An Effective Sensor Deployment Strategy by Linear Density Control in Wireless Sensor Networks Chiming Huang and Rei-Heng Cheng

Transcription

1 An ffective Sensor Deployment Strategy by Linear Density Control in Wireless Sensor Networks Ciming Huang and ei-heng Ceng 5 De c e mbe r0

2 International Journal of Advanced Information Tecnologies (IJAIT), Vol. 5, No. An ffective Sensor Deployment Strategy by Linear Density Control in Wireless Sensor Networks Ciming Huang # and ei-heng Ceng * # Department of Information Management, Hsuan Cuang University Hsin-Cu, Taiwan,.O.C. yalo.uang@gmail.com * Department of Computer Science and Information ngineering, YuanPei University Hsin-Cu, Taiwan,.O.C reieng@gmail.com Abstract Since sensors located in different positions wile tey are deployed uniformly in a wireless sensor network (WSN) bear different communication responsibilities, te energy consumption of sensors in te network are eterogeneous so tat some may die earlier tan te oters. In tis paper, we evaluate te energy consumption of sensors in networks. Furtermore, a sensor deployment by linear density control to balance te loading of sensors for WSNs is proposed, so tat te network availability is significantly improved. Simulation results sow tat te proposed metod can acieve up to 3% iger data delivery rate tan previous researc did. Keywords Wireless Sensor Network, Sensor Deployment, Linear Density Control, Compression, Data Delivery ate.. INTODUCTION A design of wireless sensor networks involves deployment of undreds or tousands of omogenous, and energy limited sensor nodes in a region were information will be collected. Hence sensor nodes will sense teir nearby environment and send te information to te BS (Base Station) wic is neiter energy nor computing power limited. Wile te power range is fixed, a node will eiter transmit te information directly to te BS or via oter nodes wic are nearer to te BS according to te distance between it and te BS. Te forwarding routes can be built by gradient based protocols []. In tis paper, a node is in -tier if it is ops away from te BS. Tus, in addition to te information generated by nodes temselves in te networks, nodes will also ave to forward te data sent by oter nodes in iger tiers to te BS. In tis way, nodes in lower tiers will ave to bear more communication responsibilities tan nodes in iger tiers. Terefore, nodes closer to te BS will lose its battery power very soon. Wile many (all) nodes in low tiers die, te data transmitted from nodes in ig tiers cannot reac te BS. Tis causes low data delivery rate and leads to a bottleneck penomenon [-5]. Previous literatures [6-3] addressed te problem of energy efficient deployment of nodes and base stations witout considering te impact of bottleneck penomenon. Tese proposed protocols may prolong te network lifetime, but te effectiveness of network suc as data delivery rate or network availability was not necessarily good as expected. In tis paper, we develop an analytic model to evaluate te energy consumption of nodes in different tiers by considering te op counts of nodes away from te BS. Based on te evaluations, a linear density control deployment metod is proposed and analysed. Simulation results sow tat te proposed metod can significantly improve te network availability. In te best case, te proposed metod can acieve up to 3% iger data delivery rate tan previous researc did. Te rest of paper is organized as follows. Section introduces some related work. Section 3 evaluates te energy consumption of sensors in network and a sensor deployment by linear density control is proposed. Section 4 presents te simulation results of our 資訊科技國際期刊第五卷第二期 -76-

