Your SlideShare is downloading. ×
Fast data collection in tree based wireless sensor networks.bak
Fast data collection in tree based wireless sensor networks.bak
Fast data collection in tree based wireless sensor networks.bak
Fast data collection in tree based wireless sensor networks.bak
Fast data collection in tree based wireless sensor networks.bak
Fast data collection in tree based wireless sensor networks.bak
Fast data collection in tree based wireless sensor networks.bak
Fast data collection in tree based wireless sensor networks.bak
Fast data collection in tree based wireless sensor networks.bak
Fast data collection in tree based wireless sensor networks.bak
Fast data collection in tree based wireless sensor networks.bak
Fast data collection in tree based wireless sensor networks.bak
Fast data collection in tree based wireless sensor networks.bak
Fast data collection in tree based wireless sensor networks.bak
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Fast data collection in tree based wireless sensor networks.bak

2,242

Published on

0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
2,242
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
57
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. 86 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 11, NO. 1, JANUARY 2012 Fast Data Collection in Tree-Based Wireless Sensor Networks ¨ Ozlem Durmaz Incel, Amitabha Ghosh, Bhaskar Krishnamachari, and Krishnakant Chintalapudi Abstract—We investigate the following fundamental question—how fast can information be collected from a wireless sensor network organized as tree? To address this, we explore and evaluate a number of different techniques using realistic simulation models under the many-to-one communication paradigm known as convergecast. We first consider time scheduling on a single frequency channel with the aim of minimizing the number of time slots required (schedule length) to complete a convergecast. Next, we combine scheduling with transmission power control to mitigate the effects of interference, and show that while power control helps in reducing the schedule length under a single frequency, scheduling transmissions using multiple frequencies is more efficient. We give lower bounds on the schedule length when interference is completely eliminated, and propose algorithms that achieve these bounds. We also evaluate the performance of various channel assignment methods and find empirically that for moderate size networks of about 100 nodes, the use of multifrequency scheduling can suffice to eliminate most of the interference. Then, the data collection rate no longer remains limited by interference but by the topology of the routing tree. To this end, we construct degree-constrained spanning trees and capacitated minimal spanning trees, and show significant improvement in scheduling performance over different deployment densities. Lastly, we evaluate the impact of different interference and channel models on the schedule length. Index Terms—Convergecast, TDMA scheduling, multiple channels, power control, routing trees. Ç1 INTRODUCTIONC ONVERGECAST, namely, the collection of data from a set of 2) raw-data convergecast where packets are individually sensors toward a common sink over a tree-based relayed toward the sink. Aggregated convergecast is applic-routing topology, is a fundamental operation in wireless able when a strong spatial correlation exists in the data, orsensor networks (WSNs) [1]. In many applications, it is the goal is to collect summarized information such as the http://ieeexploreprojects.blogspot.comcrucial to provide a guarantee on the delivery time as well as maximum sensor reading. Raw-data convergecast, on theincrease the rate of such data collection. For instance, in other hand, is applicable when every sensor reading issafety and mission-critical applications where sensor nodes equally important, or the correlation is minimal. We studyare deployed to detect oil/gas leak or structural damage, the aggregated convergecast in the context of continuous dataactuators and controllers need to receive data from all the collection, and raw-data convergecast for one-shot datasensors within a specific deadline [2], failure of which might collection. These two types correspond to two extreme caseslead to unpredictable and catastrophic events. This falls of data collection. In an earlier work [4], the problem ofunder the category of one-shot data collection. On the other applying different aggregation factors, i.e., data compressionhand, applications such as permafrost monitoring [3] require factors, was studied, and the latency of data collection wasperiodic and fast data delivery over long periods of time, shown to be within the performance bounds of the twowhich falls under the category of continuous data collection. extreme cases of no data compression (raw-data converge- In this paper, we consider such applications and focus on cast) and full data compression (aggregated convergecast).the following fundamental question: “How fast can data be For periodic traffic, it is well known that contention-freestreamed from a set of sensors to a sink over a tree-based topology?” medium access control (MAC) protocols such as TimeWe study two types of data collection: 1) aggregated Division Multiple Access (TDMA) are better fit for fast dataconvergecast where packets are aggregated at each hop, and collection, since they can eliminate collisions and retrans- missions and provide guarantee on the completion time as opposed to contention-based protocols [1]. However, the ¨. O.D. Incel is with the Department of Computer Engineering, Bogazici problem of constructing conflict-free (interference-free) University, Istanbul 34342, Turkey. E-mail: ozlem.durmaz@tam.boun.edu.tr. TDMA schedules even under the simple graph-based. A. Ghosh is with the Department of Electrical Engineering, Princeton interference model has been proved to be NP-complete. In University, F-310, Engineering Quad, Olden Street, Princeton, NJ 08544. this work, we consider a TDMA framework and design E-mail: amitabhg@princeton.edu. polynomial-time heuristics to minimize the schedule length. B. Krishnamachari is with the Ming Hsieh Department of Electrical Engineering, University of Southern California, 3740 McClintock Avenue, for both types of convergecast. We also find lower bounds EEB 300, Los Angeles, CA 90089. E-mail: bkrishna@usc.edu. on the achievable schedule lengths and compare the. K. Chintalapudi is with Microsoft Research, Bengaluru, India. performance of our heuristics with these bounds. E-mail: krchinta@microsoft.com. We start by identifying the primary limiting factors ofManuscript received 5 Nov. 2009; revised 14 Oct. 2010; accepted 15 Dec. fast data collection, which are: 1) interference in the wireless2010; published online 2 Feb. 2011. medium, 2) half-duplex transceivers on the sensor nodes, andFor information on obtaining reprints of this article, please send e-mail to:tmc@computer.org, and reference IEEECS Log Number TMC-2009-11-0482. 3) topology of the network. Then, we explore a number ofDigital Object Identifier no. 10.1109/TMC.2011.22. different techniques that provide a hierarchy of successive 1536-1233/12/$31.00 ß 2012 IEEE Published by the IEEE CS, CASS, ComSoc, IES, & SPS
  • 2. INCEL ET AL.: FAST DATA COLLECTION IN TREE-BASED WIRELESS SENSOR NETWORKS 87improvements, the simplest among which is an interfer- of aggregated convergecast. In this work, weence-aware, minimum-length TDMA scheduling that en- evaluate the behavior of an optimal power controlables spatial reuse. To achieve further improvement, we algorithm [6] under realistic settings considering thecombine transmission power control with scheduling, and limited discrete power levels available in today’suse multiple frequency channels to enable more concurrent radios. We find that for moderate size networks oftransmissions. We show that once multiple frequencies are 100 nodes, power control can reduce the scheduleemployed along with spatial-reuse TDMA, the data collec- length by 15-20 percent.tion rate often no longer remains limited by interference but . Evaluation of channel assignment methods. Usingby the topology of the network. Thus, in the final step, we extensive simulations, we show that scheduling transmissions on different frequency channels isconstruct network topologies with specific properties that more effective in mitigating interference as com-help in further enhancing the rate. Our primary conclusion pared to transmission power control. We evaluateis that combining these different techniques can provide an the performance of three different channel assign-order of magnitude improvement for aggregated converge- ment methods: 1) Joint Frequency Time Slot Schedulingcast, and a factor of 2 improvement for raw-data con- (JFTSS), 2) Receiver-Based Channel Assignment [7], andvergecast, compared to single-channel TDMA scheduling 3) Tree-Based Multichannel Protocol (TMCP) [8]. Theseon minimum-hop routing trees. methods consider the channel assignment problem Although the techniques of transmission power control at different levels: the link level, node level, orand multichannel scheduling have been well studied for cluster level. We show that for aggregated conver-eliminating interference in general wireless networks, their gecast, TMCP performs better than JFTSS and RBCAperformances for bounding the completion of data collec- on minimum-hop routing trees, while performstion in WSNs have not been explored in detail in the worse on degree-constrained trees. For raw-dataprevious studies. The fundamental novelty of our approach convergecast, RBCA and JFTSS perform better thanlies in the extensive exploration of the efficiency of TMCP, since the latter suffers from interferencetransmission power control and multichannel communica- inside the branches due to concurrent transmissionstion on achieving fast convergecast operations in WSNs. on the same channel.Besides, we evaluate the impact of routing trees on fast data . Impact of routing trees. We investigate the effect ofcollection and to the best of our knowledge, this has not network topology on the schedule length, and showbeen the topic of previous studies. As we will discuss in that for aggregated convergecast, the performanceSection 2, some of the existing work had the objective of can be improved by up to 10 times on degree-minimizing the completion time of convergecasts. How- constrained trees using multiple frequencies as http://ieeexploreprojects.blogspot.com