Philipp Hurni

Traffic-Adaptive and Link-Quality-AwareCommunication in Wireless Sensor Networks

Abstract: This paper is a summary of the main contribu-tions of the PhD thesis published in [1]. The main researchcontributions of the thesis are driven by the research ques-tion how to design simple, yet efficient and robust run-timeadaptive resource allocation schemes within the commu-nication stack of Wireless Sensor Network (WSN) nodes.The thesis addresses several problem domains with con-tributions on different layers of the WSN communicationstack.

Themain contributions can be summarized as follows:First, a a novel run-time adaptive MAC protocol is intro-duced, which stepwise allocates the power-hungry radiointerface in an on-demand manner when the encounteredtraffic load requires it. Second, the thesis outlines a metho-dology for robust, reliable and accurate software-basedenergy-estimation, which is calculated at network run-time on the sensor node itself. Third, the thesis evaluatesseveral Forward Error Correction (FEC) strategies to adap-tively allocate the correctional power of Error CorrectingCodes (ECCs) to cope with timely and spatially variable biterror rates. Fourth, in the context of TCP-based communi-cations in WSNs, the thesis evaluates distributed cachingand local retransmission strategies to overcome the perfor-mance degrading effects of packet corruption and trans-mission failures when transmitting data over multiplehops. The performance of all developed protocols are eval-uated on a self-developed real-world WSN testbed andachieve superior performance over selected existing ap-proaches, especially where traffic load and channel condi-tions are suspect to rapid variations over time.

Dr. Philipp Hurni: E-Mail: hurni@iam.unibe.ch

1 Introduction

WSNs are autonomous networks consisting of a largenumber of inexpensive small electronic devices and areequipped with sensors to measure a wide range of envir-onmental conditions (e.g., temperature, acceleration, rela-tive humidity, pressure, oxygen concentration). For dec-ades, measurements of physical values were carried outmanually with bulky and costly equipment, a tedious,

time-consuming and error-prone procedure. The conveni-ent and automated manner of being able to gain real-timemeasurement data from all kinds of distant locations andin a high resolution is the key advantage that drives re-search and innovation onWSN technologies today.

2 Traffic-Adaptive Medium AccessControl

Sensor nodes should consume as little energy as possible,since real-world sensor networks need to remain operablein an area of interest over several days,weeks or even years,given an initial battery charge, e.g., of two AA batteries.However, when keeping the radio and the onboard sensorspermanently active, a typical sensor node drains out ofenergy after not much more than a couple of days. In thepast decade, mechanisms have been developed to prolongthe time a node can live on an initial energy charge, espe-cially by designing energy-conserving communication pro-tocols on the MAC and routing layer. Instead of attemptingto balance between energy consumption and the provisionof suitable Quality of Service (QoS) parameters, most oftoday’s Energy-Efficient Medium Access (E2-MAC) proto-cols have been designed with the only goal of minimizingthe energy conservation. As a result, most of these proto-cols are able to deliver little amounts of data with a lowenergy footprint, however, introducing severe restrictionswith respect to the achievable throughput and latency, andtotally failing to adapt to varying traffic loads and changingrequirements of the imposed traffic load. The gain in en-ergy-efficiency hence comes at the cost of severely re-strained maximum throughput, as well as massively in-creased end-to-end packet latency.

The first main contribution of the PhD thesis beginswith thoroughly examining the design space of the sixmostfrequently cited E2-MAC protocols and their performanceunder variable traffic load. We then developed a tri-partitemetric tomeasure andquantify the traffic adaptivity, forma-lizinganotion for thepropertyof trafficadaptivity, forwhichameasurabledefinitionhasnotyetexistedbefore.Applyingthis metric to the selection of MAC protocols conveyed thatthe WiseMAC [2] yet achieves the best adaptivity under

DOI 10.1515/pik-2012-0138 PIK 2013; aop

variable load, a conjecture that is also supported by thecomparative study [3]. With the introduction of the Maxi-mally Traffic-AdaptiveMAC (MaxMAC)protocol [4],we spe-cifically target at alleviating the performance degradingimpact of most of today’s E2-MAC protocols with respect totrafficadaptivity.RelyingonbestpracticesofadecadeofE2-MACprotocol research,webaseour investigationsonadap-tableprotocolmechanismson themostwidelyappliedclassof asynchronous random-access and preamble-samplingbased MAC protocols. While MaxMAC operates with a lowenergy footprintat lowtraffic, it isable todynamicallyadaptto sudden changes in the network traffic load at run-time. Itintegrates established design principles of asynchronouspreamble-sampling based MAC protocols with novel run-time traffic adaptation techniques to allocate the costlyradio transceiver truly in an on demandmanner.

