design challenges for energy …wisl.ece.cornell.edu/wicker/swicker_goldsmith.pdftions for ad hoc...

ABSTRACTAd hoc wireless networks enable new and

exciting applications, but also pose significanttechnical challenges. In this article we give abrief overview of ad hoc wireless networks andtheir applications with a particular emphasis onenergy constraints. We then discuss recentadvances in the link, multiple access, network,and application protocols for these networks.We show that cross-layer design of these proto-cols is imperative to meeting emerging applica-tion requirements, particularly when energy is alimited resource.

ad hoc [Latin.]a. For this purpose, to this end; for theparticular purpose in hand or in view.b. attrib. or as adj. Devoted, appointed, andso on, to or for some particular purpose.

Oxford English Dictionary, 2nd Edition

INTRODUCTIONAn ad hoc wireless network is a collection ofwireless mobile nodes that self-configure to forma network without the aid of any establishedinfrastructure, as shown in Fig. 1. Without aninherent infrastructure, the mobiles handle thenecessary control and networking tasks by them-selves, generally through the use of distributedcontrol algorithms. Multihop connections,whereby intermediate nodes send the packetstoward their final destination, are supported toallow for efficient wireless communicationbetween parties that are relatively far apart. Adhoc wireless networks are highly appealing formany reasons. They can be rapidly deployed andreconfigured. They can be tailored to specificapplications, as implied by Oxford’s definition.They are also highly robust due to their dis-tributed nature, node redundancy, and the lackof single points of failure. These characteristics

are especially important for military applications,and much of the groundbreaking research in adhoc wireless networking was supported by the(Defense) Advanced Research Projects Agency(DARPA) [1–3]. Despite much research activityover the last several decades on wireless commu-nications in general, and ad hoc wireless net-works in particular, there remain manysignificant technical challenges in the design ofthese networks. In this tutorial we describe someof these technical challenges and possibleapproaches to solving them, with a specialemphasis on how finite node energy impactseach layer of the network protocol stack.

The lack of infrastructure inherent to ad hocwireless networks is best illustrated by contrastwith the most prevalent wireless networks today:cellular systems and wireless local area networks(WLANs). In cellular telephone networks thegeographic area of interest is divided into regionscalled cells. A mobile terminal located in a givencell communicates directly with a base stationlocated at or near the center of each cell, asshown in Fig. 2. Thus, there is no peer-to-peercommunication between mobiles. All communi-cation is via the base station through single-hoprouting, although recent work indicates that mul-tihop routing to the base station can significantlyimprove performance [4–6]). Each base stationis connected by a high-speed link (typicallywired) to a mobile switching center (MSC) thatin turn is connected to the public switched tele-phone network (PSTN). The base stations andMSC perform all control and networking func-tions, including authentication, call routing, andhandoff. The mobile units that constitute thewireless portion of this network depend entirelyon the base station/MSC/PSTN infrastructure forconnectivity and centralized control. MostWLANs have a similar centralized single-hoparchitecture: mobile nodes communicate directlywith a centralized access point that is connectedto the backbone Internet, and the access pointperforms all networking and control functionsfor the mobile nodes. In contrast, an ad hocwireless network has peer-to-peer communica-tion, distributed networking and control func-tions among all nodes, and multihop routing.

This discussion should not be taken to meanthat ad hoc wireless networks are completelyflat; that is, cannot have any infrastructure or

IEEE Wireless Communications • August 20022 1070-9916/02/$17.00 © 2002 IEEE

DESIGN CHALLENGES FOR ENERGY-CONSTRAINEDAD HOC WIRELESS NETWORKS

ANDREA J. GOLDSMITH, STANFORD UNIVERSITYSTEPHEN B. WICKER, CORNELL UNIVERSITY

The work of A. Goldsmith was supported by the Office ofNaval Research (ONR) under grants N00014-99-1-0698and N00014-02-1-0003.

This work of S. Wicker was supported in part by theDARPA’s SensIT and INCAPS programs and the Nation-al Science Foundation under grant NCR-9725251.

ENERGY-AWARE AD HOC WIRELESS NETWORKS

Ad hoc wirelessnetworks enablenew and excitingapplications, but alsopose significanttechnical challenges.We show that crosslayer design of theseprotocols is impera-tive to meetingemerging applicationrequirements,particularly whenenergy is a limitedresource.

IEEE Wireless Communications • August 2002 3

pre-established node hierarchy. Indeed, many adhoc wireless networks form a backbone infra-structure from a subset of nodes in the networkto improve network reliability and capacity [7].Similarly, some nodes may be chosen to performas base stations for neighboring nodes [8]. Thedistinguishing emphasis in the ad hoc approachlies in the design requirements. Ad hoc wirelessnetworks may exploit infrastructure to improvenetwork performance. However, while the infra-structure provides the side benefit of enhancedperformance, it is not a fundamental designprinciple of the network.

Ad hoc networks are quite common in thewired world. Indeed, most LANs, metropolitanarea networks (MANs), and wide area networks(WANs), including the Internet, have an ad hocstructure. However, the broadcast nature of theradio channel introduces characteristics in adhoc wireless networks that are not present intheir wired counterparts. In particular, a radiochannel allows a node to transmit a signal direct-ly to any other node. The link signal-to-interfer-ence-plus-noise power ratio (SINR) between twocommunicating nodes will typically decrease asthe distance between the nodes increases, andwill also depend on the signal propagation andinterference environment. Moreover, this linkSINR varies randomly over time due to themobility of the nodes that typically changes thetransmission distance, propagation environment,and interference characteristics. Link SINRdetermines the communication performance ofthe link: the data rate and associated probabilityof packet error or bit error (bit error rate orBER) that can be supported on the link. Linkswith very low SINRs are not typically used dueto their extremely poor performance, leading topartial connectivity among all nodes in the net-work, as shown in Fig. 1. However, link connec-tivity is not a binary decision, since nodes canback off on their transmission rate or increasetheir transmit power as link SINR degrades andstill maintain connectivity [9, 10]. This is illus-trated by the different line widths correspondingto different link qualities in Fig. 1. Link connec-tivity also changes as nodes enter and leave thenetwork, and this connectivity can be controlledby adapting the transmit power of existing net-work nodes to the presence of a new node [11].

The flexibility in link connectivity that resultsfrom varying link parameters such as power anddata rate has major implications for routing.Nodes can send packets directly to their finaldestination via single-hop routing as long as thelink SINR is above some minimal threshold.However, single-hop routing can cause excessiveinterference to surrounding nodes. Routing overa single hop may also require a relatively lowrate or have a high probability of bit or packeterror if the link SINR is low, thereby introducingexcessive delays. Alternatively, packets can beforwarded from source to destination by inter-mediate nodes at a link rate commensurate withthe forwarding link SINR. Routing via forward-ing by intermediate nodes is called multihoprouting. Several recent research results indicatethat ideal multihop routing significantly increas-es the capacity of ad hoc wireless networks [4,12], but achieving these gains through a decen-

tralized routing strategy remains elusive. Thechannel and network dynamics of ad hoc wire-less systems coupled with multihop routing makeit difficult to support multimedia requirementsof high speed and low delay. However, flexibilityin the link, access, network, and application pro-tocols can be exploited to compensate for andeven take advantage of these dynamics.

Energy constraints are not inherent to all adhoc wireless networks. Devices in an ad hocwireless network may be stationary and attachedto a large energy source. Mobile devices may bepart of a large vehicle, such as a car or tank, thatcan generate significant amounts of power overthe long term. However, many ad hoc wirelessnetwork nodes will be powered by batteries withlimited lifetime. Some of the most exciting appli-cations for ad hoc wireless networks follow thisparadigm. This motivates our focus on energyconstraints. Devices with rechargeable batteriesmust conserve energy to maximize time betweenrecharging. Of particular interest are devicesthat cannot be recharged, that is, sensors thatare imbedded in walls or dropped into a remoteregion. Energy constraints impact both the hard-ware operation and the signal transmission asso-ciated with node operation. It is often assumedthat the transmit power associated with packettransmission dominates power consumption.However, signal processing associated with pack-et transmission and reception, and even hard-ware operation in a standby mode, consumenonnegligible power as well [13, 14]. This entails

■ Figure 1. Ad hoc network structure.

■ Figure 2. Cellular system architecture.

Base station

IEEE Wireless Communications • August 20024

interesting energy trade-offs across protocol lay-ers. At the link layer many communicationstechniques that reduce transmit power require asignificant amount of signal processing. It iswidely assumed that the energy required for thisprocessing is small and continues to decreasewith ongoing improvements in hardware technol-ogy [13, 15]. However, the results in [14] suggestthat these energy costs are still significant. Thiswould indicate that energy-constrained systemsmust develop energy-efficient processing tech-niques that minimize power requirements acrossall levels of the protocol stack and also minimizemessage passing for network control, since theseentail significant transmitter and receiver energycosts. Sleep modes for nodes must be similarlyoptimized, since these modes conserve standbyenergy but may entail energy costs at other pro-tocol layers due to associated complications inaccess and routing. The hardware and operatingsystem design in the node can also be optimizedto conserve energy: techniques for this optimiza-tion are described in [14, 16].

Another important characteristic of ad hocwireless networks is mobility in the networknodes. Mobility impacts all layers of the networkprotocol stack. At the link layer it determineshow fast the link characteristics change andwhether or not the link connectivity is stableover time. At the medium access control (MAC)layer it affects how long measurements regardingchannel and interference conditions remain ineffect and how scheduling algorithms perform.At the network layer mobility has major implica-tions for the performance of different routingprotocols. The impact of mobility on networkperformance ultimately dictates which applica-tions can be supported on a highly mobile net-work. The impact of mobility on ad hoc wirelessnetwork design will be discussed in more detailthroughout the article.

The remainder of this article is organized asfollows. In the next section we discuss applica-tions for ad hoc wireless networks, includingdata networks, home networks, device networks,sensor networks, and distributed control. Weaddress cross-layer design in ad hoc wireless net-works: what it is, why it is needed, and how itcan be done. Link layer design issues are dis-cussed, where we describe the fundamentalcapacity limits at the link layer, and high-perfor-mance link layer designs that include coding,multiple antennas, power control, and adaptiveresource allocation. We discuss the MAC layerdesign issues, including the trade-offs inherent tofrequency/time/code channelization and theassignment of users to these channels via ran-dom access or scheduling. This section alsodescribes the role power control can play in mul-tiple access. Networking issues such as neighbordiscovery, network connectivity, scalability, rout-ing, and network capacity are outlined. Wedescribe techniques for the network to adapt tothe application requirements and the applicationto adapt to network capabilities. A summary ofthe design challenges inherent to ad hoc wirelessnetworks and some parting thoughts make upthe final section.

