Ad Hoc Networks xxx (2008) xxx–xxx

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Ad Hoc Networks

journal homepage: www.elsevier .com/locate /adhoc

Locally scheduled packet bursting for data collectionin wireless sensor networks

Ren-Shiou Liu *, Kai-Wei Fan, Prasun SinhaDepartment of Computer Science and Engineering, The Ohio State University, 395 Dreese Laboratories, 2015 Neil Avenue, Columbus, OH 43210-1277, United States

a r t i c l e i n f o

Article history:Received 13 March 2008Received in revised form 9 August 2008Accepted 15 August 2008Available online xxxx

Keywords:MAC ProtocolWireless sensor network

1570-8705/$ - see front matter � 2008 Elsevier B.Vdoi:10.1016/j.adhoc.2008.08.002

* Corresponding author. Tel.: +1 614 688 4617; faE-mail addresses: rsliu@cse.ohio-state.edu (R.-S.

state.edu (K.-W. Fan), prasun@cse.ohio-state.edu (P

Please cite this article in press as: R.-S.(2008), doi:10.1016/j.adhoc.2008.08.002

a b s t r a c t

In wireless sensor networks, the many-to-one data communication pattern induces highcollision losses as multiple transmissions cause contention and interference along thepaths from sources to the sink. This paper proposes a low-overhead MAC layer solutionto address the high contention problem to improve system throughput and reduce energyconsumption. Periods of burst transmissions with reduced contention from neighboringnodes are exploited to efficiently clear up backlogged queues and improve the performanceof CSMA. Through analytical modeling we characterize the expected performance improve-ment. Using extensive simulations on ns-2 and experiments on the 49-node sensor net-work testbed (Kansei) running TinyOS, we show that the proposed scheme can increasethe throughput by up to a factor of four.

� 2008 Elsevier B.V. All rights reserved.

1. Introduction

Energy and channel capacity are two critical resourcesin wireless sensor networks. When a large number ofnodes start reporting data, sensor networks easily get over-whelmed by high contention and interference along adja-cent multihop routing paths and in the neighborhoods ofdata collection points such as the sink. This leads to ineffi-cient use of these resources. Various approaches have beenproposed to mitigate this problem, such as improved MAClayer designs [1,2] and back-pressure techniques at thelink layer [3–6]. In [1], a hybrid TDMA/CSMA approach isproposed to address congestion near the sink. However,it requires specific capabilities only available at the sink.ZMAC [2] is another hybrid TDMA/CSMA based solution,but it requires global time synchronization and distributedslot assignment using the DRAND [7] protocol, which sig-nificantly increases the complexity and overhead of theprotocol. In addition, the computation of the TDMA sched-ules is expensive in dynamic environments where the traf-

. All rights reserved.

x: +1 614 292 2911.Liu), fank@cse.ohio-

. Sinha).

Liu et al., Locally sched

fic sources change with time. Back-pressure basedmechanisms for congestion control [3–6] operate overthe MAC layer to maintain the queue size at acceptable lev-els to avoid queue drops. As these mechanisms are notintegrated into the MAC layer where congestion is first ob-served, their impact on performance improvement islimited.

The convergecast traffic pattern of wireless sensor net-works leads to high load at nodes that are aggregating data.We demonstrate this problem by using simulations in a1000 � 1000 m2 network of 1000 nodes. An event with ra-dius 100 m is randomly generated in the network and datais forwarded over the Steiner tree joining the source nodesto the sink. We increase the offered load by increasing theevent radius. Fig. 1 shows that increase in the amount of datagenerated leads to sharp decrease in throughput. This trendis observed for data collection with or without aggregation.

In this paper we seek to design a low-overhead MAC layersolution to address the overload problem in wireless sensornetworks. Our solution is based on the observation thatthrottling sensors’ reports to prevent simultaneous trans-missions can reduce contention and increase throughput[13,14]. We propose a burst scheduling approach at theMAC layer specifically designed to mitigate the overload

uled packet bursting for data collection ..., Ad Hoc Netw.

0

5

10

15

20

25

30

35

40

40 60 80 100 120 140 160 180 200

Thro

ughp

ut [p

kts/

s]

Event radius [m]

Without aggregationWith aggregation

Fig. 1. Convergecast traffic pattern in wireless sensor networks degradesperformance of CSMA MAC.

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problem. The scheduling overhead is reduced as a burst ofpackets as opposed to a single packet is scheduled fortransmission. If a node observes an increase in its queuebacklog, it performs a low-overhead coordination withneighboring nodes to reserve a period for transmitting apacket burst. By alleviating contention during the burstperiods, throughput is boosted for transmissions fromsources to the sink. In addition, by explicitly addressingbacklogged queues, overall queue drop rate decreasesand network performance is improved. We make the fol-lowing contributions in this paper.

� We propose a low overhead packet bursting basedapproach called ClearBurst, for mitigating overload atany node in the network.

� We analytically model the expected performance gainsfor representative network scenarios by extending theanalysis techniques used in Bianchi’s work [8,9].

� We perform extensive evaluation using ns-2 and showthat Clearburst can improve throughput by two foldsunder high load and it is up to four times more energyefficient than CSMA.

� We present results from experiments on a large-scaleindoor testbed based on implementation on TinyOS,and show that ClearBurst can have four times higherthroughput and at least 20% more energy efficient thanCSMA.

The organization of the rest of the paper is as follows.We contrast our work with related work in Section 2. Sec-tions 3 and 4 present our proposed approach and analyticalmodeling of the proposed solution. Simulations and exper-imental results are presented in Sections 5 and 6. ApplyingClearBurst in moving event scenarios is explored in Section7. Section 8 concludes the paper.