3 International Journal of Advanced Information Tecnologies (IJAIT), Vol. 5, No. metod and some previous studies. Te conclusion and future work are presented in Section 5.. LATD WOK Considering te different loading on tiers, some literatures [-5] proposed solutions to problems of energy efficient deployment of nodes and base stations for prolonging te network lifetime. Sicitiu and Dutta [] proposed tat te nodes adjacent to te BS sould be equipped wit batteries of larger capacity and oter nodes aving te battery capacity inversely proportional to te distance from te BS. Since te geograpical location were a sensor network is supposed to be deployed is azardous in nature and not so easily accessible, it is very difficult to place a particular node wit a particular battery capacity at a particular location. Besides, te total manufacturing cost of te sensor nodes individually equipped wit distinct battery level will be more tan tat of mass production of exactly similar nodes. Padmanab and oy [3, 5] tougt tat te density of te sensor nodes required depends upon te distance away from te BS, probability of event occurrences, te transmission range of te nodes and te coverage area. If any of tese parameters is canged, te required density of te sensor nodes will cange as well. Increasing te number of nodes placed around BS may decrease te bottleneck effect. Te cost of deploying omogenous sensors in network is cost-efficient too. However, by converting te -dimensional problem into multiple identical - dimensional problems in data forwarding, te analysis of bottleneck in WSN will underestimate te loading of nodes in low tiers since te number of nodes in te farter tiers is mostly more tan tose in low tiers. Padmanab et al. [4] proposed to set te transmission distance between two successive nodes around te BS smaller tan tat between two periperal nodes, so tat te bottleneck around te BS could be avoided. Te smaller te transmission range, te less te power needed to transmit. However, in many practical applications, bi-directional transmission is needed for data transmitting and control. Te asymmetric transmission range design would make te communication from BS to nodes not easy. Moreover, te smaller sensing range will cause te smaller number of nodes in te corresponding tier. Tus, te smaller number of nodes in low tiers may bear muc more responsibility of forwarding from te bigger number of nodes in ig tiers. Tis may make te bottleneck effect more serious. All autors in te above studies concluded tat te loading of sensors in different tiers would be inversely proportional to te distance away from te BS. Moreover, eiter tey proposed linear deployments of sensors, i.e. te density of sensors in some tier would be linearly dependent on its tier number, or set te battery capacity of sensors linearly dependent on te distance away from te BS to balance te loading of sensors in different tiers and prolong te network lifetime terefore. Obviously, linear deployment of sensors is practical and easy to implement. Terefore, in tis paper, we also assume te density of sensors deployed in te network will be linearly dependent on teir corresponding tier number. By adjusting te slope of te linear density function, we can find te load distribution of sensors in different tiers so tat an effective sensor deployment by linear density control to balance te loading of sensors in WSN can ten be proposed to improve te network availability and increase te successful data delivery rate. 3. LINA DNSITY CONTOL A general wireless sensor network wit a single BS can be viewed as te deployment of ) nodes in a circle wit radius. Since every sensor transmits information in a fixed power range r, te circle can be split into many circular rings wic, in practice, may be possible tat all te nodes in a particular ring are present and te sape of te circular ring is not smoot [3]. However, a node deployed in a particular ring, namely i-tier, is i ops away from te BS and te number of -77- 資訊科技國際期刊第五卷第二期

4 International Journal of Advanced Information Tecnologies (IJAIT), Vol. 5, No. nodes in i-tier is denoted as. It is clear tat te fartest tier, namely, in te network is equal to /r, and. ) = () i= m=0 m=5 m=0 m=0 m=30 m=40 m=50 Assume te deployment of sensors in i-tier wit density, denoted as, wic is inversely proportional to i. Te relationsip between i and /) can be diagrammed as a line passing troug two points (0, m+) and (, ) were m 0. Te condition m 0 means tat te density of sensors in low tiers will be larger tan tat in ig tiers. An example density function for =5 is sown in Fig.. ) Tier Fig. Density functions wit respect to different values of m were te 5t-tier is te fartest tier in network. m m=0 m=5 m=0 m=0 m=30 m=40 m= =5 i Fig. Te density function for =5 and m 0. Te corresponding line function can be represented as m = [( m + ) i] ), were i and m 0 () Tere are two special cases of m=0 and m= represent for density functions of uniform deployment and sensor deployment proposed in [5] respectively. Fig. sows by deploying,000 sensors in te example network wit different values of m. In tis paper, we find te m tat makes te energy consumption of sensors te most loadbalancing, and tat te network availability, i.e. data delivery rate, can be increased Tier Fig. 3 Te number of sensors in i-tier, wit respect to different values of m. Let te area corresponding to i-tier is A(. Te number of sensors in i-tier,, can be calculated as below. = A( = (i ) πr (3) Te values of wit respect to different values of m are sown in Fig. 3. Te total number of sensors ) in network is rewritten as follows. = A( = N. ( ) = (i ) πr i= i= i= (4) By quations (), (3) and (4), we obtain 資訊科技國際期刊第五卷第二期 -78-

