An Efficient Localized Chain Construction Scheme for Chain Oriented

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2011 2011Tenth 10th International InternationalSymposium Symposiumon onAutonomous AutonomousDecentralized DecentralizedSystems Systems

An Efficient Localized Chain Construction Scheme for Chain Oriented Wireless Sensor Networks Quazi Mamun

Sita Ramakrishnan

Bala Srinivasan

Clayton School of IT Monash University VIC 3800, Australia [email protected]

Clayton School of IT Monash University VIC 3800, Australia

Clayton School of IT Monash University VIC 3800, Australia [email protected]

[email protected]

sensors to communicate with each other with little overheads, comparatively low energy, lengthened lifetime, reduced latency, and other improved facilities. Chain oriented logical topology is one of the prominent topologies in wireless sensor networks. A chain oriented sensor network is comprised of a single or multiple chains, where each chain consists of several sensor nodes. A node is elected as a leader for each chain. Other nodes of the chain are called member nodes. The communication of a sensor node in a chain is restricted only to the chain members, in particular, to its neighbouring nodes (successor and predecessor) of the chains. As a result, chain oriented sensor networks gain the advantage of avoiding wireless communication problems like interference. On the other hand, although the chain based routing protocols can effectively balance the node’s energy dissipation, and thus significantly extending the network lifetime, they very likely create serious transmission delays and redundant paths, particularly for large sensing areas [4]. Thus, chain construction is the most important part of designing a chain oriented sensor network. In this paper, we propose a chain construction scheme for chain oriented WSNs based on Voronoi tessellation. The proposed scheme would create several localized chains. The notion of localized chain is primarily based on the motivation that, if a chain is restricted to a local area, the interference problem diminishes. As a result, the network capacity increases and time required for scheduling TDMA decreases. In the proposed method, we divide the total target area into several Voronoi cells. The sensor nodes within each Voronoi cell are then self-organized into a chain, so that the summation of square to the distances between each pair would be minimum. This assures the minimum total energy consumption along the chain. An evaluation is conducted to examine the effectiveness of this approach.

Abstract— An efficient logical topology helps wireless sensor networks (WSNs) minimizing different constraints. For large-scale WSNs, chain oriented logical topologies are shown to be more energy conservative than other logical topologies. Chain construction is the main challenge to create a chain oriented logical topology. In this paper, we propose a chain construction scheme, which creates several chains for the topology using Voronoi tessellation. The main idea of this scheme is to divide the target field into a number of small areas (i.e., Voronoi cells) so that in each cell, a chain is constructed. To construct a chain in a Voronoi cell, we use a protocol, which guarantees the summation of square to the distances would be the lowest. We compare our chain construction algorithm with other similar algorithms. Simulation results show that proposed Voronoi diagram based algorithm saves more energy, lengthens lifetime of the network, and reduces data collection latency. Keywords- sensor networks; Voronoi diagram; Chain oriented sensor networks; logical topology. I.

INTRODUCTION

Wireless sensor networks (WSNs) have recently received remarkable attention from both academia and industry because of their potentials for numerous applications in both civilian and military areas [1]. A wireless sensor network consists of a large number of small sensor nodes with sensing, data processing, and communication capabilities. These tiny sensors are deployed in a region of interest, and collaborate with each other to accomplish a common task, such as environmental monitoring, industry process control, and military surveillance etc. [2]. Distinguished from traditional wireless networks, a sensor network has many unique characteristics, such as dense node deployment (high up to 20 nodes/m3 [3]), higher unreliability of sensor nodes, asymmetric data transmission, and severe power, computation, and memory constraints. These issues present many new challenges for the development and eventual application of WSNs. Moreover, the sheer number of sensor nodes, their unattended deployment, and hostile environment very often preclude relying on physical configuration or physical topology. Consequently, we need to rely only on logical topologies. An optimized logical topology facilitates the designers to design efficient protocols, which allow the 978-0-7695-4349-9/11 $26.00 © 2011 IEEE DOI 10.1109/ISADS.2011.7

II.