APPLICATIONSSince the ad hoc wireless network paradigm tai-lors the network design to the intended applica-tion, it will be useful to consider potentialapplications in some detail. In what follows weconsider both military and commercial applica-tions. We see that several design requirementsare common to both types of systems, especiallythe need for energy efficiency. Military applica-tions often require the self-configuring natureand lack of infrastructure inherent to ad hocwireless networks, even if it results in a signifi-cant cost or performance penalty. The lack ofinfrastructure is also highly appealing for com-mercial systems, since it precludes a large invest-ment to get the network up and running, anddeployment costs may then scale with networksuccess. Other commercial advantages includeease of network reconfiguration and reducedmaintenance costs. However, these advantagesmust be balanced against any performancepenalty resulting from the need for distributednetwork control.

In this section we consider the followingapplications: data networks, home networks,device networks, sensor networks, and distribut-ed control systems. Note that this list is by nomeans comprehensive, and in fact the success ofad hoc wireless networks hinges on making themsufficiently flexible so that there can be acciden-tal successes. Therein lies the design dilemmafor ad hoc wireless networks. If the network isdesigned for maximum flexibility to supportmany applications (a one-size-fits-all network), itwill be difficult to tailor the network to differentapplication requirements. This will likely resultin poor performance for some applications,especially those with high rate requirements orstringent delay constraints. On the other hand, ifthe network is tailored to a few specific applica-tions, designers must predict in advance whatthese “killer applications” will be — a riskyproposition. Ideally an ad hoc wireless networkmust be sufficiently flexible to support many dif-ferent applications while adapting its perfor-mance to the given set of applications inoperation at any given time. The cross layerdesign discussed below provides this flexibilityalong with the ability to tailor protocol design tothe energy constraints in the nodes.

DATA NETWORKSAd hoc wireless networks can support dataexchange between laptops, palmtops, personaldigital assistants (PDAs), and other informationdevices. We focus on two types of wireless datanetworks: LANs with coverage over a relativelysmall area (a room, floor, or building) andMANs with coverage over several square miles(a metropolitan area or battlefield). The goal ofWLANs is to provide peak data rates on theorder of 10–100 Mb/s, similar to what is avail-able on a wired LAN, for low-mobility and sta-tionary users. Commercial WLAN standardssuch as 802.11a and 802.11b provide data rateson this order; however, the individual user ratesare much less if there are many users accessingthe system. Moreover, these commercial LANsare not really based on an ad hoc structure. The

the success ofad hoc wirelessnetworks hinges onmaking themsufficiently flexibleso that there can beaccidental successes.Therein lies thedesign dilemma forad hoc wirelessnetworks.


normal 802.11 network configuration is a startopology with one wireless access point and sin-gle-hop routing from the mobile units to theaccess point. While the 802.11 standard doessupport a peer-to-peer architecture in the formof the Independent Base Service Set (IBSS) con-figuration option, it is not widely used and itsperformance is somewhat poor [17].

Wireless MANs typically require multihoprouting since they cover a large area. The chal-lenge in these networks is to support high datarates, in a cost-effective manner, over multiplehops, where the link quality of each hop is dif-ferent and changes with time. The lack of cen-tralized network control and potential forhigh-mobility users further complicates thisobjective. Military programs such as DARPA’sGlobal Mobile Information Systems (GLOMO)have invested much time and money in buildinghigh-speed wireless MANs that support multi-media, with limited success [18, 19]. WirelessMANs have also permeated the commercial sec-tor, with Metricom the best example [20]. WhileMetricom did deliver fairly high data ratesthroughout several major metropolitan areas,the deployment cost was quite large and signifi-cant demand never materialized. Metricom filedfor protection under Chapter XI of the FederalBankruptcy Code in fall 2000.

Note that energy efficiency is a major issue inthe design of wireless data networks. The canon-ical example of an ad hoc wireless data networkis a distributed collection of laptop computers.Laptops are highly limited in battery power, sopower must be conserved as much as possible. Inaddition, a laptop acting as a router for otherlaptops could drain its battery forwarding pack-ets for other users. This would leave no powerfor the laptop user and would initiate a changein network topology. Thus, these networks mustconserve battery power in all communicationfunctions, and devise routing strategies that useresidual power at each node of the network in afair and efficient manner.

HOME NETWORKSHome networks are envisioned to support com-munication between PCs, laptops, PDAs, cord-less phones, smart appliances, security andmonitoring systems, consumer electronics, andentertainment systems anywhere in and aroundthe home. The applications for such networksare limited only by the imagination. For exam-ple, using a PDA in the bedroom one could scanstored music titles on a PC and direct the bed-room stereo to play a favorite piece, check thetemperature in the living room and increase it bya few degrees, check the daily TV programmingfrom the Internet and direct the VCR to recorda show that night, access voice messages and dis-play them using a voice-to-text conversion soft-ware, check stocks on the Internet and sendselling instructions to a broker, and start the cof-fee maker and toaster, all without getting upfrom the bed. Other applications include smartrooms that sense people and movement, andadjust light and heating accordingly, “awarehomes” that network sensors and computers forassisted living of seniors and those with disabili-ties, video or sensor monitoring systems with the

intelligence to coordinate and interpret data andalert the home owner and the appropriate policeor fire department of unusual patterns, intelli-gent appliances that coordinate with each otherand with the Internet for remote control, soft-ware upgrades, and to schedule maintenance,and entertainment systems that allow access to aVCR, Tivo box, or PC from any television orstereo system in the home [21–24].

There are several design challenges for suchnetworks. One of the biggest is the need to sup-port the varied quality of service (QoS) require-ments for different home networkingapplications. QoS in this context refers to therequirements of a particular application, typical-ly data rates and delay constraints, which can bequite stringent for home entertainment systems.Other big challenges include cost and the needfor standardization, since all of the devices beingsupported on this type of home network mustfollow the same networking standard. Note thatthe different devices accessing a home networkhave very different power constraints: some willhave a fixed power source and be effectivelyunconstrained, while others will have very limit-ed battery power and may not be rechargeable.Thus, one of the biggest challenges in home net-work design is to leverage power in uncon-strained devices to take on the heaviestcommunication and networking burden, suchthat the networking requirements for all nodesin the network, regardless of their power con-straints, can be met.

One approach to home networking is to usean existing WLAN standard such as 802.11 [25].But 802.11 has several limitations for this type ofapplication. First, it most commonly supports astar architecture with a single access point and alldevices talking directly to this access node. Thisstar architecture eliminates the benefits of multi-hop routing, and while multihop routing is possi-ble in 802.11, as noted above, its performance ispoor. In addition, 802.11 uses a statistical multipleaccess protocol, which makes it difficult to sup-port the quality required in home entertainmentsystems. 802.11b is also somewhat limited in datarate (1–10 Mb/s), and while the 802.11a standardsupports much higher rates (10–70 Mb/s), it ismainly designed for packet data applications andnot media streaming. While protocols to supportmedia streaming on top of 802.11a are beingdeveloped (802.11e), this type of overlay will like-ly be insufficient to provide high-quality wirelesshome entertainment.

A natural choice for home networking is apeer-to-peer ad hoc wireless network. Much ofthe communication in home networks will takeplace between peer devices, so peer-to-peer com-munication eliminates the overhead of goingthrough a centralized node. In addition, many ofthe devices in a home network will be low-poweror battery-limited. In an ad hoc wireless networkthese devices need only communicate with theirnearest neighbors (typically a short distance away)to maintain connectivity with (all) other devices inthe home. Thus, multihop routing will be verybeneficial to such devices in terms of energy sav-ings. Most home networking applications involvestationary or low-mobility nodes, so the protocolsneed not support high mobility. Ad hoc wireless

Home networks areenvisioned to support

communicationbetween PCs,

laptops, PDAs,cordless phones,

smart appliances,security and

monitoring systems,consumer electronics,

and entertainmentsystems anywhere in

and around thehome. The

applications for suchnetworks are limited

only by theimagination.


networks will be challenged to provide high-quality media streaming for home entertain-ment, and this is an open area of active research.

Home networking is being pushed strongly bythe HomeRF working group, which has devel-oped an open industry standard for such net-works that combines a centralized andpeer-to-peer structure [23]. The working groupfor HomeRF was initiated by Intel, HP,Microsoft, Compaq, and IBM. The main compo-nent of the HomeRF protocol is its Shared Wire-less Access Protocol (SWAP). SWAP is designedto carry both voice and data traffic and to inter-operate with the PSTN and the Internet. SWAPis a combination of a managed network that pro-vides isochronous services (e.g., real-time voiceand video) via a centralized network controller(the main home PC) along with an ad hoc peer-to-peer network for data devices. The central-ized network controller is not required, butgreatly facilitates providing dedicated bandwidthto isochronous applications. Bandwidth sharingis enabled by frequency-hop spread spectrum at50 hops/s. HomeRF also supports a time-divisionservice for delivery of interactive voice and othertime-critical services, and a random access pro-tocol for high-speed packet data. The transmitpower for HomeRF is specified at 100 mW,which provides a data rate of 1–2 Mb/s. Howev-er, in August 2000 the FCC authorized a fivefoldincrease in the HomeRF bandwidth, effectivelyincreasing data rates to 10 Mb/s. The range ofHomeRF covers a typical home and backyard.HomeRF products operating in the 2.4 GHzband are currently on the market in the$100–$200 price range. Details on these productscan be found at http://www.homerf.org.

DEVICE NETWORKSDevice networks support short-range wirelessconnections between devices. Such networks areprimarily intended to replace inconvenient cabledconnections with wireless connections. Thus, theneed for cables and the corresponding connectorsbetween cell phones, modems, headsets, PDAs,computers, printers, projectors, network accesspoints, and other such devices is eliminated.Clearly many of these devices have limited batterylife, but are generally rechargeable. Thus, devicenetworks require energy efficiency.

The main technology driver for such networksis Bluetooth [8, 26]. The Bluetooth standard isbased on a tiny microchip incorporating a radiotransceiver that is built into digital devices. Thetransceiver takes the place of a connecting cablefor electronic devices. Up to eight Bluetoothdevices can form a star topology network (apiconet) with one node acting as a master andthe other nodes acting as slaves. The masternode is responsible for synchronization andscheduling transmissions of the slave nodes.Piconets can also be interconnected, leading to amultihop topology. Bluetooth is mainly for short-range communications, such as from a laptop toa nearby printer or from a cell phone to a wire-less headset. Its normal range of operation is 10m (at 1 mW transmit power), and this range canbe increased to 100 m by increasing the transmitpower to 100 mW. The system operates in theunregulated 2.4 GHz frequency band; hence, it

can be used worldwide without any licensingissues. The Bluetooth standard provides 1 datachannel at 721 kb/s and up to three voice chan-nels at 56 kb/s for an aggregate bit rate on theorder of 1 Mb/s. Networking is done via a packetswitching protocol based on frequency hoppingat 1600 hops/s. Energy constraints played a largerole in the design of Bluetooth, with a goal ofusing as little energy from the host device aspossible. Bluetooth uses a range of techniques inits hardware, communication, and networkingprotocols to preserve energy, including power-efficient modulation, a limited transmissionrange, smart packet detection, and intelligentsleep scheduling [26].

