An Ant-based approach to Power-Efficient Algorithm for Wireless Sensor Networks

Transcription

1 An Ant-based approach to Power-Effcent Algorthm for Wreless Sensor Networs Yaofeng Wen, Yuquan Chen, and Dahong Qan, Senor Member, IEEE Abstract We present an adaptve approach for mprovng the performance n randomly dstrbuted Wreless Sensor Networs (WSNs). The goal s to fnd the optmal routng not only to maxmze the lfetme of the networ but also to provde real-tme data transmsson servces. Consderng a wreless sensor networ where the nodes have lmted energy, we propose a novel model Energy Delay based on Ant Colony Optmzaton (ACO) algorthm (E&D ANTS) to mnmze the tme delay n transferrng a fxed number of data from the source nodes to the destnaton nodes n an energy-constraned manner. In the algorthm, an amount of artfcal ants randomly explored the networ and exchanged collected networ nformaton to perodcally update ant routng-tables whch were obtaned by havng ntegrated partal pheromones and heurstc values. Our study s focused on nfluence functons of pheromones. Because of the tradeoff of energy and delay n wreless networ systems, we propose the Renforcement Learnng (RL) algorthm to tran our model. The smulaton results show that our method boasts undoubtedly a number of attractve features, ncludng adaptaton, robustness and stablty. Index Terms ACO, Pheromones, Power consumpton, Wreless sensor networs I. INTRODUCTION The wreless sensor networs (WSNs) technology s wdely used n many felds, ncludng envronmental montorng, health montorng, mltary survellance and earthquae observaton. In the wreless systems, a lot of nodes operate on lmted batteres whle satsfyng gven throughput and delay requrements. So the development of low-cost and low-power sensor networ system has receved ncreasng attentons. Low power research s concentrated n the RF, Baseband, networ, and applcaton layers of wreless devces. A hgh performance routng algorthm s often a crucal part n networ system, because good routng can contrbute ether greater throughput or lower average delays f all the other condtons beng the same. In the paper, we propose two routng strateges. Frst of Manuscrpt receved March 12, Ths wor was supported n part by the Natonal Natural Scence Foundaton of Chna under Grant Wen Yaofeng s wth the State Specalzed Laboratory of Bomedcal Sensors, ZheJang Unversty, CO , Hangzhou, Chna (phone: ; fax: ; e-mal: yfwen@accelsem.com). Chen Yuquan s wth the Bomedcal Engneerng Department, ZheJang Unversty, Hangzhou, Chna (e-mal: yqchen@mal.bme.zju.edu.cn). Qan Dahong s wth Accel Semconductor Corporaton., CO Shangha, Chna (e-mal: qan@accelsem.com). all, we select the most power-effcent path and perform well n real tme. Secondly, we avod the heavy load lns and preserve the load balancng of the dstrbuton. Algorthms whch tae nspraton from ants behavor n fndng the paths have recently been successfully appled nto dfferent felds ncludng WSNs. Reference [1] Shows that some researchers ntroduced the ACO algorthm nto WSNs and mplemented t on the hardware. However, how to adjust the pheromone of each node by total energy level, throughput and delay of wreless networs s typcally gnored. Reference [2] ntroduced the AntNet Algorthm nto normal communcatons networs. However t seems to be unsatsfactory n WSNs. In ths paper, n order to enhance the capablty and networ lfetme, we employ an adaptve dynamc algorthm based on ACO for routng operatons. The ant routng-tables of each node are regularly updated by a bac round ant holdng networ load and delay nformaton. The rest of the paper s organzed as follows: n Secton II, we propose a practcal ACO model for WSNs. In Secton III, The scheme of routng algorthm are mplemented, Secton IV presents the smulaton results. Fnally n secton V, some concludng remars are made. II. THE PROPOSED NETWORK MODEL ON ACO In DI CARO G s paper [2], ants have the power of fndng the shortest path from ant nests to foods. They use AntNet routng algorthm to select ntermedate nodes to relay data pacets on the overall energy effcency of the networ and the capablty of ants s acheved by ther releasng one nd of volatlty pheromones along the path. Supposng