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Cube2012 Submission 359


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published in ACM CUBE 2012 pune

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Cube2012 Submission 359

  1. 1. Multihop/Direct Forwarding for 3D Wireless Sensor Networks Preety Sharma Sansar Singh Chauhan Sandeep Saxena Galgotias College of Engineering Accurate Institute of Management Galgotias College of Engineering and and Technology and Technology Technology Greater Noida, India Greater Noida, India Greater Noida, India sandeepsaxena4444@gmail.comABSTRACT well as replacement of the battery is not recommended. Therefore,Wireless Sensor Networks (WSNs) are limited in their energy, the usage of limited battery must be estimated accordingly [14].computation and communication capabilities. Energy efficiency WSN employs various data forwarding schemes. These schemes[3] and balancing is one of the primary challenges for Wireless are required to deliver the sensed data to the destination. TheySensor Networks since the sensor nodes cannot be easily play an important role in increasing the lifespan of a network [3].recharged once they are deployed. The consumption of energy is Moreover, they reduce the energy consumption of the node andmajorly determined by the data forwarding schemes. These network as a whole. There are a number of data forwardingschemes are employed to transmit the sensed information to the techniques, like Closest Forwarding (CF), Direct Forwardingfinal destination. In this work, we analyze the behavior of (DF), Multihop Forwarding (MF) and Multihop/DirectMultihop/Direct Forwarding (MDF) [6] scheme, when applied to Forwarding (MDF).the sensor network deployed in three dimensional fields. Theresults of simulation are then compared with some other data In this work, we focus on the Multihop/Direct Forwardingforwarding schemes. Simulation results show that MDF scheme in technique [6] to be implemented for 3D Wireless Sensor3D can balance the energy consumption for all sensor nodes. The Networks. These sensors are assumed to be deployed in threenetwork lifetime is prolonged in case of MDF compared to other dimensional fields. We have used an approach wherein we need todata forwarding techniques when applied in three dimensional find the optimum transmission schedule of the nodes. This can befields. determined by dividing the packet flow of each node so that the battery lifespan can be increased. The results of MDF are thenKeywords compared with different forwarding schemes on the basis ofWireless Sensor Networks, energy consumption, network lifetime, network lifetime and energy consumption. We have considered aMDF 3D Network Model with uniformly distributed nodes such that the projection of the 3D Network resembles a conical view. The Base1. INTRODUCTION Station is assumed to be present at the apex of the cone. This 3DAdvancement in the field of Wireless Communication has lead to Network Model has its applications in the field of surveillance.the development of Wireless Sensor Networks (WSN) [1]. These Our contributions in this study are twofold. First, we have derivednetworks consist of small devices known as nodes. Each sensor equations for packet flow division rules for 3D Wireless Sensornode has a processor, radio, sensor and built-in battery. A node Networks. Second, simulations for the evaluation of MDF schemesenses the region over which it is deployed and transmits the in 3D are carried out.sensed data to the Base Stations. The stations may be single or The rest of the paper is organized as follows: In section 2,multiple depending upon the nature of WSN applications. The foundation and problem composition are presented. We thenmajor contribution of the Wireless Sensor Networks lies in present the various forwarding schemes in section 3. In section 4,commercial as well as industrial areas. Some applications of WSN MDF technique in case of 3D Wireless Sensor Networks isare habitat monitoring [2], monitoring of an active volcano [13], discussed and section 5 presents and analyzes the simulationstructural health monitoring, forest fire and surveillance system results. Finally, we conclude our work in Section 6.