RELATED WORKS

There are several existing chain oriented topologies in the literature. This section overviews advantages and disadvantages of several popular topologies, namely, PEGASIS [5], COSEN [6], Enhanced PEGASIS [7], and CHIRON [8]. PEGASIS is a basic chain-based routing protocol. In this protocol, all nodes in the sensing area are first organized 3

into a chain by using a greedy algorithm, and they then take turns to act as the chain leader. In data dissemination phase, every node receives the sensing information from its closest upstream neighbor, and then passes its aggregated data toward the designated leader, first to its downstream neighbor, and then finally to the base station. Although the PEGASIS constructs a chain connecting all nodes to balance network energy dissipation, there are still some flaws with this scheme. Firstly, for a large sensing field and real-time applications, the single long chain may introduce an unacceptable data delay time. Secondly, since the chain leader is elected by taking turns, in some cases, several sensor nodes might reversely transmit their aggregated data to the designated leader, which is far away from the BS than itself. This will result in redundant transmission paths, and therefore seriously waste network energy. Thirdly and lastly, the single chain leader may become a bottleneck. In contrast to PEGASIS, COSEN is a two-tier hierarchical chain-based routing scheme. In this scheme, sensor nodes are geographically grouped into several lowlevel chains. For each low-level chain, the sensor node with the maximum residual energy is elected as the chain leader. Moreover, with the low-level leaders, a high-level chain and its corresponding chain leader will be eventually formulated. In data communications, all common (normal) nodes perform a similar procedure similar to that in PEGASIS to send their fused data, and this takes place via their respective low-level leaders and the high-level leader toward the BS. Compared to PEGASIS, although COSEN can alleviate the transmission delay and energy consumption, some chains, especially which are constructed during the end of chain formation phase, become very long. In addition, due to the greedy algorithm used for chain construction, chains may overlap with each other. In 2007, Jung et al. proposed a variation of the PEGASIS routing scheme, termed as Enhanced PEGASIS (we abbreviate it as EPEGASIS hereafter in this paper). In their method, the sensing area, centered at the BS, is circularized into several concentric cluster levels. For each cluster level, based on the greedy algorithm of PEGASIS, a node chain is constructed. In data transmission, the common nodes also conduct a similar way as the PEGASIS to transfer their sensing data to its chain leader. After that, from the highest (farthest) cluster level to the lowest (near to the BS), a multi-hop and leader-by-leader data propagation task will be followed. Although the EPEGASIS has considered the location of the BS to slightly improve the redundant transmission path and the network lifetime, there are still some problems with that scheme: 1) For large sensing areas, the node chain in each concentric cluster would still become lengthy, and thus result in a longer transmission delay. 2) Since the leader node election strategy is same as that in PEGASIS (by taking turns), it did not consider the node’s residual energy. As a node with the least residual energy is elected to act as the leader, the network lifetime would be significantly affected. 3) While the distribution of sensor nodes is not even, the transmission distance between two chain-leaders in different cluster levels might be lengthy, this would consume more energy.

In CHIRON, Chen et al. propose a hierarchical chainbased routing protocol, which creates localized chain. They use the technique of BeamStar [9] to divide the sensing area into several fan-shaped areas. The sensor nodes within each group are then self-organized into a chain for data dissemination. In this protocol, instead of taking turns, the authors consider the node with a maximum residual energy as chain leader candidate. In addition, the nearest downstream chain leader is elected for relaying the aggregated sensing information. Although this protocol solves the interference problem by enclosing the chains in a geographical region and also able to save more energy than PEGASIS and EPEGASIS, the problems of the protocol are i) the areas generated in this protocol are very much uneven, thus some chains consist of very few sensor nodes while other chains contain a very large number of nodes. ii) this protocol is not scalable, because for a large scale sensor network, the fan-shaped areas would be uncontrollably big iii) additionally, in a situation where the area is uncontrollably big, the protocol suffers from serious transmission delay iv) as the same nodes are selected always those nodes consume more energy and die soon and v) energy consumption is not evenly distributed. III. DESCRIPTION OF VORONOI DIAGRAM BASED CHAIN CONSTRUCTION SCHEME

