T-ANT: A Nature-Inspired Data Gathering Protocol for Wireless Sensor Networks 1

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1 22 JOURNAL OF COMMUNICATIONS, VOL. 1, NO. 2, MAY 26 T-ANT: A Nature-Ispired Data Gatherig Protocol for Wireless Sesor Networks 1 S. Selvakeedy School of Iformatio Techologies, Uiversity of Sydey, NSW 26, Australia. skeedy@it.usyd.edu.au S. Siappa^ ad Yi Shag* ^School of Ecoomics & Iformatio Systems, Uiversity of Wollogog, Wollogog NSW 2522, Australia. suku@uow.edu.au *Dept. of Computer Sciece, Uiversity of Missouri-Columbia, Columbia, MO 65211, USA. shagy@missouri.edu Abstract There are may difficult challeges ahead i the desig of a eergy-efficiet commuicatio stack for wireless sesor etworks. Due to the severe sesor ode costraits, protocols have to be simple yet scalable. To this ed, collective social isects behavior could be adopted to guide the desig of these protocols. We exploit the simple heuristics of at coloy i foragig ad brood sortig to desig a hierarchical ad scalable data gatherig protocol. Also, we demostrate how it could exploit data correlatios i sesor readigs to miimize commuicatios cost i the data gatherig process towards the sik. This approach selects oly a subset of sesor odes to recostruct data for the etire etwork. A distributed variace estimatio algorithm is itroduced to capture data correlatios with egligible state maiteace. It is show that this algorithm is able to predict the values rather accurately. Due to the geeral robustess of ay ature-ispired algorithm, our data gatherig protocol is reliable. It is fully distributed, ad promises scalability ad substatial eergy savigs. Idex Terms Data Gatherig, Data Correlatio, Clusterig, Swarm Itelligece, Simulatio, Sesor Networks I. INTRODUCTION Wireless sesor techology is garerig a lot of iterests due to its promises, eablig it to evolve rather rapidly. For istace, i terms of the sesor ode hardware, the Mica2 mote has roughly eight times the memory ad commuicatio badwidth as its predecessor, the Ree mote, developed i 1999 for the same power budget [1]. These sesor odes have foud use i may applicatios such as earthquake moitorig, target trackig ad surveillace, ad structural moitorig. The odes are typically less mobile due to their uique applicatio eeds, substatially more resource costraied ad more desely deployed tha mobile ad hoc etworks (MANETs). Eve though, there have bee sigificat advaces i recet years, more eergy-efficiet solutios are required withi the commuicatio stack for the coservatio of the battery power. A approach that is likely to succeed is the use of a hierarchical structure [2], which also promotes scalability of wireless sesor etworks (WSNs). Clusterig with data aggregatio is a importat techique i this directio, ad it makes the tradeoff betwee eergy efficiecy ad data resolutio. Most clusterig algorithms aim at geeratig the miimum umber of clusters ad trasmissio distace. These algorithms also distiguish themselves by how the clusterheads (CHs) are elected. The LEACH algorithm [3] ad its related extesio, TCCA [4] use probabilistic self-electio, where each sesor ode has a probability p of becomig a CH i each roud. Such role rotatio aims to distribute the eergy usage for a more load-balaced operatio. However, LEACH oly works for sigle broadcast domai etworks, ad mostly operates i a suboptimally formed hierarchical structure due to its stochastic ature. TCCA overcame the former problem by allowig multihop clusters but still suffers from the latter. Whe developig WSN protocols, aother crucial desig issue to cosider is the etwork reliability. To this ed, social isect swarm behavior may provide a ideal model for the desig of such less cotrollable systems. To our kowledge, very few researchers have cosidered or adopted such ature-ispired approaches for WSN desig. However, a umber of recet works has bee based o differet swarm behaviors i the desig of routig protocols for MANETs. As there are may importat similarities betwee these two ad hoc techologies, we believe buildig o these kowledge may be useful for WSNs. 1 Based o " Data dissemiatio based o at swarms for wireless sesor etworks", by S. Selvakeedy, S. Siappa ad Yi Shag which appeared i the Cosumer Commuicatios ad Networkig Coferece, IEEE. 26 ACADEMY PUBLISHER

