power-aware ad hoc cognitive packet networks
Post on 26-Jun-2016
215 Views
Preview:
TRANSCRIPT
Ad Hoc Networks 2 (2004) 205–216
www.elsevier.com/locate/adhoc
Power-aware ad hoc cognitive packet networks
Erol Gelenbe a,*, Ricardo Lent b
a Dennis Gabor Chair, Department of Electrical and Electronic Engineering, Imperial College, London SW7 2BT, UKb School of Computer Science, University of Central Florida, Orlando, FL 32816, USA
Available online 28 April 2004
Abstract
This paper proposes a new energy efficient algorithm to find and maintain routes in mobile ad hoc networks. The
proposal borrows the notion of learning from a previous research on cognitive packet networks (CPN) to create a
robust routing protocol. Our idea uses smart packets that exploit the use of unicasts and broadcasts to search for
routes. Because unicasts impose lower overall overhead, their use is preferred. Smart packets learn how to make good
unicast routing decisions by employing a combined goal function which considers both the energy stored in the nodes
and path delay. The end result is a dynamic discovery of paths that offer an equilibrium between low-delay routes and
an efficient use of network resources that extends the working lifetime of the network.
� 2004 Elsevier B.V. All rights reserved.
Keywords: Cognitive packet networks; Mobile ad hoc networks
1. Introduction
Mobile ad hoc networks are flexible and dy-
namic systems that can operate without the aid of
a fixed communication infrastructure. The topol-
ogy of an ad hoc network is expected to change
over time and it depends on the location of the
nodes and the resources available. Node locations
determine the establishment of links between
nodes whenever the distance and other externalfactors, such as the presence of obstacles and
interference, do not prevent nodes from commu-
nicating. In addition to acting as end systems,
nodes in ad hoc networks also act as transit nodes
* Corresponding author. Tel.: +44-2075-946342; fax: +44-
2075-946274.
E-mail addresses: e.gelenbe@imperial.ac.uk (E. Gelenbe),
rlent@cs.ucf.edu (R. Lent).
1570-8705/$ - see front matter � 2004 Elsevier B.V. All rights reserv
doi:10.1016/j.adhoc.2004.03.009
for other communications. Their participation in
the process of searching for paths (routing) andforwarding of packets depends on the availability
of internal resources. These resources are typically
scarce because of the mobile nature of the nodes.
One vital component is the stored energy in bat-
teries of mobile nodes, which is limited because of
weight and size restrictions. Furthermore, ad-
vances in battery technology lag behind advances
in computing and storage technologies [1]. Ad hocnetworks inflict extra energy consumption at
nodes, as they need to receive, process, and
transmit packets to assist others communications.
As a result, as nodes consume their resources, they
may quickly become unreliable and contribute to
create an error-prone system. Ad hoc networks are
therefore characterized by unpredictable topolo-
gies that require a highly dynamic routing algo-rithm to cope both with unreliability and mobility
ed.
206 E. Gelenbe, R. Lent / Ad Hoc Networks 2 (2004) 205–216
of nodes, while attempting to provide good quality
routes.
Flooding is a robust but resource consuming
mechanism that has been extensively employed in
the past as a searching mechanism for routes. A
request packet is addressed to the destination nodeand emitted by the source via broadcast every time a
path is to be discovered. The request is then repli-
cated at most one time by each node in the network.
This mechanism ensures the arrival of the request
packet at the destination provided that at least one
path exists. The process is expensive and pervasive
and, as a consequence, highly energy consuming.
The Cognitive Packet Network (CPN) [2–5] is afast adaptive routing algorithm that exploits learn-
ing to discover and refine routes. Routes are created
and maintained byCognitive or Smart Packets (SP),
which are sent out by source nodes when new des-
tinations are desired. SPs move in the network col-
lecting information and making decisions that take
into consideration what previous packets have
learned. Decisions can be tailored to reflect a desiredquality of service (QoS) on the path, for example,
minimize end-to-end delay or power consumption.
In this paper, we explore an extension to CPN
that enables its operation under mobile ad hoc
conditions. Ah hoc CPN (AHCPN) restricts the
use of flooding as a last-resort alternative and re-
places broadcasts with unicasts as much as possi-
ble. By employing the CPN algorithm, unicastrouting decisions can be adapted to optimize the
use of resources in mobile ad hoc networks that
reduce node unavailability due to power outages
and improve quality of service (QoS).
The rest of the paper is organized as follows.
Section 2 presents an overview of existing propo-
sals for mobile ad hoc networks. Section 3 intro-
duces the AHCPN routing algorithm, which is laterdetailed in Section 4. Section 5 reports on simula-
tion experiments that compares our proposal with
a flood-based algorithm. Finally, Section 6 con-
cludes the paper summarizing our main results.
