Enhanced Dual Level Fuzzy based Cluster Head Selection for Energy Efficient Wireless Sensor Networks

Transcription

1 Enhanced Dual Level Fuzzy based Cluster Head Selection for Energy Efficient Wireless Sensor Networks Sangeeta Rao Department of Computer Science Central University of Haryana Mahendergarh, Haryana, India ABSTRACT Wireless Sensor Networks (WSN) have emerged as a promising tool for monitoring and actuating the physical world, utilizing self organizing networks of battery powered wireless sensors that can sense the environmental conditions, process the information and then communicate with the sink. The sensor nodes have energy constraints, so such mechanisms must be developed that make the network energy efficient and also increase the lifetime of the network. Clustering is an important method for lesser energy dissipation by decreasing the number of messages to be sent to the sink. It involves selection of cluster head based on some parameters, and then send the aggregated message to the sink after receiving messages from the nodes in its vicinity. In clustered WSNs, the performance of the network highly relies on the cluster head selection scheme. One of the ways to make WSNs energy efficient is to efficiently elect the cluster head. Keeping in view this idea, a two level fuzzy based approach for cluster head selection is proposed. In first level, two parameters energy level and neighbour density are used to find out the eligibility factor of the nodes for final election as cluster heads. The aggregated eligibility factor along with distance from sink and random variable are used to finally elect the cluster heads in the later level.the proposed scheme is simulated in MATLAB and the results are compared with LEACH. It was found that network lifetime is increased using the proposed scheme. Keywords: - Wireless sensor network, fuzzy logic, cluster head, residual energy, proximity to sink, node concentration. INTRODUCTION The primary component of the Wireless Sensor Network is the sensor, essential for monitoring real world physical conditions such as sound, temperature, humidity, vibration, pressure, motion, intensity, pollutants etc. at different locations. A wireless sensor network (WSN) consists of spatially distributed autonomous sensor nodes to monitor physical or environmental conditions and to cooperatively pass their data through the network to a main location called as sink. The wireless sensor networks is useful for environmental applications, home Appliances, health applications, military applications, Monitoring friendly forces, equipment and ammunition, battlefield surveillance, reconnaissance of opposing forces and terrain, targeting, nuclear, biological and chemical attack detection and reconnaissance, battle damage assessment, office building and warehouses etc. The performance of wireless sensor networks is based on the latency, scalability, energy awareness, node processing time, and transmission scheme and network power usage. The purpose of this present research paper is to find out an energy efficient cluster head selection scheme that enhances the lifetime of the network. An efficient cluster head selection scheme in fuzzy environment is proposed in which the cluster heads are selected by the sink in centralized fashion. The various parameters that affect the cluster head performance and consider them into the cluster head selection scheme will be discussed in the paper. A fuzzy based efficient cluster head selection scheme would increase the lifetime of the network by minimizing the energy consumption of each sensor node. RELATED WORK Wireless sensor networks are an emerging field of various advancements. As the nodes are non-rechargeable means energy constrained, so main focus of the researches in this field is to make it energy efficient as par as possible and prolong its network lifetime. Clustering is one of the hierarchical routing techniques which is used to make the network energy efficient by using the efficient routing. Various advancements have been made further in the clustering methodology and many more are going on. The goal of algorithm which implement data gathering is to maximize the lifetime of network, minimize energy consumption, and the transmission occurs with minimum delay. Clustering in wireless sensor networks has to complete the routing function, node management and role of data fusion. Some of the various clustering mechanisms are as LEACH (Low Energy Adaptive Clustering Hierarchy) LEACH [1] is a self organizing, low power and adaptive clustering protocol. It uses randomization for distributing the energy load among the sensors in the network. According to this protocol, the base station is fixed and located far from the sensor nodes and the nodes are homogenous and energy