2. Related work

2.1. Congestion mitigation at MAC layer

In order to alleviate interference and contention, vari-ous TDMA based MAC protocols [19–22] and hybridCSMA/TDMA techniques [1,2,23] have been proposed.TDMA based approaches [19–22] suffer from high globaltime-synchronization and slot assignment overheads,which make them unsuitable for scenarios with changing

Please cite this article in press as: R.-S. Liu et al., Locally sched(2008), doi:10.1016/j.adhoc.2008.08.002

traffic patterns. For example, in dynamic or moving eventscenarios, not all the sensors need to report data to thesink. Thus, slots assigned to those sensors which do nothave data to report are wasted. To improve channel utiliza-tion of traditional TDMA protocols in such scenarios, hy-brid schemes have been proposed. ZMAC uses prioritizedCSMA based transmissions in TDMA slots. In other words,if an unused slot is detected, other nodes can competefor that empty slot. Though this improves channel utiliza-tion, it has been shown in [1] that the performance ofZMAC degrades with time and falls back to that of CSMAdue to clock drift. Furthermore, it is difficult for ZMAC toadapt to changing topology as it requires recomputationof slot assignments using the DRAND [7] protocol, whichincurs an extra overhead of OðDÞ per node, where D isthe maximum number of contending nodes in a neighbor-hood. Harvest [23] is also a hybrid MAC aiming for mini-mizing the slot assignment overhead. Harvest only usesfour slots in a frame, meaning that only four nodes in atwo-hop neighborhood can be assigned a slot. Harvest’sslot assignment is randomized. Whoever chooses a slotfirst wins that slot. Once a node has finished transmittingall its data, it releases its slot and its one hop neighborswho did not get a slot previously can compete for that va-cated slot. Although Harvest can complete slot assignmentin Oð1Þ time, it still requires global time synchronization.FMAC [1] is a sink-oriented hybrid MAC protocol. It onlymitigates congestion near the sink as the authors observein the experiments that contention is more intensivearound the neighborhood of the sink. For this reason, theneighborhood around the sink is named the intensity re-gion in the paper. The sink constantly monitors the trafficfrom the sensors and dynamically tunes the depth of theintensity region. Nodes in the intensity region use TDMA,while nodes outside the intensity region use CSMA. Sched-uling information is periodically computed and broadcastby the sink at a high power to all the nodes in the intensityregion. As FMAC relies on the sink to compute TDMAschedules, it does not scale well and cannot quickly reactto the onset of the congestion which arises deep in the net-work. In addition to the aforementioned global time syn-chronization and slot assignment overheads, none of theabove TDMA based protocols addresses the special require-ment of convergecast traffic pattern in sensor networks.Nodes performing data aggregation or those that are closerto the sink usually have to forward more traffic. As a result,they should have more bandwidth share of the channel.However, to the best of our knowledge, this issue is not ad-dressed by existing TDMA protocols.

2.2. Congestion mitigation at higher layers

CODA [4], Fusion [5], IFRC [6], and RAIN [3] are hop-by-hop control based strategies for mitigating congestion insensor networks. In CODA [4], a weighted moving averageof channel measurements combined with queue lengthassessment is used to determine the onset of congestion.Once congestion is detected, a node broadcasts back-pres-sure messages towards upstream nodes. Nodes that re-ceive back-pressure messages throttle their transmissionrates and determine whether to propagate the back-pres-

uled packet bursting for data collection ..., Ad Hoc Netw.

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sure further according to their local congestion assess-ment. In addition to this open loop hop-by-hop back-pres-sure mechanism, CODA also adopts a close loop rateregulation scheme. Based on the application requirementsand network conditions, the sink controls the rate of thesensors by sending or dropping ACKs. Sensors then adjusttheir rates using a function, such as AIMD, of the arrivalrate of ACK packets. In contrast to CODA, Fusion [5] usesqueue length as the only congestion indicator becausethe authors find, through experimental measurements,queue length is at least as good as channel sampling. Fur-thermore, Fusion combines hop-by-hop flow control withthe ideas of rate limiting and prioritized MAC to controlcongestion in sensor networks. A node adds a token to itsbucket every time it hears N transmissions from its parent,where N is the total number of unique sources routingthough the parent. A node is allowed to transmit a packetif and only if its token count is not zero. In order to allowcongested nodes to propagate back-pressure quickly, Fu-sion makes CSMA’s contention window size a function ofa node’s congestion state. If a sensor is congested, its win-dow size is one fourth of that of a non-congested node. Thisgives a congested node more opportunities to clear packetsaccumulated in the queue and propagate the back-pres-sure. Following Fusion, IFRC [6] also uses queue length asthe congestion indicator. The difference is that IFRC adoptsa variation of the AIMD scheme to control the rate of eachnode. Multiple queue length thresholds are employed byIFRC. If the queue length exceeds any of the thresholds, anode reduces its rate by half. The distance between kthand ðkþ 1Þth threshold gets smaller as k becomes larger.In other words, IFRC reduces the rate more aggressively ifcongestion remains and queue length continues to growafter a rate deduction. On the other hand, if there is no con-gestion, a node additively increments its rate. In additionto rate control, IFRC shares a node’s congestion with itstwo-hop neighbors by including the rate and queue lengthinformation of a node itself and its most congested child inthe packet header. Each node sets its rate to the smaller ofits own rate and the rates of its neighbors and their chil-dren. Unlike IFRC which uses multiple queue lengththresholds, RAIN [3] uses a single small queue lengththreshold (queue length threshold of size 1 is used in thepaper) to detect congestion and create a back-pressure.Using a small queue length threshold allows congestionto be detected at an early stage. It also ensures that theback-pressure can be quickly propagated toward thesource. The concept of AIMD is also adopted in RAIN, butit is implemented as a light-weight transport protocolcalled ReTP. ReTP can be light weight because packet lossesdue to contention or congestion are rare, and it only has todeal with routing failures. In contrast to these higher-layerapproaches, ClearBurst addresses congestion at the MAClayer. These approaches can be used to further improvethe performance of ClearBurst.

