DRCS: A Distributed Routing and Channel Selection Scheme forMulti-Channel Wireless Sensor Networks

Amitangshu Pal and Asis NasipuriElectrical & Computer Engineering, The University of North Carolina at Charlotte, Charlotte, NC 28223-0001

E-mail:{apal,anasipur}@uncc.edu

Abstract- We propose a joint channel selection andquality aware routing scheme for multi-channel wirelesssensor networks to improve the network lifetime. A datacollection traffic pattern is assumed, where all sensornodes perform periodic sensing and forwarding of datato a centralized base station (sink). The proposed schemeachieves lifetime improvement by reducing the energyconsumed by overhearing and also by dynamically bal-ancing the lifetimes of nodes. Performance evaluationsare presented from experimental studies as well as fromextensive simulation studies to show the effectiveness ofthe proposed scheme.

Keywords: Wireless sensor networks, multi-channel rout-ing, distributed algorithms.

I. INTRODUCTION

Development of new approaches for optimizing energyusage is a key issue for achieving reliable and long-termoperation of wireless sensor networks (WSNs). Since batteriesare hard to replenish, energy optimization is a critical designrequirement for all protocols and algorithms for WSNs. Muchof the work on energy optimization in WSNs are focusedon development of methods for minimizing the number ofradio transmissions and/or receptions, which is the largestcontributor to energy usage in sensor nodes. The complexityof this energy optimization problem in sensor networks arisesdue to the fact that it has to be addressed by network wideadaptations as opposed to independent adaptations at thenodes.

We consider large-scale WSNs for data collection appli-cations, where implementation of network-wide time synchro-nization is a significant challenge. Hence, it is difficult to applysynchronized duty cycling and scheduled transmissions in suchnetworks, which are critical for avoiding energy wastage fromoverhearing. In this work, we propose the use of multipleorthogonal channels to alleviate the overhearing problem andthereby improve the network lifetime. Current WSN platformssuch as MICAz and Telos that use CC2420 radio can operateon multiple channels, which are traditionally used to addressinterference problems. We develop a quality and battery-healthaware Distributed Routing and Channel Selection (DRCS)scheme that dynamically chooses channels and routes tooptimize network lifetime and performance. The objective isto dynamically equalize the remaining lifetimes of nodes asestimated from their current battery capacity and usage. Theperformance of DRCS is obtained from experiments using

a MICAz testbed as well as from simulations. Performancecomparison with an existing multi-channel routing protocolfor WSNs is also presented from simulations. This work isan extension of our previous work that was presented in [1],where we discussed a distributed channel selection schemebased on hop-counts and battery healths of the nodes. Thecurrent work incorporates a new link quality metric instead ofhop-count that makes this scheme suitable for applications inreal world channel varying scenarios.

II. RELATED WORK

Multi-channel routing in wireless networks has received alot of attention in recent times [2], [3], [4], [5]. However,most of the work published in this area either assume amulti-radio transceiver at each node or generate high controloverhead for channel negotiation. Much of this work focuseson reducing the complexity of solving the joint channelselection and routing problem. These schemes are not suitablefor WSNs where each sensor is typically equipped with singleradio transceiver and has limited computational capabilities. Inaddition, overhead must be minimized since energy resourcesare at a premium. Some multi-channel MAC protocols forWSNs such as MMSN [6], TMMAC [7], MMAC [8] aredesigned for single radio interfaces per node. However, theyrequire precise time synchronization, which is hard to obtainin WSNs.

Recently, some strategies for joint channel assignment androuting for WSNs were proposed in [9], [10], [11]. In [9], theauthors propose a Tree-based multichannel protocol (TMCP)where the whole network is statically divided into mutuallyexclusive single-channel subtrees to reduce interference. Au-thors in [10] propose a control theory approach that selectschannel dynamically to achieve load balancing among chan-nels, whereas in [11] authors propose a channel assignmentscheme for WSNs based on game theory to reduce inter-ference. All of the above schemes mainly consider reducingnetwork interference. Interference is proportional to packetsize as well as packet interval. Generally in WSNs the packetsize as well as packet interval are small, thus interference isusually not a primary performance factor. Also, some of theabove approaches are either centralized or need the topologyinformation that is not always possible to obtain in WSNs. Asopposed to these contributions, the proposed DRCS protocolperforms channel selection and routing together for improvingthe battery lifetime in WSNs, which is the main contributionof this paper. Furthermore, DRCS is distributed, can be applied

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Fig. 1. Experimental setup (a) to assess the activities of the radio (b) of awireless sensor node performing data collection.

without time synchronization, and requires a single transceiverper node.