2 JOURNAL OF COMMUNICATIONS, VOL. 1, NO. 2, MAY I ay radom etwork deploymet, may sesor odes may also exhibit data correlatios i their sesed data due to their overlappig sesig rages. This issue may be addressed like a topology cotrol issue where it could be formulated as a miimum graph coverig problem, or as a data aggregatio or compressio problem that miimizes the amout of data trasmitted to the sik through some i-etwork processig. I some applicatios, sesor odes may also exhibit temporal correlatio if the moitored physical characteristic has small variability. Whe the correlated data is exploited withi the etwork, the sik gathers sapshots of reduced sigal data values measured at the sesor odes, ad uses iterpolatio to derive the sigal value at other poits i the moitored regio. Here, we exploit the temporal correlatios i the sesor data. This correlatio at a ode is captured etirely based local observatios with miimal state, ad is used to cotrol the ode s participatio i the data gatherig process towards the sik. I this paper, we ivestigate the T-ANT protocol, itroduced iitially i [5], more i depth ad exted it to support some i-etwork processig to remove redudat data without the sik s cotrol. This protocol adopts the clustered strategy realized usig useful priciples of the at coloy behavior. It achieves the objective of uiform cluster formatio by exploitig two swarm behaviors, amely foragig ad brood sortig. T-ANT achieves substatially better performace tha that of a flat miimum hop routig strategy, LEACH as well as TCCA. The rest of the paper is orgaized as follows. Sectio 2 presets the perspective of this area of research. Various clusterig algorithms, ature-ispired algorithms ad data gatherig protocols proposed i the literature are discussed. I Sectio 3, the details of the T-ANT clusterig algorithm ad the associated data variability estimatio algorithm are described. The comprehesive simulator used to experimet with this protocol is described i Sectio 4. Various experimets ad the correspodig results are preseted ad aalyzed i Sectio 5. The paper cocludes with the mai fidigs of this work i the fial sectio. II. RELATED WORK Itese research i the field of sesor etwork techology i recet years has fueled further developmet i micro-sesor techology ad low-power aalog/digital electroics. To support scalable data gatherig, it is realized that the approach that is likely to succeed to provide a eergy-efficiet solutio is to adopt a hierarchical structure. To this ed, various clusterig algorithms have bee proposed i differet cotext. Geerally, clusterig algorithms segmet a etwork ito o-overlappig clusters comprisig a CH each. No-CH odes trasmit sesed data to CHs, where the data sigals could be aggregated ad trasmitted to the sik. Iitially, these algorithms focused o the coectivity problem [6-8] but later eergy-efficiecy was more of iterest i wireless ad hoc ad sesor etworks [3, 4, 9-13]. Aother crucial desig aspect of WSNs is the etwork reliability ad fault-tolerace. It has bee demostrated i differet cotext that the collective behavior of social isects has may attractive features, ot the least robustess ad reliability. However, there are oly very limited WSN proposals ispired by such biological behaviors. Due to some parallels to MANETs, we reviewed some ature-ispired algorithms proposed for this domai. The first MANET routig algorithm based o at coloy priciples is ARA [14]. It exploited the pheromoe layig behavior of ats. Pheromoe is a quality metric idicatig the goodess of a path. Although pheromoe evaporates, subsequet ats leave additioal pheromoe ad thus reiforce the path. Over time, ats establish the shortest path betwee food ad their est i a full-distributed ad autoomous maer. Ats are flooded towards destiatios while establishig the reverse paths to the at source. The gradual decay of pheromoe itroduces a form of egative feedback to prevet old routes from remaiig i the forwardig tables whe routes fall out of favor with ats. The shortest paths become preferable, ad most ats use them. However, loger paths are ot etirely lost as some ats may still maitai such routes. Routig schemes based o such at coloy behavior is both robust ad adaptable. Whe the shortest route is lost due to some evet, the loger routes provide alterative optios. Other atureispired protocols were discussed i [5]. The problem of gatherig correlated data i WSNs has bee recetly addressed by meas of either compressio or topology cotrol-like approaches. The mai focus i the compressio approach is to reduce the total umber of bits trasmitted towards the sik usig suitable codig techiques. I [15], the authors proposed a distributed compressio techique based o the Slepia-Wolf model, ad the level of compressio is determied cetrally by the sik. This ode tracks the correlatio structure amog odes, ad the, idividually iforms each sesor ode the umber of bits to be used for ecodig. They have however assumed that the etwork is a sigle broadcast domai. Moreover, the sik would prove to be a bottleeck i a larger etwork, ad could lead to the scalability problem. Sigle-iput codig strategies were