We evaluated MaxMAC in the OMNeT++[5] networksimulator and with a real-world prototype implementationon the MSB430 nodes [6]. We first compared MaxMAC’sagainst a selection of existing E2-MAC protocols in simula-tion. By applying the developed metric to network simula-tion results of MaxMAC, we showed that the developedprotocol mechanisms succeed in reaching a high trafficadaptivity. We then continued to study the real-worldfeasibility of the MaxMAC protocol by developing a real-world protocol prototype, and compared it against twoother wireless channel MAC protocols: the WiseMAC pro-tocol, and ScatterWeb2 OS’ energy-unconstrained CSMA.The experimental prototype-based evaluation relies on aseries of small to medium benchmarking experiments andseveral experiments with different traffic patterns andnetworking conditions, carried out on the testbed facilitiesat University of Bern.

Fig. 1: Indoor Distributed Testbed Scenario.

In the following, we briefly tackle one example of themanyexamined scenarios:we evaluated the behavior of the three

protocols CSMA,WiseMAC andMaxMACwith variable andcontending traffic from different areas of the network usingthe entire V-shaped network consisting of 7 MSB430 nodesdepicted in Figure 1. The sink node D is located in the topright corner and receives periodic alive status messagesfrom nodes SA, IA1, SB and IB1 each 20s. At some point, thenodes sense events and start sending 100 packets at a rateof 2 packets/s. There are overlaps of duration of 10s wheretwo nodes are concurrently attempting to send their pack-ets towards the sink. Since experimental evaluations ofWSNmechanisms usually have a high variation, we ran thedescribed experiment with 20 independent runs for each ofthe three MAC protocols. Figures 2, 3 and 4 depict the rateof received packets by the sink nodeD from each of the foursource nodes. WiseMAC obviously manages well to deliverits periodic alive status messages to the sink. However, itsuffers from major packet loss when the nodes have totransmit the 100 payload messages at a rate of 2 packets/s.Since packets have to be forwarded across multiple hops,the rather limited channel contention mechanism of Wise-MAC and the hidden node problem lead to high packetlosses. These are mainly caused by collisions and bufferoverflows after failed transmission attempts. The rate ofsuccessfully delivered packets from the nodes SA, IA1, SB, IB1therefore does not exceed 1 packet/s on average, with themajor share of packets being lost. In contrast to WiseMAC,CSMAandMaxMAC succeed in delivering the periodic alivestatus messages, but also the major share of the 100 pack-ets which are triggered at a higher rate. The small timeperiodswhere two nodes are delivering their series of pack-ets at the same time is managed best by CSMA. MaxMAC’srate of received packets reaches a slightly lower maximumthroughput, and also tends to drop some packets whenonly one event is being handled.

Fig. 2: CSMA: Packet Reception Rate from Nodes SA, IA1, SB and IB1.

Fig. 3:MaxMAC: Packet Reception Rate from Nodes SA, IA1, SB and IB1.

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Fig. 4:WiseMAC: Packet Reception Rate from Nodes SA, IA1, SB and IB1.

All our experimental evaluations demonstrated that Max-MAC reaches its goal of being clearly distinguishable fromexisting preamble-sampling based approaches by reach-ing nearly the same throughput and a similarly low la-tency as energy-unconstrained CSMA, while still exhibit-ing the same energy-efficiency during periods of sparsenetwork activity. The MaxMAC protocol hence succeeds incombining the advantages of energy unconstrained CSMA(high throughput, high PDR, low latency) with those ofclassical E2-MAC protocols (high energy-efficiency). Likemost contention-based MAC protocols, MaxMAC is a gen-eral-purpose protocol, and does not rely on assumptionsthat are cumbersome to achieve (e.g, rigid time-synchroni-zation across the entire network). It can hence be appliedwithout changes in scenarios where constant low-ratetraffic is expected, and where in most cases B-MAC and X-MAC are being used today.

3 Software-based Energy-Estimation

While commonly used networking metrics such as packetdeliveryrate,source-to-sinklatenciesormaximumthrough-put can easily be determined in real-world WSN testbeds,measuring the power consumption of sensor nodes ismuch harder: costly high-resolution digital multimeters orcathode-ray oscilloscopes need to be hooked to the nodesto sample the varying low currents and voltages.

In the second main contribution of the PhD thesis [1],we develop a reliable and robustmethodology for software-based energy estimation, cf. [7].