[9] etc. The success of any network is determined by howefficiently it delivers data to the destination. Similarly, success ofWSN is determined by how efficiently the nodes deliver the 2. FOUNDATION AND PROBLEMsensed information to the Base Station. The major issue with COMPOSITIONWSN is the dependency of each node on the battery for its We consider a 3D Wireless Sensor Network in which sensoractivities, which is severely limited. In most cases, recharge as nodes are uniformly distributed. The projection of the nodes is such that they form a conical appearance. The Base Station lies at the apex of the cone. The data generation rate of each node is one packet per unit time. The network has been divided into several logical nodes. The nodes lying at a distance i, from the Base Station constitutes the logical node i. This logical node consists of all the nodes lying at or inside its circumference. The 3D representation of WSN can be explained with the help of figure 1. In case of 3D WSN, we assume that the whole network is
  2. 2. divided into logical nodes and each logical node is at 1 unit 3. SOME DATA FORWARDINGdistance from its consecutive logical node. The number of nodesin any logical node is proportional to the difference in the surface TECHNIQUES AND THEIR ENERGYareas of the subsequent logical nodes [4][12]. Therefore, the CONSUMPTIONnumber of nodes at any logical node l having radius rl where l is There are numerous data forwarding techniques used in WSNthe distance of the node from the Base Station is given by: depending upon the requirement. The amount of energy consumed to forward the data is different for different techniques. We will (1) discuss the various techniques and present the energy consumption of the nodes for the 3D network.2.1 Assumptions  Any kind of transmission loss is not considered in the 3.1 Closest Forwarding Technique: This is the analyses. forwarding technique in which each sensor node forwards its  Receiving node does not consume extra energy in packets to its closest node towards the Base Station as shown in packet reception [7]. figure1. In this scheme, the energy consumption of each node is  Each node has the capability to adjust its transmission different. The node closest to the Base Station handles the range. maximum amount of packets [11]. Therefore, it consumes  The node can send the packet directly to the Base maximum amount of energy [9]. For any logical node u, lying at a Station if required [10]. distance u from the Base Station, the energy consumption is given by:  The distance between each logical node is assumed to ECF[u] = (3rN2 + 3 r(N-1)2 +---+ 3ru2)(k0 +1w) (5) be 1 unit.2.2 Notations  Nodes that are „x‟ units away from the Base Station are grouped into single logical node „x‟.  N is the total number of logical nodes, excluding the N Base Station. The logical nodes are indexed in the increasing order from their distance to the Base Station. N-1 The logical node closest to the sink has the least index with the index „0‟ assigned to the Base Station. N-2  r is the radius of the logical node farthest from the Base Station .i.e. lying at a distance N from the base station.  Pu,v. is the rate of packet flow from logical node u to logical node v. 3  The energy spent in sending one packet from logical node u to logical node v is given by 2 E = k0 + (u-v) w (2) where k0 is the energy constant. It includes the total 1 energy spent by the node in reception or being idle and w is the path loss exponent and its value is assumed to Base Station be 2 in this work[7][10].  The total energy consumption of node u is given by: Figure 1: Closest Forwarding Technique ETC[u] = + ] (3)  t is the optimal transmission range[8] where t )1/w) (4) 3.2 Direct Forwarding Technique: This is the forwarding technique in which each sensor node2.3 Problem Formulation forwards its packets directly to the Base Station. Therefore, Pu, v=0To evaluate the performance of the MDF scheme in a 3- except when v=0. The energy consumption of the nodes in the DFDimensional conical network. The network consists of nodes technique is also unbalanced. The energy consumption of thedeployed in such a way that the base station is present at the apex node increases with increase in distance from the Base Station. The node farthest from the Base Station consumes the maximumof the network. In order to evaluate its performance under the amount of energy. Therefore, for any node u, the energyMDF scheme, we have to find out the packet flow rate, Pu,v. where consumption is given by:u, v {0, 1… N} such that the energy spent by the whole network EDF[u] = 3ru2(k0 + uw) (6)is minimized and the lifespan of the network is maximized where ru is the radius of the logical node u.[10][5]. The lifetime of the network in our work has been definedas the time when first node of the network runs out of energy.