Certain protocols [24,25] designed primarily for reli-ability also include mechanisms for congestion control.PSFQ [24] is a reliable data dissemination protocol that alsoattempts to address congestion by controlling the rate ofpumping packets into the network. PSFQ uses two timers,Tmin and Tmax, to control the rate of pumping and relaying

Please cite this article in press as: R.-S. Liu et al., Locally sched(2008), doi:10.1016/j.adhoc.2008.08.002

packets respectively. The source node broadcasts a packetevery Tmin interval until all the data fragments are sentout. Tmin is long enough for neighboring nodes to recoverlost fragments while not overwhelming the network. Relaynodes randomly back off for an interval between Tmin andTmax before relaying data fragments to prevent collisions.Furthermore, to avoid congestion, the relay of a data frag-ment will be suppressed if it has been broadcast in thesame neighborhood for at least four times. In ESRT [25],sensor nodes monitor their buffer levels and notify the sinkof the congestion condition by setting a congestion notifi-cation bit in the packet header. The sink, in turn, informsthe source nodes to appropriately adjust their reportingrates with the goal of increasing reliability while conserv-ing energy. These higher layer mechanisms are orthogonalto our work and they can be used along with ClearBurst toprovide support for reliability and congestion control.

2.3. Architecture for congestion mitigation

In Siphon [26], a few nodes with a high-bandwidth sec-ond tier radio interface, called virtual-sinks, are randomlydeployed in the network. Whenever congestion is detected,packets are redirected to a nearby virtual sink and thensent to the next virtual sink which is closer to the real sinkusing the more powerful second-tier radio. The need for apowerful second-tier radio interface makes it a moreexpensive solution. Moreover, virtual sinks can still sufferfrom congestion.

3. Design of ClearBurst

To mitigate the overload problem at data collectionnodes, we propose ClearBurst in the MAC layer to coordi-nate media access for sensor nodes. ClearBurst has threephases. They are C-node election, congestion detection,and burst scheduling. When an event is detected, the sen-sor in the event region and closest to the sink is elected asthe C-node. A steiner tree connecting all the source nodesand the sink is then constructed. Once the tree is con-structed, all the source nodes send data toward the C-nodefor aggregation. If the application requires high reportingrate, the C-node and its immediate one-hop upstreamnodes will soon be overloaded. ClearBurst uses queuelength as an overload indicator. When any one of C-node’simmediate one-hop upstream node detects overload,ClearBurst uses a small time window to reserve the chan-nel in the two-hop region of the C-node and the node thatdetects overload. Three dedicated slots are reserved for thenode that detects overload, the C-node, and C-node’simmediate downstream node, respectively. During thesethree reserved slots, these three nodes initiate burst trans-mission in order. This reduces contention and packets canbe forwarded out of the intensive contention area towardthe sink quickly. As a result, overall throughput is in-creased and energy wastage due to collision and conten-tion is minimized. After a burst duration ends, all thenodes fall back to the CSMA operation and the next bursttransmission is not triggered until overload is detectedagain. The detailed descriptions of these three phases isprovided in the remainder of this section.

uled packet bursting for data collection ..., Ad Hoc Netw.

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3.1. C-node election

First a C-node is elected for a set of sources to act as adata collection point as well as a schedule coordinator.Although a TDMA-based approach can reduce contention,it will also incur high overhead for time synchronizationand slot assignment. Using a C-node as the coordinatornot only eliminates these overheads but also makes theschedule adaptive to dynamic traffic and unpredictabletopological changes. In addition, the C-node can serve asan aggregation point that aggregates raw data packetsand reduces the amount of information transmitted inthe network.

Many cluster-head election and tree construction algo-rithms have been proposed and can be adopted. For exam-ple, the cluster-head election, tree construction andmigration approach described in [10] can be used to electthe C-node and to construct and maintain the tree. How-ever, it is crucial that the C-node is elected quickly and effi-ciently at the routing layer so that we can respond toemerging congestion instantaneously while minimizingoverheads.

The election protocol selects one of the sensors in theevent region that is closest to the sink as the C-node andbuilds a spanning tree rooted at the C-node. This approachhas several advantages. First, instead of using the shortestpath, forwarding packets to the C-node increases theopportunity of early aggregation. Second, once data isaggregated, they can be forwarded toward the sink byburst transmissions to avoid delay and collision causedby intensive contention around the event region. Third, ifthe subgraph containing all of the source nodes are con-nected, this heuristic can compute an optimal data collec-tion tree [11]. Fourth, building a spanning tree rooted atthe C-node can be achieved by simple extensions to exist-ing routing protocols such as MintRoute [28].

When an event is detected, all nodes that can detect thisevent start generating reports at a low rate and forward thepackets to the sink using the shortest path. Besides rawdata, these low data rate reports include the ID of the nodegenerating the packet, its hop distance to the sink and to itschosen C-node, and the C-node’s hop distance to the sink.Initially, every source node chooses itself as the C-node.If a node learns that any of its neighbor’s chosen C-nodeis closer to the sink or is equally close to the sink but witha smaller ID, it chooses that C-node as its C-node, and setsthat neighbor as its parent. If more than one downstreamnodes choose the same best C-node, the one with strongersignal is picked as its parent. When a node observes that allits neighbors have reached a consensus on the C-node’slocation, it can start reporting data at the rate specifiedby the application and the C-node election and tree con-struction phases end. If the diameter of the subgraph con-necting all the source nodes is d, then the time complexityof this C-node election and tree construction process isOðdÞ.

3.2. Congestion detection

When the traffic load is low, the nodes use a CSMA-based protocol to transmit their packets since the

Please cite this article in press as: R.-S. Liu et al., Locally sched(2008), doi:10.1016/j.adhoc.2008.08.002

CSMA-based approaches perform well in low traffic sce-narios. However, when the traffic load is high, its perfor-mance drops significantly due to the congestion causedby high contention and collisions. Various congestion indi-cators have been proposed, such as the number of packetsin the queue [3,5,6], the channel utilization [4], and queuelength information of neighbor nodes [12]. To minimizethe overhead, we adopt queue length as the congestionindicator in ClearBurst as accessing queue length doesnot require any energy-consuming signalling or overhear-ing. Furthermore, it has been shown in [5,6] that usingqueue length as the congestion indicator is as good aschannel sampling for convergecast traffic in sensor net-works. When the number of packets in the transmissionqueue exceeds a predefined threshold, ClearBurst steps inand starts coordinating the transmissions. As the bottle-neck is likely to happen around the C-node, ClearBurstcoordinates the transmissions only for nodes near the C-node to minimize the control overhead. Multiple C-nodesfor coordination is possible if these C-nodes are not inter-fering with each other and we leave it for future work.