on minimum-hop trees using aever, none of the previous work discussed the effect of compared to thatmultichannel scheduling together with the comparisons single frequency. For raw-data convergecast, multi-of different channel assignment techniques and the impact channel scheduling on capacitated minimal span-of routing trees and none considered the problems of ning trees (CMSTs) can reduce the schedule lengthaggregated and raw convergecast, which represent two by 50 percent.extreme cases of data collection, together. . Impact of channel models and interference. Under As the new concepts in this paper, we introduce the setting of multiple frequencies, one simplifyingpolynomial-time heuristics for TDMA scheduling for both assumption often made is that the frequencies aretypes of data collection, i.e., Algorithms 1 and 2, and prove orthogonal to each other. We evaluate this assump-that they do achieve the lower bound of data collection time tion and show that the schedules generated mayonce interference is eliminated. Besides, we elaborate on the not always eliminate interference, thus causingperformance of our previous work, a receiver-based considerable packet losses. We also evaluate andchannel assignment (RBCA) method, and compare its compare the two most commonly used interferenceefficiency with other channel assignment methods and models: 1) the graph-based protocol model, andintroduce heuristics for constructing optimal routing trees 2) the Signal-to-Interference-plus-Noise Ratioto further enhance data collection rate. The following lists (SINR)-based physical model.our key findings and contributions: The rest of the paper is organized as follows: in Section 2, . Bounds on convergecast scheduling. We show that we discuss related works. In Section 3, we describe the if all interfering links are eliminated, the schedule problem formulation and state our assumptions. In Section length for aggregated convergecast is lower 4, we analyze the lower bounds on the schedule length for bounded by the maximum node degree in the aggregated and raw convergecast, and propose algorithms routing tree, and for raw-data convergecast by that achieve the corresponding bounds. In Section 5, we maxð2nk À 1; NÞ, where nk is the maximum number focus on power control and multichannel scheduling as of nodes on any branch in the tree, and N is the mechanisms to eliminate interference. Section 6 explains the number of source nodes. We then introduce optimal impact of routing topologies, and Section 7 presents time slot assignment schemes under this scenario detailed evaluation results. Finally, we draw our conclu- which achieve these lower bounds. sions in Section 8. . Evaluation of power control under realistic setting. It was shown recently [5] that under the idealized setting of unlimited power and continuous range, 2 RELATED WORK transmission power control can provide an un- Fast data collection with the goal to minimize the schedule bounded improvement in the asymptotic capacity length for aggregated convergecast has been studied by us
  • 3. 88 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 11, NO. 1, JANUARY 2012in [7] and [9], and also by others in [5], [10], and [11]. In [7], However, TreeMAC operates on a single channel andwe experimentally investigated the impact of transmission achieves 1=3 of the maximum throughput similar to thepower control and multiple frequency channels on the bounds presented by Gandham et al. [1] since the sink canschedule length, while the theoretical aspects were dis- receive every three time slots.cussed in [9], where we proposed constant factor and The problem of minimizing the schedule length for raw-logarithmic approximation algorithms on geometric net- data convergecast on single channel is shown to be NP-works (disk graphs). Raw-data convergecast has been complete on general graphs by Choi et al. [13]. Maximizingstudied in [1], [12], [13], and [14], where a distributed time the throughput of convergecast by finding a shortest-length,slot assignment scheme is proposed by Gandham et al. [1] conflict-free schedule is studied by Lai et al. [14], where ato minimize the TDMA schedule length for a single channel. greedy graph coloring strategy assigns time slots to theThe problem of joint scheduling and transmission power senders and prevents interference. They also discussed thecontrol is studied by Moscibroda [5] for constant and impact of routing trees on the schedule length and proposed auniform traffic demands. Our present work is different routing scheme called disjoint strips to transmit data overfrom the above in that we evaluate transmission power different shortest paths. However, since the sink remains ascontrol under realistic settings and compute lower bounds the bottleneck, sending data over different paths does noton the schedule length for tree networks with algorithms to reduce the schedule length. As we will show in this paper, theachieve these bounds. We also compare the efficiency of improvement due to the routing structure comes from usingdifferent channel assignment methods and interference capacitated minimal spanning trees for raw-data converge-models, and propose schemes for constructing specific cast, where the number of nodes in a subtree is no more thanrouting tree topologies that enhance the data collection rate half the total number of nodes in the remaining subtrees.for both aggregated and raw-data convergecast. The use of multiple frequencies has been studied The use of orthogonal codes to eliminate interference has extensively in both cellular and ad hoc networks; however,been studied by Annamalai et al. [10], where nodes are in the domain of WSN, there exist a few studies that utilizeassigned time slots from the bottom of the tree to the top such multiple channels [8], [17], [18]. To this end, we evaluate thethat a parent node does not transmit before it receives all the efficiency of three particular schemes that treat the channelpackets from its children. This problem and the one assignment at different levels.addressed by Chen et al. [11] are for one-shot raw-dataconvergecast. In this work, since we construct degree-constrained routing topologies to enhance the data collection 3 MODELING AND PROBLEM FORMULATIONrate, it may not always lead to schedules that have low We model the multihop WSN as a graph G ¼ ðV ; EÞ, where http://ieeexploreprojects.blogspot.comlatency, because the number of hops in a tree goes up as its V is the set of nodes, and E ¼ fði; jÞ j i; j 2 V g is the set ofdegree goes down. Therefore, if minimizing latency is also a edges representing the wireless links. A designated noderequirement, then further optimization, such as constructing s 2 V denotes the sink. The euclidean distance between twobounded-degree, bounded-diameter trees, is needed. A nodes i and j is denoted by dij . All the nodes except s arestudy along this line with the objective to minimize the sources, which generate packets and transmit them over amaximum latency is presented by Pan and Tseng [15], where routing tree to s. We denote the spanning tree on G rootedthey assign a beacon period to each node in a Zigbee network at s by T ¼ ðV ; ET Þ, where ET E represents the treeduring which it can receive data from all its children. edges. Each node is assumed to be equipped with a single For raw-data convergecast, Song et al. [12] presented a half-duplex transceiver, which prevents it from sending andtime-optimal, energy-efficient packet scheduling algorithm receiving packets simultaneously. We consider a TDMAwith periodic traffic from all the nodes to the sink. Once protocol where time is divided into slots, and consecutiveinterference is eliminated, their algorithm achieves the slots are grouped into equal-sized nonoverlapping frames.bound that we present here; however, they briefly mention We use two types of interference models for oura 3-coloring channel assignment scheme, and it is not clear evaluation: the graph-based protocol model and the SINR-whether the channels are frequencies, codes, or any other based physical model. In the protocol model, we assumemethod to eliminate interference. Moreover, they assume a that the interference range of a node is equal to itssimple interference model where each node has a circular transmission range, i.e., two links cannot be scheduledtransmission range and cumulative interference from simultaneously if the receiver of at least one link is withinconcurrent multiple senders is avoided. Different from the range of the transmitter of the other link. In the physicaltheir work, we consider multiple frequencies and evaluate model, the successful reception of a packet from i to jthe performance of three different channel assignment depends on the ratio between the received signal strength atmethods together with evaluating the effects of transmis- j and the cumulative interference caused by all othersion power control using realistic interference and channel concurrently transmitting nodes and the ambient noisemodels, i.e., physical interference model and overlapping level. Thus, a packet is received successfully at j if thechannels and considering the impact of routing topologies. signal-to-interference-plus-noise ratio, SINR , is greater ijSong et al. [12] extended their work and proposed a TDMA- than a certain threshold
  • 4. , i.e.,based MAC protocol for high-data-rate WSNs in [16].TreeMAC considers the differences in load at different Pi Á gijlevels of a routing tree and assigns time slots according to SINRij ¼ P ; ð1Þ k6¼i Pk Á gkj þ Nthe depth, i.e., the hop count, of the nodes on the routingtree, such that nodes closer to the sink are assigned more where Pi is the transmitted signal power at node i, N is theslots than their children in order to mitigate congestion. ambient noise level, and gij is the propagation attenuation