The Bluetooth standard was developed jointlyby 3Com, Ericsson, Intel, IBM, Lucent, Microsoft,Motorola, Nokia, and Toshiba. Over 1300 manu-facturers have now adopted the standard, andproducts compatible with Bluetooth are startingto appear on the market now. Some of the prod-ucts currently available include a wireless headsetfor cell phones (Ericsson), a wireless USB orRS232 connector (RTX Telecom, Adayma), wire-less PCMCIA cards (IBM), and wireless set-topboxes (Eagle Wireless). The prognosis for Blue-tooth has been varied, progressing from the earlyeuphoria of the late 1990s to pessimism andclaims of premature death in 2000 to the currentoutlook of guarded optimism.

SENSOR NETWORKSSensor networks have enormous potential forboth consumer and military applications. For themilitary, it is now painfully clear that the wars ofthe 21st century will differ significantly fromthose of the 20th. Enemy targets will be small,mobile, and generally found in extremely hostileterrain. If the war in Afghanistan is any indication,the targets in future combats will be small and dif-ficult to detect from great distances. Future mili-tary missions will therefore require that sensorsand other intelligence gathering mechanisms beplaced close to their intended targets. The poten-tial threat to these mechanisms is therefore quitehigh, so it follows that the technology used mustbe highly redundant and require as little supportas possible from friendly forces. An apparent solu-tion to these constraints lies in large arrays of pas-sive electromagnetic, optical, chemical, andbiological sensors. These can be used to identifyand track targets, and can also serve as a first lineof detection for various types of attacks. A thirdfunction lies in the support of the movement ofunmanned robotic vehicles. For example, opticalsensor networks can provide networked naviga-tion, routing vehicles around obstacles while guid-ing them into position for defense or attack. Thedesign considerations for some industrial applica-tions are quite similar to those for military appli-cations. In particular, sensor arrays can bedeployed and used for remote sensing in nuclearpower plants, mines, and other industrial venues.

Examples of sensor networks for the homeenvironment include electricity, gas, and watermeters that can be read remotely through wire-less connections. The broad use of simple meter-ing devices within the home can help consumersidentify and regulate devices like air condition-ers and hot water heaters that are significant

Sensor networkshave enormouspotential for bothconsumer andmilitary applications.For the military, it isnow painfully clearthat the wars of the21st century willdiffer significantlyfrom those of the20th. Enemy targetswill be small, mobile,and generallyfound in extremelyhostile terrain.


consumers of power and gas. Simple attachmentsto power plugs can serve as the metering andcommunication devices for individual appliances.One can imagine a user tracking various types ofinformation on home energy consumption from asingle terminal: the home computer. Remote con-trol of television usage and content could bemonitored in similar ways. Another importanthome application is smoke detectors that couldnot only monitor different parts of the house butalso communicate to track the spread of fire.Such information could be conveyed to local fire-fighters before they arrived on the scene alongwith house blueprints. A similar type of arraycould be used to detect the presence and spreadof gas leaks or other toxic fumes.

Sensor arrays also have great potential foruse at the sites of large accidents. One may wishto consider, for example, the use of remote sens-ing in rescue operations following the collapse ofa building. Sensor arrays could be rapidlydeployed at the site of an accident and used totrack heat, natural gas, and toxic substances.Acoustic sensors and triangulation techniquescould be used to detect and locate trapped sur-vivors. It may even be possible to avert suchtragedies altogether through the use of sensorarrays. The collapse of walkways and balconies,for example, can be predicted and tragedy avert-ed by building stress and motion sensors into thestructures from the outset. One can imaginelarge numbers of low-cost low-power sensorsbeing directly inserted into the concrete before itis poured. Material fatigue can be detected andtracked over time throughout the structure. Suchsensors must be robust and self-configuring, andwould require a very long lifetime, commensu-rate with the lifetime of the structure.

Most sensors will be deployed with non-rechargeable batteries. The problem of batterylifetime in such sensors may be averted throughthe use of ultra-small energy-harvesting radios.Research on such radios, coined the PicoRadio,promise radios smaller than 1 cm3, weighing lessthan 100 g, with a power dissipation level below100 mW [27]. This low level of power dissipationenables nodes to extract sufficient power fromthe environment — energy harvesting — tomaintain operation indefinitely. Such picoradiosopen up new applications for sensor deploymentin buildings, homes, and even the human body.

In short, important applications of the futureare enabled by large numbers of very small,lightweight, battery-powered sensors. These sen-sors must be easily and rapidly deployed in largenumbers and, once deployed, must form a suit-able network with a minimum of human inter-vention. All of these requirements must be metwith a minimum of power consumption due tobattery limitations and, for many applications,the inability to recharge these batteries

DISTRIBUTED CONTROL SYSTEMSAd hoc wireless networks enable distributedcontrol, with remote plants, sensors and actua-tors linked together via wireless communicationchannels. Such networks are imperative for coor-dinating unmanned mobile units, and greatlyreduce maintenance and reconfiguration costsover distributed control systems with wired com-

munication links. Ad hoc wireless networks arecurrently under investigation for supportingcoordinated control of multiple vehicles in anautomated highway system (AHS), remote con-trol of manufacturing and other industrial pro-cesses, and coordination of unmanned airbornevehicles (UAVs) for military applications.

Current distributed control designs provideexcellent performance as well as robustness touncertainty in model parameters. However, thesedesigns are based on closed-loop performancethat assumes a centralized architecture, syn-chronous clocked systems, and fixed topology.Consequently, these systems require that the sen-sor and actuator signals be delivered to the con-troller with a small fixed delay. Ad hoc wirelessnetworks cannot provide any performance guar-antee in terms of data rate, delay, or loss charac-teristics: delays are typically random and packetsmay be lost. Unfortunately, most distributed con-trollers are not robust to these types of communi-cation errors, and effects of small random delayscan be catastrophic [28, 29]. Thus, distributedcontrollers must be redesigned for robustness tothe random delays and packet losses inherent towireless networks. Ideally, the ad hoc wirelessnetwork can also be tailored to the requirementsof the controller. This is a relatively new area ofresearch: recent results in this area can be foundin [30] and the references therein. Energy con-straints in distributed control systems will be high-ly application-dependent: cars ON an automatedhighway will have a large renewable energysource, whereas sensors in most manufacturingapplications will have nonrechargeable batteries.

CROSS-LAYER DESIGN

The different applications discussed in the previ-ous section have a wide range of networkrequirements as well as different energy con-straints for different network nodes. The net-work requirements must be met despitevariations in the link characteristics on each hop,the network topology, and the node traffic. It isvery difficult to ensure performance of the net-work or the support of real-time or mission criti-cal data in the face of these random variations.There has been significant research directedtoward energy constraints, application require-ments, and network variability at different levelsof the network protocol stack. Examples includemultiple antennas, coding, power control, andadaptive techniques at the link layer, power con-trol and scheduling at the MAC layer, energy-constrained and delay-constrained routing at thenetwork layer, and application adaptation at theapplication layer. These techniques will be dis-cussed in more detail in the following sections.However, these research efforts have mainly tar-geted isolated components of the overall net-work design, thereby ignoring importantinterdependencies. Specifically, current ad hocwireless network protocol design is largely basedon a layered approach, as shown in Fig. 3. In thismodel each layer in the protocol stack isdesigned and operated independently, withinterfaces between layers that are static andindependent of the individual network con-straints and applications. This paradigm has

One may wishto consider, for

example, the use ofremote sensing inrescue operations

following thecollapse of a

building. Sensorarrays could be

rapidly deployed atthe site of an

accident and used totrack heat, natural

gas, and toxicsubstances.


greatly simplified network design and led to therobust scalable protocols in the Internet. Howev-er, the inflexibility and suboptimality of thisparadigm result in poor performance for ad hocwireless networks in general, especially whenenergy is a constraint or the application has highbandwidth needs and/or stringent delay con-straints. To meet these requirements a cross-layer protocol design that supports adaptivityand optimization across multiple layers of theprotocol stack is needed.

In an adaptive cross-layer protocol stack, thelink layer can adapt rate, power, and coding tomeet the requirements of the application givencurrent channel and network conditions. TheMAC layer can adapt based on underlying link andinterference conditions as well as delay constraintsand bit priorities. Adaptive routing protocols canbe developed based on current link, network, andtraffic conditions. Finally, the application layer canutilize a notion of soft QoS that adapts to theunderlying network conditions to deliver the high-est possible application quality. It is important thatthe protocols at each layer not be developed in iso-lation, but rather within an integrated and hierar-chical framework to take advantage of theinterdependencies between them. These interde-pendencies revolve around adaptivity at each layerof the protocol stack, general system constraints,such as energy and mobility, and the application(s)the network is supporting.

Adaptivity at each layer of the protocol stackshould compensate for variations at that layerbased on the timescale of these variations.Specifically, variations in link SINR are very fast,on the order of microseconds for vehicle-basedusers. Network topology changes more slowly,on the order of seconds, while variations of usertraffic may change over tens to hundreds of sec-onds. The different timescales of the networkvariations suggest that each layer should attemptto compensate for variation at that layer first. Ifadapting locally is unsuccessful, informationshould be exchanged with other layers for amore general response. For example, suppose

the link connectivity (link SINR) in the wirelesslink of an end-to-end network connection isweak. By the time this connectivity informationis relayed to a higher level of the protocol stack(i.e., the network layer for rerouting or theapplication layer for reduced-rate compression),the link SINR will most likely have changed.Therefore, it makes sense for each protocol layerto adapt to variations local to that layer. If thislocal adaptation is insufficient to compensate forthe local performance degradation, the perfor-mance metrics at the next layer of the protocolstack will degrade as a result. Adaptation at thisnext layer may then correct or at least mitigate theproblem that could not be fixed through localadaptation. For example, consider again the weaklink scenario. Link connectivity can be measuredquite accurately and quickly at the link level. Thelink protocol can therefore respond to weak con-nectivity by increasing its transmit power or itserror correction coding. This will correct for varia-tions in connectivity due to, for example, multipathflat fading. However, if the weak link is caused bysomething difficult to correct for at the link layer(e.g., the mobile unit is inside a tunnel), it is betterfor a higher layer of the network protocol stack torespond by, say, delaying packet transmissions untilthe mobile leaves the tunnel. Similarly, if nodes inthe network are highly mobile, link characteristicsand network topology will change rapidly. Inform-ing the network layer of highly mobile nodes mightchange the routing strategy from unicast to broad-cast in the general direction of the intended user.It is this integrated approach to adaptive network-ing — how each layer of the protocol stack shouldrespond to local variations given adaptation athigher layers — that forms the biggest challenge inadaptive protocol design.

Energy conservation also requires a crosslayer design. For example, Shannon theory indi-cates that the energy required to communicateone bit of information decreases as the bit timeincreases [31]. Thus, energy can be conserved bytransmitting a bit over a longer period of time.However, this will clearly impact the MAC pro-tocol and also the application. Routing is also aninteresting example. The most energy efficientrouting protocol in a sensor network may use acentrally-located sensor to forward packets fromother sensors. However, the battery of this sen-sor will be quickly exhausted, which might beundesirable from an application standpoint.Thus, the need for energy efficiency must be bal-anced against the lifetime of each individualnode and the overall life of the network.