3.3. Burst scheduling

To start the coordination, children nodes of the C-nodesignal the need for burst transmission by setting the re-quest bit in the data packet header. When the C-node re-ceives a packet with the request bit turned on and if it isnot serving any burst transmission, it grants the requestby piggybacking the acknowledgement in outgoing datapackets. The child node can overhear the data packetsand learn that its request has been granted. The child nodewhose request is granted is called an active node. The re-quest and acknowledgement handshake serves the pur-pose of reserving the channel for the burst transmission.Because the interference range is usually larger than com-munication range, this scheduling information needs to bepropagated to the nodes that may interfere with the bursttransmission as demonstrated in Fig. 2(a).

To make sure that these potential contending nodes andinterfering nodes are shut off during burst periods, Clear-Burst uses a small time window after the handshake topropagate the scheduling information before starting theburst period. During this small time window, nodes canstill access the channel using CSMA, but all the nodes,who have learned the scheduling information by overhear-ing, help propagate the information by piggybacking it inevery outgoing data packet. The number of hops to propa-gate the information can be controlled by the TTL field inthe packet header (TTL of 2 was used in simulations andexperiments). When the burst schedule propagation timeends, the node requesting for the burst transmission canstart its burst transmission.

A burst period consists of three slots. As demonstratedin Fig. 2(b)–(d), the first slot of length k is used by the ac-tive node to transmit packets to the C-node. The secondand the third slots are used by the C-node and its parentnode to forward packets to the sink. Assuming that the C-node aggregates packets with aggregation ratio q asshown in Fig. 2(e), the time required to forward theaggregated packets is l ¼ qk. If we do not reserve slots

uled packet bursting for data collection ..., Ad Hoc Netw.

Fig. 2. Demonstration of ClearBurst operation. The time slot assignments are shown along with every node. The gray ox represents the current slot.

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for the C-node and its downstream node to forward theirpackets, when a burst period ends and all the nodes re-sume their transmissions, they will have very little chanceto forward these packets because it has to compete for thechannel with other nodes in the interference range.Reserving dedicated slots for the C-node and its parentto transmit their packets in a burst can avoid queue build-up at these nodes.

During a burst period, both the active node and the C-node keep announcing the progress of the burst operationby including the remaining time of the burst period in datapacket headers. Any neighboring node who missed theschedule information during the schedule advertisementtime window and overhears the scheduling informationfreezes its transmission immediately. This further mini-mizes the chance of interference during burst periods.

Please cite this article in press as: R.-S. Liu et al., Locally sched(2008), doi:10.1016/j.adhoc.2008.08.002

Due to unpredictable channel conditions and unsyn-chronized clocks and duty cycles, some nodes may missthe scheduling information of a burst period and still tryto access the channel. In order to let the active node dom-inate its use of the channel under potential interferencefrom its neighbors, the active node uses smaller initialbackoff and contention window sizes. This helps suppressunexpected transmissions originating from the neighborsof the active node and the C-node during burst periods.The smaller initial window size also helps to minimizethe overhead of initial backoff and improve channel utili-zation during burst periods.

When the burst period ends, all the nodes go back to theCSMA mode to contend for the channel, and other childrennodes of the C-node whose queue lengths exceed thethreshold can start requesting another burst transmission.

uled packet bursting for data collection ..., Ad Hoc Netw.

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4. Performance analysis

In this section we analytically derive the throughputand energy efficiency of the ClearBurst protocol.

tCr

ras

b

Fig. 3. If a and b transmit their packets to s at the same time, a’s packet

4.1. Throughput

We adapt Bianchi’s work [9] for our analysis. However,in the simulation we found that the ‘‘capture” phenome-non has a huge impact on overall throughput when thecontention is high. The capture effect is not modeled in[9]. In this section we consider the capture effect and de-rive the corresponding throughput. First, we need theprobability that a node transmits in an idle slot, calledthe transmission probability s. The backoff mechanism inMAC layer determines the transmission probability. Thedefault MAC layer in sensor nodes, e.g. Mica2, uses fixedinitial and random backoff window sizes. The initial back-off window size is 16. If the channel is busy during the firsttransmission attempt, nodes randomly backoff with win-dow size 32. To simplify the analysis, we assume that thebackoff window size is always W.

Using a discrete time Markov chain with W states as in[9] we can easily show that in the steady state, the trans-mission probability, s, is 2

Wþ1. With s, we can derive theprobability that a transmission is a success or a collision.Second, we need to know what is the probability that a‘‘capture” will happen if two nodes transmit at the sametime. Though it is possible that capture can still happen ifthree or more nodes transmit at the same time, the proba-bility is relatively small. Therefore, we ignore this case inthe analysis.

A ‘‘capture” happens if a packet with a stronger signalcan be correctly decoded at the receiver in the presenceof interference from a weaker signal. In the simulationwe use the two-ray ground propagation model; thereforethe signal strength is inversely proportional to the squareof the distance. Suppose the capture threshold is Ct. Ifnodes a and b transmit a packet to s at the same time withthe same transmission power, the packet from node a canbe decoded if the distance between s and b is at least

ffiffiffiffiffiCtp

times longer than the distance between s and a, as shownin Fig. 3.