  • 5. INCEL ET AL.: FAST DATA COLLECTION IN TREE-BASED WIRELESS SENSOR NETWORKS 89(link gain) between i and j. We use a simple distance operating frequency, channel scheduling can be coupleddependent path-loss model to calculate the link gains as with time slot scheduling as it is the case with the JFTSSgij ¼ dÀ , where the path-loss exponent is a constant ij algorithm (Section 5.2.1) or first channels are assigned andbetween 2 and 6, whose exact value depends on external then time slot scheduling continues as in the case of RBCAconditions of the medium (humidity, obstacles, etc.), as well explained in Section 5.2.3. However, the TMCP algorithmas the sender-receiver distance. We assume that the level of (Section 5.2.2) considers tree construction and channelinterference is static and does not change over time. For assignment jointly and then does the scheduling of time slots.simplicity and ease of illustration, we use the protocolmodel in all the figures. 4 TDMA SCHEDULING OF CONVERGECASTS We study aggregated convergecast in the context ofperiodic data collection where each source node generates a In this section, we first focus on periodic aggregatedpacket at the beginning of every frame, and raw-data convergecast and then on one-shot raw-data convergecast.convegecast for one-shot data collection where each node Our objective is to calculate the minimum achievablehas only one packet to send. We assume that the size of each schedule lengths using an interference-aware TDMA pro-packet is constant. Our goal is to deliver these packets to the tocol. We first consider the case where the nodes commu-sink over the routing tree as fast as possible. More nicate on the same channel using a constant transmissionspecifically, we aim to schedule the edges ET of T using a power, and then discuss improvements using transmissionminimum number of time slots while respecting the power control and multiple frequencies in the next section.following two constraints: 4.1 Periodic Aggregated Convergecast . Adjacency constraint. Two edges ði; jÞ 2 ET and In this section, we consider the scheduling problem where ðk; lÞ 2 ET cannot be scheduled in the same time packets are aggregated. Data aggregation is a commonly slot if they are adjacent to each other, i.e., if used technique in WSN that can eliminate redundancy and T fi; jg fk; lg 6¼ . This constraint is due to the half- minimize the number of transmissions, thus saving energy duplex transceiver on each node which prevents it and improving network lifetime [19]. Aggregation can be performed in many ways such as by suppressing duplicate from simultaneous transmission and reception. messages; using data compression and packet merging . Interfering constraint. The interfering constraint de- techniques; or taking advantage of the correlation in the pends on the choice of the interference model. In the sensor readings. protocol model, two edges ði; jÞ 2 ET and ðk; lÞ 2 ET We consider continuous monitoring applications where cannot be scheduled simultaneously if they are at 2- perfect aggregation is possible, i.e., each node is capable of hop distance of each other. In http://ieeexploreprojects.blogspot.com the physical model, an aggregating all the packets received from its children as edge ði; jÞ 2 ET cannot be scheduled if the SINR at well as that generated by itself into a single packet before receiver j is not greater than the threshold
  • 6. . transmitting to its parent. The size of aggregated data Since we consider data collection to be periodic in transmitted by each node is constant and does not dependaggregated convergecast, each of the edges in ET is on the size of the raw sensor readings. Typical examples ofscheduled only once within each frame, and this schedule such aggregation functions are MIN, MAX, MEDIAN,is repeated over multiple frames. Thus, a pipeline is COUNT, AVERAGE, etc.established after a certain frame, and then onward the sink In Figs. 1a and 1b, we illustrate the notion of pipeliningcontinues to receive aggregated packets from all the source in aggregated convergecast and that of a schedule length onnodes once per frame. We explain further details about the a network of six source nodes. The solid lines represent treepipelining in the next section. On the other hand, in one-shot edges, and the dotted lines represent interfering links. Thedata collection for raw-data convergecast, the edges in ET numbers beside the links represent the time slots at whichmay be scheduled multiple times and no pipelining takes the links are scheduled to transmit, and the numbers insideplace. We use the terms link scheduling and node scheduling the circles denote node ids. The entries in the table list theinterchangeably as they are equivalent in our case. Note that nodes from which packets are received by their correspond-the two other scenarios, which we do not consider in this ing receivers in each time slot. We note that at the end ofpaper due to space constraints, are one-shot aggregated frame 1, the sink does not have packets from nodes 5 and 6;convergecast and periodic raw-data convergecast. however, as the schedule is repeated, it receives aggregated The key difference in terms of scheduling between packets from 2, 5, and 6 in slot 2 of the next frame. Similarly,periodic and one-shot data collection is that a node in the the sink also receives aggregated packets from nodes 1 andperiodic case does not have to wait for data from its children 4 starting from slot 1 of frame 2. The entries f1; 4g andbefore being scheduled. This is because a link is scheduled f2; 5; 6g in the table represent single packets comprisingonly once within each frame and each node generates a packet aggregated data from nodes 1 and 4, and from nodes 2, 5,in the beginning of every frame, so a pipelining is eventually and 6, respectively. Thus, a pipeline is established fromestablished. However, in the case of one-shot data collection, frame 2, and the sink continues to receive aggregateda node needs to wait for data from its children before being packets from all the nodes once every six time slots. Thus,scheduled, which we refer to as the causality constraint. the minimum schedule length is 6. To summarize the steps in our design, we start with treeconstruction and then continue with interference-aware 4.1.1 Lower Bound on Schedule Lengthscheduling. If the nodes can control their transmission We first consider aggregated convergecast when all thepower, scheduling phase is coupled with a transmission interfering links are eliminated by using transmission powerpower control algorithm. If the nodes can change their control or multiple frequencies. Although the problem of
  • 7. 90 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 11, NO. 1, JANUARY 2012Fig. 1. Aggregated convergecast and pipelining: (a) Schedule length of 6 in the presence of interfering links. (b) Node ids from which (aggregated)packets are received by their corresponding parents in each time slot over different frames. (c) Schedule length of 3 using BFS-TIMESLOTASSIGNMENT when all the interfering links are eliminated.minimizing the schedule length is NP-complete on general To illustrate, we show the same network of Fig. 1a in Fig. 1cgraphs, we show in the following that once interference is with all the interfering links removed, and so the network iseliminated, the problem reduces to 1 on a tree, and can be scheduled in three time slots.solved in polynomial time. To this end, we first give a lower Although BFS-TIMESLOTASSIGNMENT may not be anbound on the schedule length, and then propose a time slot approximation to ideal scheduling under the physicalassignment scheme that achieves the bound. interference model, it is a heuristic that can achieve theLemma 1. If all the interfering links are eliminated, the schedule lower bound if all the interfering links are eliminated. length for aggregated convergecast is lower bounded by ÁðT Þ, Therefore, together with a method to eliminate interference, where ÁðT Þ is the maximum node degree in the routing tree T . the algorithm can optimally schedule the network.Proof. If all the interfering links are eliminated, the Theorem 1. If all the interfering links are eliminated, the scheduling problem reduces to 1 on a tree. Now since schedule length for aggregated convergecast achieved by BFS- each of the tree edges needs to be scheduled only once TIMESLOTASSIGNMENT is the minimum, i.e., ÁðT Þ. within each frame, it is equivalent to edge coloring on a Proof. The proof is by induction on i. Let T i ¼ ðV i ; ET Þ i graph, which needs number of colors at least equal to the denote the subtree of T in the ith iteration constructed in maximum node degree. u t i the BFS order, where ET comprises all the edges that are i assigned a slot, and V comprises the set of nodes on which http://ieeexploreprojects.blogspot.com Once all the interfering links are eliminated, concurrency i i the edges in ET are incident. Note that, jET j ¼ i, because atis still limited by the adjacency constraint due to the half- every iteration, exactly one edge is assigned a slot. Forduplex transceivers, which prevents a parent from trans- i ¼ 1, clearly the number of slots used is 1, equal to ÁðT 1 Þ.mitting when it is already receiving from its children, or Now, assume that the number of slots NðiÞ needed towhen its parent is transmitting. schedule the edges in T i is ÁðT i Þ. In the ði þ 1Þst iteration, after assigning a slot to the next edge in BFS4.1.2 Assignment of Time Slots order, the number of slots needed in T iþ1 can eitherGiven the lower bound ÁðT Þ on the schedule length in the remain the same as before, or increase by 1. Thus,absence of interfering links, we now present a time slotassignment scheme in Algorithm 1, called BFS-TIMESLO- Nði þ 1Þ ¼ maxfNðiÞ; NðiÞ þ 1g: ð2ÞTASSIGNMENT, that achieves this bound. If it remains the same, Nði þ 1Þ is still the maximumAlgorithm 1. BFS-TIMESLOTASSIGNMENT degree of T iþ1 at end of ði þ 1Þst iteration. Otherwise, if it 1. Input: T ¼ ðV ; ET Þ increases by 1, the new edge must be incident on a node 2. while ET 6¼ do và , common to both T i and T iþ1 , such that the number of 3. e next edge from ET in BFS order incident edges on và that were already assigned a time 4. Assign minimum time slot t to edge e respecting slot at the end of ith iteration was ÁðT i Þ. This is so adjacency and interfering constraints because in the BFS traversal, all the edges incident on a 5. ET ET n feg node are assigned a slot first before moving on to the 6. end while next node, and because the slot assigned to the new edge is the minimum possible that is different from all that In each iteration of BFS-TIMESLOTASSIGNMENT (lines 2- already assigned to the edges incident on và until the ith6), an edge e is chosen in the Breadth First Search (BFS) iteration. Thus, at the end of ði þ 1Þst iteration, theorder starting from any node, and is assigned the minimum number of slots used NðiÞ þ 1 is equal to the number oftime slot that is different from all its adjacent edges assigned edges incident on và which, in turn, equalsrespecting interfering constraints. Note that, since we ÁðT iþ1 Þ. This proves the inductive step. Therefore, itevaluate the performance of this algorithm also for the case holds at every iteration of the algorithm until the endwhen the interfering links are present, we check for the when i ¼ jV j À 2, yielding a schedule length equal to thecorresponding constraint in line 4; however, when inter- maximum degree ÁðT Þ ¼ ÁðT jV jÀ1 Þ. Now, since assign-ference is eliminated this check is redundant. The algorithm ing different time slots to the adjacent edges of T isruns in OðjET j2 Þ time and minimizes the schedule length equivalent to edge coloring T , which requires at leastwhen there are no interfering links, as proved in Theorem 1. ÁðT Þ colors, the schedule length is minimum. u t