adopted i [16] to ecode a ode s data based o a eighbor ode. As the problem of fidig the miimumeergy data gatherig tree is NP-complete, they preseted approximatio algorithms to costruct a ear-optimal data gatherig tree for foreig-codig ad self-codig schemes. I [17], the authors proposed efficiet approximatio algorithms also based o Slepia-Wolf codig to optimize the trasmissio structure ad the rate allocatio at each ode. I all these approaches, all sesor odes are required to participate i data trasmissio at every roud eve though at reduced umber of bits trasmissios. Aother type of algorithm that aims to reduce the umber of trasmissios by makig redudat odes to sleep were proposed i [18] ad [19]. I priciple, these 26 ACADEMY PUBLISHER

3 24 JOURNAL OF COMMUNICATIONS, VOL. 1, NO. 2, MAY 26 approaches are similar to topology cotrol algorithms such as SPAN [2] ad ASCENT [21]. However, the ode redudacy i the data gatherig issue is of the applicatio perspective, whereas it is maily of the routig perspective i the latter. I [18], the authors proposed a scheme to reduce umber of trasmissios ad provided approximate results to aggregate queries through spatial data correlatio. Oly a subset of odes dissemiates data to the sik. A set of CHs is selected usig a simple localized scheme. It uses oly the edges of the forwardig tree for selectio of CHs ad routers. With a similar aim of forcig redudat odes to sleep, Gupta et al. [19] formulated this problem as fidig the miimum domiatig set problem, which is a well-kow NP-complete problem. Accordigly, they proposed a distributed approximatio algorithm ad a couple of cetralized heuristics to select a small correlatiodomiatig set, which is sufficiet to ifer data of the remaiig odes. For their distributed algorithm, each ode is expected to collect k-hop eighborhood iformatio to form the correlatio hypergraph. It is also assumed that the correlatio structure is fixed. Eve though a more complete spatial correlatio is achievable here, the computatio of the correlatio weightig coefficiets has eergy cost i the order of the trasmissio cost, ad the storage requiremet is expoetial i the umber of eighbors. Thus, i this paper, we exted the T-ANT protocol to exploit temporal data correlatio i sesor readigs, which oly ivolves local decisios, ad has smaller eergy ad storage costs. III. THE T-ANT PROTOCOL There are two mai compoets to this protocol. The first aspect is related to the CH electio ad clusterig, whereas the secod ivolves the estimatio of the data variace ad redudacy detectio. These are described i the followig subsectios, respectively. A. The Clusterig Algorithm T-ANT adopts two-phase clusterig process ivolvig the cluster setup ad steady state phases. To guide the CH electio, we chose to use a swarm of ats. Through the use of a swarm of ats, we could guaratee that the etwork always maitais a optimal umber of clusters. Durig the ode iitializatio, the sik releases a umber of ats (i.e. cotrol messages). Ramos ad Merelo [22] suggest that the ratio of the umber of ats to the umber of objects (i.e. sesor odes) should equal.1. Whe the sik releases a at, it chooses oe of its eighbors at radom. The at could travel ito the etwork as deep as restricted by its time-to-live (TTL) field. Whe a at arrives at a ode, the ext ode is radomly chose (excludig the seder) for its subsequet stop if TTL has ot expired. If TTL expires, the at remais at this ode. If however the fial at locatio overlaps with aother at, the former at must fid aother locatio. The cluster setup (CS) phase is cotrolled through a CS timer. Whe this timer expires, a ode checks to see whether it possesses a at. If the ode has a at, it becomes a CH. Whe a ode becomes a CH, it advertises to its eighbors by broadcastig a ADV message with its ode id ad a TTL field to costrai the ADV propagatio. Upo receivig a ADV message, a regular ode records the CH id, the seder s id as its paret, the hop distace to this CH, the umber of ADV messages received so far ad total hop distace to all see CHs, ad the rebroadcasts if its TTL permits. A ode decides to joi a cluster whe its joi-timer expires. It the computes its pheromoe level based o its total hop distace (h) to CHs, the umber of CHs () i its eighborhood, ad its ormalized residual eergy. The pheromoe expressio is based o the forwardig probability formula used i the at routig algorithm [23], but expaded as: where Δ p is give by: k Δp = 2 h p + Δp p = (1) 1 + Δp * E E h * is the ode s hop distace to the selected CH, E resi is the residual eergy, E max is the referece maximum battery eergy ad k is the learig rate of the algorithm ( =.1). This expressio esures that Δp is higher whe the ode is oly reachable by fewer CH