We assume that N nodes are uniformly distributedwithin a circular region with radius R. All nodes are withininterference range of each other and the receiver s is at thecenter of the circle. If we observe a tagged node whose dis-tance to s is r. If r < Rffiffiffiffi

Ct

p , only nodes within radius r �ffiffiffiffiffiCtp

can collide with its transmission. In a uniformly distributed

deployment, there are N � ðr�ffiffiffiffiCt

pÞ2

R2 � 1 such nodes other

than the tagged node. If r >¼ RffiffiffiffiCt

p , all the other nodes can

collide with the tagged node. Therefore, the probabilityfor a node to successfully transmit a packet is

p ¼ð1� sÞN�

ðr�ffiffiffiCtp

Þ2

R2 �1 if r < RffiffiffiffiCt

p

ð1� sÞN�1 if r >¼ RffiffiffiffiCt

p

8>><>>:

ð1Þ

Please cite this article in press as: R.-S. Liu et al., Locally sched(2008), doi:10.1016/j.adhoc.2008.08.002

Eq. (1) is the conditional probability of a successful trans-mission given that a sender is at a distance r to its receiver.To derive the marginal success probability, we can inte-grate p defined in Eq. (1) from r ¼ 0 to R, and we get ps ¼

R RffiffiffiCtp

0 2r � ð1� sÞN�ðr�ffiffiffiCtp

Þ2

R2 �1dr þ ðR2 � R2

CtÞ � ð1� sÞN�1

R2 ð2Þ

Note that ps is the conditional probability given that atleast one node transmits.

To compute throughput for an individual node, we firstcompute the expected time it spends on a successful trans-mission, a collision, and the time the channel is sensed asbusy and idle. These values can be approximated as theprobability of occurrence of each condition, multiplied bythe time spent on that condition. We then compute the ex-pected amount of transmitted bytes of a successful trans-mission, which can be approximated as the probability ofsuccessful transmission multiplied by the data packet size,divided by the sum of the times spent on each condition tocompute the throughput.

Therefore we need the probability and duration of eachof the following conditions:

1. A node transmits and the transmission is a success-ful transmission. The probability of this condition isPs ¼ s� ps, and the duration is Ts, where Ts is thetime to transmit a data packet, ack packet, plus DIFS,SIFS time, and two propagation delays.

2. A node transmits but the transmission collides withothers. The probability of this condition isPc ¼ s� ð1� psÞ, and the duration is Td, where Td isthe time to transmit a data packet plus DIFS and onepropagation delay.

3. A node backs off due to a busy channel. There aretwo possibilities. First, the channel is busy becauseof a successful transmission. The probability isPbs ¼ ð1� sÞ � ðN � 1Þ � s� ð1� sÞN�2, and the timeis Ts. Second, the channel is busy because of a colli-sion. The probability is Pbc ¼ ð1� sÞ � ½1� ð1� sÞN�1

�Pbs�, and the time is Td.4. The channel is idle. In such a case, the probability is

Pi ¼ ð1� sÞN , and the time is a time slot q.

Therefore the throughput of a node operating a CSMA MACis

can still be correctly decoded at s due to the capture effect.

uled packet bursting for data collection ..., Ad Hoc Netw.

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PsDPsTs þ PcTd þ PbsTs þ PbcTd þ Piq

; ð3Þ

where D is the packet size.ClearBurst reserves a few slots of the channel for

sources, the C-node, and downstream nodes of the C-node,to forward the packets to the sink in a burst, as shown inFig. 2(e). Assume that there is no other nodes transmittingduring these reserved slots and the C-node does not aggre-gate packets, i.e. q ¼ 1, and the length of schedule adver-tisement time window is l. We can compute thethroughput of the C-node as there is only one node trans-mitting, i.e. N ¼ 1, multiplied by the ratio of its slot to theentire epoch of a burst period, which is k

3kþl. However, forthose intermediate nodes on the path from the C-node tothe sink, they have to contend for the channel with theirtwo-hop upstream nodes and two-hop downstream nodes.Therefore, the throughput of intermediate nodes can becomputed by considering N ¼ 5. Note that this computedthroughput will be smaller then the throughput of the C-node. Thus, the system throughput of ClearBurst is equalto the throughput at intermediate nodes.

In simulations, the data packet size is 40 bytes, the ackpacket size is 12 bytes, and the bandwidth of the radio is19.2 kbps. By plugging these numbers into Eq. (3), wecan get the analytic throughput of CSMA. For ClearBurst,since the system throughput equals the throughput ofintermediate nodes, we use N ¼ 5 and Eq. (3) to computeits analytic throughput.

We run simulations on CSMA and ClearBurst and com-pare the results with our analysis. The simulation method-ology is described in Section 5. For 100 � 100 m2 eventsize, there are approximately 15–30 source nodes whenthe network is deployed with 500–1000 sensors, all arewithin interference range. The results are shown in Fig. 4.We can see that the throughput of CSMA drops as the net-work density increases, while ClearBurst remains similaracross different network densities and performs much bet-ter than CSMA. This confirms our claim and demonstratesthe benefit of ClearBurst.

4.2. Energy consumption

In this section, we analyze the energy consumption ofCSMA and ClearBurst protocols. When contention is inten-sive, the probability of collision is high. If a collision hap-

0

500

1000

1500

2000

2500

3000

500 600 700 800 900 1000

Thro

ughp

ut [b

ps]

Number of nodes

ClearBurst-AnalysisClearBurst-Simulation

CSMA-AnalysisCSMA-Simulation

Fig. 4. Throughput analysis and simulation results for CSMA andClearBurst.

Please cite this article in press as: R.-S. Liu et al., Locally sched(2008), doi:10.1016/j.adhoc.2008.08.002

pens, a node has to retransmit the packet. Therefore, weuse the expected number of transmissions to successfullytransmit one packet as the metric to compare the energyefficiency of ClearBurst and CSMA. The metric called nor-malized number of transmissions is defined as:

Etx ¼TXsuccess þ TXcollision þ TXack

TXsuccessð4Þ

where TXsuccess is the number of successfully transmittedpackets, TXcollision is the number of collisions, and TXack isthe number of ACK packets. Assume that there is no colli-sion for ACK packets, TXack ¼ TXsuccess. Therefore Eq. (4)becomes

Etx ¼ 2þ TXcollision

TXsuccessð5Þ

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The expected number of transmissions for the entire net-work can be approximated by N � Etx. For ClearBurst, whennodes are in the burst transmission mode, only the C-nodecan transmit, and there is no collision. Therefore, the ex-pected number of transmissions for a successful transmis-sion is two. When nodes are in the CSMA mode, theexpected number of transmissions is simply Etx. In simula-tions, since burst transmissions occupy 90% of the simula-tion time and CSMA accounts for 10% of the simulationtime, the expected number of transmissions for ClearBurstis 2� 0:9þ ð2þ pc=psÞ � 0:1.