  • 8. INCEL ET AL.: FAST DATA COLLECTION IN TREE-BASED WIRELESS SENSOR NETWORKS 91 which requires each node to maintain a buffer that stores at most two packets and minimizes the schedule length. In the following, we describe a time slot assignment scheme that computes a schedule of length exactly equal to the lower bound when interference is eliminated and does not requireFig. 2. Raw-data convergecast: largest top-subtree with nk nodes. to store more than one packet in buffers at any time. 4.2.2 Assignment of Time Slots4.2 One-Shot Raw-Data Convergecast We now describe a time slot assignment scheme inIn this section, we consider one-shot data collection where Algorithm 2, called LOCAL-TIMESLOTASSIGNMENT, whichevery sensor reading is equally important, and so aggrega- is run locally by each node at every time slot. The key idea istion may not be desirable or even possible. Thus, each of the to: 1) schedule transmissions in parallel along multiplepackets has to be individually scheduled at each hop en route branches of the tree, and 2) keep the sink busy in receivingto the sink. As before, we focus on minimizing the schedule packets for as many time slots as possible. Because the sinklength. Unlike in the case of periodic aggregated converge- can receive from the root of at most one top-subtree in anycast where a pipelining takes place and each of the tree edges time slot, we need to decide which top-subtree should beis scheduled only once within each frame, here the edges made active. We assume that the sink is aware of thecould be scheduled multiple times and there is no pipelining. number of nodes in each top-subtree. Each source node The problem of minimizing the scheduling length for maintains a buffer and its associated state, which canraw-data convergecast is proved to be NP-complete even be either full or empty depending on whether it contains aunder the protocol interference model by a reduction from packet or not. Our algorithm does not require any of thethe well known Partition Problem [13]. Before getting into the nodes to store more than one packet in their buffer at anydetails, we first define the following terms: a branch is time. We initialize all the buffers as full, and assume thatdefined as a subtree containing the sink as an endpoint; a the sink’s buffer is always full for the ease of explanation.top-subtree is defined as a subtree that has a child of the sinkas its root. For instance, in Fig. 3, the branches are fs; 1; 4g, Algorithm 2. LOCAL-TIMESLOTASSIGNMENTfs; 2; 5; 6g, and fs; 3; 7g, while the top-subtrees are f1; 4g, 1. node.buffer ¼ fullf2; 5; 6g, and f3; 7g. 2. if {node is sink} then 3. Among the eligible top-subtrees, choose the one with4.2.1 Lower Bound on Schedule Length the largest number of total (remaining) packets, say http://ieeexploreprojects.blogspot.comAs mentioned in Section 4.1.1, if all the interfering links are top-subtree ieliminated using multiple frequencies, the only limiting 4. Schedule link ðrootðiÞ; sÞ respecting interferingfactor in minimizing the schedule length is the half-duplex constrainttransceivers. In the following, we give a lower bound on the 5. elseschedule length under this scenario. 6. if {node.buffer == empty} thenLemma 2. If all the interfering links are eliminated, the schedule 7. Choose a random child c of node whose buffer length for one-shot raw-data convergecast is lower bounded by is full maxð2nk À 1; NÞ, where nk is the maximum number of nodes 8. Schedule link ðc; nodeÞ respecting interfering in any top-subtree of the routing tree, and N is the number of constraint sources in the network. 9. c.buffer = emptyProof. Let ni denote the number of nodes in top-subtree i. 10. node.buffer = full Order the top-subtrees in nonincreasing order of their 11. end if sizes: nk ! nkÀ1 ! Á Á Á ! n1 . Consider the routing tree 12. end if shown in Fig. 2. Since the nodes cannot receive multiple The first block of the algorithm in lines 2-4 gives the packets simultaneously, N is a trivial lower bound to scheduling rules between the sink and the roots of the top- receive all the packets. Next, consider the largest top- subtrees. We define a top-subtree to be eligible if its root has at subtree k, the root of which has to transmit nk packets to least one packet to transmit. For a given time slot, we the sink, and the children of this root have to forward schedule the root of an eligible top-subtree which has the nk À 1 packets in total. Because of the half-duplex largest number of total (remaining) packets. If none of the top- transceivers, time slots assigned to the root of this top- subtrees are eligible, the sink does not receive any packet subtree must be distinct from all those assigned to its during that time slot. children. Thus, in total, we need at least nk þ ðnk À 1Þ ¼ Inside each top-subtree, nodes are scheduled according 2nk À 1 distinct time slots. u t to the rules in lines 5-12. We define a subtree to be active if there are still packets left in the subtree (excluding its root) We note that this bound of maxð2nk À 1; NÞ, which to be relayed. If a node’s buffer is empty and the subtreeapplies only when all the interfering links are removed, is rooted at this node is active, we schedule one of its childrensmaller than the lower bound of 3N for general networks at random whose buffer is not empty. Our algorithmand that of maxð3nk À 3; NÞ for tree networks, as computed guarantees (as proved in Lemma 3) that in an active subtree,by Gandham et al. [1] for the 2-hop interference model. They there will always be at least one child whose buffer is notproposed a time slot assignment scheme for tree networks, empty, and so whenever a node empties its buffer, it will
  • 9. 92 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 11, NO. 1, JANUARY 2012 . Node i is full, while pðiÞ and cðiÞ are both empty. . Nodes i and pðiÞ are both full, while cðiÞ is empty. For the first case, we need to show that both pðiÞ and cðiÞ (since now they are empty) have a child and a parent whose buffers are full. Clearly, pðiÞ has a child with a full buffer because i is now full. Similarly, pðiÞ also has a parent with a full buffer because a transmission took place from pðiÞ to its parent at t ¼ k þ 1. For the latter,Fig. 3. Raw-data convergecast using algorithm LOCAL-TIMESLOTAS- cðiÞ has a parent with a full buffer because transmissionSIGNMENT: (a) Schedule length of 7 when all the interfering links are took place from cðiÞ to i at t ¼ k þ 1. If the child of cðiÞ,removed. (b) Schedule length of 10 when the interfering links are i.e., gcðiÞ, was empty at t ¼ k, then gcðiÞ also had a childpresent. with a full buffer because the lemma was true at t ¼ k. Therefore, at t ¼ k þ 1, the child of gcðiÞ transmits andreceive a packet in the next time slot, thus emptying buffers fills up its parent’s buffer. Otherwise, if gcðiÞ was full atfrom the bottom of the subtree to the top. t ¼ k, then it also remains full at t ¼ k þ 1 because it We run through an example shown in Fig. 3a to explain cannot transmit to its parent cðiÞ, which was full at t.the algorithm. In the first time slot, since the eligible top- For the second case, cðiÞ transmitted and pðiÞ did not.subtree containing the largest number of remaining packets For this to happen, gpðiÞ was full at t ¼ k and eitheris f2; 5; 6g, we schedule the link ð2; sÞ, and the sink receives a empties or remains full at t ¼ k þ 1. If it empties, gpðiÞpacket from node 2 in slot 1. In the second time slot, the has a parent with a full buffer because it transmitted ateligible top-subtrees are f1; 4g and f3; 7g, both of which have t ¼ k þ 1, and also has a child with a full buffer becausetwo remaining packets. We choose one of them at random, pðiÞ did not transmit. If it remains full, at t ¼ k þ 1 nodessay f1; 4g, and schedule the link ð1; sÞ. Also, in the same time i, pðiÞ, and gpðiÞ are full, cðiÞ is empty, and gcðiÞ is full asslot since node 2’s buffer is empty, it chooses one of its we showed in the first case. So, the lemma holds forchildren at random, say node 5, and schedule the link ð5; 2Þ. t ¼ k þ 1, and the proof follows. u tIn the third time slot, the eligible top-subtrees are f2; 5; 6gand f3; 7g, both of which have two remaining packets. We Theorem 2. If all the interfering links are eliminated, thechoose the first one at random and schedule the link ð2; sÞ, schedule length for raw-data convergecast achieved byand so the sink receives node 5’s packet (relayed by node 2). algorithm LOCAL-TIMESLOTASSIGNMENT is the minimum,We also schedule the link ð4; 1Þ in the third time slot because i.e., maxð2nk À 1; NÞ.node 1’s buffer is empty at this point. http://ieeexploreprojects.blogspot.com number of nodes in top-subtree i. This process continues Proof. Let ni be theuntil all the packets are delivered to the sink, yielding an Order the top-subtrees in nonincreasing order of their Passignment that requires seven time slots. Note that, in this sizes: nk ! nkÀ1 ! Á Á Á ! n1 . Suppose nk kÀ1 ni ; then i¼1example, 2nk À 1 ¼ 5, and so maxð2nk À 1; NÞ ¼ 7. In Fig. 3b, maxð2nk À 1; NÞ ¼ 2nk À 1. From Lemma 1, we knowwe show an assignment when all the interfering links are that it takes at least 2nk À 1 slots to schedule all thepresent, yielding a schedule length of 10. packets originated in top-subtree k. Out of these, the sink In the following, we prove that the algorithm requires can use at most nk À 1 slots to receive packets from theexactly maxð2nk À 1; N) slots when all the interfering links other top-subtrees, which P have a total of at most nk À 1are eliminated. Before giving the details of the proof, we kÀ1 packets. Also, when nk i¼1 ni , the root of the largestfirst highlight the two key insights of the algorithm: 1) the top-subtree k gets scheduled once in every two timesink is kept busy in receiving packets for as many time slots slots. Therefore, the schedule length is at most 2nk À 1.as possible, and 2) a node’s