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Wireless sensor networks (WSN) have been widely used in various applications. …

Wireless sensor networks (WSN) have been widely used in various applications.
In these networks nodes collect data from the attached sensors and send their data to a base
station. However, nodes in WSN have limited power supply in form of battery so the nodes
are expected to minimize energy consumption in order to maximize the lifetime of WSN. A
number of techniques have been proposed in the literature to reduce the energy
consumption significantly. In this paper, we propose a new clustering based technique
which is a modification of the popular LEACH algorithm. In this technique, first cluster
heads are elected using the improved LEACH algorithm as usual, and then a cluster of
nodes is formed based on the distance between node and cluster head. Finally, data from
node is transferred to cluster head. Cluster heads forward data, after applying aggregation,
to the cluster head that is closer to it than sink in forward direction or directly to the sink.
This reduction in distance travelled improves the performance over LEACH algorithm
significantly.

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  • 1. A New Energy Efficient Clustering based Communication Protocol for Wireless Sensor Networks Aman Singhal1 and K.K. Shukla2 1 Department of Computer Science & Engineering IIT (BHU), Varanasi, India Email: aman.singhal.cse09@iibhu.ac.in 2 Department of Computer Science & Engineering IIT (BHU), Varanasi, India Abstract— Wireless sensor networks (WSN) have been widely used in various applications. In these networks nodes collect data from the attached sensors and send their data to a base station. However, nodes in WSN have limited power supply in form of battery so the nodes are expected to minimize energy consumption in order to maximize the lifetime of WSN. A number of techniques have been proposed in the literature to reduce the energy consumption significantly. In this paper, we propose a new clustering based technique which is a modification of the popular LEACH algorithm. In this technique, first cluster heads are elected using the improved LEACH algorithm as usual, and then a cluster of nodes is formed based on the distance between node and cluster head. Finally, data from node is transferred to cluster head. Cluster heads forward data, after applying aggregation, to the cluster head that is closer to it than sink in forward direction or directly to the sink. This reduction in distance travelled improves the performance over LEACH algorithm significantly. Index Terms— Wireless sensor networks (WSN), clustering algorithm, data aggregation, energy efficient communication. I. INTRODUCTION Wireless Sensor Networks (WSN) have drawn a lot of attention in recent times due to their wide range of applications in various fields. WSN can be deployed in places where human presence is not feasible such as military and environment monitoring [1]. WSN are also useful in field monitoring to observe any abnormal growth behaviour in different types of plants. In WSN, number of nodes may vary from a few hundred to several thousands. Nodes send data collectively to a base station in some predefined manner. Each node may have several parts including, radio transceiver, microcontroller and a battery [5]. Usually, the battery has limited power supply, and reception and transmission of data spends a considerable amount of energy. Also, reinstallation of batteries in so many nodes is not feasible. So this process of data reception and transmission should be as energy efficient as possible so as to maximize the lifetime of WSN. Also sensors in WSN have lot of data and for user to make use of that data, aggregation techniques [3, 4] are needed to be applied. So, in order to conserve energy, nodes can form clusters and instead of sending data directly to sink, each node can send their data to the cluster head. Then cluster head can apply data aggregation techniques to remove redundancy and then can DOI: 02.ITC.2014.5.69 © Association of Computer Electronics and Electrical Engineers, 2014 Proc. of Int. Conf. on Recent Trends in Information, Telecommunication and Computing, ITC
  • 2. 123 send the data to the sink. This is the idea used in LEACH [2] protocol. There are various energy efficient communication protocols available for WSN. We make use of improved LEACH to select cluster head as the first step of our algorithm. Then we apply data aggregation technique at each cluster head and then each cluster head forwards its data to the nearest cluster head in forward direction or to the sink. In the rest of the paper first the preliminaries are described, then the proposed scheme is illustrated and in the last section the simulation results of proposed scheme are discussed. II. PRELEMINARIES A. Wireless Sensor Network Model The sensor network model considered in this project has following characteristics:  The sensor field consists of n sensor nodes scattered randomly in a fixed sized field. Once deployed, positions of these sensors are fixed in the field.  Sink is at distance ds far away from the sensor field whose position is also fixed.  Each of these sensors is provided an initial fixed energy.  Each sensor performs one of the two tasks: sensing the data or being the cluster head. Data sensed by the sensor is forwarded to the cluster head of the cluster to which it belongs, which in turn aggregates the data and forwards that data to either sink or a cluster head which is closer to it than sink. B. Energy Model [2] While forwarding and receiving the data there is some amount of energy spent at each node. This energy depends upon the distance travelled by the data and number of bits in the data. Model (Fig. 1) for energy expansion is given by: To transmit a k bit message at distance d, energy expansion at transmitter is: ETx(k, d) = ETx-elec(k) + ETx-amp(k, d) (1) ETx(k, d) = Eelec*k + Efs*k*d2 (2) To receive this message, energy expanded at receiver is: ERx(k) = ERx-elec(k) (3) ERx(k) = Eelec*k (4) If distance d>dth, the energy dissipated is given by: ETx(k, d) = Eelec*k + Eamp*k*d4 (5) where, dth is given by: dth = √( Efs / Eamp ) (6) C. Data Aggregation Model [3] For data aggregation, simple model is used. This model simply reduces the redundant data. In this model, if there are x nodes in a cluster and each of the node sends a packet of same length to cluster head, then at cluster head there are x packets each of same length. After data aggregation, cluster head produces ζ(x) packets of same length. So the number of packets in the output is a function of number of packets in the input given by: ζ(x) = m*x + c (7) where, c: overhead of aggregation m: the compression ratio. We can say that 0 ≤ m ≤ 1 because after aggregation, number packets in the output will be lesser than the number of packets in the input. If m = 0 and c > 0 then it corresponds to the case when any number of packets can be compressed into a single packet of fixed length [3]. We are considering this scenario into our simulations.