2. Routing in ad hoc networks
The area of ad hoc routing has been very active
in recent years, and there appear to be two well-
defined trends in the design of routing protocols for
mobile ad hoc networks: table driven protocols
(proactive protocols) and source-initiated on-
demand driven protocols (reactive protocols) [6].
Proactive protocols operate with global informa-
tion about the network so as to maintain routes forevery possible source-destination pair. To acquire
this information, proactive algorithms require
nodes to periodically exchange routing tables.
Reactive protocols, on the other hand, create
routes only as needed. In general, and under low to
moderate network traffic, a periodic exchange of
packets will result in higher energy consumption
than the search for routes on-demand. A few ofthese proposals consider energy constrains in the
establishment of routes. We start this section by
reviewing non-energy aware protocols, and later on
we discuss energy aware protocols.
The Destination Sequenced Distance Vector
(DSDV) [7] is a proactive protocol that implements
the Bellman–Ford distance vector algorithm with
suitable adaptations for wireless environments.DSDV introduces a sequence number to each route
entry in order to solve the count-to-infinity prob-
lem. This sequence number allows nodes to discern
stale from new information so that only the most
recent routes are used. Global State Routing
(GSR) [8] is a proactive protocol that relies on a
variation of a link state algorithm to calculate
shortest paths. GSR requires nodes to periodicallyexchange their entire topology table with their
neighbors and then calculate paths with a variant
of the Dijkstra’s algorithm. Each entry in a topol-
ogy table is tagged with a sequence number to
allow nodes to recognize updates. The Wireless
Routing Protocol (WRP) [9] is another proactive
approach that relies on a link state algorithm to
calculate shortest paths. As defined by the link statealgorithm, nodes exchange link state information
with their neighbors either periodically or when a
change occurs. WRP tracks the length of the pre-
decessor-to-destination in the shortest path calcu-
lation to eliminate the count-to-infinity problem.
A major inconvenience of DSDV, WRP, and
GSR is the large number of control packets that
are needed to circulate routing information to allnodes in the network. Hierarchical-based algo-
rithms partition the network into areas in order to
E. Gelenbe, R. Lent / Ad Hoc Networks 2 (2004) 205–216 207
restrict the flow of routing control packets. Clus-
terhead Gateway Switch Routing (CGSR) [10] is a
proactive algorithm that partitions the network
into clusters, which contain a dynamically elected
clusterhead. Within these clusters, nodes that are
able to reach more than one clusterhead are elec-ted as gateways. CGSR uses distance vector rout-
ing to maintain paths that describe the series of
clusterheads and gateways required to reach a
destination, which can significantly help in reduc-
ing the size of the routing tables. Fisheye State
Routing (FSR) [11] is an improved version of
GSR. FSR periodically exchanges entire link state
tables with neighbors on different frequencies,which depend on hop distances.
Most on-demand protocols use a variation of
flooding to discover and maintain routes. This
usually consists in sending a route request packet
to find a route from a source to a destination.
Flooding is restricted by introducing a sequence
number for request packets and limiting each node
to process one request at most once, thusrestricting the control overhead to OðnÞ for an nnode network.
Dynamic Source Routing (DSR) [12] is an on-
demand protocol that sends request packets to
desired destinations and stores the discovered
paths in source nodes. Request packets record the
path that they need to follow to reach their desti-
nations and the route information is sent to thesource inside route reply packets. DSR takes
advantage of existing routing information in inter-
mediate nodes to accelerate route discovery and
possibly reduce routing overhead. The Ad Hoc
On-Demand Distance Vector (AODV) [13,14]
protocol uses query packets to discover new des-
tinations. A query packet behaves similarly to a
route request packet in DSR. However, AODVstores paths in a distributed fashion. AODV uses
backward learning, which consists in nodes acquir-
ing the path to the source when they receive a
query packet.
A number of proposals discover routes on-
demand but require a periodic transmission of
control packets to check for neighbor connectivity
and other metrics. These protocols are as expensivein terms of energy consumption as proactive pro-
tocols [15] but are able to provide a different type of
route quality. Associativity-Based Routing ABR
[16] is similar to DSR but employs a new metric
in the establishment of routes. Nodes periodically
transmit hello messages to their neighbors and the
count of these messages at a node provides an
indicative measurement of the link strength withthe origin of the hello messages. Signal Stability
Routing (SSR) [17] is another routing protocol that
requires periodic beacons to determine the link
stability of a node. This information is employed to
determine routing tables. The Temporary-Ordered
Routing Algorithm (TORA) is a distributed rout-
ing protocol that is based on local maxima for
propagation. Although TORA is an on-demandprotocol, it is designed to work over the Internet
MANET Encapsulation Protocol (IMEP) [18],
which requires periodic transmission of packets.