Our aim is to construct a number of chains with the deployed sensor nodes, and for each chain, to select a sensor node as a leader. In the process of chain construction for chain oriented WSNs, determining chain length is very crucial. If the constructed chain becomes larger, networks suffer from serious transmission delay. On the other hand, if constructed chains are too small in length, energy consumption increases. Another important issue that needs to be addressed during the chain construction process is that, the constructed chains should experience low interferences. If a chain overlaps with other chain(s), interference problem becomes acute, and more time delay occurs for scheduling the sensor nodes for time division multiple access (TDMA) process. For these reasons, this is a good idea to limit the chains in geographical regions. We adopt this policy in designing chains for chain oriented WSNs. Limiting a chain in geographical boundaries also gives the upper bound for the distance between two nodes of a chain. For designing geographical boundaries among the chains, we use voronoi tessellation method. Figure 1 shows two methods of constructing chains. Fig 1(a) shows construction of chains that follows a greedy method (closest neighbour method, used in PEGASIS). On the other hand, 1(b) shows all the chains are restricted in a region, thus no chain is crossing / overlapping with another chain and chain lengths are restricted by the region. Our chain construction algorithm consists of four sequential phases – i) Selecting tentative leaders ii) constructing voronoi cells with respect to tentative leaders iii) voronoi cell management and iv) constructing a single chain in each voronoi cell. Here we describe them briefly:

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Figure 3: Straight line L divides the polygon P into two parts Figure 1: (a) Chains constructed using greedy algorithms; (b) chain constructed using voronoi diagram.

ii) Constructing voronoi cells:

i) Selecting tentative leaders: The Voronoi diagram has been reinvented, used, and studied in many domains. It is believed that the Voronoi diagram is a fundamental construct defined by a discrete set of points. In 2D, the Voronoi diagram of a set of discrete sites (points) partitions the plane into a set of convex polygons such that all points inside a polygon are closest to only one site. This construction effectively produces polygons with edges that are equidistant from neighboring site. In wireless sensor networks, Voronoi cells can provide a means to help monitoring and tracking targets [10], conserving energy [8], and balancing workload [11]. Several Voronoi diagram construction algorithms exist in literature [12, 13]. However, the difference between these algorithms and the proposed algorithm is the number of nodes on which the protocol should run. In our case there is a limited number of nodes (5%-8% of the total sensor nodes deployed in the target field), whereas the rest of the protocols should consider all deployed sensor nodes. Now, given a set of points P { p1 , p 2 ,  , p n } on a two-

At the very first stage, we like to select the tentative leaders, which are used in the second phase to construct Voronoi diagrams. The nodes selected in this phase may become the leaders of constructed chains depending on the voronoi cell size. If the voronoi cell created is of optimal size, the node with respect to which the voronoi cell is created becomes the leader of the chain for the voronoi cell. We consider the residual energy of a node as a factor for selecting as a tentative leader. Other factor we consider is the number of times the node was previously selected as a leader. In doing so, each node chooses a random number from 0 to n. If that number is less than a threshold Tn, it is nominated as a tentative leader. To select a nominated sensor node we put Tn with mode’s residual energy ER in a function and get the final decision in .p(S).

p( S )

f ( E R , Tn ) 1 p /{1 p *( r mod )} ; if n  G p Where Tn = otherwise 0

{

dimensional plane R; the Voronoi Diagram divides R into n cells. Each cell Ci centers at point pi. Any point in cell Ci is closer to pi than to any other points. Formally defined as

here p is the desired number of chains (in our simulation we find the value of p should be 0.05 to 0.08, that means, provided with 100 sensor nodes deployed on the field we create 5 to 8 chains in total) r is the current round, and G is the set of nodes that have not been local leaders in the last rounds. Using this threshold, each node will be nominated as a leader at some point within n rounds.

 ^p dist ( p , p) d dist ( p , p) : p  R` n

Ci

i

j

iz j , j 1

where p is a point in plane R; dist ( p j , p ) denotes the Euclidean distance between pi and p. Voronoi_Cell (sensor S, polygon A, bisectors set B) S.Cell = A for each bi in B if (S.cell ɀ

Abi (S))  Ø

S.cell = S.cell —

Abi (S)

return S.cell Figure 4: Voronoi diagram construction algorithm for a sensor node S

Figure 2: Voronoi diagram

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Figure 5: Voronoi cell construction for node S1. (a) S1 computes b12, b13 and b14, initially S1 assumes the entire region as its voronoi cell, S1.cell=A. (b) S1 refines its voronoi cell (shaded region is excluded) by S1.cell S1.cell  Ab ( S1 ) (c) S1 12

refines its voronoi cell by S1.cell S1.cell Ab (S1 ) ; as S .cell  Ab ( S1 ) I , b13 does not affect the voronoi cell S1.cell. 14 13

The lines at the boundaries of the Voronoi diagram extend to infinity. However, since here we are dealing with a finite area and there are only few nodes that take part, we clip the Voronoi diagram to the boundaries of the field. Since traveling along the bounds of the sensor field also constitutes a valid path, we introduce extra edges in the sensors’ sensing and transmission areas are modelled as disks and the radii of such disks are equal for all the sensors. Thus, the network is modelled as a unit graph. We assume that the induced unit graph is connected. The sensors are also required to know their locations. This is usually achieved through GPS or other techniques [14, 15]. The Euclidean distance from a sensor to a given point in the plane is, thus, computable. In the following part, we describe the essential notations we use in the algorithm for creating voronoi cells.