Fig. 5 shows the analytical and simulation results. Inanalysis, we do not consider the transmissions of interme-diate nodes that are on the path from the C-node to thesink because it depends on the distance between the C-node and the sink. However, in simulations, all transmis-sions in the network are considered for computing Etx.Therefore there are small gaps between the analysis andsimulation results. However the trends are similar. Whennode density increases, CSMA consumes more energy foreach successfully received packet.

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8 R.-S. Liu et al. / Ad Hoc Networks xxx (2008) xxx–xxx

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5. Simulations

To study the performance of ClearBurst, we use ns-2 toconduct extensive simulations using random topologieswith various node densities, event sizes and source rates.We also studied the impact of event mobility, aggregationratio and different queue length thresholds that are usedto trigger Clearburst. Performance metrics include through-put, energy tax, latency, and fairness. Energy tax is definedas ðTXdata þ TXackÞ=R, where R is the number of packets re-ceived at the sink, TXdata is the total number of transmissionsfor data packets and TXack is the total number of transmis-sions for ACK packets. Energy tax represents the averagenumber of transmissions required to forward a packet tothe sink. Fairness is evaluated by Jain’s fairness index [27].

Table 1 shows the parameters used in the simulations.We compare the performance of ClearBurst with CarrierSense Multiple Access (CSMA) and CSMA with ShortestPath Routing (CSMA + SP). In CSMA, all data packets areforwarded to the same C-node as in ClearBurst before theyare forwarded to the sink. Whereas, in CSMA + SP, datapackets follow shortest-path routes to the sink. All resultsare generated based on the ER model (all source nodes arewithin a certain radius of the event center), and packetswith the same time stamp can always be aggregated.

5.1. Network density

We randomly generated six sensor networks of area1000 � 1000 m 2 to evaluate performance for different net-work densities, with 500–1000 nodes uniformly distrib-uted in the network. The sink is at the bottom left corner.Sensors within the event region generate traffic at a con-stant rate of 2 pkts/s. For each simulation, 30 static eventswith 100 m radius are randomly generated. The averagethroughput, energy tax, latency, and fairness index with95% confidence intervals are plotted in Fig. 6(a), (b), (c)and (d) respectively.

As network density increases, more sensors are in theevent region. This increases the channel contention and re-sults in more collisions which leads to packet drops inCSMA. Even worse, the C-node has little chance to forwardpackets accumulated in its queue. Therefore, even thoughpackets are successfully delivered to the C-node, only afew of them can be forwarded toward the sink, which re-sults in low throughput and energy efficiency in CSMA as

Table 1Parameters used in ns-2 simulation

Parameter Value

Communication range 100 mCarrier sensing range 220 mChannel bit-rate 19.2 kbpsInitial backoff window size 15Congestion window size 32Capture threshold 10Queue size 50 pktsCongestion threshold 20 pktsBurst duration 1.9902 sCSMA duration 0.438575 sEvent radius 100 m

Please cite this article in press as: R.-S. Liu et al., Locally sched(2008), doi:10.1016/j.adhoc.2008.08.002

shown in Fig. 6(a) and (b). By observing the queue lengthat the C-node, we found that the problem is due to conten-tion induced queue overflow. Similar results can also beobserved in CSMA + SP. However, it is important to notethat using shortest path routes reduces the chance of earlyaggregation. Thus, CSMA + SP has the lowest throughput.Furthermore, in CSMA + SP, packets along interfering pathshave to compete for the channel, which results in 50%higher energy consumption and latency than ClearBurst.In contrast to CSMA and CSMA + SP, ClearBurst has thehighest throughput, lowest energy tax and a moderate la-tency between CSMA and CSMA + SP. This shows thatClearBurst successfully resolves the contention aroundthe C-node. However, it should be noted that the designof ClearBurst favors the C-node and its children nodes be-cause burst transmission is only applied at these nodes.Thus, we restrict these node such that they cannot gener-ate a packet until a total number of n packets are received,where n is the number of immediate upstream nodes. Afterapplying this policy, ClearBurst shows the same fairness le-vel as the other two protocols as shown in Fig. 6(d).

5.2. Source rates

In this set of simulation, we fixed the number of nodesat 1000, and event radius at 100 m and varied the sourcerate from 0.5 pkt/s to 6 pkt/s. In Fig. 7(a) and (b), we canobserve similar results as those for different network den-sities in Fig. 6(a) and (b). As the source rate increases, con-tention becomes more intensive causing the throughput ofCSMA and CSMA + SP decline. However, when the sourcerate is as low as 0.5 - 1 pkts/s, CSMA outperforms Clear-Burst. This is due to the fact that there are only 31 sensorsin the event region and they generate traffic at a low rate(less than 1 pkt/s). CSMA can efficiently arbitrate transmis-sions among sensor nodes without incurring signallingoverhead. However, as the source rate increases beyond1 pkts/s, CSMA’s throughput decreases and latency and en-ergy tax increase dramatically. Note that when the sourcerate is low, CSMA + SP still has the lowest throughput. Thisis, again, due to the fact that shortest-path prohibits aggre-gation and long interfering routing paths increase thetransmission delay and the collision probability (see Fig. 8).

5.3. Event radius

To evaluate the impact of the event size, we fix the net-work density at 1000 nodes and vary event radius from50 m to 200 m. In this set of simulations, we can see a cleartrend of performance degradation of CSMA. When theevent radius is small, CSMA can arbitrate the channel ac-cess efficiently. Thus, it has similar performance as Clear-Burst. As the event radius grows, CSMA’s throughputdrops and energy tax and latency increase dramatically.