The Zone Routing Protocol (ZRP) [19,20] is a
hybrid approach that combines both proactive and
reactive algorithms. ZRP creates zones and uses
proactivity within these zones. Reactivity is used
when a node needs to reach nodes outside its zone.Although, the use of Global Positioning Sys-
tems (GPS) may introduce additional energy
requirements and possibly extra packet control
overhead to disseminate positional information,
geographic position information can be exploited
to reduce the total number of control packets for
routing purposes. Location Aided Routing (LAR)
[21] utilizes the position of the nodes and a vari-ation of DSR to restrict the area of search for
routes. Similarly, geoTORA [22] is a variation of
TORA using geographic position.
Research in power-efficient techniques em-
braces all seven layers of the OSI model. Power
conserving methods have been investigated in the
design of hardware [23], operating systems, and
applications, ranging from the design of energy-efficient schedulers for processors [24,25] to power-
aware applications for database transactions [26].
A recent commercial product that exemplifies the
need for power-efficient components is the new
Intel Centrino technology.
2.1. Power-aware protocols
Power efficient, routing protocols include the
work of Singh et al. [27] who investigated the use
208 E. Gelenbe, R. Lent / Ad Hoc Networks 2 (2004) 205–216
of power-aware metrics in the calculation of
shortest paths. These metrics describe the power
required for transmitting and receiving a packet
on a link, so as to minimize the end-to-end power
requirements for routing. This proposal did not
take into account the remaining energy in thenodes and it can result in a severe drain of energy
in the batteries of the nodes on the least-cost route.
Other proposals overcame this problem by
using battery lifetime information. Toh [28] pro-
posed a new metric, which calculates the summa-
tion of the inverses of the remaining battery
capacities of the nodes on the path. In addition,
Toh proposed the Min–Max algorithm to main-tain a fair use of resources by avoiding the use of
nodes with the least remaining-battery capacity in
the network. Li et al. [29] propose an algorithm
(denoted by max–min zPmin) that computes the
paths with minimal energy consumption while
maximizing the minimal residual power of the
network.
Power-aware Source Routing (PSR) [30] issimilar to DSR, but the destination calculates the
link cost based on the remaining battery capacity
and transmission power of the nodes. The draw-
back of this approach is that the destination needs
to wait some time after the arrival of the first route
request, so as to receive more than one possible
route, and then selects the one with the minimum
cost.Finally, an alternative approach is used by the
Adaptive Fidelity Algorithm (AFA) [31], which
operates on top of on-demand ad hoc routing
protocols, such as AODV and DSR. AFA saves
battery power by turning off certain transceivers
whenever the applications allow a reduction in the
quality of the connections. The algorithm trades
quality for battery lifetime, network bandwidth, ora number of active sensors.
3. Ad hoc cognitive packet network (AHCPN)
Cognitive packet networks use three types of
packets to accomplish all routing and forwarding
functions: smart packets (SPs), dumb packets(DPs), and acknowledgments (ACK). SPs are
responsible for discovery of routes and for main-
tenance. As more information is learned from the
network, smart packets can refine routes by taking
better routing decisions. DPs employ source
routing with the paths discovered by SPs to move
payload from source to destination. Finally,
ACKs are employed to relay the information ac-quired by SPs of DPs. There are three elements
in the structure of any CPN packet. A header
transports source and destination addresses and
other useful information for the processing of the
packet, such as the packet’s length. A cognitive
map is an area that the packet uses to store net-
work status information. DPs use the data area to
transport payload.
3.1. Route discovery
In ad hoc networks, each node needs to become
aware of its neighbors dynamically and continu-
ously, as opposed to the CPN algorithm where
that information is known a priori and is not ex-
pected to change much over time. A node canlearn the existence of a neighbor simply by listen-
ing to its transmissions, and assuming that the
neighbor is alive as long as it keeps transmitting
packets. In AHCPN, we refrain from forcing each
node in the network to send out periodically a
bacon packet to indicate its existence because of
the energy consumption that this implies [15,
Chap. 9]. Nodes simply listen any transmission todetect neighbors.
To discover routes, SPs may use either broad-
cast or unicast transmissions to move in the net-
work. Broadcasts are obviously more expensive
than unicasts because they may be processed by
all nodes in the communication vicinity, and may
get reproduced as many times as the number
of neighbors. To use a unicast transmission, a SPneeds to select a target neighbor and therefore a
routing decision needs to take place. The process
of taking the routing decision is defined by the
CPN algorithm.