a certan path s selected by more ants than other paths, more pheromones ncrements wll be saved to the path, and as a result, more ants wll select the path at the next tme. Thus, the amount of the pheromones n the specfc path wll grow gradually because of accumulated postve feedbac. In the end, ants wll fnd the shortest path on a stable state. However, the ey dea of our E&D ANTS scheme s tang advantages of the conjuncton of energy and delay n wreless networs n order to update nodes pheromones. The wreless networ n consderaton s modeled as a drected graph GNA, (, ) where N s the set of all the nodes whch can queue and transfer pacets and A s the set of all drected lns (, j) where, j N. Let L be the set of all nodes that can be reached by node wth a certan power level n ts dynamc

2 range. We assume that ln (, j ) exsts f and only f j L. Each node has the resdual energye (ts ntal value E.). 0 Assume that the transmsson energy requred for node to transmt an nformaton unt to ts neghborng node j se. j Assume the ant routng-tables of node are denoted as follows: () t () t... () t N a a a () t () t... () t N [ ( t)] a a a = a =. Α a () t () t... () t L1 al2 aln N, j L, d N (1) Where each row n the upper matrx must meet the followng constraned equaton: s. t. a = 1; d [ 1, N] (2) j L Where represents the probablty of selectng from the a current node to destnaton node d va the node j [3]. ω () (1 ) τ t + ω η j a = (3) ω + (1 ω)( N 1) Where ω [ 0,1] s a weghtng factor and the denomnator s a normalzaton term. The ant routng-tables Α are obtaned by ntegratng partal pheromone tral values τ () t and heurstc values. The pheromone tral values are calculated by (3): η j τ ( t 1) ρ ( t) j τ j τ j Where [ 0,1] τ j f The functon f () t + = + Δ (4) ρ and Δ = () t. s the soluton of teraton. In the followng subsectons, ths functon s our crucal object whch needs further research on the tradeoff between power consumpton and delay n wreless sensor networs. In [1], the heurstc values are set as the followng equaton: η = e [0,1] (5) n N e n Ths enables an ant to mae a decson accordng to neghbor nodes energy levels. A node wll have less opportunty to be selected when t has a lower energy source. III. DESCRIPTION OF ADAPTIVE DYNAMIC ACO ALGORITHM A. The mplementaton of ACO In ths ACO algorthm, all the ants are dentfed nto two types of artfcal ants, a forward F and a bacward B by ther functons. An artfcal ant F represents an ant agent moves from a source node s to a destnaton node d hoppng from one node to the next tll node d s reached. An artfcal ant B represents an ant agent moves bacward from a destnaton node d to a source node s. Each ant researches for a mnmum cost path between a par of nodes of the networ. All the ants are equally allocated from each networ node towards destnaton nodes randomly selected to match the traffc load. Each ant has a memorytab whch contans the already vsted nodes. The memory L { tab } s used to defne, for each ant, the set of nodes that an ant started from node stll has to vst. By explotng{ tab } an ant can buld feasble solutons. That s to say, ant can try to avod vst a node twce whch s shown as follows: () t f j () t a p = tab (6) 0 f j tab Where p () t s the probablty of selectng the next node j. The ant routng-tables of node are denoted by P = () p t. Also, memory allows the ant to compute tme delays and power consumpton of the tour generated and to cover the same path bacward to depost pheromones on the vsted nodes. When an F ant arrves at ts destnaton node d, the node d wll produce one B ant to go bac to the source node s along the. At the same tme, the B ant wll update same path { tab } pheromones of each node n { tab } based on nformaton of the F ant s collectng delays and power levels. The followng two paragraphs descrbe the expressons of delay model and energy model. Each node n networ wll send pacets by a certan speed. A splt pacet from each source s called and F ant whose destnaton node s selected by random probablty. The ntermedate nodes memorze and transfer F ants accordng to the FIFO prncple. The strategy of transferrng F ants depends on the ant routng-tables n node.the tables