  3. 3. MF scheme leads to much more balanced energy consumption as compared to CF and DF scheme. N 5 x 10 8 CF N-1 DF 7 MF N-2 6 Energy Consumption, E[u] 5 3 4 2 3 1 Base Station 2 Figure 2: Direct Forwarding Technique . 13.3 Multihop Forwarding Technique:This is the forwarding technique in which each node forwards its 0 5 10 15 20 25 30 35 40 45 50data packets to the node lying at the optimum hop distance, t Node index,utowards the Base Station as shown in figure 3.The logical node Nis forwarding its packets to the node (N-t), which is at hop Figure 4: Comparison of node energy consumption for CF, DFdistance t. Therefore, Pu, v=0 except when u-v= t or when u<t and and MF techniques (N=50, k0=100)v=0EMF[u] = (3rN2 + 3r(N-t)2 + …+3ru2)(k0+min(t,u)w) (7) 4. MULTIHOP/DIRECT FORWARDINGwhere ru is the radius of the logical node u. (MDF) FOR 3D WSN In the Multihop/Direct Forwarding Scheme each logical node x divides its data packets into two components. The first component N is sent to the logical node which is t distance away from x, denoted by Px, (x-t). The second component is sent directly to the Base Station denoted by Px,0 . If the logical node lies at a distance N-t which is less than the optimal transmission range t i.e. x ≤ t then all the packets are sent directly to the Base Station. The number of packets generated by each logical node is equal to the number of nodes present. The number of nodes in a logical node is 3rl2 where rl is the radius of the logical node (as calculated in eq(1)). Since the number of nodes in each logical node is different, t+1 therefore each logical node is heterogeneous in terms of energy reserve as well as packet generation. The energy reserve and the number of packets are proportional to the number of nodes at that 1 logical node. Hence, all the nodes which are at the same distance from the Base Station are grouped into a single logical node having energy reserve and as the total number of packets generated. Base Station The logical nodes in the whole network are divided into t Figure 3: Multihop Forwarding Technique subgroups. Each logical node except the last node in a single subgroup is separated from its consecutive logical node by aWe calculated the energy consumption of different logical nodes distance t. The last node may be at a distance lesser than t units toas per the above mentioned schemes (CF, DF and MF). The the Base Station. We further assume that each subgroup has itsresults are shown in Figure 4. We have observed that the node own Base Station. Hence, the number of Base Stations is equal toenergy consumption of the DF scheme increases with increase in the number of subgroups i.e. t. Each subgroup sends its packetsthe distance from the Base Station. The CF scheme exhibits a separately to the Base Station. We will analyze the behavior ofreverse trend. In the case of CF, node energy consumption only one of these subgroups as shown in figure 5 since each ofincreases with decrease in the distance from the base station. The them is essentially the same.
  4. 4. Since the energy consumption of nodes 2x and x must be same. Therefore: zt P2t,t * (k0 + tw) + P2t,0* (k0 + 2tw) = Pt,0* (k0 + tw) 4 P2t, t + P2t, 0 * (k0 + 2tw) = 4Pt,0 (16) (z-1)t (k0 + tw ) Therefore, eq (15) can be rewritten as: G2 = (17) (z-2)t Similarly, combining eq (15) and eq (16), we get: H2 = (18) 2t Hence: G2 + H2 = (19) t From eq (13), we get: Hx = Pt, 0 – Gx (20) Base Station We can calculate the value of Pxt, 0 from eq (12b): Pxt, 0 = [x2* Hx ] (21) where x=2, 3…z. Figure 5: Representation of a subgroup in a 3D network. Putting the value of x=2 in eq (21), we get: implementing MDF scheme P2t,0 = [3Pt,0 + ] (21a)We initiate the study of 3D WSN, by analyzing the behavior ofone of the subgroups. In a subgroup, the total number of logical Similarly, substituting the values for x=3, 4…z, we get eq (21) innodes that are sending data to the Base Station is denoted by z the form:where z = (8) Pxt, 0 = mxPt, 0 + nx (22)where N is the total number of logical nodes and t is the optimum The boundary condition may be obtained through traffichop distance. If we analyze any logical node say x where 1<x<z, generation of all nodesthen the packet flow of node xt can be represented as: = = (23)P(x+1) t, xt + = Pxt, (x-1) t + Pxt ,0 (9)where xt = 3(rxt) 2 i.eThe energy spent in sending a packet from node (x+1) t to node xt =Pt, 0 = (24)is given by: Therefore:P(x+1) t, xt *(k0 + tw) + P(x+1) t, 0 *(k0 +(x+1) tw) (10) –Therefore, in order to balance the consumption of the energy in Pt, 0 = (25)the network, we need to make sure that the energy spent by logical and from eq (22) and eq (25)nodes (x+1) t and xt must be equal. Hence: Pxt,(x-1)t = x2 Pt,0 – Pxt,0* (26) = In order to apply MDF scheme, a node u needs to know its index (11) i.e. its distance from the base station. Therefore, the value of x in aWe can define subgroup can be calculated as: x = (27) where is the ceiling function returning the smallest integer thatGx = (12a) is not smaller than n. In order to calculate the energy consumption of any logical nodeHx = (12b) say u, we are required to know the index of that node. The indexwhere x= 2, 3 4… z of node u can be greater than or less than the optimum forwarding distance t, which results in two cases:Therefore, eq (12a) and (12b) can be rewritten as: Case A: When u>t,Gx+1 + Hx+1 = Gx + Hx =…..=G3 + H3 = G2 + H2 (13) ETC [u] = Pxt, 0 (k0 + uw) + Pxt,(x-1)t(k0 + tw ) (28) where Pxt,0 , Pxt,(x-1)t and x are given by eq (22) ,(26) and (27)Similarly, eq (11) can also be rewritten as: respectively.Gx = Gx-1 + P(x-1) t, 0 (14) Case B: when u<t, ETC [u] = Pt, 0(k0 + uw) (29) where Pt,o is given by eq(25) .Putting x=1 in eq (9), we get:P2t, t + = Pt ,0 (15)
  5. 5. 5. RESULT ANALYSIS In order to show the optimality more clearly in figure 7, weThis section provides some numerical and simulation results on present normalized energy consumption, which is calculated asthe MDF scheme. The MDF scheme in 3D has been evaluated and the average energy consumption divided by the minimum value ofcompared with other techniques by using MATLAB. We have energy consumption along all possible t, i.e. E/Emin.. It can be seenused the following model for simulation: that the energy consumption is higher, at small as well as largerWe have assumed a 3D network. The nodes are deployed in a values of t. The least value of energy consumption is at theconical projection. All the nodes lying at the same distance from optimum hop distance which is calculated in eq.(4). The resultsthe Base Station are grouped into a single logical node. N is the are shown in Figure number of logical nodes. Each logical node contains 3*rad^2number of nodes (where rad is the radius of the logical node).These nodes are assumed to generate 1 data packet per unit time. 3.5The distance between any two consecutive logical nodes is 1 unit.We have compared the results of MDF scheme with other K0=50schemes such as MF, DF and CF in terms of energy consumption K0=100 3and network lifetime. K0=200 Normalized energy Consumption , En 25 2.5 22.5 DF 20 MF 2 MDF Normalized Energy Consumption, En 17.5 1.5 15 12.5 1 10 7.5 0.5 5 10 15 20 25 30 35 40 45 50 hop distance, t 5 2.5 Figure 7: Normalized Energy Consumption of different hop distance t, when MDF scheme is employed and k0=100 0 25 30 35 40 45 50 Node Index,u .Figure 6: Comparison of node energy consumption for the DF, 3.5the MF, and the MDF schemes (N = 50, k0 = 100). N=50Figure 6 shows the energy consumption of logical nodes under N=100 N=200MF, DF and MDF schemes. We have shown the results in terms 3of Normalized Energy Consumption. Each normalized value ofenergy consumption of a logical node is actually the ratio of the Normalized Energy Consumption, Enfractional consumption of total energy to the minimum value offractional energy consumption along all logical nodes. We have 2.5observed that the fractional consumption of total energy of eachlogical node is equivalent in case of MDF whereas in case of MF,it decreases with increase in node index. The DF scheme in caseof 3D model follows the same trend as in one-dimensional model. 2The fractional consumption of total energy decreases as thedistance from Base Station increases.In figure 7, we evaluate the values of energy consumption and 1.5present the Normalized Energy Consumption of the MDF schemeas a function of t for different values of k0. The number of logicalnodes is fixed at N= 50. It is observed that the value of optimumhop distance t, increases with increase in k0. 1 5 10 15 20 25 30 35 40 45 50 hop distance,tWhen MDF scheme is implemented in 3D, we have evaluated thevalues of energy consumption with different hop distance and Figure 8: Normalized energy consumption of different hopk0=100. We have analyzed the results with different values of N. distance, t (N = 50)