When nodes have no knowledge about their
surroundings (e.g. when they have just entered the
network), no unicast decision can be made and
broadcast is used to transmit SPs. Under thesecircumstances, a node desiring a path to a desti-
nation emits a SP and a broadcast query and
E. Gelenbe, R. Lent / Ad Hoc Networks 2 (2004) 205–216 209
await-reply cycle takes place. Each SP is tagged
with a packet identifier that is used along with the
source address to avoid processing a smart packet
more than once by any node. Therefore, a node
stores the source address and packet identifier of
each SP that it receives and discards any sub-sequent copy. Also, for any source–destination
(S,D) pair, the identity of the most recently re-
ceived SP is stored. As a SP moves in the network,
it collects the addresses of and arrival times at each
node. The first arriving SP at the destination
generates an ACK that heads back to the source to
inform the discovery and travels on the reverse
path recorded by the SP. Battery information isrecorded by ACKs and not by SPs for conve-
nience, as it will be shown later.
3.2. Information structures
CPN use a learning system whose ability to
create and maintain routes depends on the avail-
ability of accurate network information. Packetsacquire this information as they move, and it is
stored in several structures in a distributed fash-
ion. These structures are: route caches, cognitive
maps, mailboxes, weight parameters, and neighbor
tables.
Route caches are located at source nodes; they
store complete paths for every destination as nee-
ded. As detailed before, SPs create paths as a resultof their exploration of the network. The path
information arrives at the source via ACKs. To
maintain the quality of a connection, SPs are
continuously sent out by the source as a small
fraction of the data traffic, possible resulting in a
route switch to keep or improve the performance
of data packets flow. As a result, only active
connections are maintained.A cognitive map exists within each packet to
store addresses and network metrics. SPs dynam-
ically construct their cognitive maps with the ad-
dresses of the nodes that they visit. Dumb packets
and acknowledgments use their cognitive map to
list the complete path they follow. Also, SPs and
DPs record their arrival time at each node in their
cognitive map. ACKs distribute the cognitive mapinformation along with battery information to the
nodes on an active path.
ACKs visit the nodes on the path listed in their
cognitive map and update their mailboxes. A
mailbox is a structure within a node that maintains
statistics about the performance of active connec-
tions. Mailboxes keep the average round-trip delay
and other metrics to every known destinationthrough every neighbor. This information is re-
quired to operate the CPN algorithm that uses
reinforcement learning to update weight tables of
random neural networks stored within the node.
Neighbor tables are maintained at every node
with entries that define all known neighbors. As
explained before, neighbors are acquired by lis-
tening to their transmissions. Each entry in thetable details the address of the neighbor and an
expiration time that is used to remove nodes that
are no longer present.
3.3. Unicast routing
CPN routing decisions are performed with the
aid of a random neural network, which is trainedwith a reinforcement-learning algorithm. AHCPN
employs a procedure virtually identical to CPN to
make unicast routing decisions, but with a small
difference. CPN replaces a small fraction of deci-
sions with random decisions to avoid trapping the
algorithm in local minima. In AHCPN, we replace
random decisions with broadcasts to allow the
algorithm a better exploration of the mobile net-work. We will omit further details about CPN as
the algorithm is well documented in the literature
[2–5].
3.4. Specific routing goals for ad hoc networks
Ad hoc networks are characterized by a high
transmission error probability, which is caused bymobility, the use of wireless links, and the limited
resources of nodes. We introduce a new routing
metric, in AHCPN, that takes into account the
quality of the links and nodes involved in a con-
nection. We call this metric ‘‘path availability’’. A
combined routing goal, which endeavors to max-
imize path availability and minimize the round-
trip delay of the packets, can offer a good balanceamong the selection of fast paths, the use of stable
paths, and a better use of network resources.
210 E. Gelenbe, R. Lent / Ad Hoc Networks 2 (2004) 205–216
Path availability is defined as the probability to
find nodes and links available for routing on a
path. Formally, suppose that a SP takes the path:
(n1; n2; . . . ; nd), where ni represents the ith node on
that path and (ni; niþ1) represents the link between
nodes ni and niþ1.Assume that node ni is available for routing
with probability PnðniÞ and that link (ni; niþi) is
available with probability Plðni; niþ1Þ. Path avail-
ability Ppðni; ndÞ, from node ni to node nd , is ex-
pressed as
Ppðni; ndÞ ¼Yd�1
j¼i
Pnðniþ1ÞPlðni; niþ1Þ: ð1Þ
A recursive, combined routing goal function Gid
consisting of round-trip delay from ni to nd and
path availability information is defined as
Gid ¼ Ppðni; ndÞDðni; ndÞ þ ½1� Ppðni; ndÞ�ðTo þ GiÞ;ð2Þ
where Dðni; ndÞ is the round-trip delay from ni to ndand To is a predefined timeout interval. To can be
interpreted as the delay before a packet, which has
been lost, can be retransmitted.