are appled to all the nodes unvsted by ant. In formng the path from the source node to the destnaton node, ants F use the same queue wth numbers of pacets to transfer. By ths way, ants or data pacets are totally delayed. So we record the delay s tme D that t costs when movng from the node s to the d node d as one mportant factor of evaluatng the qualty of the traffc of the path. Let D be the set of tme delay of each nodes whch are denoted as the followng matrx: d 11 d 12 d 1N N D d d d (7) =, j L; d N d L1 d L2 d LN Where d represent the delay from the node to the destnaton d va the node j. In ths paper D s regarded as one Α factor of evaluatng the model () t f. That s because the factor of delay can show the quanttes when an ant passes by, the capablty of transferrng pacets and the performance of the ln. Another reason s that t shows the status of congeston when artfcal ants pass by the stagnated nodes. In [5] [6], the studes show that sngle hop routng s almost

3 always more power effcent compared to mult-hop under realstc envronments when thnng of the basc consumpton such as RF crcut, channel fadng and path effcency. Also t s ponted out that mult-hop networ schemes wll result n sgnfcant overhead when we assume a larger number of short hops replace a smaller number of long hops. In our optmzaton we mnmze the networ power consumpton across all the nodes. So ths optmzaton crteron maxmzes average node lfetme n a long run f we assume that the data rate generated at each node s randomly changng to the same dstrbuton, where the path length s a vector whose elements are the ln costs gven by x c = j e. (8) j Where x s nonnegatve weghtng factors for power consumpton of the ln, Assume C s the set ofc.therefore, j we formulate the power consumpton problem wth the objectve of maxmzng the system lfetme gven the sets of source and destnaton nodes. B. Pheromones and E&D ANTS Model As havng been dscussed above, energy and delay are two crucal factors n the update of pheromones whch contrbute on the soluton of the path. The soluton s to mnmze the Energy Delay model. The mathematcal expresson s shown as follows: g () t = Mn( Energy Delay), (9) However, generally ncreasng energy savng comes wth a penalty of ncreased delay. Therefore there s a tradeoff between energy consumpton spent and delay cost. Assume ant F passes along the path from the source s to the P : s,,..., d destnaton d denoted as the set { 1 }. When ant B moves bacward from the destnaton node d to the source node s, we can calculate energy consumpton and tme delay of each stage n each agent. Further Integratng (7), (8) and (9), we determne the E&D ANTS module as follows: s g() t = C D = ( ) (10) j L c d P j Where s the number of solutons repeatedly constructed by all ants, ther movng average z s computed and each new soluton z s compared wth z We can determne the new ncrement of pheromones of the model as follows: g () t new () t f () t 1 z Δ τ = = j τ 0 (11) z g() t Where z s the average of the last solutons, n order to acheve the optmal soluton cost, we mnmze the g() t value to a low bound as much as possble. So n (10), we use RL algorthm to fnd the lowest value g () t whch are shown as follows: x g() t = (1 ρ) g( t 1) + ρed (12) j Where ρ s the learnng rate, ρ [0,1), obvously, whle the smaller the energy consumpton s, the shorter delay tme s on the path j and the bgger the resdual energy of node has, the g() t value wll become smaller. That s to say, the model estmatng value wll approach to the soluton. As a result, the ncrement of pheromones on the path grows bgger and bgger. When the node j s the choce and ants have less chance to select other paths,the result of repeatedly searchng s absolutely p () t 1. It conforms to the constraned (2). IV. SIMULATION To evaluate the above analyss, we use networ smulator OPNET to construct the networ topology graph whch s shown n Fg. 1. For the ACO mplementaton, program s wrtten n C++. Besdes, we also mplement AntNet [2] algorthm n OPNET. The networ s constructed by twelve nodes and eghteen lns. Fg 1: A Topology Graph of Wreless Networ In Fg.1, the numbers wthn panes ndcate node dentfers. Each lne n the graph