The above discussion indicates that in orderto support an adaptive cross-layer design, thedesign and operation of the protocol stack mustevolve to that shown in Fig. 4. This figure indi-cates that information must be exchanged acrossall layers in the protocol stack. This informationexchange allows the protocols to adapt in a glob-al manner to the application requirements andunderlying network conditions. In addition, allprotocol layers must be jointly optimized withrespect to global system constraints and charac-teristics such as energy and high-mobility nodes.In order to design a protocol stack based on Fig.4, two fundamental questions must be answered:• What information should be exchanged across

■ Figure 3. The OSI model for protocol stackdesign and operation.

AdaptivityApplication layer

Network layer

MAC layer

Link layer

It is this integratedapproach to adaptivenetworking — howeach layer of theprotocol stack shouldrespond to localvariations givenadaptation at higherlayers — that formsthe biggest challengein adaptiveprotocol design.


protocol layers and how should that informa-tion be adapted to?

• How should global system constraints andcharacteristics be factored into the protocoldesigns at each layer?In the next several sections we will discuss the

design of the different layers in the protocolstack, and then revisit cross-layer design relativeto these two questions. Cross-layer design is anactive theme in ad hoc wireless network designtoday. However, there remain many open ques-tions in the understanding and implementationof this design philosophy.

LINK DESIGN ISSUES

The link layer design of energy-constrained adhoc wireless networks introduces many chal-lenges. Wireless channels are generally a diffi-cult communications medium, with relatively lowcapacity per unit bandwidth, random amplitudeand phase fluctuations due to multipath fading,intersymbol interference due to delay spread,and interference from other nodes due to thebroadcast nature of the radio channel. The goalof link layer design in ad hoc wireless networksis to achieve rates close to the fundamentalcapacity limits of the channel while overcomingchannel impairments using relatively little ener-gy. In this section we explore capacity limits ofenergy-constrained nodes along with the tech-niques and design strategies to provide good linklayer performance under an energy constraint.

FUNDAMENTAL CAPACITY LIMITSThe fundamental capacity of a channel dictatesthe maximum data rate that can be transmittedover the channel with arbitrarily small probabili-ty of error. Channel capacity for wireless chan-nels under average or peak power constraintshas been the subject of intense research formany years. This research was pioneered byClaude Shannon in 1948 when he determinedthe capacity of an additive white Gaussian noise(AWGN) channel to be C = Blog2(1 + SNR)b/s, where B is the channel bandwidth and SNRis the received signal-to-noise power ratio withinthis bandwidth. There has been much recentwork on obtaining capacity expressions for chan-nel models that better reflect the channelsunderlying current wireless systems. Recentresults in this area include the capacity of single-and multiuser fading channels under differentassumptions about channel information [32–35],capacity of diversity channels [36], and capacityof channels with multiple antennas at both thetransmitter and receiver [37, 38]. Capacity resultsfor fading channels indicate that power and rateshould be increased in good channel conditionsand decreased in poor channel conditions. Themultiple antenna results indicate that the capaci-ty of wireless channels increases linearly with thenumber of antennas at the transmitter/receiver;however, this requires perfect channel estimates.Degradation in these estimates can significantlydegrade the capacity gains resulting from multi-ple antennas. In general, the capacity-achievingcodes for wireless channels have asymptoticallylarge block lengths. The long codes and complexdecoding in this optimal scheme drive the proba-

bility of error to zero for any data rate belowcapacity, but the complexity of these schemesmakes them hard to approximate with practicalimplementations.

Channel capacity under a hard transmit ener-gy constraint, as opposed to a peak or averagepower constraint, is a relatively new area ofresearch. With finite energy it is not possible totransmit any number of bits with asymptoticallysmall error probability. This is easy to see intu-itively by considering the transmission of a singlebit. The only way to ensure that two different val-ues in signal space, representing the two possiblebit values, can be decoded with arbitrarily smallerror is to make their separation arbitrarily large,which requires arbitrarily large energy. Since arbi-trarily small error probability is not possibleunder a hard energy constraint, a different notionof reliable communication is needed. The pio-neering work by Gallager [39] in this area definesreliable communication under a finite energy con-straint in terms of the capacity per unit energy.This capacity per unit energy is defined as themaximum number of bits per unit energy that canbe transmitted such that the maximum likelihoodrandom coding error exponent is positive. Thisdefinition ensures that for all rates below thecapacity per unit energy, error probability decreas-es exponentially with the total energy, although itwill not be asymptotically small for finite-energychannels. Gallager also shows that the capacityper unit energy is achieved using an unlimitednumber of degrees of freedom per transmittedbit. This translates to either very wideband com-munication [40] or using many symbols per bit,the opposite of high-rate transmission schemesunder a power constraint (e.g., M-QAM, with Mb/symbol for M large).

Capacity per unit energy is also explored in[31], and these results can be used to obtain thecapacity of finite-energy channels in terms of bits[41]. Capacity in bits dictates the maximum num-ber of bits that can be transmitted over a chan-nel using finite energy given some nonzero

■ Figure 4. Adaptive cross-layer design and operation.

OperationDesign

Application layer

Network layer

MAC layer

Link layer

Cross-layeradaptivity

Systemconstraints


probability of bit error (recall that this errorprobability cannot be driven to zero with finiteenergy). The capacity of a finite-energy channelin bits is an important concept, since it indicatesthat ad hoc wireless networks with finite energynodes only have a finite number of bits that agiven node can transmit before exhausting itsenergy. Allocating those bits to the differentrequirements of the network — informationtransmission, exchange of routing information,forwarding bits for other nodes, channel estima-tion, and so on — becomes an interesting andchallenging optimization probl em that clearlyrequires cross-layer design.

CODINGChannel coding can significantly reduce the powerrequired to achieve a given BER and is thereforea common feature in energy-constrained link layerdesign. Deep space communications engineershave aggressively pursued the use of very complexcodes to minimize power. Most wireless systemsalso use some form of error control coding toreduce power consumption. Conventional errorcontrol codes use block or convolutional codedesigns: the error correction cabability of these

codes is obtained at the expense of an increasedsignal bandwidth or a lower data rate [42]. Trelliscodes, invented by Ungerboeck in the ’70s [43],use a joint design of the channel code and modu-lation to provide good error correction withoutany bandwidth or rate penalty.

For AWGN channels the challenge of achiev-ing very low BERs with minimum power has forthe most part been met by turbo codes [44] andthe more general family of codes on graphs withiterative decoding algorithms [45, 46]. While themain ideas behind current research in codes ongraphs was introduced by Gallager in 1962, atthe time these coding techniques were thoughtimpractical and were generally not pursued byresearchers in the field. The landmark 1993paper by Berrou, Glavieux, and Thitimajshimaon turbo codes [44] provided more than enoughmotivation to revisit Gallager’s and other’s workon iterative, graph-based decoding techniques.

As first described by Berrou et al., turbo errorcontrol consists of two key components: parallelconcatenated encoding and iterative “turbo”decoding [44, 47]. A typical parallel concatenatedencoder is shown in Fig. 5. It consists of two par-allel convolutional encoders separated by aninterleaver, with the input to the channel beingthe data bits m along with the parity bits X1 andX2 output from each of the encoders in responseto input m. The key to parallel concatenatedencoding lies in the recursive nature of theencoders and the impact of the interleaver on theinformation stream. Interleavers also play a signif-icant role in the elimination of error floors [47].

Iterative or turbo decoding exploits the com-ponent-code substructure of the turbo encoderby associating a component decoder with each ofthe component encoders. More specifically, eachdecoder performs soft input/soft output decoding,as shown in Fig. 6. In this figure decoder 1 gener-ates a soft decision in the form of a probabilitymeasure p(m1) on the transmitted data bits basedon the received codeword (m¢,X1¢). This reliabilityinformation is passed to decoder 2, which gener-ates its own probability measure p(m2) from itsreceived codeword (m¢,X2¢) and the probabilitymeasure p(m1). This reliability information isinput to decoder 1, which revises its measurebased on this information and the originalreceived codeword. Decoder 1 sends the new reli-ability information to decoder 2, which revises itsmeasure using this new information. Turbo decod-ing proceeds in an iterative manner, with the twocomponent decoders alternately updating theirprobability measures. Ideally the decoders eventu-ally agree on probability measures that reduce tohard decisions m = m1 = m2. However, the stop-ping condition for turbo decoding is not welldefined, in part because there are many cases inwhich the turbo decoding algorithm does not con-verge; that is, the decoders cannot agree on thevalue of m. Several methods have been proposedfor detecting convergence (if it occurs), includingbit estimate variance [48] and neural net-basedtechniques [49].

Figure 7 indicates the performance of turbocodes. It can be seen that at a BER of 10–5 arecursive convolutional systematic (RCS) turbocode provides an 8.5 dB coding gain over uncod-ed systems and lies within 1.5 dB of the Shannon

■ Figure 5. A turbo encoder.

X = (m, X1, X2)

X1

m

X2

Encoder

Datasource

C1

Encoder

C2

Interleaver

■ Figure 6. A turbo decoder.

(m, X1)

P(m1)

m1

m2(m, X2)

P(m2)

Decoder 1

Decoder 2

DeinterleaverInterleaver


capacity limit. The intuitive explanation for thisamazing performance is that the code complexityintroduced by the encoding structure is similarto the codes that achieve Shannon capacity. Theiterative procedure of the turbo decoder allowsthese codes to be decoded without excessivecomplexity [46]. Similar performance gains havebeen shown for bandwidth-constrained systemsthrough turbo trellis coded modulation [50–52].

The soft decisions of the turbo decoding algo-rithm are highly dependent on the channelmodel. The performance shown in Fig. 7 is for asimple AWGN channel model. Turbo codesdesigned for AWGN channels do not necessarilyperform well on wireless channels. However,recent work has shown that turbo codes workwell on many wireless channels as long as anaccurate channel model is embedded into theturbo decoding algorithm [53]. Recent resultsalso indicate that for wireless systems with poorchannel estimates, turbo codes may have inferiorperformance to simpler codes like trellis codes[54]. Note also that the turbo decoding algo-rithm typically requires more signal processingpower than other decoders due to its iterativenature. This additional energy requirement mayreduce the net energy gain of turbo codes.

MULTIPLE ANTENNASMultiple antennas at the transmitter and/orreceiver play a powerful role in improving theperformance and reducing the required transmitpower for wireless link layer designs. Multipleantenna systems typically use either diversity,beamsteering, or multiple input multiple output(MIMO) techniques. Diversity combining is acommon technique to mitigate flat fading bycoherently combining multiple independentlyfading copies of the signal [55]. By significantlyreducing the impact of flat fading, diversity com-bining can lead to significant power savings [56].This is indicated in Fig. 8, where we show theperformance of maximal-ratio combining (MRC)diversity in Rayleigh fading with one (no diversi-ty), two, and four branches. We see that com-pared to no diversity, at a BER of 10–3

two-branch diversity saves 5.5 dB of power andfour-branch diversity saves 8.5 dB.