5 International Journal of Advanced Information Tecnologies (IJAIT), Vol. 5, No. N. ( ) = (i ) πr and i= 6 ) D ) =. π r ( m 3m + m m [( m + ) i] ) (5) ( + 6 ) (6) Now we can evaluate te energy consumption of sensors in different tiers. Te energy required for a single node is described below. At any node energy consumption takes place in ( sensing, (i computation, and (ii communication (reception and transmission). Te energy consumption in sensing, computation and reception are independent from te transmission distance. According to te data given by [3], we consider tat te energy consumption in computation is negligibly small in comparison to oter counter parts. Since te energy needed for sensing is almost constant, we take only te energy consumption in transmission and reception into consideration in te following analysis. Tus wen a bit of information is generated at a source node and forwarded to te BS, te energy is consumed in ( transmission at eac node in te forwarding pat, and (i reception at eac node in te forwarding pat except te source one. Te energy required to transmit and receive a packet of information is assumed as ξ T and ξ in tis paper respectively. At te beginning, we analyse te average energy required for a sensor in i-tier to forward packets from all sensors in x-tier, namely i (, were 0<i x. Assume tat V events are randomly triggered among sensors; te expected amount of packets triggered and transmitted by a sensor is V/N. We use ρ =V/N for abbreviation. Te number of events sensed and packets transmitted by te nodes in x-tier is ρ, tus te energy consumed for transmitting tese packets to (x-)-tier is equal to ρξ T. quation (7) sows te average transmitting energy consumed for eac node in x-tier. x ρ ξ T x ( ) = = ρξt (7) All packets transmitted from x-tier would be received totally by sensors in (x-)-tier. And all received packets will be retransmitted to sensors in (x-)-tier. So tat te average energy consumed for eac node in tier x- to receive and forward all te packets transmitted from x-tier is as sown in quation (8). ( x ρ ξ + ρ ξt = x ) ρ = ( ξ ) x ) (8) quation (9) is a general form for te nodes in i-tier to bear te communication responsibility for sensors in x-tier. ρ i ( = ( ξ ) were i is in {, x- } (9) However, once te density of sensor in tiers is controlled, it is no more uniform random distribution. So te expected amount of packets triggered and transmitted by a sensor in tier x is defined as follow. V A( ρ x = were A( ) is te overall A( ) area of network (0) For a sensor density controlled network, te ρ in equations (7) to (9) sould be replaced by ρ x. At last, we can get te overall energy consumption of a sensor in i-tier, denoted as i, after V events are randomly triggered among te network as sown below. i = i ( = ρ x ( ξ ) x= i x= i V = ( ξ ) (x ) x= i () -79- 資訊科技國際期刊第五卷第二期