buffer is not empty for two or PkÀ1 Now suppose nk i¼1 ni ; then maxð2nk À 1; NÞ ¼more consecutive time slots so long as the subtree rooted at N. We need to show that there always exists an eligiblethis node is active. The first one is evident from the top-subtree to complement for the largest one when it isscheduling rule between the sink and the top-subtrees. We not eligible. In this case, the sink will receive packets inprove the second insight in the following lemma: every slot, because otherwise it remains idle during some PLemma 3. In an active subtree, a node with an empty buffer time slots and the first condition nk kÀ1 ni will be met. i¼1 always has a child and a parent whose buffers are full. Thus, we will P prove that the algorithm keeps the kÀ1 inequality nk i¼1 ni as an invariant.Proof. We prove it by induction on time slot t. The parent In any given time slot t, the algorithm schedules an and grandparent of node i are denoted by pðiÞ and gpðiÞ; eligible top-subtree that has the largest number of similarly, a child and a grandchild of i are denoted by remaining packets. At slot t þ 1, therefore, we have cðiÞ and gcðiÞ, respectively. Slightly abusing notation, we nk ¼ nk À 1, and the following three cases might arise: also use these symbols to denote the state of the buffers on the respective nodes. . Top-subtree k still has the largest number of At t ¼ 1, the lemma is trivially true because all the remaining packets with nk ! nkÀ1 ! Á Á Á ! n1 . buffers are full. Suppose the lemma holds for t ¼ k, i.e., Then, the root of k is again chosen to transmit every node whose buffer is empty has a child and a parent whose buffers are full. At t ¼ k þ 1, each node PkÀ1 1, and the inequality still holds as nk À 1 at t þ i¼1 ni . with an empty buffer schedules one of its children whose . Top-subtree k and at least another one, say j, have buffer is full. The following two situations can occur: an equal number of remaining packets. Then, the
  • 10. INCEL ET AL.: FAST DATA COLLECTION IN TREE-BASED WIRELESS SENSOR NETWORKS 93 root of j is chosen, and the inequality still holds PkÀ1 higher than what is required by the threshold value, it because nj À 1 i¼1 ni À 1 (since nj ¼ nk À 1). should decrease its power and if it is below the threshold, it . Top-subtree k does not have the largest number of should increase its transmission power, within the available remaining packets, implying that there were other range of power levels on the radio. If all the nodes meet top-subtrees with an equal number of packets left the interfering constraint, the algorithm proceeds with the as k in slot t. Then, the root of a new largest top- schedule calculation for the next time slot. On the other subtree j P chosen, and the inequality holds since is hand, if the maximum number of iterations is reached and kÀ1 nj À 1 i¼1 ni À 1 (since nj ¼ nk ). there are nodes which cannot meet the interfering con- Thus, the algorithm keeps the inequality as an straint, the algorithm excludes the link with minimum invariant, and there always exists a top-subtree that can SINR from the schedule and restarts the iterations with the be alternatelyP scheduled with the largest top-subtree. new subset of nodes. The power control phase is repeated kÀ1 until an admissible transmission scenario is found. When nk ¼ 1, i¼1 ni À 1 ¼ 1, which means that there are two packets left at two different top-subtrees that can be 5.2 Multichannel Scheduling scheduled in alternate slots. Since this inequality holds for all the N steps, the sink always finds a top-subtree to Multichannel communication is an efficient method to receive packets from, and therefore it takes N slots. eliminate interference by enabling concurrent transmissions Moreover, Lemma 1 implies that a top-subtree becomes over different frequencies [21]. Although typical WSN radios eligible after a transmission because its root is filled up in operate on a limited bandwidth, their operating frequencies the next slot. Therefore, the theorem follows. u t can be adjusted, thus allowing more concurrent transmis- sions and faster data delivery. Here, we consider fixed- bandwidth channels, which are typical of WSN radios, as5 IMPACT OF INTERFERENCE opposed to the possibility of improving link bandwidth bySo far, we have focused on computing spatial-reuse TDMA consolidating frequencies. In this section, we explain threeschedules where transmissions take place on the same channel assignment methods that consider the problem atfrequency at a constant transmission power. In this section, different levels allowing us to study their pros and cons forwe focus on different methods to mitigate the effects of both types of convergecast. These methods consider theinterference on the schedule length. First, we discuss the channel assignment problem at different levels: the link levelbenefits of using transmission power control and explain (JFTSS), node level (RBCA), or cluster level (TMCP).the basics of a possible algorithm. Then, we discuss theadvantages of using multiple channels by considering three 5.2.1 Joint Frequency Time Slot Scheduling http://ieeexploreprojects.blogspot.com joint solution for constructing adifferent channel assignment schemes. JFTSS offers a greedy maximal schedule, such that a schedule is said to be5.1 Transmission Power Control maximal if it meets the adjacency and interfering constraints,In wireless networks, excessive interference can be elimi- and no more links can be scheduled for concurrentnated by using transmission power control [6], [20], i.e., by transmissions on any time slot and channel withouttransmitting signals with just enough power instead of violating the constraints. Approximation bounds on JFTSSmaximum power. To this end, we evaluate the impact of for single-channel systems and its comparison with multi-transmission power control on fast data collection using channel systems are discussed in [22] and [23], respectively.discrete power levels, as opposed to a continuous range JFTSS schedules a network starting from the link that haswhere an unbounded improvement in the asymptotic the highest number of packets (load) to be transmitted.capacity can be achieved by using a nonlinear power When the link loads are equal, such as in aggregatedassignment [5]. We first explain the basics of one particular convergecast, the most constrained link is considered first,algorithm that we use in our evaluations in Section 7. i.e., the link for which the number of other links violating The algorithm proposed by ElBatt and Ephremides [6] is the interfering and adjacency constraints when scheduleda cross layer method for joint scheduling and power control simultaneously is the maximum. The algorithm starts withand it is an optimal distributed algorithm to improve the an empty schedule and first sorts the links according to thethroughput capacity of wireless networks. The goal is to loads or constraints. The most loaded or constrained link infind a TDMA schedule that can support as many transmis- the first available slot-channel pair is scheduled first andsions as possible in every time slot. It has two phases: 1) added to the schedule. All the links that have an adjacencyscheduling and 2) power control that are executed at every constraint with the scheduled link are excluded from the listtime slot. First, the scheduling phase searches for a valid of the links to be scheduled at a given slot. The links that dotransmission schedule, i.e., largest subset of nodes, where no not have an interfering constraint with the scheduled linknode is to transmit and receive simultaneously, or to can be scheduled in the same slot and channel whereas thereceive from multiple nodes simultaneously. Then, in the links that have an interfering constraint should be sched-given valid schedule, the power control phase iteratively uled on different channels, if possible. The algorithmsearches for an admissible schedule with power levels chosen continues to schedule the links according to the mostto satisfy all the interfering constraints. In each iteration, the loaded (or most constrained) metric. When no more linksscheduler adjusts the power levels depending on the can be scheduled for a given slot, the scheduler continuescurrent RSSI at the receiver and the SINR threshold with scheduling in the next slot. Fig. 4a shows the same tree
  • 11. according to the iterative rule: Pnew ¼ SINR Á Pcurrent . Accord- given in Fig. 1a which is scheduled according to JFTSSing to this rule, if a node transmits with a power level where aggregated data are collected. JFTSS starts with link
  • 12. 94 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 11, NO. 1, JANUARY 2012 are on frequency F 1. RBCA starts with the most interfered parent, node 2 in this example, and assigns F 2. Then, it continues to assign F 3 to node 3 as the second most interfered parent. Since all interfering parents are assigned different frequencies, sink can receive on F 1. 6 IMPACT OF ROUTING TREESFig. 