Beamsteering creates an effective antennapattern at the receiver with high gain in thedirection of the desired signal and low gain in allother directions. Beamsteering is accomplishedby combining arrays of antennas with signal pro-cessing in both space and time. The signal pro-cessing typically adjusts the phase shifts at eachantenna to “steer” the beam in the desired direc-tion. A simpler technique uses sectorized anten-nas with switching between the sectors.Beamsteering significantly improves energy effi-ciency since transmitter power is focused in thedirection of its intended receiver. Beamsteeringalso reduces interference power along with fad-ing and intersymbol interference due to multi-path, since the interference and multipath signalsare highly attenuated when they arrive fromdirections other than that of the line-of-sight (ordominant) signal. Results in [57] indicate thatbeamsteering can significantly improve the trans-mission range, data rates, and BER of wirelesslinks. Highly mobile nodes can diminish these

gains, since the beamsteering direction will beshifting and difficult to determine accurately.

MIMO systems, where both transmitter andreceiver use multiple antennas, can significantlyincrease the data rates possible on a given chan-nel. In MIMO systems, if both the transmitter andreceiver have channel estimates, with N antennasat the transmitter and receiver the MIMO systemcan be transformed into N separate channels thatdo not interfere with each other, providing aroughly N-fold capacity increase over a systemwith a single antenna at both the transmitter andreceiver. When the transmitter does not know thechannel, the optimal transmission strategy is aspace-time code, where bits are encoded over bothspace and time [58]. These codes are highly com-plex, so in practice suboptimal schemes like lay-ered space-time codes are used and tend toperform very well [59].

While multiple antenna techniques save trans-

■ Figure 7. Turbo code performance.

10–9

Signal-to-noise ratio (dB)

–8.5 dB(7Y)

No coding used

Statistical theoretical limit

RCS turbo

Turbo product codeV

iterbi with Reed Solom

on

Coding system comparisons

Bit

erro

r ra

te

9 10–1

10–4

10–1

100

10–2

10–3

10–5

10–6

10–7

10–8

876543210

■ Figure 8. Performance of diversity systems.

Average SNR/branch18 200

10–9

10–8

BER

10–7

10–6

10–5

10–4

10–3

10–2

10–1

0

161412108642

4-branch diversity

No diversity

5.5 dB 3 dB


mission power, they are often highly complexand therefore require significant power for sig-nal processing. Given a total energy constraintthis trade-off must be examined relative to eachsystem to determine if multiple antenna tech-niques result in a net savings in energy.

POWER CONTROLPower control is a potent mechanism for improv-ing wireless network performance. At the linklayer power control can be used to compensatefor random channel variations due to multipathfading, reduce the transmit power required toobtain a given data rate and error probability,minimize the probability of link outage, andreduce interference to neighboring nodes. It canalso be used to meet hard delay constraints andprevent buffer overflow.

Power control strategies at the link layer typi-cally either maintain SINR on the link above arequired threshold by increasing power relativeto fading and interference or use a “water-fill-ing” approach where power and rate areincreased for good channel conditions, decreasedfor poor channel conditions, and set to zerowhen the channel quality falls below a given cut-off threshold [36]. The constant SINR strategyworks well for continuous stream traffic with adelay constraint, where data is typically sent at afixed rate regardless of channel conditions. How-ever, this power control strategy is not power-efficient, since much power must be used tomaintain the constant SINR in deep fading con-ditions. Optimal variation of transmission rateand power maximizes average throughput [60]and channel capacity [61], but the associatedvariable-rate transmission and channel-depen-dent delay may not be acceptable for some appli-cations. Power control has also been used tomeet delay constraints for wireless data links. Inthis approach power for transmission of a packetincreases as the packet approaches its delay con-straint, thereby increasing the probability of suc-cessful transmission [62]. A more complexapproach uses dynamic programming to mini-mize the transmit power required to meet a harddelay constraint [63], and the resulting powerconsumption is much improved over power con-trol that maintains a constant SINR.

Before closing this section, we want to empha-size that power control has a significant impact onprotocols above the link layer. The level of trans-mitter power defines the “local neighborhood” —the collection of nodes that can be reached in asingle hop — and thus in turn defines the contextin which access, routing, and other higher-layerprotocols operate. Power control will thereforeplay a key role in the development of efficientcross-layer networking protocols. We will discussintegration of power control with multiple accessand routing protocols in later sections.

ADAPTIVE RESOURCE ALLOCATIONAdaptive resource allocation in link layer designis a relatively new technique that provides robustlink performance with high throughput whilemeeting application-specific constraints. Thebasic premise is to adapt the link transmissionscheme to the underlying channel, interference,and data characteristics through variation of the

transmitted power level, symbol transmissionrate, constellation size, coding rate/scheme, orany combination of these parameters. Recentwork shows that adaptation can increase the linkspectral efficiency by a factor of five or moreover nonadaptive techniques while maintainingthe required link performance [10]. Moreover,adaptive modulation can compensate for SINRvariations due to interference as well as multi-path fading and can be used to meet differentQoS requirements of multimedia [64] by priori-tizing delay-constrained bits and adjusting trans-mit power to meet BER requirements.

Recent work in adaptive resource allocationhas investigated combinations of power, rate,code, and BER adaptation ([60] and the refer-ences therein). These schemes typically assumesome finite number of power levels, modulationschemes, and codes, and the optimal combina-tion is chosen based on system conditions andconstraints. Only a small number of power lev-els, rates, and/or codes are needed to achievenear-optimal performance, since there is a criti-cal number of degrees of freedom needed forgood performance of adaptive resource alloca-tion, and beyond this critical number additionaldegrees of freedom provide minimal perfor-mance gain [60]. In particular, power control inaddition to variable-rate transmission providesnegligible capacity increase in fading channels[61], cellular systems [65, 66], and ad hoc wire-less networks [4]. Code-division multiple access(CDMA) systems, in addition to varying power,data rate, and channel coding, can also adjusttheir spreading gain or the number of spreadingcodes assigned to a given user [67, 68]. The ben-efits of assigning multiple spreading codes peruser are greatest when some form of multiuserdetection is used, since otherwise self-interfer-ence is introduced [69]. Note also that in adap-tive CDMA systems all transmitters sending to agiven receiver must coordinate since they inter-fere with each other.

Other adaptive techniques include variationof the link layer retransmission strategy as wellas its frame size. The frame is the basic informa-tion block transmitted over the link and includesoverhead in the form of header and error con-trol bits. Shorter frames entail higher overhead,but are less likely to be corrupted by sporadicinterference and require less time for retrans-mission. Recent results have shown that optimiz-ing frame length can significantly improvethroughput as well as energy efficiency [70].

Data communications require corruptedpackets to be retransmitted so that all bits arecorrectly received. Current protocols typicallydiscard the corrupted packet and start overagain on the retransmission. However, recentwork has shown that diversity combining ofretransmitted packets or retransmitting addition-al redundant code bits instead of the entirepacket can substantially increase throughput [71,references therein]. A performance comparisonof incremental redundancy against that of adap-tive modulation is given in [72].

MEDIUM ACCESS CONTROL DESIGN ISSUES

The medium access control protocol dictates

At the link layerpower control can beused to compensatefor random channelvariations due tomultipath fading,reduce the transmitpower required toobtain a given datarate and errorprobability, minimizethe probability oflink outage, andreduce interferenceto neighboringnodes.


how different users share the available spectrum.There are two components to this spectrum allo-cation: how to divide the spectrum into differentchannels, and then how to assign these differentchannels to different users. In the first subsec-tion below we discuss channelization: the differ-ent methods that can be used to divide thespectrum into different channels. The next twosections deal with channel assignment. Whenusers have very bursty traffic the most efficientmechanism to assign channels is random access,where users contend for a channel wheneverthey have data to transmit. This contention isinefficient when users have continuous streamdata or long packet bursts. In this case someform of scheduling helps to prevent collisionsand ensure continuous connections. We con-clude this section with a discussion of the rolepower control plays in multiple access strategies.

CHANNELIZATIONMethods to divide the spectrum into differentchannels include frequency division, time divi-sion, code division, and hybrid combinations ofthese methods. In code division either orthogo-nal or semi-orthogonal spreading code tech-niques may be used, although orthogonal codesoften do not maintain orthogonality after trans-mission through a wireless channel. Time divi-sion, frequency division, and orthogonal codedivision are equivalent in that they all divide upthe spectrum orthogonally, and this orthogonaldivision results in the same number of channels[73]. Without frequency reuse, code division withsemi-orthogonal codes has inferior capacity tothat of orthogonal techniques due to its intro-duction of interference between users. However,multiuser detection can increase the capacity ofsemi-orthogonal code division above that of theorthogonal channelization methods. Thesecapacity conclusions are based on idealizedimplementations. In practice, due to implemen-tation and signal propagation issues, these capac-ity conclusions are not necessarily accurate.Frequency reuse and bursty traffic make thecapacity trade-offs between the different chan-nelization methods even more difficult to deter-mine. We now describe each of these divisiontechniques in more detail.

In frequency division the system bandwidth isdivided into nonoverlapping channels, and eachactive user is assigned a different channel. If thechannel bandwidth does not exceed the inverse ofthe multipath delay spread in the channel, fre-quency-division systems do not require intersym-bol interference compensation (e.g., equalizationor multicarrier modulation) [55, 74]. Frequencydivision is generally the simplest channelizationtechnique to implement, but it is rather inflexible.In particular, it is difficult to allocate multiplechannels on demand to a single user, since thisrequires simultaneous demodulation of multiplechannels in different frequency bands.

Time division is an alternate technique, wheretime is divided into orthogonal time slots. Activeusers with continuous stream data are typicallyassigned a cyclically repeating timeslot. Theseactive users do not transmit continuously, sofunctions such as channel probing can be doneduring idle times. One difficulty of using time

division is the need for synchronization amongall nodes transmitting to the same receiver.Since these different nodes will have differentpropagation delays to the receiver, the timeslotsmust be synchronized so that they remainorthogonal after these respective delays. Thechannels associated with a time-division systemare typically wideband relative to multipathdelay spread, and thus require intersymbol inter-ference mitigation, often in the form of an equal-izer. Time division has more flexibility fordynamic channel allocation than frequency divi-sion, since multiple time slots can be assigned toa user on demand with no complexity increase intransmission or reception.