represents a bdrectonal ln and the orgnal weghts each ln are ndcated as <power consumpton, propagaton delay>, n whch power consumpton s measured n nj/bt/message and propagaton delay n mllsecond (ms). We assume the bandwdth B of each ln s dvded nto two parts for bdrectonal communcatons, and the lns are constructed accordng to the Drop-Tal model (a fnte FIFO queue). After source nodes produce a quantty of artfcal ants or pacets conformng to Posson dstrbuton, the destnaton nodes are randomly chosen by average probablty. Each pacet wth an ntal energy of 1 joule has a sequence number ncreased step by step. When one pacet passes through a node by a certan speed, the node taes the frst step to put all the ant agents nto buffer storage and then selects the optmal path from ts routng table to transfer pacets. In ths way all the ants dsperse n as much paths as possble to acheve the balance of the load. The dfferent szes of one pacet are consdered n our smulatons. So some of the expermental parameters used n the smulatons are lsted n Table I. In order to avod cycles and TABLE I PARAMETERS IN NETWORK THROUGHPUT MODEL Networ traffc model Parameters descrpton

4 Intal energy 0 : 1 Joule per node Pacet Sze ( S ): 1, 2, 4, 8, Posson 32 or 64 bts Bandwdth ( B ): 1Mbt/s Traffc load ( Load ): 15 pacets/s routng table s freezng, we need ntalzeτ to 1. In ths 0 L case, ant agents can adjust to the more effcent path when networ traffc loads have changed and congeston fades away. Reference [4] ntroduced some smulaton methods to fnd the parameters x n (8). Consderng (11) and (12), we assume the parameters of ρ s 0.1. In ths paper, the performance metrcs are used as follows: Energy: Power that all the nodes have consumed on sendng a quantty of pacets. We use the total energy consumed by sendng messages as the ndcator of the lfetme of networ. Average delay tme: It conssts of watng tme n queues and transferrng tme, whch s namely the average tme delay of all the ants arrvng at the destnaton node. We repeated experments for more than ten tmes and calculated the average of those expermental data. In Fg.2, comparng our smulaton results wth AntNet s, we can see that our mproved algorthm has better convergence propertes. Otherwse, when we change the structure of networ topology (to shut down the node ) or decrease the bandwdth of nodes to 0.5Mbt/s when t goes to 210, t s shown n Fg.2 that our model undergoes a short tme fluctuaton and approaches very fast bac to the balance status. It has a wonderful robustness. Whereas, AntNet can not adapt to these changes, and the whole curve went up straghtly. (a) e n 12 (b) Fg.2 The comparson of E&D ANTS and AntNet Fg.3 Delay and Energy tradeoff each pacet The tradeoff curve of delay and energy s shown n Fg3, where we conclude that E&D ANTS behaves better than AntNet n WSNs. In our experments, we also found that the tradeoff curve s nfluenced by the topology graphs of networs and networ load. Reference [7] proposed the tradeoff for dfferent source rates and dfferent networ topologes n TDMA-based sensors networs. V. CONCLUSIONS AND FUTURE WORK In ths paper, we propose a new algorthm model, E&D ANTS, whch ntroduces a great energy-effectve soluton to communcate nformaton from source nodes to destnaton nodes and sgnfcantly smplfes the topology of networ at the same tme. From the above research and smulaton results, we obtaned an amazng effect on determnng the ncrement of pheromones by mnmzng the model Energy Delay. Wthout foundng an enormous model, we optmze t by usng the RL algorthm. Our study shows that E&D ANTS acheves up to 127% hgher communcaton throughput whle consumng 80% less per pacet energy than AntNet. However, n wreless sensor devces, the memory of each node s lmted. So when networ traffc load s heavy, retransmssons wll consume a lot of energy because of hgh pacet loss rate. So the next step s to develop a mechansm whch allows a node to accurately estmate the traffc and adjust ts waeup rate accordngly. REFERENCES [1] S. Odem and D. Karaboga, Routng n Wreless Sensor Networs Usng Ant Colony Optmzaton, Proceedngs of the Frst NASA/ESA

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