4. Scheduling with multichannels for aggregated convergecast: Besides transmission power control and multiple channels,(a) Schedule generated with JFTSS. (b) Schedule generated with the network topology and the degree of connectivity alsoTMCP. (c) Schedule generated with RBCA. affect the scheduling performance. In this section, we describe schemes to construct topologies with specific(2; sink) on frequency 1 and then schedules link (4,1) next properties that help to reduce the schedule length.on the first slot on frequency 2. Then, links (5,2) on 6.1 Aggregated Data Collectionfrequency 1 and (1; sink) on frequency 2 are scheduled onthe second slot and links (6,2) on frequency 1 and (3; sink) We first construct balanced trees and compare theiron frequency 2 are scheduled on the last slot. performance with unbalanced trees. We observe that in both An advantage of JFTSS is that it is easy to incorporate the cases, the sink often creates a high-degree bottleneck. Tophysical interference model; however, it is hard to have a overcome this, we then propose a heuristic, as described indistributed solution since the interference relationship Algorithm 3, by modifying Dijkstra’s shortest path algorithmbetween all the links must be known. to construct degree-constrained trees. Note that constructing such a degree-constrained tree is NP-hard. Each source node5.2.2 Tree-Based Multichannel Protocol i in our heuristic keeps track of the number of its children,TMCP is a greedy, tree-based multichannel protocol for data CðiÞ, which is initialized to 0, and a hop count to the sink,collection applications [8]. It partitions the network into HCðiÞ, which is initialized to 1. The algorithm starts with 0multiple subtrees and minimizes the intratree interference by the sink node, and adds a node i 62 T at every iteration to the 0assigning different channels to the nodes residing on tree such that HCði Þ is minimized. It stops when jT j ¼ jV j,different branches starting from the top to the bottom of or when no more nodes can be added to the tree because thethe tree. Fig. 4b shows the same tree given in Fig. 1a which is neighbors of all these new nodes have reached the limit onscheduled according to TMCP for aggregated data collec- their maximum degree. Consequently, in this latter situation, http://ieeexploreprojects.blogspot.comtion. Here, the nodes on the leftmost branch are assigned the heuristic might not always generate a spanning tree. Infrequency F 1, second branch is assigned frequency F 2, and our evaluation presented in Section 7.3, we consider onlythe last branch is assigned frequency F 3 and after the those instances of the topologies where spanning trees withchannel assignments, time slots are assigned to the nodes the specified degree constraint are produced.with the BFS-TimeSlotAssignment algorithm. The advantage Algorithm 3. DEGREE-CONSTRAINED TREESof TMCP is that it is designed to support convergecast traffic 1. Input: GðV ; EÞ, s, max degreeand does not require channel switching. However, conten- 2. T fsgtion inside the branches is not resolved since all the nodes 3. for all i 2 V doon the same branch communicate on the same channel. 4. CðiÞ 0; HCðiÞ 15.2.3 Receiver-Based Channel Assignment 5. end for 6. HCðsÞ 0In our previous work [7], we proposed a channel assign- 7. while jT j 6¼ jV j doment method called RBCA where we statically assigned thechannels to the receivers (parents) so as to remove as many 8. Choose i0 62 T such that:interfering links as possible. In RBCA, the children of a 9. (a) ði; i0 Þ 2 E, for some i 2 T with CðiÞ common parent transmit on the same channel. Every node max degree À 1in the tree, therefore, operates on at most two channels, thus 10. (b) HCði0 Þ is minimizedavoiding pairwise, per-packet channel negotiation over- 11. T T [ fi0 gheads. The algorithm initially assigns the same channel to 12. HCði0 Þ ¼ HCðiÞ þ 1all the receivers. Then, for each receiver, it creates a set of 13. CðiÞ CðiÞ þ 1interfering parents based on SINR thresholds and itera- 14. if 8i 2 V , CðiÞ ¼ max degree thentively assigns the next available channel starting from the 15. breakmost interfered parent (the parent with the highest number 16. end ifof interfering links). However, due to adjacent channel 17. end whileoverlaps, SINR values at the receivers may not always To illustrate the gains of degree-constrained trees,be high enough to tolerate interference, in which case the consider the case when all the N nodes are in range ofchannels are assigned according to the ability of the each other and that of the sink. If the nodes select theirtransceivers to reject interference. We proved approxima- parents according to minimum hop without a degreetion factors for RBCA when used with greedy scheduling in constraint, then all of them will select the sink, and this[9]. Fig. 4c shows the same tree given in Fig. 1a scheduled will give a schedule length of N. However, if we limit thewith RBCA for aggregated convergecast. Initially, all nodes number of children per node to 2, then this will result in
  • 13. INCEL ET AL.: FAST DATA COLLECTION IN TREE-BASED WIRELESS SENSOR NETWORKS 95two subtrees rooted at the sink, and if there are enoughfrequencies to eliminate interference, the network can bescheduled using only two time slots, thus achieving a factorof N=2 reduction in the schedule length.6.2 Raw-Data CollectionAs emphasized in [13], routing trees that allow more paralleltransmissions do not necessarily result in small schedulelengths. For instance, the schedule length is N for a network Fig. 5. Balanced tree construction: Node 4 is attached to b2 based on theconnected as a star topology, whereas it is ð2N À 1Þ for a line search sets; load on both b1 and b2 is 5.topology once interference is eliminated. Theorem 1suggests that the routing tree should be constructed such the largest cardinality for further processing, breaking tiesthat all the branches have a balanced number of nodes and based on the smallest id. We define the growth set of a nodethe constraint nk ðN þ 1Þ=2 holds. In this section, we as the set of neighbors (potential children) that are not yetconstruct such routing trees. connected to the tree and have larger hop counts. A balanced tree satisfying the above constraint is a Rule 3. Once a node is chosen based on the growth setsvariant of a capacitated minimal spanning tree [24]. The CMST according to Rule 2, we construct search sets (SS) to decideproblem, which is known to be NP-complete, is todetermine a minimum-hop spanning tree in a vertex which potential branch the node should be added to. Aweighted graph such that the weight of every subtree search set is thus branch specific and includes the nodeslinked to the root does not exceed a prescribed capacity. Inthat are not yet connected to the tree and are neighbors of aour case, the weight of each link is 1, and the prescribed node that are at a higher hop count. In particular, if thecapacity is ðN þ 1Þ=2. Here, we propose a heuristic, as chosen node has access to branch b, and has a neighbor thatdescribed in Algorithm 4, based on the greedy scheme can connect to only branch b if b is selected, then thispresented by Dai and Han [25], which solves a variant of theneighbor and its potential children are included in theCMST problem by searching for routing trees with an equal search set for b. However, if the neighbor has access to atnumber of nodes on each branch. We augment their scheme least one other branch even after b is selected, then it is notwith a new set of rules and grow the tree hop by hop included in the search set.outward from the sink. We assume that the nodes know The search sets guarantee that the choices for the nodestheir minimum-hop counts to sink. at longer hops to join a particular branch are not limited by the decision of the joining node. This balances out the http://ieeexploreprojects.blogspot.comAlgorithm 4. CAPACITATED-MINIMALSPANNINGTREE number of nodes on different branches and prevents one to 1. Input: GðV ; EÞ, s grow faster than others. Once the search sets are con- 2. Initialize: structed, we choose the branch for which the sum of its load 3. B roots of top subtrees // the branches (W ) and the size of the search set is minimum. 4. T fsg [ B To illustrate the merit of search sets, consider the 5. 8i 2 V , GSðiÞ unconnected neighbors of i situation shown in Fig. 5. Dotted lines represent potential at further hops communication links and solid lines represent already 6. 8b 2 B, W ðbÞ 1 included tree edges. At this point, node 4 is being 7. h 2 processed, and the loads on branches b1 and b2 are 2 and 8. while h 6¼ max hop count do 4, respectively, where bi denote the branch rooted at node i. 9. Nh unconnected nodes at hop distance h The search set SSð4; b1Þ is f8; 9; 10g, because the neighbor 010. Connect nodes Nh that have a single potential node 8 has access to only b1 if b1 is selected by node 4. S 0 parent: T T Nh However, the search set SSð4; b2Þ is empty, because the 0 neighbor node 8 has access to another branch b1 (via node11. Update Nh Nh n Nh12. Sort Nh in non-increasing order of jGSj 3). Therefore, the sum of the load and the size of the search13. for all i 2 Nh do set for b1 is 5, and that for b2 is 4. So, we attach node 4 to b2,14. for all b 2 B to which i can connect do and in the next step attach node 8 to b1. This balances out15. Construct SSði; bÞ the number of nodes over the two branches.16. end for17. Connect i to b for which W ðbÞ þ jSSði; bÞj is 7 EVALUATION minimum In this section, we evaluate the impact of transmission18. Update GSðiÞ and W ðbÞ S S power control, multiple channels, and routing trees on the19. T T fig SSði; bÞ scheduling performance for both aggregated and raw-data20. end for convergecast.21. h hþ1 We deploy nodes randomly in a region whose dimen-22. end while sions are varied between 20 Â 20 m2 and 300 Â 300 m2 to Rule 1. Nodes with single potential parents are con- simulate different levels of density. The number of nodes isnected first. kept fixed at 100. For different parameters, we average each Rule 2. For nodes with multiple potential parents, we point over 1,000 runs. We use an exponential path-lossfirst construct their growth sets (GS) and choose the one with model for signal propagation with the path-loss exponent
  • 14. 96 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 11, NO. 1, JANUARY 2012Fig. 6. Scheduling on minimum-hop trees with and without power Fig. 7. Scheduling on minimum-hop trees with multiple channels:control: (a) Aggregated convergecast. (b) Raw-data convergecast. (a) Aggregated convergecast. (b) Raw-data convergecast.varying between 3 and 4, which is typical for indoor 7.1.2 Raw-Data Convergecastenvironments. We also use the physical interference model For raw-data convergecast, we observe in Fig. 6b that theand simulate the behavior of CC2420 radios that are used schedule length increases as the network gets sparser onon Telosb and TmoteSky motes and are capable of minimum-hop trees. This is counterintuitive because inoperating on 16 different frequencies. The transmission sparse networks, the reuse of slots should be higher whichpower can be adjusted between À24 and 0 dBm over eight would reduce the schedule length. However, as the networkdifferent levels, and the SINR threshold is set to