We implemented a simple Three States Model that esti-mates the energy consumption of a WSN nodes using thethree states of the transceiver receive, transmit and sleep, amodel which is used in most studies on WSN MAC proto-cols. To find optimal values for the estimation model para-meters, we used a multivariate Ordinary Least Squares(OLS) regression model. By comparing physical measure-ments with the software-based energy-estimations com-putedon thenodes themselves,weobserved that themodel

achieves a mean absolute estimation error (MAE) in therange of 3% and more compared to the real physicallymeasured energy consumption. We then investigatedfurther means to improve the estimation accuracy by en-hancing the model basing on three state variables. By tak-ing into account the transceiver switches and integratingthem into our model, we could indeed significantly im-prove the estimation accuracy. We could reduce the MAEand its standard deviation (denoted as μ ± σ) of the soft-ware-based energy estimations to 1.13% ± 1.15%. Figure 6illustrates the enhanced model’s concept of a node’s cur-rent draw.We refer to thismodel as Three StatesModel withState Transitions hereafter, as specified in equation (E1).

E = PrcvTrcv+PtxTtx+PslpTslp+αsrcv+βstx+γsslp (E1)

According to this enhanced model, the energy consumedby an arbitrary node is a function of the total time it has itsradio transceiver in the three different states (denoted asTrcv, Ttx, Tslp) and the three adjustment terms αsrcv, βstx, andγsslp. The parameters α, β, γ compensate for the transceiverswitches to the states receive, transmit and sleep. Applyingevenmore sophisticated parameter calibration of per-nodeand per-protocol calibration using the multivariate OLSmodel has been shown to reduce the mean absolute errorand standard deviation to as few as only 0.42% ± 0.72%across the four evaluated wireless channel MAC protocolsS-MAC, T-MAC,WiseMAC, and IEEE802.11-likeCSMA.

Fig. 5: Current Draw of physicallymeasured node.

Fig. 6: Current Draw as modeled by our software-based energyestimation basing on the Three States Model with State Transitions.

4 Link-Quality-Aware AdaptiveForward Error Correction (FEC)Strategies

WSN nodes are typically preconfigured with the mostcrucial parameter settings at compile-time, hence much

Traffic-Adaptive and Link-Quality-Aware Communication in Wireless Sensor Networks 3

Fig. 7: PDRs per Link and Overall Source-to-Sink PDR.

before the actual network deployment. This can result insuboptimal performance in case the actually encounteredenvironment differs much from the conditions expectedduring planning. When deciding to apply FEC, a crucialdesign question consists in selecting the appropriate ErrorCorrecting Code (ECC). While a too weak code might notbe able to correct many errors, a too strong code wouldwaste precious time and energy for encoding/decodingand transmitting additional parity data.

The third main contribution of the PhD thesis [1] ad-dresses the challenges related to the inherently unreliablewireless channel in WSNs by exploring the potential ofrun-time adaptive Forward Error Correction (FEC) schemes.We have implemented eight different ECCs and have pro-posed three run-time adaptive FEC strategies, which reactto deteriorating link quality by allocating the correctionalpower of ECC codes in an on demandmanner. The conceptapplied in this context shares many similarities to that ofMaxMAC, cf. Section 2. The parameter adaptation algo-rithm follows a similar finite-state-machine-based model,where each state describes a certain set of parameters.Input variables such as the success of previous transmis-sions then define the state transitions, allocating less com-putational power when the link quality is good and unen-

coded transmissions are successfully acknowledged, andmore sophisticated coding when link qualities are deterio-rated.

Our three developed FEC adaptation strategies arereferred-to as Stateful Adaptive FEC (SA-FEC), StatefulHistory Adaptive FEC (SHA-FEC) and Stateful Sender Re-ceiver Adaptive FEC (SSRA-FEC) hereafter. SA-FEC selectsthe ECC for the next transmission to the specified destina-tion according to the success of the last one. If the lasttransmission of an ECC packet has been successful, SA-FEC selects the next less powerful ECC. The second strat-egy SHA-FEC maintains a variable denoting the currentlyused ECC and a history of entries representing the recentpast transmissions. In case a transmission succeeds andan ACK is received, SHA-FEC stores an integer value repre-senting the next lower ECC into the history, since it as-sumes that a lower ECC would have sufficed as well. Incase the transmission fails and no ACK is received, SHA-FEC stores a value representing the next higher ECC, as-suming that more correctional power is necessary. Thethird strategy SSRA-FEC extends SHA-FEC by taking intoaccount an additional history containing receiver informa-tion into the ECC selection process. Given that a packetpayload consists of n blocks b1, b2, . . . bn, the receiver

4 Philipp Hurni

calculates for each block the number of occurred andcorrected errors e1, e2, . . . en during the decoding process.If all the blocks could be correctly decoded, the receiversends emax = max (e1, e2, . . . en) back to the sender in theACK frame.