6 International Journal of Advanced Information Tecnologies (IJAIT), Vol. 5, No. Te average energy consumption for a sensor in different tiers wit different values of m is sown in Fig. 4. is equal to as derived from [5], and m is 0 in uniformly random deployment. i m=0 m=5 m=0 m= Tier Fig. 4 Average energy consumption for a sensor in i-tier. In order to minimize te difference of loading between sensors in low and ig tiers, sould be as close to as possible. Tat is, / approaces to one if te load distribution of sensors is te most loadbalancing. Tus te best value of m is defined as te one wic makes equal to ereafter. It is easy to find tat ( ξ ) V ( ξ ) V = (x ) = ) x= ) () and ( ξ ) V ( ξ ) V = ( ) = ( ) x ) x= ) (3) Terefore, 3 ) ) = = = ) m [( m + ) ] ) m + m (4) Wile / is equal to, te value of m is +. Different values of m represent different deployment strategies. For instance, m 4. SIMULATION SULTS Te simulation program is written wit Borland c and run on a Pentium 4 CPU 3.0 GHz macine wit G AM. In te simulations,,000 stationary sensor nodes are deployed by linear density control in a circular area wit 300 meters in radius, and te BS is set up at te centre of te area (300, 300). Power range of eac sensor is 60 meters. To simulate te network operations, 35,000 events are triggered randomly among sensors wic need to deliver a fixed-lengt packet to te BS. According to te energy model used by [4], we ave equations (5) and (6). Tus te energy consumptions of transmitting and receiving a bit witin 60 meters for a sensor are 40 nj (ξ T ) and 50 nj (ξ ) respectively. Te initial power of eac sensor is 0,000 nj. ac result is te average value of 30 different simulations. We assume sensors are omogeneous and initially ave te same energy. ξ T ( d ) = 50 nj / bit + 0. nj / bit / m d were d is te transmitting range (5) ξ = 50 nj bit (6) / From te analysis in Section 3, we know tat te uniform random deployment and te deployment metod proposed in [5] are equal to linear deployment wit m=0 and 5 respectively, and tese two metods are referred as ANDOM and PADMANABH in te following paragraps. On te oter and, te linear density control strategy proposed in tis paper uses te best value of m wic is equal to +, and tis strategy is referred as OUS ereafter. Since te value of in our simulation is equal to /r = 300/60 = 5, te best value of m is equal to 30. Tat is to say, in Figs. 5 and 6, OUS uses m=30 to determine te number of nodes deployed in eac tier. 資訊科技國際期刊第五卷第二期 -80-

7 International Journal of Advanced Information Tecnologies (IJAIT), Vol. 5, No. Survival rate ANDOM PADMANABH OUS Number of events Fig. 5 Te Comparison of survival rates in te first tier wit respect to different deployments of sensors. Since sensors in te first tier always die earlier to result in te bottleneck penomenon tat lowers te amount of data successfully delivered to te BS, we analyse te survival rate of sensors in te first tier wit different values of m and sow te results in Fig. 5. Meanwile, Fig. 6 sows te data delivery rate and sensors survival rate corresponding to sensor deployments by linear density control wit different values of m. rate and sensors survival rate so tat te network availability is enanced. Actually, PADMANABH (m=5) is also better tan te ANDOM significantly. Tis can also explain tat te bottleneck penomenon does exist for multi-opped routing protocols implemented in WSN. Increasing te number of sensors or density around te BS could effectively balance te loading of network so tat te network availability can be improved correspondingly. However, autors in [5] underestimated te loading of sensors around te BS so tat sensors in low tiers would die earlier tan oter sensors in ig tiers, wic results in te same bottleneck penomenon wit lower data delivery. Wen we increase te value of m to 30, te sensors deployed around te BS are increased, and te bottleneck penomenon is smoot away. Fig. 6(a) depicts tat OUS can acieve up to 3% iger data delivery rate tan PADMA- NABH did..0 Data delivery rate (%) Sensors survival rate (%) ANDOM PADMANABH OUS Number of events ANDOM PADMANABH OUS (a) Number of events (b) Fig. 6 Te Comparisons of data delivery rate and sensors survival rate wit respect to different deployments of sensors. 5. CONCLUSIONS In tis paper, we evaluate te energy consumption of sensors in network more precisely tan previous related works did by considering te energy consumption for sensors in eac tier instead of only a single route. Wit te knowledge of load distribution, a deployment strategy of sensors by linear density control is proposed to balance te loading of sensors in different tiers suc tat te bottleneck penomenon can be proibited as possible to improve te network availability. Simulation results sow our metod outperform te uniform random deployment significantly and also better tan deployment proposed in previous researc in data delivery rate and survival rate. To include te compression mecanism in our evaluation model migt be addressed in future researc. Clearly, te OUS metod (m=30) outperforms te oter two metods in data delivery -8- 資訊科技國際期刊第五卷第二期

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