In real systems, many factors contribute to the
values of Pn and Pl. For example, the processing
load of the nodes, type of obstacles, nature of
working environment, etc. In this paper and asexplained next, Pn will be expressed only in terms
of the remaining battery lifetime of the nodes. We
will assume Pl ¼ 1.
3.5. Energy sensitivity
Batteries are the major source of energy in
mobile nodes. To provide greater portability,batteries need to be small and lightweight, which
unfortunately restricts the total energy that they
can carry. Once batteries exhaust their energy,
they need to be replaced or recharged, which typ-
ically reduces the independence of a mobile node
to a few hours of operation.
Energy consumption, in communication-related
tasks, depends on the communication mode of anode. A node may be transmitting, receiving, or in
idle mode. Naturally, transmission consumes more
energy than the other two modes. From the rout-
ing perspective, our interest is in selecting routes in
such a way that the transmission and reception of
packets is intelligently distributed on the network
so as to maximize the overall average battery
lifetime of the nodes. Therefore, we are interested
in getting SPs to select, with greater frequency,
those nodes which have the longest remainingbattery lifetime.
If Bi represents the remaining battery lifetime of
node ni, PnðniÞ can be expressed as
PnðniÞ ¼Bi
Bm; ð3Þ
where Bm is the lifetime of a fully charged battery.
Assume Plðni; niþ1Þ ¼ 1 in Eq. (1), for all
i 2 ½1; d � 1�, to simplify this discussion. Thus,
Ppðni; ndÞ ¼Yd�1
j¼i
Pnðniþ1Þ: ð4Þ
From (4) and (3), the path availability to destina-
tion nd from a node on the path becomes
Ppðni; ndÞ ¼Yd�1
j¼i
Bjþ1
Bm:
4. AHCPN protocol description
This section presents a detailed description of
the AHCPN protocol. AHCPN packets are
structured in three main areas: header, cognitive
map, and payload. Payload is only carried in DPs.
Fig. 1 illustrates the format of the packets:
1. Type (4 bits)––identifies the packet type:
0¼ dumb packet, 1¼ smart packet, 2 and 3
indicate acknowledgments originated by dumb
and cognitive packets respectively.
2. QoS (4 bits)––indicates the desired quality of
service for the packet. The quality-of-service
goal determines the reinforcement learning
function to be used to update the random neu-ral network in the CPN algorithm.
3. Header and cognitive map length in words of 32
bits.
4. Cognitive map cursor––indicates the position in
the cognitive map of the node transmitting the
packet.
Reserved
Fig. 1. Packet format in AHCPN.
E. Gelenbe, R. Lent / Ad Hoc Networks 2 (2004) 205–216 211
5. Packet identification (32 bits)––uniquely distin-
guish the packet. Acknowledgments carry the
same packet identification as their originators
(dumb or smart packets). The identification is
used at the source node to remove packets wait-
ing for retransmission, after the correspondingacknowledgment arrives. In addition, smart
packets leave a copy of their packet identifica-
tion in the nodes to identify previously visited
nodes.
6. Destination address (32 bits)––uses an IPv4
addressing space.
7. Cognitive map (variable)––is an area where
packets store information about the network.
A cognitive map in AHCPN consists of the fol-
lowing field:
• Source and intermediate hop addresses––usesIPv4 address format. Smart packets attach a
new record with every node that they visit.
On the other hand, dumb and acknowledg-
ment packets carry the cognitive map given
at the source node.
• Timestamp fields––record the arrival time at
each intermediate node. The entry corre-
sponding to the source node indicates thepacket’s departure time.
• Path availability information field––expresses
the probability to find all nodes and links
available for routing from the present loca-
tion to the destination (calculated from bat-
tery lifetime of nodes).
8. Payload––area to transport IP datagrams.
4.1. Operation
DPs source route datagrams to their desti-
nations, using the paths stored in the route
cache of the source node. If a route to a des-
tination is yet to be determined, datagrams wait
in a queue while SPs seek the route. Fig. 2illustrates the logic in the generation of packets
at source nodes. Until a route to a requested
destination is discovered, source nodes continu-
ously send out smart packets while datagrams
arrive from their upper layer and are locally
stored.
SPs use either unicast or broadcast to propagate
on the network. They decide what method to usedepending on the information available in the
node where they are located. When the available
information is not sufficient to make a unicast
(RNN based) decision, then broadcast is used
(Fig. 3). Note that, at least three neighbors are
required to use the RNN algorithm at any inter-
mediate node. One of the neighbors is simply the
one from which the packet was received and doesnot participate in the computation (split-horizon
principle). At the source node at least two neigh-
bors are required.