odes (smaller ), far from CHs ( h ), has higher residual eergy (E resi ) i or is earer to its selected CH ( ). A regular ode chooses the best cluster to joi based o its hop distace to the CH, which would esure miimal eergy dissipatio durig the data collectio rouds. The ode jois a cluster by sedig a JOIN message with its id, the selected CH id ad its pheromoe level. If the CH is i rage, the message is trasmitted directly; otherwise it is forwarded through its paret to the CH. Whe a CH receives JOIN messages, it fids the member with the highest pheromoe level to attract its at for the followig CS phase. Before the ext CS timer expires, the ats wader to the odes with the highest pheromoe level amog their eighbors, ad these odes will be the subsequet CHs. Before a at leaves its curret ode, a amout of atipheromoe is laid to mimic a rapid decay of pheromoe level [5]. This esures that the ats do ot retur to the same ode too soo, which promotes load balacig. The give pheromoe expressio guides the evolutio of the swarm to achieve the separatio behavior betwee ats i the swarm [5]. It is foud empirically that separatio is attaied rather quickly withi 3-5 rouds as a optimal swarm size is used. Aother useful swarm behavior is aligmet [5]. I our cotext, the area served by each at represets the aligmet property. It is reflected by the umber of members i a cluster. Whe the swarm evolves to achieve separatio, aligmet is also achieved as a side-beefit. The pheomeo due to resi max h * i= 1 h i (2) 26 ACADEMY PUBLISHER

4 JOURNAL OF COMMUNICATIONS, VOL. 1, NO. 2, MAY both behaviors is captured by the followig fitess fuctios, respectively. The CH electio fitess fuctio S to capture the separatio behavior is: S = c i i i= 1 h ij j= 1 where c is the umber of CH odes, i is the umber of ADVs see by CH i ad h ij is CH i s hop distace to CH j. The clusterig fitess fuctio A to represet the aligmet behavior is as follows: A = r h i i= 1 where r is the umber of regular odes ad h i is ode i s hop distace to its CH. I the steady state phase, if a regular ode is cosidered redudat, it seds or forwards its sesory data to its CH. It is possible that the foragig ats may die due to the evirometal ucertaity or ode failure. To avoid a reducig umber of ats i the etwork over time, ats have a fiite lifetime. Whe ats die, the sik rereleases the same optimal umber of ats to restart the process. I order to determie the odes that have redudat iformatio ad thus, could be made to sleep, we itroduce the followig algorithm to capture data correlatios. B. The Variace Estimatio Algorithm I order to decide a ode s participatio i the data gatherig process, we eed to determie whether its data is redudat. We choose ot to base this selectio decisio o spatial correlatio due to the amout of data to be collected ad stored from the k-hop eighbors as well as the subsequet computatio cost ivolved i the value predictio eve as a liear combiatio of the eighbor values. To reduce the amout of state at each ode, oly temporal correlatio is maitaied. If the variability of the moitored value falls below the specified applicatio boud, the ode s data is cosidered redudat, ad it locally decides to sleep. This decisio is made durig the CS phase. Fidig a factor s variability problem could be aalogized to the roud-trip time (RTT) variace estimatio problem i settig of the retrasmissio timeout value i the TCP trasport protocol. The timeout algorithm allows a TCP etity to cope with the highly dyamic Iteret traffic. This RTT variace estimatio is based o the Jacobso s algorithm, ad is specified as part of TCP i RFC2988 [24]. Jacobso itroduced a variatio measure called mea deviatio, ad used it with the expoetial smoothig techique to capture the dyamic ature of Iteret traffic. As it is obvious that TCP is successful i adaptig to this dyamism, a similar estimatio algorithm could prove useful for our purpose to capture the sesor data variability. To eable such estimatio, each ode maitais a average value (represeted as s_val) that stores the (3) (4) weighted sesor data value based o preset ad past values as follows: s_val k+1 = (1-g) s_val k + g val k+1 (5) where s_val i is the smoothed value at the i th time istat, val i is the actual sesed value at the i th time istat ad g is a costat, <g<1. I order to capture the measure of dispersio of the sesed data, we adopt the mea deviatio metric as follows: s_dev k+1 = (1-h) s_dev k + h val k+1 - s_val k (6) where s_dev i is the smoothed variability of the sesed values at the i th time istat ad h is a costat, <h<1. Fially, the sesed value for the (k+1) th time istat ca be predicted as follows: i k + 1 val = s_val k+1 + s_dev k+1 (7) * where val * is the predicted value