We have examined the different implemented ECCs aswell as the adaptive FEC strategies in a series of experi-ments, of which the majority were conducted again on thedistributed testbed facilities of University of Bern.

In the following, we outline the results of one particu-lar experiment scenario, where each sensor node exceptthe sink node generates and sends packets to its gatewaynode towards the sink node. We evaluated 1000 packetsgenerated on each node for each run, and 20 runs for eachconfiguration. The total amount of transmissions withinthe network amounts to roughly 2 transmissions per sec-ond, resulting in a channel utilization of roughly 14%across the entire testbed. With this level of channel utiliza-tion, interferences due to other ongoing transmissions arelikely to occur, which may render the application of ECC tobe a valuable countermeasure.

Figure 7 depicts the Packet Delivery Rate (PDR) barsof the examined ECC and the adaptive FEC approaches.The figure depicts for each examined setting the PDRs ofthe different links and the overall source-to-sink PDR.When comparing with the case of unencoded transmis-sions (top left corner of Figure 7), one can clearly see thatthe application of FEC made transmissions along theerror-prone links more reliable, especially on SA → IA1. Theapplication of ECCs has clearly paid off with respect toalleviating the deteriorating impact of the lossy links IB1 →IB2 and SA → IA1: the improvement in the achieved PDR isup to 15% of the total generated packets (cf. Figure 7,DECTED and BCH). Comparing the results of Hamming(7,4), DECTED(16,8) and the BCH-variants with the adap-tive approaches, we can conclude that the adaptive FECsachieved astonishingly good results. The three strategiesSA-FEC, SHA-FEC and SSRA-FEC outperformed almostevery other static and network-wide setting of any of theimplemented ECC codes. Since the adaptive schemes haveonly employed FEC on weak links and in periods of ele-vated BER, the majority of packets could be sent unen-coded, which may also have led to fewer interference dueto shorter transmission times, compared to static FEC set-tings. The major advantage of the adaptive approaches isthe on-demand nature of using the correctional power ofECCs: with simple state-based concept, the adaptive ap-proaches have managed to reach the same or better PDRs.Since the application of ECC comes at the cost of time andhence energy spent for encoding and decoding, ECCsshould be limited to weak and error-prone links and/or

time periods where the link quality suffers from deteriorat-ing influences.

5 TCP Optimizations for WirelessSensor Networks

TCP/IP has been shown to perform rather poorly [9] [10] inWSNs with multiple hops, due to the unreliable nature ofthe wireless channel (higher bit error rates and packetloss), particular properties of and interactions with theunderlying wireless channel MAC protocols (exponentialbackoff mechanisms, hidden node and exposed node pro-blem), and the design of the TCP congestion control me-chanisms.

In the final contribution of the PhD thesis [1], weexperimentally evaluate the performance of TCP/IP acrossmultiple hops in WSNs, cf. [8] for more details. We take upbasic concepts of distributed TCP caching and local re-transmissions proposed in [9] [10] and self-developed ex-tensions of the latter, and implement them – in contrast tothe studies [9] [10] – in a MAC-layer independent mannerinto our Caching and Congestion Control (cctrl) module.The cctrlmodule developed within this experimental studyis a modular add-on for the μIP stack [11] of the Contiki OS[12], which implements and augments the distributedcaching and local retransmission features proposed inDTC [9] and TSS [10]. On top of the basic retransmissionmechanism, we further designed and implemented addi-tional extensions for our cctrl module. These extensionsconsist in monitoring the channel for activity on the linklayer (activity monitoring) in order to defer retransmis-sions and in splitting the TCP connection in parallelstreams (dual-connections). We show that our contri-bution is able to significantly increase the end-to-endthroughput across multiple hops in various real-worldWSN topologies. We study the performance of cctrl usingthree different E2-MAC protocols (X-MAC, LPP, Contiki-MAC) and Contiki’s energy-unconstrained CSMA variantNullMAC. We test our implementations in an indoor wire-less sensor node testbed in two scenarios where data hasto be transmitted reliably across routes of increasinglength.

Figure 8 is an excerpt from the results of numerousexperiments conducted on our distributed testbed labora-tories. In general, we encountered a rather high variabilityamong the results of the different examined protocol con-figurations. Some protocols (e.g., NullMAC, LPP) generallyreacted positively to the local retransmission scheme,whereas others (i.e., ContikiMAC) performed rather worse.