Voronoi diagram corresponding to the bounds. In subsequent discussions, when we refer to the Voronoi diagram, we are actually referring to the bounded diagram. Figure 2 shows an example of voronoi diagram Throughout this paper, we assume all sensors are identical and can be modelled as points in the plane. The Algorithm in Figure 3 requires only few messages. A sensor node does not need to collect location information from all nodes, especially from distant nodes, as those nodes do not affect the shape of the voronoi cell essentially. Figure 5 depicts an example of the situation.

Let a point x be inside a polygon P (see figure 3). A straight line L divides the polygon P into two polygons PL (x) and PL (x) such that i) PL ( x)  PL ( x) P, ii) PL ( x)  PL ( x) I , iii) x  PL (x) and iv) x  PL (x) . (See Figure 3.) Let S = {S1, S2, …, Sk} be the set of sensor nodes for which we construct voronoi cells. Each sensor broadcasts its location information to all other sensor nodes. After collecting the broadcasted messages, a sensor constructs a set of straight lines, which are computed as the perpendicular bisector between that node and every other node. Let a sensor node S constructs a perpendicular bisector lines set B={b1, b2, …, bk} where b1 is the bisector line between sensor S and sensor S1, b2 is the bisector line between sensor S and sensor S2 and so on. Using the notations above, below we give the voronoi-cell construction algorithm for a sensor node S that is deployed in a filed A and calculate its voronoi cell in S.cell: Algorithm in Figure 4 constructs voronoi cells for each of the tentative leaders selected in the previous phase.

(a)

(b) Figure 6: Merging two voronoi cells to get rid of an undersized voronoi cell for S3

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Figure 7: Chain construction starts with node 1. Initially the chain is 1-3, (round 1) then a new node 2 is inserted and the chain becomes 1-2-3 (round 2). The ultimate chain is 1-2-4-6-3-5. Chain= leader node while there is a sensor node not included in the chain for each nodex which is not in a chain for each pair of nodes (nodej, nodej+1) which are member of the chain construct dist-matrix[xi]=[dist(nodex, nodei)]2+ [dist(nodex,nodei+1)]2 - [dist (nodei,nodei+1)]2 choose the node which has minimum valued distance-matrix and insert in between nodei and nodei+1

iii) Voronoi Cell management: After constructing the voronoi diagram with respect to the tentative leaders, voronoi cells are compared against a threshold value. If the cell is found too small it is merged with one of its neighbour cell, on the other hand if a cell becomes too large we split the cell into two cells and elect new leaders for new cell using the same steps followed in phase (i). In our simulations we put (avg _ cell _ size r 20%) as threshold value. Figure 6 shows an example of merging two cells into one.

Fig 8: Chain construction algorithm inside a voronoi cell

iv) Constructing chain in a voronoi cell When all the voronoi cells are fixed, tentative leaders are converted to leaders of chains. They declare themselves as leaders and initiate the process of chain formation. A single chain is constructed in a voronoi cell. The chain formation starts by the leader node. Each time a node is selected as the next member of chain depending on its contribution to d2 (where d is the distance between two nodes along the chain). Thus, a new member node increases to the minimum possible extent compared to the old chain. In this process, the chain may be broken to insert the new node to the chain. The algorithm is given in figure 8, and illustrated with an example in figure 7.