at the ith time istat. If the predicted ad actual values deviate lesser tha the applicatio boud, this value is uiterestig for the applicatio ad could be approximated by the sik from the historic data. Thus, this ode should ot participate i further data gatherig rouds util data variability exceeds the boud. Sice redudat odes are decided durig the CS phase, these odes will ot be ivolved i the clusters formatio. Also, the ats will oly forage amog the active odes. As sesig the eviromet is cotiuously performed by all odes, a iactive ode may rejoi durig the ext CS phase, if its value falls outside the threshold. IV. SIMULATION FRAMEWORK The performace of T-ANT clusterig is evaluated usig a discrete-evet simulator. To eable a comprehesive study, the effects of both routig ad MAC protocols are itegrated. I the descriptio of the simulator, we assume that each sesor ode is aware of: its eighbors due to the occasioal beacoig by the sik ad the cluster setup phase; ad the etwork is sychroized by meas of ay time sychroizatio protocols. The radio model is assumed to follow isotropic propagatio. As for the MAC choice, we adopted the CSMA protocol due to its simplicity ad its promise of scalability. However, a straightforward applicatio of this protocol i a covergecast sceario is a recipe for failure. I the periodic moitorig type applicatio, whe the sesor data timer expires, all odes capture their sesory value ad covert to digital via a aalog-to-digital coverter (ADC) liked to the sesig hardware. Assumig a time-sychroized clustered etwork, all odes geerate the sesory message for trasmissio towards their CHs at the same time. If o precautio was take, their trasmissios would iterfere resultig i may collisios ad retrasmissios. I order to reduce such collisios, Huag ad Zhag [25] proposed that a ode should delay its trasmissio relative to its distace to the destiatio (h) ad the ode desity. As there is 26 ACADEMY PUBLISHER

5 26 JOURNAL OF COMMUNICATIONS, VOL. 1, NO. 2, MAY 26 likely to be may odes at each hop, a additioal radom offset is also icluded to further reduce the collisio probability. We choose to adopt a similar temporal coordiatio with respect to a ode s CH. However, it was modified here to be less coservative to reflect the smaller scope of a cluster rather tha a etire etwork as i [25]. The radom wait time fuctio (T) is accordigly give as: T(h) = (1 + r).τh (8) Where r is a uiformly distributed radom umber from to 1 ad τ is the average oe-hop delay. At the etwork layer, we adopted a simple routig mechaism i Greedy Routig Scheme (GRS) to cotrol the etwork s forwardig behavior. The forwardig objective is to miimize the umber of hops betwee the sik ad the other odes. To establish this miimum hop routig tree, the sik occasioally broadcasts a beaco message with a hop cout, which is iitialized to zero. Upo receivig the beaco, each ode records the seder id, icremets the hop cout by oe, ad the rebroadcasts it. A ode oly rebroadcasts if the ew hop cout is smaller tha its stored value. Sice we are focusig o a quasi-statioary type of applicatio, the sik ode oly eeds to perform occasioal beacoig to avoid sigificat overhead. This forwardig rule establishes a miimum hop tree rooted at the sik. Fially, the T-ANT scheme is implemeted betwee the applicatio ad the etwork layer, ad thus, the overall system framework is as show i Fig. 1. Figure 1. Applicatio T-ANT GRS CSMA Radio ADC Sesor The simulatio framework. eergy parameters are set as: E elec = 5 J/bit ad ε fs = 1 pj/bit/m 2. The eergy for data aggregatio is set to E DA = 5 J/bit per sigal [3]. The cotrol ad data message sizes are fixed at 3 bytes, ad sesory data is geerated at 2- secod iterval. Each CH ode retais its CH status for 2 secods. The umber of ats is fixed at 1 ad the ati-pheromoe rate is.1. The performace metrics beig ivestigated are: Clusterig fitess: This metric is based o the fitess fuctio give as (4). It represets the goodess of the cluster formatio i terms of aligmet ivolvig all regular odes. CH electio fitess: This metric is based o the fitess fuctio give i (3). It represets the goodess of all the elected CH odes i terms of CH separatio. Average eergy per roud: This metric represets the average eergy dissipated by all the odes i a roud of data dissemiatio. Network lifetime: This metric represets the time period from the istat the etwork is deployed to the momet whe the first sesor ode rus out of eergy. Fig. 2 depicts the clusterig fitess value at differet simulatio time. For T-ANT, the iitial value is high idicatig that the swarm has ot yet achieved the aligmet behavior as the ats are radomly released ito the etwork. However, as pheromoe is laid ad atipheromoe takes effect durig CS