Traffic-Adaptive and Link-Quality-Aware Communication in Wireless Sensor Networks 5

Fig. 8: PDRs per Link and Overall Source-to-Sink PDR.

The results reveal that the cctrl module can definitely in-crease the throughput of TCP across multi-hop WSNs inmany of the examined situations. However, due to thedifferences in the examined MAC protocols algorithms, theimpact of the proposed cctrl extensions varied heavily,such that it was impossible to find a single cctrl configura-tion thatmaximizes the throughput with all evaluatedMAClayers. We observed that the results depend not only onMAC protocol characteristics, but also on the network to-pology and the presence of interfering traffic. The cctrlmodule managed to increase the end-to-end TCP through-put of NullMAC by up to 84% across routes with 2–6 hopslengths. X-MAC and LPP tended to exhibit improvements,whereas ContikiMAC’s performance was significantly de-graded. The best results with respect to the energy-effi-ciency (measured as radio on-time per TCP segment) wereachieved with NullMAC in combination with our cctrlmod-ule. If a large portion of data has to be transmitted to acertain node across multiple hops, it is a better strategy tolet the entire route temporarily operate without duty-cy-cling the radio, because the net radio on-time per trans-mitted TCP segment is lower thanwith any existing E2-MACprotocol. This observation basically confirms and justifiesthe MaxMAC concept, which proposes to temporarilyswitch to CSMA if the encountered load conditions can notbe handled anymore without major packet loss when stick-ing to the periodic radio duty-cycling pattern.

References

1 Philipp Hurni, “Traffic-Adaptive and Link-Quality-Aware Commu-nication in Wireless Sensor Networks,” PhD Thesis submittedand accepted at University of Bern, Switzerland, December 2011.

2 A. El-Hoiydi and J. D. Decotignie, “WiseMAC: An Ultra Low PowerMAC Protocol for Multihop Wireless Sensor Networks.” Interna-tional Workshop on Algorithmic Aspects of Wireless Sensor Net-works (ALGOSENSORS), Turku, Finland, July 2004, pp. 18–31.

3 K. Langendoen and A. Meier, “Analyzing MAC Protocols for LowData-Rate Applications,” ACM Transactions on Sensor Networks(TOSN), New York, USA, vol. 7, no. 2, pp. 1–40, August 2010.

4 P. Hurni and T. Braun, “MaxMAC: a Maximally Traffic-AdaptiveMAC Protocol for Wireless Sensor Networks.” European Con-ference on Wireless Sensor Networks (EWSN), Coimbra, Portu-gal, February 2010, pp. 289–305.

5 A. Varga, “The OMNeT++ Discrete Event Simulation System.”European Simulation Multiconference (ESM), Prague, CzechRepublic, June 2001, pp. 319–324. [Online]. Available:http://www.omnetpp.org.

6 M. Baar, E. Koeppe, A. Liers, and J. Schiller, “The ScatterWebMSB-430 Platform for Wireless Sensor Networks.” SICS ContikiWorkshop, Kista, Sweden, March 2007.

7 P. Hurni, B. Nyffenegger, T. Braun, “On the Accuracy of Soft-ware-based Energy Estimation Techniques.” European Confer-ence on Wireless Sensor Networks (EWSN), Bonn, Germany,February 2011, pp. 49–64.

8 P. Hurni, U. Bürgi, T. Braun, and M. Anwander, “PerformanceOptimizations for TCP in Wireless Sensor Networks.” EuropeanConference on Wireless Sensor Networks (EWSN), Trento, Italy,February 2012.

9 A. Dunkels, J. Alonso, T. Voigt, and H. Ritter, “Distributed TCPCaching for Wireless Sensor Networks.”Mediterranean Ad-HocNetworks Workshop (Med-Hoc-Net), Bodrum, Turkey, June 2004,pp. 13–28.

10 T. Braun, T. Voigt, and A. Dunkels, “TCP Support for SensorNetworks.”Wireless On demand Network Systems and Services(WONS), Obergurgl, Austria, January 2007, pp. 162–169.

11 A. Dunkels, “Full TCP/IP for 8-Bit Architectures.” InternationalConference on Mobile Systems, Applications, and Services(MobiSys), San Francisco, USA, May 2003, pp. 85–98.

12 A. Dunkels, B. Groenvall, and T. Voigt, “Contiki – a Lightweightand Flexible Operating System for Tiny Networked Sensors.”IEEE Workshop on Embedded Networked Sensors (EmNets),Tampa, Florida, November 2004, pp. 455–462. [Online].Available: {http://www.sics.se/contiki/}.

Dr. Philipp Hurni: Institute of ComputerScience and Applied Mathematics,University of Bern, Switzerland

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