  • 3. 124 Energy expansion for data aggregation is given by: EDAx(k) = EDA*k (8) where, EDA represents energy for data aggregation. Figure 1: Energy model used [2] III. PROPOSED SCHEME The proposed scheme can be subdivided into following phases: A. Selection of Cluster-Head After the deployment of nodes in the sensor field, cluster heads are chosen according to improved LEACH (QBCDCP)[4]. In this scheme, every node i selects a random number between 0 and 1. If this number is found below a threshold T(i), the node is decided as a cluster-head for current round. In this case, the threshold T(i) is defined as: T(i) = * _ _ + ( ∗ ) 1 − _ _ if iϵG (9) 0 otherwise where, P : the desired percentage of cluster heads G : the set of nodes that have not been cluster heads in last 1/P rounds rs : the number of successive rounds in that a node has not been cluster head. It is reset to 0 after a node becomes cluster head in a round and is increased for each round in which it doesn’t become cluster head. r : current round. Ei_current : current energy of node i Ei_max : initial energy of node i Each node, that has elected itself as a cluster-head for the current round, broadcasts an advertisement message to the rest of the nodes. B. Cluster Set-up Phase Each node calculates its distance from each of the cluster-head. It will belong to the cluster for which distance from that cluster head and node is minimum. After each node has decided to which cluster it belongs, it must inform the cluster head node that it will be a member of the cluster. C. Data transmission Phase Once the clusters are created and the schedule is fixed, data transmission can begin. If we assume that each node always has data to send, then they send it during their allocated transmission time to the cluster head.
  • 4. 125 Data aggregation is applied at each cluster head. But a different strategy is applied before sending the data collected to the sink. If the distance of cluster head is greater than threshold distance dth, then following strategy is used:  Distances between this cluster head and all other cluster heads, whose distance from the sink is lower than this cluster head’s distance from the sink, are calculated.  Minimum of these distances is found.  All of the data at this cluster head is forwarded to the cluster head whose distance is found minimum in the last step. Otherwise, the data collected is sent from the cluster head to the sink directly. In the algorithm, following symbols are used Array rs[], where rs[i] represents number of rounds node i has not been cluster head. Array distance[], where distance[ch] represents distance between cluster head ch and sink or another cluster head to which ch forwards its data. Array E[], where E[i] represents remaining energy of node i. Number of nodes is n. Algorithm- improved wireless communication protocol 1. for each round r, from 1 to rmax do 2. for each node i do 3. calculate T(i) 4. select a random number between 0 and 1. 5. if T(i) is greater than this number then 6. select this node as a cluster-head and set rs[i] = 0; 7. else 8. rs[i] ← rs[i] + 1; 9. end 10. end 11. for each cluster-head ch do 12. distance[ch] = distance(ch, sink) 13. if distance(ch, sink) > dth then 14. mindist ← ∞ 15. for each ch1 other than ch do 16. if distance(ch1, sink) < distance(ch, sink) then 17. mindist ← min(distance(ch1, ch), mindist) 18. end 19. end 20. if mindist ≠ ∞ then 21. assign forward_cluster[ch] as ch1 with mindist 22. distance[ch] ← distance(ch1, ch) 23. end 24. end 25. if distance[ch] > dth then 26. E[ch] ← E[ch]–{(Eelec+EDA)*k + Eamp*distance[ch]4 } 27. else 28. E[ch] ← E[ch]–{(Eelec+EDA)*k + Efs*distance[ch]2 } 29. end 30. end 31. for each node i do 32. calculate distance(i, ch) for all ch 33. find min_dis as minimum of these distances and ch1 as cluster-head for which this min_dis comes 34. if min_dis > dth then 35. E[i] ← E[i]–(Eelec*k + Eamp*min_dis4 ) 36. else