  • 15. ¼ À3 dB.1 gets sparser, the number of nodes that can directly reach theWe first evaluate the schedule length for single-channel sink decreases and packets have to be relayed over moreTDMA, and then its improvement using transmission hops. Thus, more packets need to be scheduled than in apower control, multiple channels, and routing trees. single hop. We see that the number of packets to be scheduled7.1 Impact of Transmission Power Control increases faster than the reuse ratio. In the densest settingWe investigate two cases: 1) when nodes transmit at where all the nodes can directly reach the sink, the schedulemaximum power, and 2) when nodes adjust their length is 99, which is equal to the number of sources.transmission power according to the algorithm described With power control, we observe a reduction in thein Section 5.1. In both cases, nodes communicate on the schedule length in Fig. 6b as some of the interfering links http://ieeexploreprojects.blogspot.comsame channel and use minimum-hop routing trees. In the are eliminated, thus increasing slot reusability. Whenfirst case, time slots are assigned according to BFS- ¼ 3:0, most of the interference can be eliminated byTIMESLOTASSIGNMENT for aggregated data, and accord- power control, and beyond which the structure of theing to LOCAL-TIMESLOTASSIGNMENT for raw data. In the routing tree, especially the number of nodes nk on thesecond case, we follow the scheduling rules in [6]. largest branch with ð2nk À 1Þ N, becomes the bottleneck. However, for ! 3:5, power control cannot always elim-7.1.1 Aggregated Convergecast inate interference as networks get sparser and nodes tend toFig. 6a shows the variation of schedule length with density transmit at their maximum power.for different values of on minimum-hop trees. Weobserve that the schedule length decreases as the deploy- 7.2 Impact of Multichannel Schedulingment gets sparser. This happens because at low densities, In this section, we analyze the performance of the channelthe interference is less, and so more concurrent transmis- assignment methods discussed in Section 5.2. We usesions can take place. In the densest deployment (L ¼ 20) CC2420 radios that have 16 channels in the 2.4 GHz range,when all the nodes are within the range of each other, the with adjacent channels overlapping according to thesink is the only parent, and the network is scheduled in rejection and blocking values given in the data sheet.99 time slots regardless of power control. However, in We assume that the nodes transmit at maximum power andsparser scenarios, using power control, the network can be use minimum-hop trees. In TMCP and RBCA, time slots arescheduled with fewer time slots as the level of interference assigned according to BFS-TIMESLOTASSIGNMENT forgoes down. We achieve a 10-20 percent reduction in aggregated convergecast and LOCAL-TIMESLOTASSIGN-schedule length for the best case. MENT for raw-data convergecast. The path-loss exponent We also observe that power control is more effective in is taken as 3.5.reducing the schedule length for denser deployments thanin sparser ones where the results tend to be similar. This is 7.2.1 Aggregated Convergecastdue to the discrete power levels and limited power range. Comparing the plots in Figs. 7a and 6a, we observe that theMoreover, due to the À95 dBm threshold for the transcei- channel assignment methods achieve schedule lengths thatvers to be able to decode a signal successfully, further are shorter than those achieved by power control. While it’spower reduction is limited. true that power control helps in reducing the effects of 1. Due to variation in signal strength, a fading margin can be included interference, this gain is limited due to the discrete levels andsuch that some of the packets can still be captured if the RSSI is slightly limited range of transmission power (e.g., CC2420 has eightlower than the threshold. Such a model [26] can easily be incorporated inour experiments, in which case retransmissions of lost packets should also different power levels between 0 and À24 dBm). In sparsebe considered in calculating the schedule length. deployments, nodes cannot reduce their transmit power
  • 16. INCEL ET AL.: FAST DATA COLLECTION IN TREE-BASED WIRELESS SENSOR NETWORKS 97below a certain threshold because a transceiver cannotdecode signals below the sensitivity level À95 dBm). Asshown in Fig. 6a, for L 200, the schedule lengths are similarwhen nodes transmit at maximum power or when theyadjust their power levels. Moreover, in mid-sparse deploy-ments (60 L 180, Fig. 6a), the limited range and discretepower levels restrict the nodes to adjust their transmitpowers. On the other hand, multichannel communicationeven with just two frequencies (Fig. 7a) can eliminate theinterference limitations, and beyond this, the performancegains are limited by the connectivity structure. By transmit-ting on different channels, interference is eliminated by the Fig. 8. (a) Bounds on the number of frequencies. (b) Percentage ofhigh adjacent/alternate channel rejection values of the C2420 incorrectly scheduled links.radio, and the channels behave like orthogonal. In Fig. 7a, sparser deployments (L 140) with multi- With RBCA and JFTSS, the number of channels required ischannel communication show a 40 percent reduction in low for dense networks as the number of receivers is low. Inschedule length as compared to transmitting on a single particular, when L ¼ 20, all the nodes can directly connect tochannel with maximum power. However, in denser the sink, and so only one frequency is needed. As the networkdeployments, multiple channels do not help much due to gets sparser, the number of receivers increases, and thusincreased connectivity, with the sink as a bottleneck in the more frequencies are required to support concurrentdensest setting. From Fig. 7a, we observe that JFTSS and transmissions. However, for L ! 80, the number of nodesRBCA can optimally schedule the network using 16 that are being connected to the same parent slowly dominateschannels, i.e., they achieve the lower bound, as shown by the effect of the number of receivers, and since the networkthe line “Lower Bound-MHST.” The advantage of RBCA gets very sparse, the number of channels required furtherover JFTSS is that it takes into account the topological goes down as the level of interference decreases.characteristics: a parent node receives data on the same The trends of both RBCA and JFTSS are quite similar, andchannel from its children, and does not have to switch the number of channels required is no more than the numberchannels in every slot. In dense deployments, TMCP of available channels on CC2420 radios (16 channels). On theperforms better due to the different routing trees con- other hand, TMCP requires many more channels as eachstructed, i.e., when L ¼ 20, RBCA and JFTSS construct a star branch is on a different channel. This is expensive fortopology, whereas TMCP constructshttp://ieeexploreprojects.blogspot.com of nodes can directly connect to the a 2-branch tree with deployments where a lottwo channels and a 16-branch tree with 16 channels. sink, and thus are assigned different channels because they form different branches. Thus, one needs to optimize the7.2.2 Raw-Data Convergecast channel usage.In Fig. 7b, we observe that none of the methods caneliminate interference completely with two channels; 7.2.4 Interference Models and Orthogonal Channelshowever, JFTSS and RBCA can do so with six or more We now evaluate the impact of interference and channelchannels at different densities (plots are not shown due to models on the schedules, in particular, the idealisticlack of space). We also see that TMCP needs 16 channels to assumption of the protocol model and the orthogonalityreach a performance similar to that achieved by RBCA and assumption. We examine the feasibility of the schedulesJFTSS with only two channels. This is because in JFTSS and based on the adjacent and alternate channel rejection valuesRBCA, when a node is receiving from its children, its parent of the transceivers and the SINR threshold.can transmit simultaneously on a different channel, which Fig. 8b shows the results for JFTSS in terms of theis not possible due to intrabranch interference in TMCP. percentage of nodes that are incorrectly scheduled (hence-The results also verify that JFTSS and RBCA can achieve a forth, referred to as errors). The top two lines show theschedule length which is bounded by maxð2nk À 1; NÞ,shown as “Lower Bound-MHST,” so long as the number of errors for two and 16 channels with both the assumptions,available channels is sufficient to eliminate interference. whereas the bottom line shows the errors only for theCompared to the results on a single channel in sparser orthogonality assumption. We observe that the errorsscenarios, we achieve a reduction of up to 40 percent on the are much higher in sparser deployments, because althoughschedule length. In very dense scenarios, the improvement the interference created by an individual sender is not highis small because most of the nodes can directly reach the enough to jam concurrent transmissions, the cumulativesink, and so the limiting factor becomes the half-duplex effect from multiple senders is very high, which is nottransceiver. captured in the protocol model. On the other hand, in dense deployments, a single transmitter can jam another one7.2.3 Required Number of Channels because of smaller internode distances and higher level ofIn this section, for the different channel assignment interference. In such cases, some of the nodes might selectmethods, we evaluate the required number of channels to the next available channels for concurrent transmissions;completely eliminate interference as a function of deploy- however, interference can still be high because the channelsment density. In our simulation results, as shown in Fig. 8a, in reality are not perfectly orthogonal. After the peak, thewe assume that the number of available channels is network gets sparser and interference reduces. We noteunlimited so as to show the upper bounds. that, our simulations corroborate previous results [27] that