Packet arriving from IPlayer
Fig. 2. Generation of packets at source nodes.
Fig. 3. Decision logic in smart packets.
212 E. Gelenbe, R. Lent / Ad Hoc Networks 2 (2004) 205–216
After a route to a new destination has been
established, additional smart packets may depart
to maintain (or improve) the quality-of-service of
the connection. The additional smart packets are
generated as a small percentage of dumb packet
Fig. 4. Smart packet
rate. Fig. 4 illustrates the flow of smart packet-
related processes. Note that smart packets may get
destroyed if a node’s resources do not meet their
needs. This happens, for example, when the
remaining battery lifetime of the node is too low or
when a replica of the same request has already
visited the node.
flow diagram.
Fig. 5. Dumb packet flow diagram.
E. Gelenbe, R. Lent / Ad Hoc Networks 2 (2004) 205–216 213
To accelerate the establishment of bidirectional
connections, a smart packet creates a route back
to the source node if no such route exists yet in
the destination’s route cache. As in CPN, an
acknowledgment departs toward the source nodeon the reverse route described in its cognitive map.
As they travel, acknowledgments update mail-
boxes along the path and the route cache at the
source node. If there are packets awaiting for a
route to be discovered at the source, they depart
immediately after the first acknowledgment for
that connection arrives.
A flow control diagram for dumb packets isdepicted in Fig. 5. As in CPN, dumb packets are
given the complete path to transport payload (in
this case, datagrams). A copy of the original
data remains within the source node until the
packet is successfully delivered to its destination.
The acknowledgments that are originated by
dumb packets confirm to the source the deliv-
ery of packets and update mailboxes along thepath.
Dumb packets collect timestamps as they tra-
vel on the network to keep the mailboxes up-
to-date whereas their acknowledgments collect
battery related and link-quality information on
the path.
5. Simulation experiments
An implementation of the AHCPN algorithm
was developed and integrated into the popular
Network Simulator-2 (NS-2). Our experimentsconsisted in observing the establishment and use of
routes in a dynamic network, which incorporated
random node motion. Each experiment simulated
the operation of the network for 15 min.
We employed 50 nodes divided into two popu-
lations. The first population consisted of 10 nodes
that started the experiment with full battery
charge. A full battery charge allows up to 2 h ofoperation in the lack of communication related
tasks. It was assumed that batteries of all mobile
nodes were all identical with a maximum capacity
of 2 Wh. A mobile node was assumed to consume
10 W to uphold non-communication related
operations. The second population (40 nodes)
started the experiment with 1/8 of the full capacity
of their batteries, which provides 15 min of oper-ation excluding communication tasks.
All nodes started the experiment at a random
location within a rectangular working area of
1500 · 500 meters and moved as defined by the
random waypoint model. For this, each node se-
lected a random destination within the working
Fig. 6. Average number of nodes whose battery powder is
completely depleted, as a function of simulated time.
Fig. 7. Average number of data packets that were delivered to
their destination.
214 E. Gelenbe, R. Lent / Ad Hoc Networks 2 (2004) 205–216
area and moved linearly to that location at a speed
of 2 m/s (approximately equivalent to walking
speed). After reaching their destination, the nodes
selected a new random location with no pause.
Five connections were established and removed
during the simulation of each experiment. Theseconnections were performed only among nodes
from the first population to avoid having shortage
of energy at sources or sinks.
Energy consumption for communication re-
lated tasks was recreated after the linear model by
Feeney and Nilsson [32]. The model predicts the
energy consumption for reception and transmis-
sion of packets. The model takes into account thetype of transmission (unicast or broadcast) and
size of packets. To speed up our simulations and
reduce storage requirements, the energy required
to send and receive packets was assumed to be 100
times larger than the value predicted by the Fee-
ney’s model (consequently, assuming the use of
energy inefficient network interface cards).
The reports that follow show the average valueof the observed variable and the 95% confidence
interval of 100 runs of the experiment. We com-
pare the operation of AHCPN with the response
of a source routing, flood-based ad hoc algorithm
where broadcasts packets are emitted to discover
routes while acknowledgments inform the instan-
taneous fastest route (in a way similar to DSR or
AODV).Under AHCPN, smart packets were sent at a
ratio of 0.01 (on average, one SP every 100 DPs).
Their objective function included both round-trip
delay and battery information as detailed in Sec-
tion 3. To obtain a fair comparison, the flood-
based model sent broadcast packets at the same
rate as that of smart packets.