  6. 6. Figure 9 shows the network lifetime for MF, CF and MDF [4] Chang, N. and Liu, M. 2004. Revisiting the TTL-basedforwarding techniques. It can be seen that the network lifetime of controlled flooding search: Optimality and randomization. InMDF scheme is better as compared to other techniques. The Proceedings of the 10th Annual ACM/IEEE Internationallifetime of the CF technique is significantly shorter than all other Conference on Mobile Computing and Networkingschemes. This is because of the imbalance energy consumption of (MobiCom ‟04). IEEE, 85–99.nodes in the network. We have defined network lifetime, in Figure [5] D. Ganesan, A.Cerpa, W. Ye, Y. Yu, J. Zhao, D. Estrin,9, as the time when the first node in the network runs out of “Networking Issues in Wireless Sensor Networks”, Journalbattery energy. of Parallel and Distributed Computing, Vol. 64 (2004) , pp. 799-814. 180 CF [6] Deng, J. 2009. Multihop/Direct Forwarding (MDF) for Static MF MDF Wireless Sensor Networks. ACM Trans. Sens. Networks, 5, 160 4, Article 35 (November 2009) 140 [7] Feeney, L. M. and Nilsson. 2001. Investigating the energy consumption of a wireless network interface in an ad hoc networking environment. In Proceedings of the 20th 120 Network Lifetime ,TL Conference of the IEEE Communications Society (Infocom‟01). Vol. 3. IEEE, 1548–1557. 100 [8] Gao, J. L. 2002. Analysis of energy consumption for ad hoc wireless sensor networks using a bit-meter-per-joule metric. 80 IPN Progress Report 42-150, California Institute of Technology, Jet Propulsion Lab. 60 [9] G. Simon, M. Maroti, A. Ledeczi, G. Balogh, B. Kusy, A. Nadas, G. Pap, J. Sallai, K. Frampton, “Sensor network- 40 based counter sniper system”, In Proceedings of the 2nd International Conference on Embedded Networked Sensor 20 10 20 30 40 50 Systems (Sensys), Baltimore, MD, 2004 Energy Constant ,k0 [10] Heinzelman, W. B., Chandrakasan, A. P., and Balakrishnan, H. 2002. Application-specific protocol architecture forFigure 9: Network Lifetime for MF, MDF and CF schemes wireless micro sensor networks. IEEE Trans. Wireless. Comm. 1, 4, 660–670. [11] J. Li, P. Mohapatra, “Analytical Modeling and Mitigation6. CONCLUSION Techniques for the Energy Hole Problem in SensorIn this paper, we presented the MDF technique in case of 3D Networks”, Pervasive Mobile Computing, 3(3):233-254,WSN and presented the network lifetime and energy consumption June 2007.of the nodes. We have identified that the MDF scheme performs [12] Perillo, M., Cheng, Z., and Heinzelman, W. 2005. Anclose to some very efficient but complex techniques in terms of analysis of strategies for mitigating the sensor network hotenergy consumption. The network lifetime of MDF scheme is far spot problem. In Proceedings of the 2nd Annualbetter as compared other schemes when evaluated in 3D. Thus, it International Conference on Mobile and Ubiquitouscan be said that MDF scheme shows consistent performance even Systems: Networking and Services. IEEE, 474– case of 3D. [13] G. Werner-Allen, K. Lorincz, M. Ruiz, O. Marcillo, J. Johnson, J. Lees, M. Welsh, “Deploying a WirelessREFERENCES SensorNetwork on an Active Volcano”, IEEE Internet[1] Akyildiz, I. F., SU, W., Sankarasubramaniam, Y., and Computing, Special Issue on Data-Driven Applications in Cayirci, E. 2002. A survey on sensor networks. IEEE Comm. Sensor Networks, March/April 2006. Mag. 40, 8, 102–114. [14] Sankar, A. and Liu, Z. 2004. Maximum lifetime routing in[2] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler and wireless ad-hoc networks. In Proceedings of the 23rd J. Anderson, “Wireless Sensor Networks for Habitat Conference of the IEEE Communications Society Monitoring”, Proc. ACM Workshop on Wireless Sensor (Infocom’04). IEEE, 1089–1098 Networks and Applications, pp. 88-97, Atlanta (USA),September 2002[3] A. Warrier, S.Park J. Mina and I. Rheea, “How much energy saving does topology control offer for wireless sensor networks? – A practical study”, Elsevier/ACM Computer Communications, Vol. 30 (14-15), Pp. 2867-2879, 15 October 2007