In contrast, ClearBurst’s throughput and energy tax re-main steady until the event radius becomes larger than100 m. When the event radius goes beyond 100 m, the per-formance of ClearBurst starts falling as well. By examiningthe packet reception and transmission of C-node, we foundthat ClearBurst successfully clears up all packets accumu-lated in C-node’s queue. But the reception rate during the

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R.-S. Liu et al. / Ad Hoc Networks xxx (2008) xxx–xxx 9

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CSMA duration is low. Therefore, the overall performancedegrades.

5.4. Aggregation ratio and queue length thresholds

In sensor networks, data aggregation is an effectivetechnique for reducing energy consumption and traffic

Please cite this article in press as: R.-S. Liu et al., Locally sched(2008), doi:10.1016/j.adhoc.2008.08.002

load in the network. Here, we study the impact of aggrega-tion ratio on CSMA, CSMA + SP and ClearBurst. The aggre-gation ratio is defined as the number of raw packets thatcan be aggregated to a single packet. To find out the impactof different burst sizes on the performance of ClearBurst,different queue length thresholds are used to trigger bursttransmissions.

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Figs. 9(a) and (b) present the impact of aggregation ratioon throughput and energy efficiency. We observe that, thehigher the aggregation ratio, the higher the throughputand energy efficiency. This is true for all three protocols.However, it should be noted that when the aggregation ra-tio is high, CSMA outperforms CSMA + SP in terms ofthroughput. This performance gain comes from aggrega-tion. ClearBurst increases this gain further by reducingcontention around the C-node. Furthermore, it should alsobe noted that even though CSMA’s throughput improves asthe aggregation ratio increases, its energy efficiency is stillat least three times higher than the other two protocols(Fig. 9(b)). In contrast, ClearBurst provides highestthroughput with lowest energy tax.

Fig. 10(a) shows the throughput of ClearBurst with dif-ferent burst sizes when aggregation is turned off. It can beseen that, if aggregation is turned off, the larger the burstsize, the higher the throughput. However, a larger burst

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Please cite this article in press as: R.-S. Liu et al., Locally sched(2008), doi:10.1016/j.adhoc.2008.08.002

size means packets must wait in the queue for a longertime before a burst transmission can be initiated. The long-er queueing delay is reflected on latency which can be ob-served in Fig. 10(b). In contrast, as shown in Fig. 11(a), ifaggregation is always possible, smaller burst sizes are bet-ter. This is because aggregation greatly reduces the amountof traffic in the network. If a larger burst size is used, pack-ets must wait in the queue for an even longer time. Thislonger delay not only increases the overall packet deliverylatency (Fig. 11(b)) but also lowers the throughput as few-er burst transmissions are triggered. These results suggestthat when aggregation is always possible, smaller burstsizes are favored.

6. Experiments

We implement ClearBurst on TinyOS [15] to evaluate itsperformance in a real environment. Fig. 12 shows the

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architecture of ClearBurst TinyOS implementation [15]. Tosupport the functions required by ClearBurst, we extendedthe MacControl interface to include the following com-mands and events.

1. SetPriority: Specify the priority. When MAC is in thehigh priority mode, it uses smaller initial backoff andcontention window size.

2. SetSlotLength: Set the length of the burst periodneeded to flush BURST_THRESHOLD packets in thequeue.

3. SlotExpired: An event used by the MAC layer toinform ClearBurst module that the specified burstperiod or CSMA period has ended.

A circular queue of size 32 is implemented for Clear-Burst. The ClearBurst module interacts with the circularqueue through the CirQueueControl interface. CirQueue-

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Fig. 12. TinyOS implementation.

Please cite this article in press as: R.-S. Liu et al., Locally sched(2008), doi:10.1016/j.adhoc.2008.08.002

Control interface provides three commands which includeEnqueue, Dequeue, and Length. This circular queue servesas the TX queue which is shared by all the applications.Messages generated locally and packets being routedthrough a node are sent to the queue first. The ClearBurstmodule then pops one packet from the queue each timeand transmits it to the next hop.

The ClearBurst module continuously monitors thequeue length. If it exceeds the congestion threshold, Clear-Burst intervenes packet transmission. Before sending apacket to the MAC layer, the ClearBurst module calls Mac-Control.SetSlotLength() command to pass the remainingtime of the burst period to the MAC layer. The MAC layerstamps the remaining time in the header right before thepacket is to be transmitted. Every time the SpiByteFifo.data-Ready() interrupt handler in the MAC is executed, theremaining time of current burst period is decremented.When the time reaches zero, the MAC layer informs Clear-Burst module by signaling a MacControl.SlotExpired event.

Experiments are conducted on the Kansei testbed[16,17] with 49 nodes in grid topology. The sink is locatedat the bottom right corner. Eight nodes at the top left cor-ner periodically send a packet to the C-node. In the exper-iments, C-nodes are manually selected.

As shown in Fig. 13(a) and (b), ClearBurst achieves fourtimes higher throughput than TinyOS’s CSMA MAC and yetis more energy-efficient. The results are similar to the re-sults presented in the previous section. However, it clearlydemonstrates that, even with a small network, the many-to-one traffic pattern in sensor networks has a severe im-pact on data delivery. Thus, transmission in the neighbor-

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hood of C-node must be coordinated, which validates thedesign of our protocol.

7. Discussion

Though we focused on static event in previous sections,we explore the possibility of applying ClearBurst in thescenarios where the event is mobile. In contrast to staticevents, when an event moves, the data collection treeshould be reconfigured. New edges must be establishedto connect new source nodes to the tree, and old edgesconnecting to nodes that falls out of the event regionshould be pruned. Event tracking in sensor networks is achallenging problem [18]. Although protocols such asDCTC [10] can be used to construct and reconfigure thedata collection tree to track the event, its signalling over-head is high. Furthermore, DCTC chooses the node closestto the event center as the root. In high data rate applica-tions, contention around the tree will be extremely high.Thus, it is not applicable under high data rate applications.Therefore, instead of applying DCTC directly, we extendour C-node election protocol and design a distributed C-node’s migration protocol for the purpose of event trackingand tree reconfiguration.