III. MOTIVATION BEHIND THIS WORK

Radio transmissions as well as receptions are the criticalenergy-consuming tasks in typical low-powered wireless sen-sor nodes. For instance, the MICAz nodes draw about 20mAof current while transmitting and receiving, whereas it drawsabout 20 µA in idle mode and 1µA in sleep mode. Hence,a key aspect of designing energy-efficient wireless sensornodes is to minimize the radio active periods, allowing thenode to sleep as long as possible. Popular energy efficientwireless sensor networking protocols such as XMesh [12]employs low-power (LP) operation by letting nodes duty cyclein their sleep modes for brief periods of time to detect possibleradio activity and wake up when needed. While this principleextends the battery life (lifetime) of the nodes considerably, akey factor that leads to energy wastage is overhearing, i.e.receiving packets that are intended for other nodes in theneighborhood. The traditional mechanism used for avoidingoverhearing is transmission scheduling, which requires timesynchronization that we assume is absent in the WSNs.

The effect of overhearing is illustrated in Figure 1, whichdepicts an experiment using six MICAz motes and a sink.The network is programmed with the collection tree protocol(CTP) [13] application where each node transmits periodicdata packets comprising of sensor observations with an in-terval of 10 seconds and routing packets (beacons) with aninterval that varies between 128 and 512000 milliseconds.The network uses the beacons to build link quality basedleast-cost routes from all nodes to the sink. All nodes usean extremely low transmit power of −28.5 dBm and applythe LowPowerListening scheme [14] with a wake-up intervalof 125 milliseconds. We run this experiment for 10 minutesand record the total number of beacons and data packetssent/received throughout the network as well as the networkwide overhearing. The results, shown in Figure 1(b), indicatethat even with sleep cycles, overhearing is a dominating factorin the energy consumption in the nodes. Consequently, amechanism to optimally distribute the network traffic overmultiple channels would lead to reduction in overhearing andsignificant improvement in the lifetime of the network.

In addition to reducing overhearing, a second considerationfor improving the network lifetime is to address the effect

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Fig. 2. Illustration of the layout (a) of ParadiseNet [12], a 122-node WSNdeployed for equipment health monitoring from a power substation, and theaverage battery usage of nodes in different geographical zones over a period offive months (b). ParadiseNet uses a single-channel link quality based routingprotocol. The goal of this work is to develop a multi-channel tree for suchWSNs to extend its lifetime (c).

of differential battery drainage among the nodes. This ismotivated by experimental observations from a WSN testbedthat was developed by the authors for health monitoring ofhigh-power equipment in a power substation in Figure 2. TheWSN, called PradiseNet [15], consists of 122 wireless sensornodes that were deployed in 1000 × 400 feet area, and usesa link-quality based routing protocol. Figure 2(a) depicts thelocation of nodes in ParadiseNet and Figure 2(b) depicts theaverage drops in the battery levels in the four regions ofthe network over a period of five months of operation. Itcan be observed that while nodes closer to the base stationgenerally have higher voltage drops, Zone-C has the highestdrop. This is basically due to the fact that sensor nodes inZone C are responsible for forwarding most of the packetsfrom Zone A and Zone B. In addition, nodes from Zone C alsoexperience higher amount of overhearing traffic. This type ofenergy imbalance ultimately results in, nodes in Zone C dyingearlier than the ones in other zones which will collectivelyresult in network partitioning and decrease in the lifetime ofthe network. Consequently, it is important that in addition toaddressing the overhearing problem, the routing and channelselection scheme should balance the energy consumption inthe nodes so that the network lifetime is maximized.

IV. MULTI-CHANNEL ROUTING IN WSNS

In data collecting wireless sensor networks the forwardingscheme follows a tree structure connecting the nodes to thesink. With a single channel, a node overhears all nodes thatare in the receiving range of that node. Our first objective isto use a multi-channel tree so that the overhearing problem isreduced. In our scheme, the available channels are distributedamong the nodes so that each node listens on its selectedchannel by default. For data transmissions and forwarding,each node temporarily switches to the channel of its parent and

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Fig. 3. Battery discharge curve of a typical node in Paradisenet

switches back to its designated channel when the transmissionis completed. Selection of designated channels as well asparents are performed based on a battery health parameterH and a link quality parameter (ETX), as explained below.While channel selection builds a multi-channel tree that is theprimary mechanism for overhearing reduction (see illustrationin Figure 2(c), where different channels are shown in differentcolors), it also builds the framework for dynamic route andchannel selection to achieve load balancing, which is designedto meet our second objective of lifetime equalization.