  • 17. 98 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 11, NO. 1, JANUARY 2012 holds. In such cases, the schedule length is limited by 2nk À 1. These results indicate that RBCA and JFTSS combined with a suitable tree construction mechanism can achieve a reduction of up to 50 percent in the schedule length as compared to single-channel communication on minimum-hop spanning trees. 8 CONCLUSIONS In this paper, we studied fast convergecast in WSN where nodes communicate using a TDMA protocol to minimizeFig. 9. (a) Scheduling on degree-constrained minimum-hop trees. the schedule length. We addressed the fundamental(b) Scheduling on CMST. limitations due to interference and half-duplex transceivers on the nodes and explored techniques to overcome thethe protocol model may result in serious interference, and same. We found that while transmission power controladjacent channel interference cannot always be ignored. helps in reducing the schedule length, multiple channels are more effective. We also observed that node-based (RBCA)7.3 Impact of Routing Trees and link-based (JFTSS) channel assignment schemes areIn the preceding sections, we observed that although more efficient in terms of eliminating interference asinterference can be substantially eliminated by using power compared to assigning different channels on differentcontrol and multiple channels, connectivity of the tree still branches of the tree (TMCP).limits the performance. In the following, we discuss the Once interference is completely eliminated, we provedimprovements with routing tress. that with half-duplex radios, the achievable schedule length is lower bounded by the maximum degree in the routing7.3.1 Aggregated Convergecast on Degree-Constrained tree for aggregated convergecast, and by maxð2nk À 1; NÞ Trees for raw-data convergecast. Using optimal convergecastFig. 9a shows the variation of schedule length with density scheduling algorithms, we showed that the lower boundswhen the maximum tree degree is 3 (in sparser scenarios, are achievable once a suitable routing scheme is used.with a maximum degree of 2, it was not always possible to Through extensive simulations, we demonstrated up to an top two lines are for order of magnitude reduction in the schedule length forconstruct connected topologies). Thehttp://ieeexploreprojects.blogspot.comnodes transmitting at maximum power, and nodes using aggregated, and a 50 percent reduction for raw-datapower control. The bottom two lines are for JFTSS and convergecast. In future, we will explore scenarios withRBCA. When nodes transmit with maximum power, we variable amounts of data and implement and evaluate theobserve a reduction in the schedule length in dense combination of the schemes considered.deployments as compared to non-degree-constrained treesshown in Fig. 6a. We also notice further improvement with REFERENCESpower control in denser deployments than in sparser ones. [1] S. Gandham, Y. Zhang, and Q. Huang, “Distributed Time-Optimal When nodes are assigned channels using RBCA, we see a Scheduling for Convergecast in Wireless Sensor Networks,”factor of more than 2 reduction in the schedule length in Computer Networks, vol. 52, no. 3, pp. 610-629, 2008. [2] K.K. Chintalapudi and L. Venkatraman, “On the Design of MACdense deployments (L 120), as compared to that using Protocols for Low-Latency Hard Real-Time Discrete ControlRBCA on minimum-hop spanning trees. We also observe Applications over 802.15.4 Hardware,” Proc. Int’l Conf. Informationthat the schedule lengths are much larger than the Processing in Sensor Networks (IPSN ’08), pp. 356-367, 2008.maximum degree in the routing tree for dense deploy- [3] I. Talzi, A. Hasler, G. Stephan, and C. Tschudin, “PermaSense: Investigating Permafrost with a WSN in the Swiss Alps,” Proc.ments, as compared to those in sparse scenarios (L ! 120). Workshop Embedded Networked Sensors (EmNets ’07), pp. 8-12, 2007.Considering deployments at different densities, routing [4] S. Upadhyayula and S.K.S. Gupta, “Spanning Tree Basedover minimum-hop degree-constrained spanning trees Algorithms for Low Latency and Energy Efficient Data Aggrega-together with RBCA achieves an order of magnitude tion Enhanced Convergecast (DAC) in Wireless Sensor Net- works,” Ad Hoc Networks, vol. 5, no. 5, pp. 626-648, 2007.improvement than routing over minimum-hop spanning [5] T. Moscibroda, “The Worst-Case Capacity of Wireless Sensortrees while transmitting at maximum power. When we use Networks,” Proc. Int’l Conf. Information Processing in SensorJFTSS, the schedule length is close or equal to the maximum Networks (IPSN ’07), pp. 1-10, 2007.degree since it can handle interference using multiple [6] T. ElBatt and A. Ephremides, “Joint Scheduling and Power Control for Wireless Ad-Hoc Networks,” Proc. IEEE INFOCOM,channels more effectively by reusing and assigning them to pp. 976-984, 2002.the links instead of the receivers. [7] ¨ O. Durmaz Incel and B. Krishnamachari, “Enhancing the Data Collection Rate of Tree-Based Aggregation in Wireless Sensor7.3.2 Raw-Data Convergecast on CMST Networks,” Proc. Ann. IEEE Comm. Soc. Conf. Sensor, Mesh and Ad Hoc Comm. and Networks (SECON ’08), pp. 569-577, 2008.Fig. 9b shows the variation of schedule length on CMST. [8] Y. Wu, J.A. Stankovic, T. He, and S. Lin, “Realistic and EfficientThe impact of such routing trees is more prominent in Multi-Channel Communications in Wireless Sensor Networks,”sparser networks (L ! 200) than routing over minimum- Proc. IEEE INFOCOM, pp. 1193-1201, 2008. [9] ¨ A. Ghosh, O. Durmaz Incel, V.A. Kumar, and B. Krishnamachari,hop spanning trees (Fig. 7b). When L 200, the length is “Multi-Channel Scheduling Algorithms for Fast Aggregatedbounded by N. Beyond this point, it is almost always not Convergecast in Sensor Networks,” Proc. IEEE Int’l Conf. Mobilepossible to construct trees where the constraint 2nk À 1 N Adhoc and Sensor Systems (MASS ’09), pp. 363-372, 2009.
  • 18. INCEL ET AL.: FAST DATA COLLECTION IN TREE-BASED WIRELESS SENSOR NETWORKS 99[10] V. Annamalai, S.K.S. Gupta, and L. Schwiebert, “On Tree-Based ¨ Ozlem Durmaz Incel received the BSc and Convergecasting in Wireless Sensor Networks,” Proc. IEEE MSc degrees in computer engineering from the Wireless Comm. and Networking Conf. (WCNC ’03), vol. 3, Yeditepe University, Turkey, in 2002 and 2005, pp. 1942-1947, 2003. respectively. She received the PhD degree in[11] X. Chen, X. Hu, and J. Zhu, “Minimum Data Aggregation Time computer science from the University of Problem in Wireless Sensor Networks,” Proc. Int’l Conf. Mobile Ad- Twente, Netherlands, in March 2009. She is Hoc and Sensor Networks (MSN ’05), pp. 133-142, 2005. currently a postdoctoral researcher in the[12] W. Song, F. Yuan, and R. LaHusen, “Time-Optimum Packet Networking Laboratory (NETLAB) of Bogazici Scheduling for Many-to-One Routing in Wireless Sensor Net- University, Turkey. Her dissertation is focused works,” Proc. IEEE Int’l Conf. Mobile Ad-Hoc and Sensor Systems on efficient data collection in wireless sensor (MASS ’06), pp. 81-90, 2006. networks and was entitled “Multi-Channel Wireless Sensor Networks:[13] H. Choi, J. Wang, and E. Hughes, “Scheduling for Information Protocols, Design and Evaluation.” She was a visiting student in the Gathering on Sensor Network,” Wireless Networks, vol. 15, pp. 127- Autonomous Networks Research Group of the University of Southern 140, 2009. California as part of her PhD studies from 2007-2008.[14] N. Lai, C. King, and C. Lin, “On Maximizing the Throughput of Convergecast in Wireless Sensor Networks,” Proc. Int’l Conf. Amitabha Ghosh received the BS degree in Advances in Grid and Pervasive Computing (GPC ’08), pp. 396-408, physics from the Indian Institute of Technology 2008. Kharagpur, India, in 1996, and the ME degree[15] M. Pan and Y. Tseng, “Quick Convergecast in ZigBee Beacon- in computer science and engineering from the Enabled Tree-Based Wireless Sensor Networks,” Computer Comm., Indian Institute of Science, Bengaluru, in 2000. vol. 31, no. 5, pp. 999-1011, 2008. He received the PhD degree in electrical[16] W. Song, H. Renjie, B. Shirazi, and R. LaHusen, “TreeMAC: engineering from the University of Southern Localized TDMA MAC Protocol for Real-Time High-Data-Rate California in August 2010. Between 2000 and Sensor Networks,” J. Pervasive and Mobile Computing, vol. 5, no. 6, 2004, he worked for Alcatel-Lucent and pp. 750-765, 2009. Honeywell on GSM/GPRS networks and wire-[17] G. Zhou, C. Huang, T. Yan, T. He, J. Stankovic, and T. Abdelzaher, less ad hoc networks. He is currently a postdoctoral researcher at “MMSN: Multi-Frequency Media Access Control for Wireless Princeton University. His research interest is in design and analysis Sensor Networks,” Proc. IEEE INFOCOM, pp. 1-13, 2006. of algorithms for scheduling, routing, and power control for cellular[18] Y. Kim, H. Shin, and H. Cha, “Y-MAC: An Energy-Efficient Multi- and wireless sensor networks. Channel MAC Protocol for Dense Wireless Sensor Networks,” Proc. Int’l Conf. Information Processing in Sensor Networks (IPSN ’08), Bhaskar Krishnamachari received the BE pp. 53-63, Apr. 2008. degree in electrical engineering from The Cooper[19] B. Krishnamachari, D. Estrin, and S.B. Wicker, “The Impact of Union in 1998, and the MS and PhD degrees in Data Aggregation in Wireless Sensor Networks,” Proc. Int’l Conf. electrical engineering from Cornell University in Distributed Computing Systems Workshops (ICDCSW ’02), pp. 575- 1999 and 2002, respectively. He is an associate 578, 2002. professor in the Ming Hsieh Electrical Engineer-[20] J. Zander, “Performance of Optimum Transmitter Power Control ing Department at the University of Southern in Cellular Radio Systems,” IEEE Trans. on Vehicular Technology, California Viterbi School of Engineering. His vol. 41, no. 1, pp. 57-62, Feb. 1992. http://ieeexploreprojects.blogspot.com research interests are in the design and perfor-[21] P. Kyasanur and N.H. Vaidya, “Capacity of Multi-Channel mance analysis of protocols for next-generation Wireless Networks: Impact of Number of Channels and Inter- wireless networks. He received the US National Science Foundation faces,” Proc. ACM MobiCom, pp. 43-57, 2005. CAREER Award in 2004.[22] G. Sharma, R.R. Mazumdar, and N.B. Shroff, “On the Complexity of Scheduling in Wireless Networks,” Proc. ACM MobiCom, Krishnakant Chintalapudi received the MS pp. 227-238, 2006. degree in electrical engineering from Drexel[23] X. Lin and S. Rasool, “A Distributed Joint Channel-Assignment, University and the PhD degree in computer Scheduling and Routing Algorithm for Multi-Channel Ad-Hoc science from the University of Southern Wireless Networks,” Proc. IEEE INFOCOM, pp. 1118-1126, 2007. California. He is currently working as an[24] C.H. Papadimitriou, “The Complexity of the Capacitated Tree associate researcher at Microsoft Research, Problem,” Networks, vol. 8, no. 3, pp. 217-230, 1978. India. His research interests include delay-[25] H. Dai and R. Han, “A Node-Centric Load Balancing Algorithm sensitive and fault-tolerant wireless sensor for Wireless Sensor Networks,” Proc. IEEE Conf. Global Telecomm. networks, high-data-rate wireless sensor net- (GlobeCom ’03), pp. 548-552, 2003. work applications, hybrid sensor networks, and[26] M. Zuniga and B. Krishnamachari, “An Analysis of Unreliability wireless control and automation. and Asymmetry in Low-Power Wireless Links,” ACM Trans. Sensor Networks, vol. 3, no. 2, p. 7, 2007. ¨[27] J. Gronkvist and A. Hansson, “Comparison between Graph-Based and Interference-Based STDMA Scheduling,” Proc. ACM Mobi- . For more information on this or any other computing topic, Hoc, pp. 255-258, 2001. please visit our Digital Library at www.computer.org/publications/dlib.

×