phases, the swarm aligmet improves. Withi the third evolutio, the swarm is able to alig. As for the other schemes, the fitess value varies rather wildly. Ulike T-ANT, TCCA mostly operates i sub-optimal fashio. Also for m- LEACH, the fitess value is always smaller tha the other schemes due to the ADV messages beig limited to firsthop eighbors. Ay ucovered odes would have to resort to direct trasmissio to the sik. Sice m-leach ad TCCA have probabilistic CH electio, it is possible that the CHs may eve be clumped. Whe the CHs are clumped, the disparity amog clusters is large i terms of their umber of members, as each CH coteds for the same regular odes pool. Based o the give simulatio framework, we ivestigated T-ANT s performace agaist LEACH, TCCA ad a flat strategy (i.e. the applicatio sits directly o GRS). However, sice LEACH ca t be applied directly to a multihop etwork, we modified this algorithm to use a routig protocol to forward messages wheever the destiatio is ot withi a ode s radio rage. We termed this modified algorithm as multihop- LEACH (or m-leach). The results from this compariso ad other evaluatios are preseted further. V. RESULTS AND DISCUSSIONS For these simulatio experimets, we assumed that there are 1 sesor odes distributed radomly i a square M M regio with M = 5 m. The trasceiver Clusterig Fitess 25 T-A NT TCCA 2 m-lea CH Figure 2. Clusterig fitess at differet simulatio time of T-ANT, m-leach ad TCCA. 26 ACADEMY PUBLISHER

6 P o ly.(t -A N T ) P o ly.(t C C A ) JOURNAL OF COMMUNICATIONS, VOL. 1, NO. 2, MAY I Fig. 3, the CH electio fitess is depicted for the same three algorithms. Agai, cosistet behavior as above is obtaied. For T-ANT, it has a higher fuctio value iitially, but it quickly coverged somewhat. The ats move to better locatio based o the computed pheromoe level, ad withi the fifth roud, the swarm is able to achieve the separatio behavior. This behavior esures the elected CHs are distributed as uiformly as possible. Eve after the uiformity is achieved, the ats keep movig at each roud to esure that the CH role is shared amog odes, ad eergy-load balacig is attaied. As for the other schemes, the topology barely settles ad mostly has a lower value tha T-ANT. A lower value idicates that the CHs i these schemes are mostly too close to each other. I m-leach, the fitess fuctio quite ofte assumes a zero value compared to TCCA. This is maily due to its restricted ADV propagatio, where a CH is uable to recogize aother CH located oly two hops away. CH Electio Fitess T-A NT TCCA m-lea CH Figure 3. CH electio fitess at differet simulatio time of T- ANT, m-leach ad TCCA. Sice cluster size was show to have a sigificat impact o clusterig algorithms [4], we varied ADV s TTL value ad compared these algorithms. I Fig. 4, these algorithms exhibit the presece of a optimal cluster size. However, T-ANT achieves sigificatly more eergy savigs tha m-leach ad TCCA for cluster sizes up to four. T-ANT achieves eergy savigs of more tha 3% agaist m-leach. Whe cluster size is two, T-ANT dissipates 27% lesser eergy compared to TCCA. This observatio is cosistet with fitess values reported i Figs. 2 ad 3. Sice m-leach ad TCCA maily operates with sub-optimally formed topology, their eergy dissipatios are higher. However, for larger cluster sizes, T-ANT s beefit is less apparet. This is maily caused by the eergy expeded durig the cluster setup phase that is sigificatly larger as ADV messages are flooded further, ad the JOIN messages have to be forwarded may hops before reachig their CHs. Similarly, durig the steady state phase, sigificat itracluster traffic is geerated egatig T-ANT s beefits. Average Eergy per Roud (J) Cluster Size (hops) T-A NT TCCA Figure 4. Average eergy usage per roud agaist the cluster size of T-ANT ad TCCA (ad m-leach similar to TCCA with cluster size oe). I Fig. 5, the improvemet gaied through T-ANT is further exemplified by the etwork lifetime graph. For this ivestigatio, we have fixed the iitial battery eergy, E max at.1j. It is evidet that T-ANT exhibits the logest lifetime with all odes remaiig fully fuctioal. It is foud that T-ANT achieves almost 3.5 times the lifetime of m-leach ad almost five times of the flat approach. It also achieves up to 5% loger lifetime tha TCCA. Number of Nodes Alive T-A NT TCCA m-lea CH Figure 5. Network lifetime agaist simulatio time of T-ANT, TCCA, m-leach ad the flat strategy. To ivestigate the effectiveess of the proposed estimatio algorithm, we gathered daily temperature data (i Fahreheit) of 1 Australia weather statios for the whole year of 24 [26]. Usig aturally gathered data, we exhibit the exted of correlatios iheret i real data. For the variace estimatio algorithm, the