A. Preliminaries

We define the battery health-metric H of a node to representits remaining battery lifetime, i.e. the estimated time until itsbattery is depleted under its currently estimated energy usage.We assume H∝B

I , where B is the remaining capacity of thebattery and I represents the estimated current drawn at thenode. Based on the experimentally validated model [15], thecurrent drawn in each node is calculated as follows:

I = IBtTBtTB

+M.IDtTDt +N. IBrTBrTB

+O.IDrTDr

+ F.IDtTDt +IsTsTD

+NP .IPTP (1)

where Ix and Tx represent the current drawn and the du-ration, respectively, of the event x; and TB represents thebeacon interval. Transmission/reception of beacons is denotedby Bt/Br, data transmit/receive is denoted by Dt/Dr andprocessing and sensing are denoted as P and S, respectively. Oand F are the overhearing and forwarding rates, respectively,and N is the number of neighbors. M is the rate at which anode transmits its own packets. If there are no retransmissions,then M = 1

TD, where TD is the data interval. NP represents

the number of times that a node wakes per second to checkwhether the channel is busy, and is set to 8 in our applica-tion. We assume that each node is able to estimate all thedynamic parameters that are used in equation (1), by periodicassessment of its overheard and forwarded traffic.

In this work, we assume that the battery capacity B isestimated from the battery voltage. We consider MICAz nodes,which operate in a voltage range of 2.7V to 3.3V [16]. Ex-perimental data from ParadiseNet indicates that the dischargecurve for alkaline cells under typical usage (i.e. < 1mAaverage current) is approximately linear within this range. Thisis illustrated in Figure 3. The actual battery voltage is relatedto the ADC reading as follows: Vbat = 1.223×1024

ADC reading . Thus,assuming that the capacity is 100% when the battery voltageis greater than or equal to 3V (ADC reading = 417 from

All nodes are on same channel Runs CTP, choose receiver channel

Nodes switch to their receiver channel Change transmit channel dynamically

0 τ time

Fig. 4. The proposed channel selection scheme in DRCS

MICAz voltage sensor), and 0% when it drops below 2.6V(ADC reading = 482), the battery capacity can be estimatedas B = min

(100,

482−ADC reading0.65

). Although this is not

an accurate estimate, it provides a computationally simpleassessment of the battery health1.

To estimate the quality of a route, we use the expectednumber of transmissions (ETX) that is used in CTP. An ETXis the expected number of transmission attempts required todeliver a packet successfully to the receiver. Hence, a low ETXvalue indicates a good end to end quality of a route, and viceversa. In our scheme, ETX is calculated similar to [13].

B. The Proposed DRCS Scheme

We now present the proposed distributed channel selec-tion and routing scheme DRCS for single-radio WSNs thatdistributes transmission over multiple channels and tries tobalance the remaining lifetimes of all nodes for extendingthe overall lifetime of the network. We define the receiverchannel of a node to be its designated channel for receivingall incoming packets. On the other hand, a transmit channel isthe channel to which a node switches temporarily to transmit,which is the receiver channel of its intended destination.According to DRCS, nodes select their receiver channels toenable distribution of traffic over multiple orthogonal chan-nels. Since nodes listen to their receiver channels by default,overhearing is limited to neighboring transmissions on a node’sreceiver channel only. Transmit channels are chosen dynami-cally to prolong the lifetime of the neighboring node with theworst battery health-metric. Note that channel selection is tiedto parent selection, which leads to route determination. Hencethe proposed approach leads to a joint channel selection androuting in the WSNs.

As shown in Figure 4, the channel selection scheme inDRCS runs in two stages, which is described below. Weassume that all nodes broadcast periodic beacon messages,which include their node ID, their receiver channel, ETX valueand their battery health-metrices.First stage: In this stage, all nodes use a common defaultchannel. Each node chooses a random backoff (this ensuresthat nodes choose channels one after another) and selects theleast used channel in its neighborhood when the backoff timerexpires. This channel becomes the node’s receiver channel,which it announces to its neighbors via beacon packets. If thereare multiple channels that are least used, the tie is broken bychoosing a random channel among the channels that make thetie. All nodes store their neighbors as well as the neighbors’

1A more accurate method for estimating the battery capacity is currentlybeing implemented, which has not been considered in this work.