IV. ANALYSIS AND SIMULATION RESULTS The proposed Voronoi diagram (VD) construction algorithm is very efficient. Although the construction of VD’s is a long-studied topic in the fields of computational geometry, computing the VD in a distributed fashion, especially in wireless sensor networks, is a relatively new topic. In wireless sensor networks, the challenges of distributed computation are added to the vulnerability of the network such as, energy limitations, wireless link failures, and low bandwidth. Therefore, not only time and space of computation are important, but efficiency in terms of power consumption, bandwidth usage, and fault tolerance. It is noteworthy that we do not need to construct Voronoi diagram with all deployed sensors. Only very few (5% to

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8%) of the nodes take part in this process. In addition, a node does not require to collect broadcast messages, but from only its neighbours. Our chain construction algorithm always guarantees to construct a chain that has lowest d2 value. We know in wireless communication transmission energy is directly proportional to the square of distance between sender and receiver nodes. Thus, our chains consume lowest transmission energy. Voronoi cells keep the chain lengths under restriction. None of the chains created is too large or too small. In addition, the distance between any two nodes in a chain is restricted by the Voronoi edges. In this way the energy consumption of constructed nodes are evenly distributed. This affects the lifetime of the networks. Following section describes some of our simulation results.

Here d is the distance between sender and receiver measured in meters. In case of receiving message, the energy consumption equation is given by Equation 2.

E rx (k )

(2)

E elec * k

Simulation results: Our first simulation experiment calculates the total chain length among PEGASIS, COSEN and our proposed algorithm. In PEGASIS, only one chain is constructed, whether in COSEN and our proposed Voronoi diagram based algorithm we create multiple chains. Obviously if the total chain length can be kept low, more energy can be saved. Figure 9 shows the comparison among PEGASIS, COSEN and our proposed Voronoi diagram based algorithm.

Simulation environment and parameters:

n

We use Total chain length =

We compare our simulation results with PEGASIS, EPEGASIS and COSEN in terms of total chain length, total energy consumption, average transmission delay and network lifetime. We developed our simulation program written in object oriented programming language C++. We consider a network of 100 nodes and a fixed base station. The nodes are placed randomly in a place of 50 meter × 50 meter and the BS is located at (25, 150). We use Cartesian coordinates to locate the sensors. We assume each sensor starts with one Joule of initial energy. In practice, it is difficult to model energy expenditure in radio wave propagation. Therefore, in order to measure the energy expenditure in the network, we choose to use the same simplified radio model used in PEGASIS. In PEGASIS, the first order radio model is used, and it is assumed that the sources of energy dissipation are the transmitter and receiver. The transmitter dissipates energy to run radio electronics and power amplifier, whereas the receiver dissipates energy to run the radio electronics. They approximate that the transmitter amplifier requires Eamp=100 pJ/bit/m2 to amplify the signal at an acceptable signal to nose ratio (SNR). In addition energy required in running transmitter and receiver electronics are equal and given by Etx-elec=Erx-elec=Eelec=50nJ/bit. Moreover, the energy cost for data aggregation is considered as 5nJ/bit/message [16]. The bandwidth of the channel is set to 1 Mb/s [17]. In our experiments, each data message is 2000 bits long and information-processing time in a node is taken between 5 to 10 milliseconds [17]. The medium is assumed symmetric such that the energy required for transmitting a message from nodes A to B and from B to A are same at a fixed SNR. Therefore, we can say, for free space propagation loss, energy dissipation is certainly dominated by the long distance transmissions.

¦ Chain

i

where n is the

i 1

number of chains constructed. CHIRON claims to save more energy than PEGASIS and EPEGASIS. However, the main problem of CHIRON is that, as we mentioned earlier, if the number of cell is higher some chains becomes too large. Our proposed algorithm ensures that each chain is restricted by size. In a network of around 100 nodes some chain constructed by CHIRON may have only few (2 or 3) nodes while other chain may have (20 to 24) nodes. However, in our protocol all the chains have almost same number of nodes. That means scheduling like TDMA becomes more efficient in our design. Figure 10 shows the comparison between CHIRON and our proposed algorithm in respect of number of nodes per chain. Figure 11 shows the comparison of lifetime among PEGASIS, EPEGASIS, COSEN and our proposed algorithm. Assuming the same data collection technique applied in Voronoi diagram based designed chains, we find the constructed chains offer better lifetime of the network.