In code division, time and bandwidth are usedsimultaneously by different users, modulated byorthogonal or semi-orthogonal spreading codes.The receiver then uses the spreading code prop-erties to separate out the different users. One ofthe big advantages of channelization using spreadspectrum is that little dynamic coordination ofusers in time or frequency is required, since theusers can be separated by the code propertiesalone. In addition, since time and frequency divi-sion carve up time and bandwidth into orthogonalpieces, there is a hard limit on how many userscan simultaneously occupy the system. This is alsotrue for code division using orthogonal codes. Incontrast, if semi-orthogonal codes are used, thenumber of users is interference-limited. Specifical-ly, there is no hard limit on how many users cansimultaneously share the channel. However, sincesemi-orthogonal codes interfere with each other,the more users that are packed into the samechannel, the higher the level of interference,which degrades the performance of all the users.Moreover, semiorthogonal code-division systemstypically require power control to compensate forthe near-far problem. The near-far problem arisesbecause users modulating their signals with differ-ent spreading codes interfere with each other.Suppose that one user is very close to the receiv-er, and another user is very far away. If bothusers transmit at the same power level, the inter-ference from the close user will swamp the signalfrom the far user. Thus, power control is usedsuch that the received signal powers from allusers are roughly the same. This form of powercontrol, which essentially inverts any attenuationand/or fading introduced by the channel, causeseach interferer to contribute an equal amount ofpower, thereby eliminating the near-far problem.

Performance of code-division systems can besignificantly improved using both RAKEreceivers and multiuser detection. A RAKEreceiver takes advantage of the structure of thespreading codes to coherently combine the mul-tipath components of the received signal [55].Multiuser detection schemes, pioneered byVerdu, take advantage of the fact that the inter-ference from other users arises from a knownsemi-orthogonal spreading code sequence. Thus,this interference can be decoded and subtractedout, thereby eliminating most interferencebetween users and the need for power control[75]. While multiuser detection promises signifi-cant performance enhancement, the detectionscheme must have a low probability of bit error,since bits that are incorrectly detected are sub-

The benefits ofassigning multiple

spreading codes peruser are greatest

when some form ofmultiuser detection

is used, sinceotherwise

self-interference isintroduced. Note also

that in adaptiveCDMA systems all

transmitters sendingto a given receiver

must coordinatesince they interfere

with each other.


tracted from the signals of other users, whichmay cause them to be decoded in error as well(error propagation). These detection strategiescan also be highly sensitive to channel estimationerrors. Finally, multiuser detection significantlyincreases the complexity (and associated energyrequirements) of the receivers. For these reasonsmultiuser detection has not yet permeated exist-ing code-division system designs. Even withoutmultiuser detection, code-division systems havethe most complex receivers of all the channeliza-tion techniques due to spreading code acquisi-tion and synchronization requirements alongwith power control. Note also that multiuserdetection requires knowledge at the receiver ofthe spreading codes associated with each signalit receives, which entails some control overheadin an ad hoc wireless network.

RANDOM ACCESSGiven a channelization scheme, each user can beassigned a different channel for some period oftime. However, most data users do not requirecontinuous transmission, so dedicated channelassignment can be extremely inefficient. More-over, most systems have many more total users(active plus idle users) than channels, so at anygiven time channels can only be allocated tousers that need them. Random access strategiesare used in such systems to assign channels toactive users. Random access techniques werepioneered by Abramson with the Aloha protocol[76]. In Aloha users are assumed to share a setof common channels. In unslotted or “pure”Aloha, the users transmit on one of these chan-nels whenever they have data to send. Shouldtwo users “collide,” they both wait a randomamount of time before retransmitting. The goal,of course, is to prevent the users from collidingonce again when they retransmit. In slottedAloha, the users are further constrained by arequirement that they only begin transmitting atthe start of a time slot. The use of such timeslots increases the maximum possible throughputof the channel [77], but also introduces the needfor a synchronization mechanism of some sort.Even in a slotted system, collisions occur when-ever two or more users attempt transmission inthe same slot. Error control coding can result incorrect detection of a packet even after a colli-sion, but if the error correction is insufficient,the packet must be retransmitted, resulting in acomplete waste of the energy consumed in theoriginal transmission. A study on design opti-mization between error correction and retrans-mission is described in [78].

Collisions can be reduced by carrier sense

multiple access (CSMA), where users sense thechannel and delay transmission if they detectthat another user is currently transmitting [77].CSMA only works when all users can hear eachother’s transmissions, which is typically not thecase in wireless systems due to the nature ofwireless propagation. This gives rise to the hid-den terminal problem, illustrated in Fig. 9, whereeach node can hear its immediate neighbor butno other nodes in the network. In this figureboth nodes 3 and 5 wish to transmit to node 4.Suppose node 5 starts its transmission. Sincenode 3 is too far away to detect this transmis-sion, it assumes that the channel is idle andbegins its transmission, thereby causing a colli-sion with node 5’s transmission. Node 3 is saidto be hidden from node 5 since it cannot detectnode 5’s transmission. Aloha with CSMA alsocreates inefficiencies in channel utilization fromthe exposed terminal problem, also illustrated inFig. 9. Suppose the exposed terminal in this fig-ure, node 2, wishes to send a packet to node 1 atthe same time node 3 is sending to node 4. Whennode 2 senses the channel it will detect node 3’stransmission and assume the channel is busy,even though node 3 does not interfere with thereception of node 2’s transmission by node 1.Thus, node 2 will not transmit to node 1 eventhough no collision would have occurred.

The collisions introduced by hidden terminalsand inefficiencies introduced by exposed termi-nals are often addressed by a four-way hand-shake prior to transmission, as in the 802.11WLAN protocol [79, 80]. However, this hand-shake protocol is based on single-hop routing,and recent work indicates that its performancein multihop networks may be suboptimal [81,82]. Another technique to avoid hidden andexposed terminals is busy tone transmission. Inthis strategy users first check to see whether thetransmit channel is busy by listening for a busytone on a separate control channel [77]. This istypically not an actual busy tone; instead, a bit isset in a predetermined field on the control chan-nel. This scheme works well in preventing colli-sions when a centralized controller can be“heard” by users throughout the network. In aflat network without centralized control, morecomplicated measures are used to ensure thatany potential interferer on the first channel canhear the busy tone on the second [83]. Hybridtechniques using handshakes, busy tone trans-mission, and power control are investigated in[84]. Note that while the four-way handshakeand busy tone transmission both reduce colli-sions due to the hidden terminal problem, theytend to aggravate the exposed terminal problem,leading to less efficient utilization of the avail-able channels in the network. A solution to thisproblem is to have both transmitter and receiversend busy tones [83].

Random access protocols can be more ener-gy-efficient by limiting the amount of time agiven node spends transmitting and receiving.The paging industry developed a solution to thisproblem several decades ago by scheduling“sleep” periods for pagers. The basic idea is thateach pager need only listen for transmissionsduring certain short periods of time. This is asimple solution to implement when a central

■ Figure 9. Hidden and exposed terminals in random access.

Exposedterminal

Hiddenterminal

1 2 3 4 5


controller is available. It is less obvious how toimplement such strategies within the frameworkof a distributed control algorithm. Access proto-cols that utilize node sleep times to minimizeenergy consumption are investigated in [13].

Random access schemes can be made moreflexible in general, and more energy aware inparticular, by adopting a dynamic programmingapproach to decisions about transmissions.Under dynamic programming, decision makingis based on utility (cost) functions: an agent willact or not, depending on utility of the action asindicated by a utility function computed oversome time period. A given protocol can be madeenergy aware by introducing the cost of a trans-mission into the utility function.

Consider the case of Aloha. In work conduct-ed by MacKenzie at Cornell, a game-theoreticversion of Aloha was developed that initiallyfocused on a simple “collision game” [85]. It isassumed in this game that players know the num-ber of backlogged users, n. G(n) is then the gamein which there are currently n users backlogged.In each stage of G(n) each of the n backloggedplayers must decide whether to transmit or wait.If one player decides to transmit and the restdecide to wait, the player who transmits willreceive a payoff of 1 – c, where c is the cost of atransmission, and each of the other (n – 1) play-ers will then proceed to play G(n – 1) in the nextperiod. If either no users transmit or more thanone user transmits, all players will play G(n) againin the next period. Players place a lower value onpayoffs in later stages than on current payoffs viaa discount factor. The cost of delay and the ener-gy cost of transmission are parameters of the costfunction that can be tailored to the particularapplication. The resulting system is both stable (inthe language of game theory, there is a Nashequilibrium) and distributed. It allows for individ-ual nodes to make autonomous decisions onretransmission strategies. This simple version ofthe game assumes that the users know the num-ber of backlogged users within the local neighbor-hood, but it is possible to develop utility functionsthat reflect less ideal situations. In general, thedecision-theoretic approach provides a convenientway to embed the cost of transmission decisionsinto random access protocols.

SCHEDULINGRandom access protocols work well with burstytraffic where there are many more users thanavailable channels, yet these users rarely trans-mit. If users have long strings of packets or con-tinuous stream data, then random access workspoorly as most transmissions result in collisions.Thus channels must be assigned to users in amore systematic fashion by transmission schedul-ing. In scheduled access the available bandwidthis channelized into multiple time, frequency, orcode division channels. Each node schedules itstransmission on different channels in such a wayas to avoid conflicts with neighboring nodeswhile making the most efficient use of the avail-able time and frequency resources. While therehas been much work on transmission scheduling,or channel assignment, in cellular systems [86],the centralized control in these systems greatlysimplifies the problem. Distributed scheduled

access in ad hoc wireless networks in general isan NP-hard problem [87]. Selman et al. haverecently discovered that NP-hard problemsexhibit a rapid change in complexity as the sizeof the problem grows [88, 89]. The identificationof this “phase transition” provides an opportuni-ty for bounding the complexity of problems likescheduled access by staying on the good side ofthe phase transition.

Even with a scheduling access protocol, someform of Aloha will still be needed since a prede-fined mechanism for scheduling will be, by defi-nition, unavailable at startup. Aloha provides ameans for initial contact and the establishmentof some form of scheduled access for the trans-mission of relatively large amounts of data. A sys-tematic approach to this initialization that alsocombines the benefits of random access for burstydata with scheduling for continuous data is packetreservation multiple access (PRMA) [90]. PRMAassumes a slotted system with both continuousand bursty users (e.g., voice and data users). Mul-tiple users vie for a given time slot under a ran-dom access strategy. A successful transmission byone user in a given time slot reserves that timeslot for all subsequent transmissions by the sameuser. If the user has a continuous or long trans-mission, after successfully capturing the channelhe/she has a dedicated channel for the remainderof his/her transmission (assuming subsequenttransmissions are not corrupted by the channel:this corruption causes users to lose their slots,and they must then recontend for an unreservedslot, which can entail significant delay). When thisuser has no more packets to transmit, the slot isreturned to the pool of available slots that usersattempt to capture via random access. Thus, datausers with short transmissions benefit from therandom access protocol assigned to unused slots,and users with continuous transmissions getscheduled periodic transmissions after successfullycapturing an initial slot. A similar technique usinga combined reservation and Aloha policy isdescribed in [14].

Scheduling under an energy constraint furthercomplicates the problem. As discussed in an earli-er section, channel capacity under a finite energyconstraint is maximized by transmitting each bitover a very long period of time. However, whenmultiple users wish to access the channel, thetransmission time allocated to each user must belimited. Recent work has investigated optimalscheduling algorithms to minimize transmit energyfor multiple users sharing a channel [91]. In thiswork scheduling was optimized to minimize thetransmission energy required by each user subjectto a deadline or delay constraint. The energy mini-mization was based on judiciously varying packettransmission time (and corresponding energy con-sumption) to meet the delay constraints of thedata. This scheme was shown to be significantlymore energy efficient than a deterministic sched-ule with the same deadline constraint.