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receiver channel information. After a certain time interval τ ,the second stage begins.Second stage: In the second stage, all nodes switch to theirreceiver channels. In this stage, nodes dynamically performparent selection, and consequently, their transmit channels,based on periodic assessments of the battery health and ETXparameters. This is done as follows. For any channel c, eachnode calculates Hc = min{Hi} ∀ i ∈ Sc where Sc is theset of neighbors that are in receiver channel c and Hi is thehealth metric of node i. In order to transmit to the sink, nodesthat are immediate neighbors of the sink switch to the commondefault channel for transmitting DATA packets. All other nodeschoose a transmit channel c with a probability of Hc

H .1ec

, whereH =

∑Hi ∀ channel i in the node’s neighborhood such

that there is at least one neighbor that is in channel i andwhose ETX is less than the node’s ETX. ec is the ETX ofthe link between a node and the neighbor in c that has thelowest path metric to the sink. The term Hc

H ensures that thereceiver channel of the node with the worst health-metric ischosen with the lowest probability. This mechanism minimizesthe overhearing for the neighboring node that has the worsthealth-metric. The term 1

ecrepresents the probability that the

packets sent by a node are received successfully by its parentif channel c is chosen. After choosing the transmit channel,a node chooses the parent among all its neighbors on c thathas the best path metric to the sink. Nodes choose transmitchannels as well as their parents in periodic intervals, calledroute-update intervals (RUI).

The routing and channel selection scheme should ensurethat new nodes that are added to the network at any time areable to get connected to the network and send informations tothe sink. In our proposed scheme, this is ensured by sendingthe beacon messages in different channels in rotation. Hence, anew node is always able to receive beacons from its neighborsand get connected, irrespective of its initial choice of a defaultchannel.

The proposed routing and channel selection scheme takesinto account a number of factors that are explained as follows:Battery state of individual nodes: The battery state of a nodeis taken into account by the term B. If the battery conditionof any node deteriorates, the value of its health-metric willdrop. This will result in a lower probability of selectionof that node’s channel by its neighboring nodes for DATAtransmission.Load balancing between nodes: If a node’s load increases, itsI will increase, causing its health-metric to decrease. This willcause that node’s channel to be chosen with lower probabilityin the next RUI. Also after choosing the transmit channel, aparent is chosen based on the lowest ETX. Thus, if a parentis overloaded, its ETX will increase, resulting in other nodesto avoid selecting that node.Load balancing between channels: If a channel is overused,the forwarding and overhearing traffic on that channel willincrease. This will decrease the health-metric of the nodes inthat channel. Thus, that channel is avoided in the next RUIswith higher probability.

Route quality: The ETX value quantifies the quality of a route.The route quality is important as bad routes result in higherretransmissions, which reduce the network lifetime.Channel quality: DRCS favors selection of channels withbetter quality, i.e. lower interference, as follows. A high levelof channel interference will result in higher number of retrans-missions and overhearing on that channel, causing the health-metrices of the nodes on that channel to reduce. Moreover,it will increase the ec for that channel. Consequently, thecorresponding channel will be chosen with lower probabilityin the next RUIs.

The proposed scheme does not incur any additional controloverhead other than periodic beacon updates. Also, to avoididle listening, nodes use low-power listening where they sleepmost of the time and wake up in periodic intervals. If theysense some channel activity, they remain on. Otherwise, theygo back to sleep to conserve energy. Problems such as routingloop detection and repairing are tackled similar to CTP. Onepossible drawback of the DRCS is channel switching whenthe receive and transmit channels of a node are different.However, we show that data collection application with lowdata rates, this does not pose any problems. For high data rateapplications, frequent channel switching may result in somedata loss.

V. PERFORMANCE EVALUATION

This section presents evaluation results of DRCS fromexperiments on a real testbed as well as from simulations.We first demonstrate that our proposed multi-channel schemeeffectively reduces overhearing using an experimental testbedcomprising of 18 MICAz motes. We also show the effec-tiveness of the dynamic channel selection scheme based onindividual node’s battery health metrices. To show the perfor-mance of our scheme in a larger network, we implement thisscheme in the Castalia simulator [17] on a 150-node network.Finally, we compare the performance of DRCS with a well-known channel assignment scheme TMCP. Parameters usedfor experiments and simulations are listed in Table I.