costats asume these values: g =.125 ad h =.25. I Fig. 6, the accuracy of the adopted variace estimatio algorithm is ivestigated. The cotiuous lie represets the predicted values, whereas the crosses represet the actual observed data agaist the left y-axis. The error ratio betwee these two values is show usig the triagle marker o the right y-axis. As expected, the iitial forecasts are quite far from the actual values. However, as more values are used for the smoothig process, the predictio improves sigificatly. Occasioally, whe there are sudde chages i the temperature with steep vertical rises or drops, the error ratio becomes quite large idicatig the eed for the affected ode to report back to the sik. Otherwise, it is Flat 26 ACADEMY PUBLISHER

7 28 JOURNAL OF COMMUNICATIONS, VOL. 1, NO. 2, MAY 26 evidet that the chose variace estimator algorithm accurately predicts the aturally geerated data. Temperature ('F) actual average Figure 6. Actual ad predicted temperature data values of a sigle ode show o the primary y-axis agaist simulatio time. The secodary y-axis shows the error ratio betwee the two values. To observe the effect of the temporal correlatio suppressio o the etwork, a plot of umber of sleepig odes agaist simulatio time is show with the error ratio i Fig. 7. Whe the error ratio is higher tha the applicatio boud, more odes are ivolved i the data gatherig process. However, whe the predictio is rather accurate, more odes are made to sleep as evidet from the peaks of the cotiuous lie i Fig. 7. For this applicatio data ad the chose boud, the scheme is able to make more tha 3% odes to sleep o average. Eve without ay expesive spatial correlatio exercise, it is evidet that we have the potetial to make sigificat eergy savigs due to the iheret temporal data correlatios i this aturally geerated data. Number of odes iactive Figure 7. Number of iactive odes (i.e. sleepig) show o the primary y-axis agaist simulatio time. The secodary y-axis shows the error ratio betwee the two values. Fially, to ivestigate the effect of data sesitivity o the etwork performace, we vary the applicatio boud. As expected, Fig. 8 cofirms that the eergy cost reduces with the icrease i the applicatio boud. As the applicatio boud is icreased, the eergy cost reduces expoetially. Whe the applicatio is lesser sesitive to mior chages, there would be may more odes iactive i the etwork with lesser radio usage. Thus, the proposed scheme exploits the icreased isesitivity of the applicatio by makig more odes to sleep. error sleep erro r Error ratio (%) Error ratio (%) Average Eergy per Roud (J) Applicatio Boud (%) Figure 8. Average eergy cost per data collectio roud agaist the applicatio boud. VI. CONCLUSIONS To our kowledge, the T-ANT is the first atureispired approach for data gatherig i wireless sesor etworks. The algorithm uses a swarm of ats to cotrol the clusterhead electio i a distributed maer. It is show that T-ANT achieves two desirable swarm behaviors, amely separatio ad aligmet. Due to these, a uiform distributio of clusterhead is guarateed eablig the etwork to operate i a optimal maer throughout its lifetime. Eve though this is possible i a cetralized approach as i LEACH-C [3], our algorithm is distributed, robust ad does ot require positio kowledge. T-ANT also stores less state overhead i memory tha LEACH or TCCA. The T-ANT protocol is also able to exploit the iheret data correlatios i the sesed data sigals. To avoid the amout of state ecessary to capture the spatial correlatio amog eighbors, we resorted to capture temporal correlatio oly. This ivolves oly local decisio-makig. The variace estimatio algorithm itroduced here captures sesor data variability with egligible state maiteace. It is demostrated that T- ANT with data redudacy detectio achieves sigificat eergy savigs for periodic moitorig applicatios. ACKNOWLEDGMENT This work was supported i part by grats from ARC DP ad USyd R&D L2844 U323. REFERENCES [1] J. Hill, M. Horto, R. Klig, ad L. Krishamurthy, "The platforms eablig wireless sesor etworks," Comms. of the ACM, vol. 47, pp , Ju 24. [2] K. Akkaya ad M. Youis, "A Survey o Routig Protocols for Wireless Sesor Networks," Elsevier Ad Hoc Networks, vol. 3, pp , May 25. [3] W. B. Heizelma, A. P. Chadrakasa, ad H. Balakrisha, "A applicatio-specific protocol architecture for wireless microsesor etworks," IEEE Tras. o Wireless Comm., vol. 1, pp , Oct 22. [4] S. Selvakeedy ad S. Siappa, "The Time-Cotrolled Clusterig Algorithm for Optimized Data Dissemiatio i Wireless Sesor Networks," i Proc. IEEE Coferece o LCN, Sydey, Australia, 25, pp ACADEMY PUBLISHER