In order to track the event, the C-node periodicallymonitors the movement of the event. If the event hasmoved farther then a predefined distance, it releases itsrole and designates one of the neighbors, which is in theevent region and has the smallest hop distance to the sink,

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Please cite this article in press as: R.-S. Liu et al., Locally sched(2008), doi:10.1016/j.adhoc.2008.08.002

as the new C-node. If the C-node is migrated too often, sig-nalling overhead will increase and ClearBurst is appliedonly for a short period of time before the next migration.On the contrary, if the C-node does not migrate until theevent is far away, congestion arising at the event’s newlocation cannot be resolved. Furthermore, if the C-node’slocation becomes deep in the event region after event’smovement, it will experience more intensive contentionand the number of slots needed for burst transmission willhave to be increased. As a compromise, we choose to mi-grate the C-node when the event has moved farther thanhalf of the communication range. After the migration iscompleted, both the new C-node and the old C-node an-nounce the new C-node’s location in their neighborhoods.This information is then propagated in the event regionfor tree reconfiguration. When a node receives the newC-node’s location information, it follows the tree construc-tion procedure described in Section 3 to select its new par-ent. To reduce the tree reconfiguration overhead, new C-node’s location information is only propagated in the re-gion where reconfiguration is needed. In other words, if anode finds that its parent does not change after the C-nodemigration, it does not forward this information to down-stream nodes. If the event does not move further, thenew C-node can reduce or stop announcing its locationinformation to minimize overhead.

We simulate moving events with 100 m event radiusand random motion. The model used for events’ movingpattern is random waypoint mobility model. All the sen-

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sors that can detect the event generate event status updateat the rate of 2 pkts/s. Fig. 14 shows the throughput andenergy tax under various event moving speeds. Whenevent moves slowly, ClearBurst outperforms CSMA andCSMA + SP. Therefore, ClearBurst does have the potentialto increase network throughput if a more efficient eventtracking and tree reconfiguration protocol can be applied.

8. Conclusion

This paper addresses the overload problem aroundnodes that are aggregating data, and provides a solutionto improve system throughput and reduce energy con-sumption. The proposed MAC layer solution makes use ofburst transmissions with low-overhead local advertise-ments to avoid contention during the bust-periods. Usingextensive simulations, we observe that our proposed ap-proach can achieve up to two times higher throughputand four times higher energy efficiency than CSMA in staticevent scenarios, with an increasing performance gap as thenetwork gets overloaded (higher node densities and/or lar-ger event sizes). These observations are also supported bythe experiments on the Kansei testbed on different datarates. To apply ClearBurst in moving event scenarios, thelocation of C-node must migrate as the event moves.Through extensive simulations, we showed that ClearBurstwith the C-node migration protocol can have four timeshigher throughput and two times more energy efficiencythan CSMA. However, when the event moves fast, Clear-Burst does not show significant performance gain in com-parison to CSMA + SP. The reason for this performancedegradation is due to the overhead of C-node migrationand the reconfiguration of the data collection tree. The lat-ter is a time consuming operation which limits how fastthe protocol can react to the congestion arising at thenew location of the event. In summary, our proposed ap-proach is highly suited for data collection applications insensor networks, especially for static and slow movingevents. As to fast moving events, a more efficient data col-lection protocol is needed for event tracking and fast con-gestion resolution. These requirements impose manychallenges in different network layers and will be furtherstudied in our future works.

Acknowledgements

This material is based upon work supported by the Na-tional Science Foundation under Grants CNS-0546630 (CA-REER Award), CNS-0721434, and CNS-0403342. Anyopinions, findings, and conclusions or recommendationsexpressed in this material are those of the author(s) anddo not necessarily reflect the views of the National ScienceFoundation.

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Ren-Shiou Liu received his BS and MS degreein Computer Science and Information Engi-neering from National Chiao Tung University,Taiwan in 1998 and 2000. He worked at BenQMobile System as a software engineer andresearcher from 2000–2002 and 2003–2005,respectively. He is pursuing his Ph.D. inDepartment of Computer Science and Engi-neering at Ohio State University. His researchfocuses on design and analysis of networkprotocols for sensor networks and meshnetworks.

Kai-Wei Fan received his BS and MS degree inComputer Science and Information Engineer-ing from National Chiao Tung University,Taiwan in 1997 and 1999, respectively. Hewas a senior engineer and project manager innetwork security division in a start-up com-pany from 1999 to 2004. He is currentlypursuing his PhD degree in Department ofComputer Science and Engineering at TheOhio State University. His research interestsinclude wireless sensor networks and meshnetworks where he focuses on design and

implementation of energy efficient protocols.

Please cite this article in press as: R.-S. Liu et al., Locally sched(2008), doi:10.1016/j.adhoc.2008.08.002

Prasun Sinha received his PhD from Univer-sity of Illinois, Urbana-Champaign in 2001, MS

from Michigan State University in 1997, andB. Tech. from IIT Delhi in 1995. He worked atBell Labs, Lucent Technologies as a Member ofTechnical Staff from 2001 to 2003. Since 2003he is an Assistant Professor in Department ofComputer Science and Engineering at OhioState University. His research focuses ondesign of network protocols for sensor net-works and mesh networks. He has served onthe program committees of various confer-

ences including INFOCOM, MOBICOM and MOBIHOC. He has won severalawards including Ray Ozzie Fellowship (UIUC, 2000), Mavis MemorialScholarship (UIUC, 1999), and Distinguished Academic Achievement

rks xxx (2008) xxx–xxx

Award (MSU, 1997). He received the prestigious NSF CAREER award in2006.

uled packet bursting for data collection ..., Ad Hoc Netw.