TABLE ISIMULATION ENVIRONMENT

Var Values Var Values Var Values Var ValuesIBt 20 mA TBt 140 ms IBr 20 mA TBr 140 msIDt 20 mA TDt 140 ms IDr 20 mA TDr 140 msIP 8 mA TP 3 ms IS 7.5 mA TS 112 ms

A. Evaluation in a real testbed

We implement our proposed scheme DRCS in TinyOSusing MICAz motes that use LowPowerListening with wake-up intervals of 125 milliseconds. The beacon interval, DATAinterval and τ are chosen to be 30, 60 and 180 secondsrespectively. The transmit power is chosen to be −28.5 dBm toenable experimentation in a small place. We place 18 motesthat periodically sense and forward sensor data to the sinkusing our proposed multi-channel routing scheme DRCS. Theposition of the sink is varied to form three different network

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Fig. 5. Experimental deployment scenarios with sink locations marked by yellow circles: 1 (a), 2 (b), and 3 (c); and comparison of the number of packetsdelivered at the sink (d) and the total packets overheard (e), with 1, 2, and 4 channels.

topologies as shown in Figure 5(a)-(c). For ease of obtainingpacket counts, we disable retransmissions. The results obtainedover a duration of 15 minutes are shown in Figure 5(d)-(e). Itis observed that in all three topologies, the number of packetsreceived at the sink drops only marginally with increasingnumber of channels, even with no retransmissions. This im-plies that the packet delivery performance is not affected bychannel switching in these data-rates. However, there is asignificant reduction in the total number of overhearing packetsby using 2 and 4 channels. This experiment demonstratesthat DRCS can significantly reduce energy wastage due tooverhearing without sacrificing the delivery performance.

To show the effectiveness of the dynamic channel selectionscheme, we set up a small network as shown in Figure 6(a),and monitor the variations of the number of packets overheardin a specific node when its battery voltage (and hence, itscapacity) is changed manually. In this experiment, we useonly 2 channels and a data interval of 15 seconds. Initially, thebattery capacities of all nodes are made to be 100%. After 30minutes, the battery voltage of node D is reduced to representa capacity of 50%, keeping all others unchanged. Figure 6(b)shows the variation of the number of packets overheard byD over time. Each bar on the x-axis shows the number ofoverheard packets by D over a duration 5 minutes. It canbe observed that after 30 minutes the overhearing on nodeD starts reducing as all other nodes switch their transmitchannels to avoid the receiver channel of D. This experimentdemonstrates that our proposed scheme helps in reducingenergy consumption at a node with bad health-metric, whichcan occur due to deteriorating battery health.

B. Simulation Results

We conduct simulations to evaluate the performance of ourproposed scheme in a larger network and to also evaluatethe lifetime improvement achieved by DRCS. We consider anetwork of 150 nodes that are uniformly placed in an area of200×200 meters. The transmission power is assumed to be 0dBm. The initial battery capacities of the nodes are assumedto be uniformly (randomly) distributed between 75% to 100%.The capacity of a fresh battery (100% capacity) is assumed tobe 5000mAH. The beacon interval is set to 30 seconds and themaximum retransmission count is set to 30. Each simulationis run for 500 seconds and all the results are averaged overfive independent simulations.

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Fig. 6. Experiment to evaluate the effectiveness of dynamic transmit channelselection.

Comparison with different datarates: Fig 7 shows the variationof the packet delivery ratios, overhearing counts and theworst case network lifetime with different number of channelsand transmission rates. The worst case network lifetime isdefined as the time when the first node of the network dies.It is observed that the packet delivery ratio is above 90%for all cases. This is consistent with the findings from theexperimental testbed, indicating that at these data rates, thepacket delivery ratio is not significantly affected by the channelswitching scheme employed in DRCS. However, overhearingis reduced by nearly 60% with 2 channels and by almost 50%with 4 channels. This significantly reduces the average currentconsumption in the nodes and improves the network lifetime.

Comparison with TMCP [9]: Fig 8 shows the comparison ofDRCS with another well-known channel assignment schemeTMCP with different number of channels. We assume acommunication range of 40 meters and an interference rangethat is 1.5 times of the communication range. Here, we set thedata interval to 3 seconds. Fig 8 shows that DRCS generatesa higher packet delivery ratio in comparison to TMCP. Thisis due to several reasons. Firstly, TMCP uses a distance-basedcommunication and interference model that does not effec-tively capture the link qualities, especially with a high channelvariance σ2. Secondly, DRCS uses channels more efficientlythan TMCP. In TMCP nodes select the same channels as thatof their parents. Hence, if the sink has n immediate neighborsand there are k channels where k > n, then at least k − nchannels will be unused, since there will be at most n sub-treesin the network. On the other hand, nodes on the same sub-tree in DRCS may use multiple channels, thereby improvingchannel utilization. Also in case of TMCP, the parent and

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Fig. 8. Comparison of (a) packet delivery ratio, (b) network-wide packets overheard (c) worst case network lifetime with different number of channels

channel assignments are static. These do not change even withvariations of congestion and link quality. These result in poorroute quality that leads to higher packet loss, retransmissions,and overhearing. Moreover, the channel quality may vary overtime, which requires a dynamic protocol. It should be notedthat the performance of DRCS and TMCP are similar in termsof the total reduction of overhearing with multiple channels.However, DRCS provides a much higher network lifetimethat is achieved by dynamically balancing the lifetimes ofindividual nodes.