POWER CONTROLAccess protocols can be made more efficient anddistributed by taking advantage of power con-trol. Work in this area has mainly focused onmaintaining the SINR of each user sharing thechannel above a given threshold, which may be

Even with ascheduling access

protocol, someform of Aloha will

still be neededsince a predefined

mechanism forscheduling will be,

by definition,unavailable atstartup. Aloha

provides a means forinitial contact and

the establishment ofsome form of

scheduled access forthe transmission of

relatively largeamounts of data.


different for different users [11]. Necessary andsufficient conditions to ensure that a feasible setof transmit powers for all users exists underwhich these users can meet their threshold SINRlevels given the link gains between them aredetermined in [11]. Battery power for each useris minimized by finding the minimum power vec-tor within the feasible set. This algorithm can alsobe performed in a distributed manner, whicheliminates the need for centralized power control.Access to the system can be based on whether thenew user causes other users to fall below theirSINR targets. Specifically, when a new userrequests access to the system, a centralized con-troller can determine if a set of transmit powersexists such that he/she can be admitted withoutdegrading existing users below their desired SINRthreshold. This admission can also be done usingthe distributed algorithm, where the new usergradually ramps up his/her power, which causesinterference to other existing users in the system.If the new user can be accommodated in the sys-tem without violating the SINR requirements ofexisting users, then the power control algorithmsof the new and existing users eventually convergeto the feasible power vector under which all users(new and existing) meet their SINR targets. If thenew user cannot be accommodated, as he/sheramps up his/her power the other users willincrease their powers to maintain their SINRssuch that the new user remains far from his/herSINR target. After some number of iterationswithout reaching his/her target, the new user willeither back off from the channel and try againlater or adjust his/her SINR target to a lowervalue and try again.

A power control strategy for multiple accessthat takes into account delay constraints is pro-posed and analyzed in [62]. This strategy opti-mizes the transmit power relative to bothchannel conditions and the delay constraint viadynamic programming. The optimal strategy

exhibits three modes: very low-power transmis-sion when the channel is poor and the tolerabledelay large, higher power when the channel anddelay are average, and very high-power transmis-sion when the delay constraint is tight. Thisstrategy exhibits significant power savings overconstant power transmission while meeting thedelay constraints of the traffic.

Power control has also been extensively stud-ied for cellular systems [92, references therein].In cellular systems the power adaptation of eachmobile is typically controlled by a centralizedbase station in each cell, and the base stationscoordinate to optimize power allocation acrossall mobiles in the network. Distributed powercontrol for cellular systems has also been investi-gated [93]. In general, power control in cellularsystems, often in combination with channel orrate allocation, improves spatial channel reuseby managing interference, thereby significantlyincreasing cellular network capacity. The bene-fits of power control for both voice and data incellular systems has been well established, andmost next-generation cellular system designsinclude some form of power control. However,there are few results outside of [11] on thedesign and performance of power controlschemes in ad hoc wireless networks, and thisremains an active area of research.

NETWORK DESIGN ISSUES

NEIGHBOR DISCOVERY ANDNETWORK CONNECTIVITY

“Neighbor discovery” is one of the first steps inthe initialization of a network of randomly dis-tributed nodes. From the perspective of the indi-vidual node, this is the process of determining thenumber and identity of network nodes with whichdirect communication can be established givensome maximum power level and minimum linkperformance requirements (typically in terms ofdata rate and associated BER). Clearly the higherthe allowed transmit power, the greater the num-ber of nodes in a given neighborhood.

Neighbor discovery begins with a probe ofneighboring nodes using an initial power con-straint. If the number of nodes thus contacted isinsufficient to ensure some minimal connectivityrequirements then the power constraint isrelaxed and probing repeated. The minimal con-nectivity requirements will depend on the appli-cation, but most ad hoc wireless networkapplications assume a fully-connected networkwhereby each node can reach every other node,often through multiple hops. The exact numberof neighbors that each node requires to obtain afully-connected network depends on the exactnetwork configuration but is generally on theorder of six to eight for randomly distributedimmobile nodes [77, 94]. An analysis of the mini-mum transmit power required at each node tomaintain full connectivity is done in [95, 96].Clearly the ability of the network to stay con-nected will decrease with node mobility, somaintaining full connectivity under high mobilitywill require larger neighborhoods and an associ-ated increase in transmit power at each node.

It is interesting to note that, given a random

■ Figure 10. Network connectivity vs. node density.

Communication radius

0.9 100

0.7

Prob

abili

ty (

netw

ork

is c

onne

cted

)0.9

1

0.8

0.6

0.5

0.4

0.3

0.2

0.1

0.80.70.60.50.40.30.20.1

n = 20n = 40n = 60n = 80n = 100


distribution of nodes, the likelihood of completeconnectivity changes abruptly from zero to one asthe transmission range of each node is increased.The sensitivity of the location of this “zero-one”transition to the density of nodes is shown in Fig.10 from [97] for a static network. This figureshows several curves, each corresponding to a dif-ferent number (n) of randomly distributed nodesin a region of fixed size. The curves thus reflect,from left to right, a decreasing level of node den-sity. Note that the transmission range required forthe network to be fully connected increases as thenode density decreases, reflecting the increasedprobability of deep holes, to borrow a term fromthe theory of lattices.

Connectivity is heavily influenced by the abilityto adapt various parameters at the link layer suchas rate, power, and coding, since communicationis possible even on links with low SINR if theseparameters are adapted [19]. The sensitivity ofconnectivity to transmit power level can be repre-sented abstractly through the theory of transmis-sion graphs [97]. Figure 11 shows a series oftransmission graphs: graphs in which edges existbetween pairs of nodes if and only if the distancebetween the nodes is less than or equal to a givenbound. The series of graphs shows how connectiv-ity can increase dramatically with transmit powergiven a random distribution of nodes.

From the standpoint of power efficiency andoperational lifetime, it is also very important thatnodes be able to decide whether or not to take anap. These sleep decisions must take into accountnetwork connectivity, so it follows that these deci-sions are local but not autonomous. Mechanismsthat support such decisions can be based onneighbor discovery coupled with some means ofordering decisions within the neighborhood. In agiven area, the opportunity to sleep should be cir-culated among the nodes, ensuring that connec-tivity is not lost through the coincidence of severalidentical decisions to go to sleep.

ROUTINGThe multihop routing protocol in an ad hoc wire-less network is a significant design challenge,

especially under energy constraints where theexchange of routing data consumes precious ener-gy resources. Most work in multihop routing pro-tocols falls into three main categories: flooding,proactive routing (centralized or distributed), andreactive routing [98–100, references therein].

In flooding a packet is broadcast to all nodeswithin receiving range. These nodes also broad-cast the packet, and the forwarding continuesuntil the packet reaches its ultimate destination.Flooding has the advantage that it is highlyrobust to changing network topologies andrequires little routing overhead. In fact, in highlymobile networks flooding may be the only feasi-ble routing strategy. The obvious disadvantage isthat multiple copies of the same packet traversethrough the network, wasting bandwidth andbattery power of the transmitting nodes. Thisdisadvantage makes flooding impractical for allbut the smallest of networks.

The opposite philosophy to flooding is cen-tralized route computation. In this approachinformation about channel conditions and net-work topology are determined by each node andforwarded to a centralized location that com-putes the routing tables for all nodes in the net-work. The criterion used to compute the“optimal” route depends on the optimization cri-terion: common criteria include minimum aver-age delay, minimum number of hops, andrecently, minimum network congestion [101].While centralized route computation providesthe most efficient routing according to the opti-mality condition, it cannot adapt to fast changesin the channel conditions or network topology,and also requires much overhead for collectinglocal node information and then disseminatingthe routing information. Centralized route com-putation, like flooding, is typically only used invery small networks.

Distributed route computation is the mostcommon routing procedure used in ad hoc wire-less networks. In this protocol nodes send theirconnectivity information to neighboring nodes,and then routes are computed from this localinformation. In particular, nodes determine the

■ Figure 11. Connectivity increases with transmission radius R (scales with power).

Sparse (R = 0.2)

0.5 100

0.5

1

Medium (R = 0.35)

Fixed radius random graphs G = G(n1, R)

0.5 100

0.5

1

Dense (R = 1.2)

0.5 100

0.5

1


next hop in the route of a packet based on thislocal information. There are several advantagesof distributed route computation. First, the over-head of exchanging routing information withlocal nodes is minimal. In addition, this strategyadapts quickly to link and connectivity changes.The disadvantages of this strategy are that globalroutes based on local information are typicallysuboptimal, and routing loops are often commonin the distributed route computation.

Both centralized and distributed routingrequire fixed routing tables that must be updat-ed at regular intervals. An alternate approach isreactive (on-demand) routing, where routes arecreated only at the initiation of a source nodethat has traffic to send to a given destination.This eliminates the overhead of maintainingrouting tables for routes not currently in use. Inthis strategy a source node initiates a route dis-covery process when it has data to send. Thisprocess will determine if one or more routes areavailable to the destination. The route or routesare maintained until the source has no moredata for that particular destination. The advan-tage of reactive routing is that globally efficientroutes can be obtained with relatively little over-head, since these routes need not be maintainedat all times. The disadvantage is that reactiverouting can entail significant delay, since theroute discovery process is initiated when there isdata to send, but this data cannot be transmitteduntil the route discovery process has concluded.Recently a combination of reactive and proactiverouting has been proposed to reduce the delayassociated with reactive routing as well as theoverhead associated with proactive routing [99].

Mobility has a huge impact on routing proto-cols as it can cause established routes to nolonger exist. High mobility especially degradesthe performance of proactive routing, since rout-ing tables quickly become outdated, requiring anenormous amount of overhead to keep them upto date. Flooding is effective in maintainingroutes under high mobility, but has a huge pricein terms of network efficiency. A modification offlooding called multipath routing has beenrecently proposed, whereby a packet is duplicat-ed on only a few paths with a high likelihood ofreaching its final destination [100]. This tech-nique has been shown to perform well underdynamically changing topologies.

Energy constraints in the routing protocol sig-nificantly change the problem. First of all, theexchange of routing information between nodesentails an energy cost: this cost must be tradedagainst the energy savings that result from usingthis information to make routes more efficient. Inaddition, even with perfect information about thelinks and network topology, the route computa-tion must change to take energy constraints intoaccount. Specifically, a route utilizing a smallnumber of hops (low delay) may use significantlymore energy (per node and/or total energy) thana route consisting of a larger number of hops.Moreover, if one node is often used for forward-ing packets the battery of that node will die outquickly, making that node unavailable for trans-mitting its own data or forwarding packets forothers. Thus, the routing protocol under energyconstraints must somehow balance delay con-

straints, battery lifetime, and routing efficiency.There has been much recent work on evaluating

routing protocols under energy constraints. In [102]simulations were used to compare the energy con-sumption of different well-known routing protocols.Their results indicate that reactive routing is moreenergy efficient. This is not surprising since proac-tive routing must maintain routing tables via contin-uous exchange of routing information, which entailsa significant energy cost. This work was extended in[103] to more accurately model the energy con-sumption of radios in a “listening” mode. The ener-gy consumption for this mode, ignored in [101], wassignificant; based on this more accurate model, itwas concluded that the proactive and reactive rout-ing schemes analyzed in [102] have roughly thesame energy consumption. The article goes on topropose a sleep mode for nodes that reduces ener-gy consumption by up to 40 percent. Other work inthis area combines routing, power control, andadaptive coding to minimize the energy cost ofroutes [104]. Power control to optimize energy effi-ciency in routing is also studied in [105].