8 JOURNAL OF COMMUNICATIONS, VOL. 1, NO. 2, MAY [5] S. Selvakeedy, S. Siappa, ad Y. Shag, "Data Dissemiatio Based o At Swarms for Wireless Sesor Networks," i Proc. IEEE CCNC, Las Vegas, NV, USA, 26, pp [6] M. Gerla ad J. T.-C. Tsai, "Multicluster, mobile, multimedia radio etwork," Wireless Networks, vol. 1, pp , Aug [7] F. Kuh, T. Moscibroda, ad R. Wattehofer, " Iitializig ewly deployed ad hoc ad sesor etworks," i Proc. 1th It. coferece o Mobile computig ad etworkig, Philadelphia, PA, USA, Sept 24, pp [8] D. Baker ad A. Ephremides, "The Architectural Orgaizatio of a Mobile Radio Network via a Distributed Algorithm," IEEE Tras. o Commuicatio, vol. 29, pp , Nov [9] G. Gupta ad M. Youis, "Load-balaced clusterig of wireless sesor etworks," i Proc. IEEE ICC, Achorage, AL, USA, May 23, pp [1] A. D. Amis ad R. 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Ramachadra, "A distributed ad adaptive sigal processig approach to reducig eergy cosumptio i sesor etworks," i Proc. INFOCOM, 23, pp [16] P. v. Rickebach ad R. Wattehofer, "Gatherig correlated data i sesor etworks," preseted at Workshop o Foudatios of mobile computig, Philadelphia, PA, USA, 24, pp [17] R. Cristescu ad M. Vetterli, "Power efficiet gatherig of correlated data: optimizatio, NP-completeess ad heuristics," ACM SIGMOBILE Mobile Computig ad Commuicatios Review, vol. 7, pp , 23. [18] S. Yoo ad C. Shahabi, "Exploitig spatial correlatio towards a eergy efficiet clustered aggregatio techique (CAG)," i Proc. IEEE Iteratioal Coferece o Commuicatios, 25, pp [19] H. Gupta, V. Navda, S. R. Das, ad V. Chowdhary, "Efficiet gatherig of correlated data i sesor etworks," i Proc. MobiHoc '5, Urbaa-Champaig, IL, USA, 25, pp [2] B. Che, K. Jamieso, H. Balakrisha, ad R. Morris, "Spa: a eergy-efficiet coordiatio algorithm for topology maiteace i ad hoc wireless etworks," Wireless Networks, vol. 8, pp , 22. [21] A. Cerpa ad D. Estri, "ASCENT: Adaptive Self- Cofigurig sesor Networks Topologies," IEEE Tras. o Mobile Computig, vol. 3, pp , 24. [22] V. Ramos ad J. J. Merelo, "Self-orgaized stigmergic documet maps: Eviromet as mechaism for cotext learig.," i Proc. Spaish Coferece o Evolutioary ad BioIspired Algorithms, 22, pp [23] Y. Ohtaki, N. Wakamiya, M. Murata, ad M. Imase, "Scalable ANT-based Routig Algorithm for Ad-Hoc Networks," i Proc. 3rd IASTED Iteratioal Coferece o Commuicatios, Iteret, ad Iformatio Techology, St. Thomas, US Virgi Islads, Nov 24. [24] V. Paxso ad M. Allma, "Computig TCP's Retrasmissio Timer," RFC2988, 2. [25] Q. Huag ad Y. Zhag, "Radial coordiatio for covergecast i wireless sesor etworks," i Proc. 29th IEEE Iteratioal Coferece o Local Computer Networks, Tampa, FL, USA, Nov 24, pp [26] NESDIS, " US Natioal Oceaic & Atmospheric Admiistratio, 25. S. Selvakeedy obtaied his PhD i computer commuicatios i 1999 from the Uiversity of Putra, Malaysia. Earlier i 1996, he obtaied his first-class hoors degree i computer sciece also from the same uiversity. He is curretly servig as a Seior Lecturer at the Uiversity of Sydey, Australia. He has published more tha 5 articles i jourals ad cofereces maily i the area of commuicatios protocols for all-optical etworkig ad wireless etworkig. His curret research iterests lies i developig algorithms for media access, routig, localizatio ad topology cotrol issues i wireless sesor, ad hoc ad mesh etworks. Dr. Selvakeedy is a member of ACM ad IEEE. He has served o may coferece program committees. Sukuesa Siappa got his doctorate from the Uiversity of Newcastle, Australia i 24. He is curretly with the School of Ecoomics ad Iformatio Systems, Uiversity of Wollogog, Australia. He has published i umerous fields such as olie cosumer behavior, website aalysis ad wireless sesor etworks. His curret research iterests iclude olie self-profilig, locatio-based services, olie privacy ad trust apart from wireless sesor etworks. Yi Shag was bor i Chia i He received the PhD degree i Computer Sciece from the Uiversity of Illiois at Urbaa-Champaig, Urbaa, IL, USA, i 1997; MS i Computer Egieerig from the Istitute of Computig Techology, Chiese Academy of Scieces, Beijig, Chia, i 1991; ad BS i Computer Sciece from Uiversity of Sciece ad Techology of Chia, Hefei, Chia, i His research fields iclude artificial itelligece, oliear optimizatio, wireless etworks, ad distributed systems. He is a Associate Professor i the Departmet of Computer Sciece at the Uiversity of Missouri at Columbia, USA. He was a seior research scietist at the Xerox Palo Alto Research Ceter at Palo Alto, CA from 21 to 23. His curret research iterests are wireless sesor etworks ad multi-aget systems. He has published more tha 8 techical papers ad has 6 patet applicatios pedig. Dr. Shag is a member of ACM ad IEEE. He has bee program committee member of may cofereces. 26 ACADEMY PUBLISHER