Figure 9: Total chain length comparison

V. CONCLUSION In this paper, we propose a chain construction scheme for chain oriented sensor network. Constructed chains using the proposed scheme have the following characteristics: i) All constructed chains are restricted to a geographical are, thus all chains have an upper bound for the chain length. ii) The chains are optimal in chain lengths. These two important

Thus, the total transmission cost for a k-bit message is given by the Equation 1. (1) Etx (k , d ) E elec * k  E amp * k * d 2

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[4]

[5]

[6]

[7]

Figure 10: Number of nodes per chain

characteristics assures low interference and low energy consumptions. We compare our algorithm with other chain construction algorithms and find that the ‘total chain length’ of the proposed algorithm is smaller than that of other algorithms (30% smaller than PEGASIS and 22% smaller than COSEN). As energy consumption is directly proportional to the distance between source and destination nodes, our proposed algorithm saves more energy. The proposed scheme also constructs chains of similar sizes and put the restriction on the distance between any two consecutive nodes. Thus, energy consumption in our algorithm is evenly distributed. This contributes to the longer lifetime of the network.

[8]

algorithm design for energy-efficient wireless sensor networks,” in Proceedings of the 7th annual international conference on Mobile computing and networking, ser. MobiCom ’01. ACM, 2001, pp. 272–287. K.-H. Chen, J.-M. Huang, and C.-C. Hsiao, “Chiron: An energy-efficient chain-based hierarchical routing protocol in wireless sensor networks,” in Wireless Telecommunications Symposium, 2009. WTS 2009, 2009, pp. 1 –5. S. Lindsey and C. Raghavendra, “Pegasis: Power-efficient gathering in sensor information systems,” in Aerospace Conference Proceedings, 2002. IEEE, vol. 3, 2002, pp. 3– 1125 – 3–1130 vol.3. N. Tabassum, Q. Mamun, and Y. Urano, “Cosen: A chain oriented sensor network for efficient data collection,” in Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on, 2006, pp. 262 –267. [7] S.M. Jung, Y.-J. Han, and T.-M. Chung, “The concentric clustering scheme for efficient energy consumption in the pegasis,” in Advanced Communication Technology, The 9th International Conference on, vol. 1, 2007, pp. 260 –265. Z. Zhou, S. Das, and H. Gupta, “Variable radii connected sensor cover in sensor networks,” in Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004. 2004 First Annual IEEE Communications Society Conference on, 2004, pp. 387 – 396.

[9] Mao, S. and Hou, Y. T., “BeamStar: An Edge-Based

Approach to Routing in Wireless Sensor Networks,” IEEE Transactions on Mobile Computing, Vol. 6, Issue 11, pp. 1284 -1296, 2007. [10] Chen, W, Hou, J. and Sha, L, “Dynamic clustering for acoustic target tracking in wireless sensor networks”, IEEE Transaction on Mobile Computing, 3(3), pp. 258 – 271, 2004. [11] Byers, J., Considine, J. and Mitzenmacher, M., “Geometric generalizations of the power of two choices. In 16th ACM Symp. on Parallel Algorithms and Architectures, 2004. [12] Fortune, S.,“A sweepline algorithm for voronoi diagrams. In SCG ’86: 2nd Annual Symposium on Computational Geometry, ACM Press, pp. 313 – 322, 1986. [13] Sharifzadeh, M. and Shahabi, C., “Supporting spatial aggregation in sensor network databases.” In 12th International Symposium of ACM GIS, 2004. [14] Priyantha N., Chakraborty, A. and Balakrishnan, H., “The cricket location-support system”. In 6th Annual International Conference on Mobile Computing and networking, pp. 32– 43, 2000. [15] Girod, L. and Estrin, D., “Robust range estimation using acoustic and multimodal sensing”, In IEEE/RSJ Conference on Intelligent Robots and Systems. Vol 3, pp.1312 – 1320, 2001. [16] Heinzelman, W. R., Chandrakasan, A., and Balakrishnan, H., “Energy-efficient Communication Protocolsrdfor Wireless Micro sensor Networks,” Proc. of the 33 Hawaii International Conference on System Sciences, 2000. [17] Kulik, J., Heinzelman, W. R. and Balakrishnan, H., “Negotiation-based protocols for disseminating information in wireless sensor networks", Wireless Networks, vol. 8: 169-185, 200

Figure 11: Lifetime comparison

REFERENCES [1] C.-Y. Chong and S. Kumar, “Sensor networks: evolution, opportunities, and challenges,” Proceedings of the IEEE, vol. 91, no. 8, pp. 1247 – 1256, 2003. [2] I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks,” Communications Magazine, IEEE, vol. 40, no. 8, pp. 102 – 114, Aug. 2002. [3] E. Shih, S.-H. Cho, N. Ickes, R. Min, A. Sinha, A. Wang, and A. Chandrakasan, “Physical layer driven protocol and

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