Fig. 6 reports the average number of nodesfrom population 2 whose battery power is com-
pletely depleted, as a function of the simulated
time. A flooding-based algorithm selects fast
routes without considering the availability of en-
ergy in the nodes; this quickly drains the battery
of a large number of nodes. The learning capacity
of smart packets makes a more efficient use of
resources. However, the total number of datapackets that get to arrive to their destinations is
slightly shorter when using AHCPN (Fig. 7),
which can not be avoided because AHCPN re-
quires more time to adapt to changes in topology.
Flooding provides faster adaptation but have to
pay a high price in consumption of resources.
Figs. 8 and 9 report the average total number of
packets that were transmitted and received during
the simulation. The figures illustrate the aggre-gated number of data packets and control packets.
Flooding produces many more packets than
AHCPN, so a higher consumption of resources is
to be expected with flooding.
Fig. 8. Average number of packets (data packets and control
packets) transmitted during the experiment.
Fig. 9. Average number of packets (data packets and control
packets) received during the experiment.
E. Gelenbe, R. Lent / Ad Hoc Networks 2 (2004) 205–216 215
6. Conclusions
This paper has specified as evaluated the
AHCPN protocol, which uses our previous re-
search on Cognitive Packet Networks (CPN) to
produce an innovative solution that supports the
operation of a mobile, ad hoc network.
In AHCPN, SPs use broadcasts to create a total
or partial flooding that allows nodes acquire
neighboring information while SPs move on thenetwork since flooding is expensive in terms of
resource utilization. Whenever possible, SPs use
unicast-based transmissions based on the CPN
routing algorithm.
We have introduced a new routing metric to
cope with the limited power resources of mobile
networks. Path availability, which models theprobability to find available nodes and links on a
path, was employed to take into account the en-
ergy available at nodes. The end result is that
packets choose nodes which have longer remaining
battery lifetime with a higher probability than
nodes with shorter remaining lifetime. By com-
bining path availability and round-trip delay in a
single goal function, selected paths are still of alength close to the shortest path length.
We have discussed a formal definition of an
Ad Hoc Cognitive Packet Network protocol that
provides an implementation framework to test our
ideas, and a simulation model was developed and
integrated into the Network Simulator 2 (NS-2).
Our results show that the CPN protocol (1) is able
to dynamically discover neighbors and routes, (2)can discover and maintain routes without the need
of a large number of broadcasts, (3) will distribute
network traffic so as to extend the battery lifetime
of the nodes and (4) maintains a comparable per-
formance to more energy consuming, broadcast
based approaches.
Acknowledgements
The authors gratefully acknowledge the support
that made this research possible via the Engineer-
ing and Physical Science Research Council (UK)
under Grant GR/S52360/01, by US Army Stricom
via NAWC under Contract No. N61339-02-
C0117, by NSF under Grant No. EIA0203446,and by the US Army Research Office under Con-
tract No. DAAD190310135.
References
[1] C.E. Jones, K.M. Sivalingam, P. Agrawal, J.-C. Chen, A
survey of energy efficient network protocols for wireless
networks, Wireless Networks 7 (4) (2001) 343–358.
[2] E. Gelenbe, R. Lent, Z. Xu, Networks with cognitive
packets, in: Proceedings of the Eight International
216 E. Gelenbe, R. Lent / Ad Hoc Networks 2 (2004) 205–216
Symposium on Modeling, Analysis and Simulation of
Computer and Telecommunication Systems (IEEE Com-
puter Society) San Francisco, CA, 2000, Opening Key-
Note Paper, pp. 3–12.
[3] E. Gelenbe, R. Lent, Z. Xu, Toward networks with
cognitive packets, in: K. Goto, T. Hasegawa, H. Takagi,
Y. Takahashi (Eds.), Performance and QoS of Next
Generation Networking, Springer, London, 2000.
[4] E. Gelenbe, R. Lent, Z. Xu, Measurement and perfor-
mance of cognitive packet networks, Computer Networks
37 (2001) 691–701.
[5] E. Gelenbe, R. Lent, Z. Xu, Design and performance
of cognitive packet networks, Performance Evaluation 46
(2–3) (2001) 155–176.
[6] E. Royer, C.-K. Toh, A review of current routing protocols
for ad-hoc mobile wireless networks, IEEE Personal
Communications 6 (2) (1999) 46–55.
[7] C.E. Perkins, P. Bhagwat, Highly dynamic destination-
sequenced distance-vector routing (dsdv) for mobile com-
puters, in: Proceedings of the ACM Conference on
Communications Architectures, Protocols and Applica-
tions, London, England, 1994, pp. 234–244.
[8] X. Chen, L. Qi, D. Sun, Global and superlinear conver-
gence of the smoothing Newton method and its application
to general box constrained variational inequalities, Math-
ematics of Computation 67 (222) (1998) 519–540.
[9] S. Murthy, J. Garcia-Luna-Aceves, An efficient routing
protocol for wireless networks, Mobile Networks and
applications 1 (2) (1996) 183–196.