SCALABILITY AND DISTRIBUTED PROTOCOLSScalability arises naturally in the design of self-configuring ad hoc wireless networks. The key toself-configuration lies in the use of distributednetwork control algorithms — algorithms thatadjust local performance to account for local con-ditions. To the extent that these algorithms forgothe use of centralized information and controlresources, the resulting network will be scalable.Work on scalability in ad hoc wireless networkshas mainly focused on self-organization [13, 106],distributed routing [107], mobility management[7], QoS support, and security [108]. Note thatdistributed protocols often consume a fair amountof energy in local processing and messageexchange: this is analyzed in detail for securityprotocols in [109]. Thus, interesting trade-offsarise as to how much local processing should bedone vs. transmitting information to a centralizedlocation for processing. Most work on scalabilityin ad hoc wireless networks has focused on rela-tively small networks, less than 100 nodes. Manyof the applications described earlier, especiallysensor networks, could have hundreds to thou-sands of nodes or even more. The ability of exist-ing network protocols to scale to such largenetwork sizes remains an open question.

NETWORK CAPACITYThe fundamental capacity limit of an ad hocwireless network — the set of maximum datarates possible between all nodes — is a highlychallenging problem in information theory. Infact, the capacity for simple channel configura-tions within an ad hoc wireless network, such asthe general relay and interference channel,remain unsolved [110]. In a recent landmarkpaper an upper bound on the performance of anasymptotically large ad hoc wireless network interms of the uniformly achievable maximum datarate was determined [12]. Surprisingly, this resultindicates that even with optimal routing andscheduling, the per-node rate in a large ad hocwireless network goes to zero. To a large extentthis pessimistic result indicates that in a largenetwork all nodes should not communicate with

Both centralized anddistributed routingrequire fixed routingtables that must beupdated at regularintervals. An alternateapproach is reactive(on-demand) routing,where routes arecreated only at theinitiation of a sourcenode that has trafficto send to a givendestination. In thisstrategy a sourcenode initiates aroute-discoveryprocess when it hasdata to send.


all other nodes: there should be distributed pro-cessing of information within local neighbor-hoods. This work was extended in [111] to showthat node mobility actually increases the per-node rate to a constant (i.e., mobility increasesnetwork capacity). This result follows from thefact that mobility introduces variation in the net-work that can be exploited to improve per-userrates. Other recent work in this area has deter-mined achievable rate regions for ad hoc wire-less networks using adaptive transmissionstrategies [4] and an information theoretic analy-sis on achievable rates between nodes [112].

APPLICATION DESIGN ISSUES

In true cross-layer protocol design, the highestlayer — the application — can play a significantrole in network efficiency. In this section weconsider network adaptation to the applicationrequirements and application adaptation to theunderlying network capabilities.

ADAPTIVE QOSThe Internet today, even with high-speed high-quality fixed communication links, is unable todeliver guaranteed QoS to applications in terms ofguaranteed end-to-end rates or delays. For ad hocwireless networks, with low-capacity error-pronetime-varying links, mobile users, and a dynamictopology, the notion of being able to guaranteethese forms of QoS is simply unrealistic. There-fore, ad hoc wireless network applications mustadapt to time-varying QoS parameters offered bythe network. While adaptivity at the link and net-work levels as described earlier will provide thebest possible QoS to the application, this QoS willvary with time as channel conditions, networktopology, and user demands change. Applicationsmust therefore adapt to the QoS offered. Therecan also be a negotiation for QoS such that userswith higher priority can obtain better QoS by low-ering the QoS of less important users.

As a simple example, the network may offerthe application a rate-delay trade-off curvederived from the capabilities of the lower-layerprotocols [30]. The application layer must thendecide at which point on this curve to operate.Some applications may be able to tolerate ahigher delay but not a lower overall rate. Exam-ples include data applications in which the over-all data rate must be high but latency might betolerable. Other applications might be extremelysensitive to delay (e.g., a distributed controlapplication) but might be able to tolerate alower rate (e.g., via a coarser quantization ofsensor data). Energy constraints introduce anoth-er set of trade-offs related to network perfor-mance versus longevity. Thus, these trade-offcurves will typically be multidimensional toincorporate rate, delay, BER, longevity, and soon. These trade-off curves will also change withtime as the number of users on the network andthe network environment change.

APPLICATION ADAPTATION ANDCROSS LAYER DESIGN REVISITED

In addition to adaptive QoS, the applicationitself can adapt to the QoS offered. For exam-

ple, for applications like video with a hard delayconstraint, the video compression algorithmmight change its compression rate such that thesource rate adjusts to the rate the network candeliver under the delay constraint. Thus, underpoor network conditions compression would behigher (lower transmission rate) and the endquality poorer. There has been much recentwork on application adaptation for wireless net-works [113–115, references therein]. This workindicates that even demanding applications likevideo can deliver good overall performanceunder poor network conditions if the applicationis given the flexibility to adapt.

The concept of application adaptation returnsus to the cross-layer design issue discussed earli-er. While the application can adapt to a rate-delay-performance trade-off curve offered by thenetwork and underlying links, by making thelower-layer protocols aware of the trade-offsinherent to the application adaptation, thattrade-off curve might be adjusted to improveend-to-end performance without using up moreresources in the network. In other words, if theapplication is aware of the lower-layer protocoltrade-offs and these protocols are aware of theapplication trade-offs, these trade-off curves canbe merged to operate at the best point relativeto end-to-end performance. While implementingthis philosophy remains a wide open researchproblem, it holds significant promise for the per-formance of ad hoc wireless networks.

SUMMARY AND CONCLUSIONS

We have described recent advances in ad hocwireless network protocols and the strong inter-action that occurs across different protocol lay-ers. While there is still work to be done onimproving link, medium access, network, andapplication protocols, the interactions acrossthese different protocol layers invite novel cross-layer designs that exploit these interdependen-cies. Cross-layer design is particularly importantunder energy constraints, since energy across theentire protocol stack must be minimized.

The biggest design challenges in ad hoc wire-less networks are the lack of centralized control,limited node capability, and variability of the linksand network topology. Methods to address thesechallenges include intelligent distributed controlprotocols, node redundancy and coordination,and adaptivity at each layer of the protocol stackto compensate for and exploit variability. Recentbreakthroughs in ad hoc wireless network designhave come from thinking outside the box: the boxof layered protocol designs, the box of wirelineprotocols, the box of guaranteed QoS fordemanding applications, and the box of applica-tion-agnostic networks. Much more out-of-boxthinking is required to deliver the great promiseoffered by ad hoc wireless networks.

ACKNOWLEDGMENTSThe authors gratefully acknowledge T. Holliday,X. Liu, B. Krishnamachari, M. Medard, and S.Toumpis for their detailed comments and sug-gestions. They are also grateful to S. Cui and A.MacKenzie for providing some of the figures.

The Internet today,even with high-speed

high-quality fixedcommunication links,

is unable to deliverguaranteed QoS to

an application interms of guaranteed

end-to-end rates ordelays. For ad hocwireless networks,with low-capacity

error-pronetime-varying links,

mobile users, and adynamic topology,

the notion of beingable to guarantee

these forms of QoSis simply unrealistic.


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BIOGRAPHIESANDREA GOLDSMITH ([email protected]) received herB.S., M.S., and Ph.D. degrees in electrical engineering fromthe University of California at Berkeley in 1986, 1991, and1994, respectively. From 1986 to 1990 she was affiliated

While there is stillwork to be done on

improving link,medium access,

network, andapplication protocols,

the interactionsacross these differentprotocol layers invite

novel cross layerdesigns thatexploit these

interdependencies.


with Maxim Technologies, where she worked on packetradio and satellite communication systems, and from 1991to 1992 she was affiliated with AT&T Bell Laboratories,where she worked on microcell modeling and channel esti-mation. She was an assistant professor of electrical engi-neering at the California Institute of Technology from 1994to 1998, and then joined the Electrical Engineering Depart-ment at Stanford University where she is now an associateprofessor. Her research includes work in capacity of wire-less channels and networks, wireless communication theo-ry, adaptive modulation and coding, multiantennachannels, communications and control, and adaptiveresource allocation for cellular systems and ad hoc wirelessnetworks. She is a Terman Faculty Fellow at Stanford and arecipient of the Alfred P. Sloan Fellowship, a National Sci-ence Foundation CAREER Development Award, the Officeof Naval Research Young Investigator Award, a NationalSemiconductor Faculty Development Award, an OkawaFoundation Award, and the David Griep Memorial Prizefrom the University of California at Berkeley. She is an edi-tor for IEEE Transactions on Communications and IEEEWireless Communications.

STEPHEN B. WICKER ([email protected]) received hisB.S.E.E. with High Honors from the University of Virginia in1982. He received an M.S.E.E. from Purdue University in1983 and a Ph.D. degree in electrical engineering from theUniversity of Southern California in 1987. He is professorand associate director for research of the School of Electri-cal and Computer Engineering at Cornell University. He isthe author of Codes, Graphs, and Iterative Decoding (Kluw-er, 2002), Turbo Coding (Kluwer, 1999), and Error ControlSystems for Digital Communication and Storage (PrenticeHall, 1995). He is also co-editor of Reed-Solomon Codesand Their Applications (IEEE Press, 1994), and was Associ-ate Editor for Coding Theory and Techniques for IEEETransactions on Communications. He recently concludedhis second term as a member of the Board of Governors ofthe IEEE Information Society. He teaches and conductsresearch in wireless information networks, self-configuringsystems, and error control coding. His current researchfocus is on the use of probabilistic models and game theo-ry in the development of highly distributed, adaptive, wire-less networks. He was awarded the 1998 Cornell College ofEngineering Michael Tien Teaching Award and the 2000Cornell School of ECE Teaching Award. He has served as aconsultant in wireless telecommunication systems, errorcontrol coding, and cryptography for various companiesand governments in North America, Europe, and Asia.

Recent breakthroughsin ad hoc wirelessnetwork design havecome from thinkingoutside the box:the box of layeredprotocol designs, thebox of wirelineprotocols, the boxof guaranteedquality-of-servicefor demandingapplications, andthe box ofapplication-agnosticnetworks.

design challenges for energy …wisl.ece.cornell.edu/wicker/swicker_goldsmith.pdftions for ad hoc...

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