  • 5. 126 37. E[i] ← E[i]–(Eelec*k + Efs*min_dis2 ) 38. end 39. E[ch1] ← E[ch1] – (Eelec+EDA)*k 40. end 41. end Functions used: distance(p1, p2): returns the Euclidean distance between p1 and p2. min(a,b): returns the minimum of a and b. In the above algorithm, Lines 3-9 represent cluster-head selection phase. Complexity of this phase O(n). Lines 13-24 represent, for cluster head ch, selection of nearest cluster head, if sink is located far than dth. Lines 32-33 represent, for node i, selection of cluster head nearest to i. D. Illustration Figure 2: Algorithm illustration Proposed technique can be best illustrated with the help of Fig. 2. If we suppose that all shaded nodes are cluster-heads and distances between A and Sink, C and Sink, and D and Sink all are greater than dth. These distances are shown by shaded edges in the figure. Now each of these cluster-heads, A, C and D, will find a cluster-head which is nearest to them and has distance to sink lower than their own distance to Sink. For A, that cluster-head comes out to be B. Now, instead of sending directly to Sink, A will send all data collected at A to B. Similarly, distance between C and sink is also greater than dth. So, C will find a cluster-head whose distance from Sink is lower than this distance. For C, that cluster-head comes out to be D. Now, instead of sending directly to Sink, C will send all data collected at C to D. Now, D is also having its distance from sink greater than dth. This time D will find the forwarding cluster- head as E. Now, instead of sending directly to Sink, D will send all data collected at D to E. These paths are shown by dark arrows in the network diagram. As it is demonstrated in the figure, distance that had to be covered from a cluster-head to sink has been reduced significantly. This is the main reason for the improvements observed in the proposed algorithm. IV. SIMULATION RESULTS Implementation of the proposed technique was simulated using MATLAB. Values taken for different parameters were:  n (no of nodes) = 100  Size of field = 100x100  Location of sink = (150, 50) and (200, 50)  Initial energy = 1 J  P = 0.1 or (10%)
  • 6. 127  Eelec = 50 nJ/bit  Efs = 10 pJ/bit/m2  EDA = 5 nJ/bit/message  Eamp = 0.0013 pJ/bit/m4  dth = √( Efs / Eamp )  k = 4000  c (overhead of aggregation) = 1  m (compression ratio) = 0 These parameters are similar to the ones used in [6]. Using these parameters values experiments were performed and following results were found (Fig. 3 & 4). On comparison of the results of proposed scheme with that of LEACH, we can easily say that proposed scheme is showing almost 160% improvement in first case while 90% in second case. For these simulations we have taken compression ratio as 0. V. CONCLUSION AND FUTURE WORK In this paper we described an energy efficient communication protocol for wireless sensor networks. First we introduced the need of energy efficient communication protocol. Then we discussed the model used. Then the proposed scheme was described and finally results obtained from simulation of proposed scheme with the help of MATLAB were shown. As part of future scope, modifications in the selection of cluster head can be done. Also, security aspects of WSN can be included while selecting cluster head. Figure 3: Bar plot of results when sink is at (150, 50) Figure 4: Bar plot of results when sink is at (200, 50) 0 500 1000 1500 2000 2500 3000 3500 4000 4500 First Node Dead 25th Node Dead 50th Node Dead 75th Node Dead 100th Node Dead Lifetime(innumberofrounds) LEACH Proposed 0 500 1000 1500 2000 2500 3000 First Node Dead 25th Node Dead 50th Node Dead 75th Node Dead 100h Node Dead Lifetime(innumberofrounds) LEACH Improved
  • 7. 128 REFERENCES [1] I.F. Akyildiz, Weilian Su, Sankara subramaniam, E. Cayirci, “A survey on sensor networks”, IEEE Communications, Volume: 40, August 2002, pp.102-114. [2] Wendi Rabiner Heinzelman, Anantha Chandrakasan, and HariBalakrishnan, “Energy-Efficient Communication Protocol for Wireless Microsensor Networks”, Proceedings of the 33rd Hawaii International Conference on System Sciences, Volume: 2, Jan 2000, pp.1-10. [3] Vivek Mhatre, Catherine Rosenberg, “Design guidelines for wireless sensor networks: communication, clustering and aggregation”, Elsevier’s J on Ad Hoc Networks, 2 (2004) pp. 45–63. [4] M.Sheik Dawood, S.Sadasivam, G.Athisha, “Energy Efficient Wireless Sensor Networks based on QoS Enhanced Base Station controlled Dynamic Clustering Protocol”, International Journal of Computer Applications (0975 8887), Volume: 13, Jan 2011, pp.44-49. [5] Wireless Sensor Network: http://en.wikipedia.org/wiki/Wireless_sensor_network [6] Ezzati Abdellah et al, “Advanced Low Energy Adaptive Clustering Hierarchy”, (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 07, 2010, 2491-2497.