[10] C. Chiang, H. Wu, W. Liu, M. Gerla, Routing in clustered
multihop, mobile wireless networks, in: Proceedings of the
IEEE Singapore International Conference on Networks,
1997, pp. 197–211.
[11] G. Pei, M. Gerla, T.-W. Chen, Fisheye state routing: A
routing scheme for ad hoc wireless networks, in: Proceed-
ings of ICC 2000, New Orleans, IEEE, 2000.
[12] D.B. Johnson, D.A. Maltz, Y.-C. Hu, J.G. Jetcheva, The
dynamic source routing protocol for mobile ad hoc
networks, IETF Draft, March 2001.
[13] C.E. Perkins, E.M. Royer, Ad hoc on demand distance
vector routing, in: 2nd IEEE Workshop on Mobile
Computing Systems and Applications, 1999.
[14] C.E. Perkins, S.R. Das, Ad hoc on-demand distance vector
(aodv) routing, IETF Draft, March 2001.
[15] C.E. Perkins, Ad Hoc Networking, Addison-Wesley,
Reading, MA, 2000.
[16] C.-K. Toh, A novel distributed routing protocol to support
ad-hoc mobile computing, in: Proceedings of the 1996
IEEE Fifteenth Annual International Phoenix Conference
on Computers and Communications, 1996, pp. 480–486.
[17] R. Dube, C. Rais, K. Wang, S. Tripathi, Signal stability
based adaptive routing (ssa) for ad hoc mobile networks,
IEEE Personal Communication 4 (1) (1997) 36–45.
[18] S. Corson, S. Papademetriou, P. Papadopoulos, V. Park,
An Internet manet encapsulation protocol (imep) specifi-
cation, IETF Draft, August 1998.
[19] Z.J. Haas, M.R. Pearlman, P. Samar, The intrazone
routing protocol (iarp) for ad hoc networks, IETF Draft,
January 2001.
[20] Z.J. Haas, M.R. Pearlman, P. Samar, The interzone
routing protocol (ierp) for ad hoc networks, IETF Draft,
January 2001.
[21] Y.-B. Ko, N.H. Vaidya, Location-aided routing (lar) in
mobile ad hoc networks, in: Proceedings of ACM/IEEE
Mobicom, Dallas, TX, 1998, pp. 66–75.
[22] Y. Ko, N. Vaidya, Geotora: A protocol for geocasting
in mobile ad hoc networks, in: Proceedings of the 2000
International Conference on Network Protocols, Osaka,
Japan, 2000.
[23] M. Bhardwaj, R. Min, A. Chandrakasan, Power-aware
systems, in: Proceedings of 34th Asilomar Conference on
Signals, Systems and Computers, November 2000.
[24] A. Sinha, A. Chandrakasan, Energy efficient real-time
scheduling, in: Proceedings of the International Conference
on Computer Aided Design (ICCAD), San Jose, Novem-
ber 2001.
[25] H. Mehta, R. Owens, M. Irwin, R. Chen, D. Ghosh,
Techniques for low energy software, in: Proceedings of the
International Symposium on Low Power Electronics and
Design, August 1997.
[26] L. Gruenwald, S.M. Banik, A power-aware technique to
manage real-time database transactions in mobile ad hoc
networks, in: 4th International Workshop on Mobility
in Database and Distributed Systems, International Con-
ference on Database and Expert Systems Applications
(DEXA), September 2001.
[27] S. Singh, M. Woo, C.S. Raghavendra, Power-aware
routing in mobile ad hoc networks, in: Proceedings of the
Fourth Annual ACM/IEEE International Conference on
Mobile Computing and Networking, 1998, pp. 181–190.
[28] C.-K. Toh, Maximum battery life routing to support
ubiquitous mobile computing in wireless ad hoc networks,
IEEE Communications Magazine 39 (6) (2001) 138–147.
[29] Q. Li, J.A. Aslam, D. Rus, Online power-aware routing
in wireless ad-hoc networks, in: Mobile Computing and
Networking, MobiComm 2001, 2001, pp. 97–107.
[30] M. Maleki, K. Dantu, M. Pedram, Power-aware source
routing protocol for mobile ad hoc networks, in: Proceed-
ings of the 2002 International Symposium on Low Power
Electronics and Design, 2002.
[31] T. Xu, J. Heidemann, D. Estrin, Adaptive energy-conser-
ving routing for multihop ad-hoc networks, USC/ISI
Research Report 527, October 2000.
[32] L.M. Feeney, M. Nilsson, Investigating the energy con-
sumption of a wireless network interface in an ad hoc
networking environment, in: IEEE INFOCOM, 2001.
top related