VI. CONCLUSIONS

In this paper, we propose a scheme for building a multi-channel tree in data gathering wireless sensor networks formaximizing the network lifetime. The proposed scheme DRCSinvolves distributed channel selection to enable nodes toreduce overhearing, and dynamic parent selection for minimiz-ing the load of nodes that have the worst expected lifetime.Through simulations and experiments, we demonstrate thatDRCS significantly improves the network lifetime without sac-rificing the packet delivery ratio. The proposed scheme has noadditional overhead other than periodic beacon updates, whichmakes it suitable for implementations in real-life applicationsto prolong the network lifetime.

ACKNOWLEDGEMENT

This work was supported in part by NSF grant CNS-1117790.

REFERENCES

[1] A. Pal and A. Nasipuri, “A distributed channel selection scheme formulti-channelwireless sensor networks,” in MobiHoc, 2012, pp. 263–264.

[2] A. Raniwala, K. Gopalan, and T. cker Chiueh, “Centralized channelassignment and routing algorithms for multi-channel wireless meshnetworks,” ACM Mobile Computing and Communications Review, vol. 8,pp. 50–65, 2004.

[3] P. Kyasanur and N. H. Vaidya, “Routing and link-layer protocols formulti-channel multi-interface ad hoc wireless networks,” SIGMOBILEMob. Comput. Commun. Rev., vol. 10, no. 1, pp. 31–43, 2006.

[4] M. K. Marina and S. R. Das, “A topology control approach forutilizing multiple channels in multi-radio wireless mesh networks,” inBROADNETS, 2005, pp. 412–421.

[5] A. Pal and A. Nasipuri, “JRCA: A joint routing and channel assignmentscheme for wireless mesh networks,” in IEEE IPCCC, 2011.

[6] G. Zhou, C. Huang, T. Yan, T. He, J. A. Stankovic, and T. F. Abdelzaher,“MMSN: Multi-frequency media access control for wireless sensornetworks,” in INFOCOM, 2006.

[7] J. Zhang, G. Zhou, C. Huang, S. H. Son, and J. A. Stankovic, “TMMAC:An energy efficient multi-channel mac protocol for ad hoc networks,”in ICC, 2007, pp. 3554–3561.

[8] X. Chen, P. Han, Q.-S. He, S. liang Tu, and Z. long Chen, “A multi-channel mac protocol for wireless sensor networks,” in CIT, 2006.

[9] Y. Wu, J. A. Stankovic, T. He, and S. Lin, “Realistic and efficient multi-channel communications in wireless sensor networks,” in INFOCOM,2008, pp. 1193–1201.

[10] H. K. Le, D. Henriksson, and T. F. Abdelzaher, “A control theoryapproach to throughput optimization in multi-channel collection sensornetworks,” in IPSN, 2007, pp. 31–40.

[11] Q. Yu, J. Chen, Y. Fan, X. Shen, and Y. Sun, “Multi-channel assignmentin wireless sensor networks: A game theoretic approach,” in INFOCOM,2010, pp. 1127–1135.

[12] “Xmesh user manual, Crossbow technology,”http://www.xbow.com/Products, 2007.

[13] O. Gnawali, R. Fonseca, K. Jamieson, D. Moss, and P. Levis, “Collectiontree protocol,” in SenSys, 2009, pp. 1–14.

[14] D. Moss, J. Hui, and K. Klues, “Low Power Listening, Core WorkingGroup, TEP 105.”

[15] A. Nasipuri, R. Cox, J. Conrad, L. V. der Zel, B. Rodriguez, andR. McKosky, “Design considerations for a large-scale wireless sensornetwork for substation monitoring,” in LCN, 2010, pp. 866–873.

[16] “MIB/MPR user manual,” http://www.xbow.com.[17] “Castalia: A Simulator for WSN,” http://castalia.npc.nicta.com.au/.

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