2 constrained. It divides the total operation into rounds each round consisting of two phases: set-up phase and steady phase. In set-up phase, cluster formation process takes place after electing the cluster heads. Sensor node generates a random number range 0-1. If this random number is less than the threshold T (n), then it releases the information that he is the cluster head node to the nodes within the cluster. When only a node is not elected, T(n) = 1, so this node will be elected. T (n) formula can be expressed as: where P is the percentage of cluster head node in all nodes, r is an election rounds, r mod (1 / P) is on behalf of the number of node which was elected as cluster head nodes in the cycle, G is the node set which is not elected as a cluster head node in this cycle. Work in a steady phase, member nodes continuous collected monitoring data, and send data to the cluster head node in their own time slots. While the other time, it can turn off the radio module, into hibernation, and it is one of the main ways to save energy for LEACH. However, there are a number of deficiencies [5] in LEACH algorithm. HEED (Hybrid, Energy-Efficient Distributed) HEED (Hybrid, Energy-Efficient Distributed clustering) [2] is a clustering algorithm that improves the problems in the LEACH. HEED follows a clustering concept. The probability to become CH is based on the ratio of the initial electric power Emax and the current residual electric power Eresidual in HEED. Therefore, the node that has the more electric power is easier to become CH. There are two states in the CH, the tentative CH and final CH. If the node broadcasts the final CH advertisement, the node serves the CH in the round. On the other hand, if the node broadcasts the tentative CH advertisement, the node may cancel the advertisement and join to other cluster that the total communication cost becomes small. In HEED, the probability of the node that try to become CH (CHprob) is given as CH prob = max(ch prob * E residual / E max, P min ) Here, CHprob is the rate of the CH given beforehand. Pmin is the minimum value of the CHprob, that is decided in inverse proportion to Emax. So, in order to make the network energy efficient, HEED considers a hybrid of energy and communication cost. HEED uses two parameters; residual energy and intra cluster communication cost, in order to form good cluster. Residual energy is used to probabilistically select an initial set of CHs, and communication cost is used to determine which CH the node should belong to when the node has two or more possible CHs. The efficiency of clustered protocols also depend on the technique how the cluster heads are elected. We can optimize the cluster head selection by using various optimization techniques. Fuzzy logic is also an optimization technique. A lot of work on fuzzy based cluster head selection has been done by various researchers some of them are listed below: In the paper [6] An Energy Efficient Fuzzy Logic Cluster Formation Protocol in Wireless Sensor Networks by Rogaia Mhemed, Nauman Aslam, William Phillips and Frank Comeau; the parameters considered for cluster head selection are - energy level of the CH, distance between the BS and the CH, and distance between the CHs. In [7] Fuzzy Logic Controlled Cluster Head Selection for Wireless Sensor Networks the authors have considered parameters residual energy, neighbor density and node centrality for the selection of cluster heads. In Multilayer Fuzzy Interface Design for Cluster Head Selection in Single Sink Wireless Ad-hoc sensor network dual stage of cluster head selection is used. In the preliminary stage energy & node density are used and in advanced stage centrality, proximity to sink & CH to CH distance are used for final selection of cluster heads.[11] In the paper [8] named Cluster-head Election using Fuzzy Logic for Wireless Sensor Networks by Indranil Gupta Denis Riordan, Srinivas Sampalli; the parameters used for cluster head selection are energy, centrality and node concentration. The authors of the paper An Energy Efficient Approach for Clustering in WSN using Fuzzy Logic have considered energy and centrality, only two parameters for cluster head selection.[9] In the paper [10] Cluster Head Selection in Wireless Sensor Networks under Fuzzy Environment by Puneet Azad and Vidushi Sharma; energy, no. of Neighbors and proximity to sink are taken for cluster head selection in fuzzy environment. All the above techniques have been used to efficiently elect cluster head in fuzzy environment to minimize energy consumption and prolong network lifetime. Randomization plays a very important role in Cluster Head Selection. Cluster Head selection can be further optimized by using a randomized parameter. PROPOSED SCHEME There are many possible models for these wireless sensor networks. In this work, we consider wireless sensor networks where: The base station is fixed and located far from sensors. All the nodes in the network are energy constrained. Cluster Head Selection is centralized i.e. by the base station. The communication between the sensor nodes and the base station is expensive, and there are no high energy nodes through which communication can proceed. Sensor networks contain too much data for an end user to process. Therefore, automated methods of combining or aggregating the data into a small set of meaningful information are required [3,4]. In addition to helping avoid information overload, data

3 aggregation, also known as data fusion, can combine several unreliable data measurements to produce a more accurate signal by enhancing the common signal and reducing the uncorrelated noise. Large energy gains can be achieved by performing the data fusion or classification algorithm locally, thereby requiring much less data to be transmitted to the base station. NETWORK ENERGY MODEL To transmit an l-bit data to a distance d, the radio expands energy: E TX (l,d) = l *E elec + l *ξ fs *d 2, when d< d o l *E elec + l *ξ amp *d 4, when d>=d o where E elec,ξ fs and ξ amp are the parameters of the transmission/reception circuitry. Depending on the distance between the transmitter and receiver, free space (ξ fs ) and multi-path fading (ξ amp ) channel models is used. While receiving, the radio expands energy: E RX (l) = l *E elec In this work, we assume a simple model where the radio dissipated Eelec= 50 nj/bit to run the transmitter or receiver circuitry and for Eamp = 100 pj/bit/m 2, the transmit amplifier to achieve a acceptable Eb/No. We also assume an r 2 energy loss due to channel transmission. PROPOSED SCHEME OF CLUSTER HEAD SELECTION The operation is broken up into rounds, where each round begins with a setup phase in which cluster head selection takes place followed by cluster formation. Cluster heads are elected by dual stage fuzzy logic giving node and network parameters as input. This cluster head selection scheme is centralized assuming base station keeping all knowledge of the network scenario. The proposed model is based on the following assumptions: Cluster Head Selection is Centralized i.e.bs elect the CHs using fuzzy. BS has global information about the network i.e. all information about the nodes. All the sensor nodes are identical and are stationary once deployed. The nodes are uniformly distributed in the network. Single hop communication.(nch -> CH -> BS) BS has high power, high storage and high computation abilities. BS is equipped with fuzzy logic toolbox. PARAMETERS CONSIDERED FOR FUZZY INPUT As our model implements dual stage of fuzzy logic, each stage takes two parameters as inputs: ELIGIBILITY PHASE: Node Energy - energy level available in each node, designated by the fuzzy variable residual-energy, Node Concentration - number of nodes present in the vicinity, designated by the fuzzy variable concentration, The fuzzy rules of Eligibility phase are listed below: Residual_energy Concentration Eligibility for Selection Phase Low Low Less Low Avg Medium Low High Less Avg Low Large Avg Avg Medium Avg High Medium High Low Large High Avg Large High High Medium The surface view of the eligibility with respect to the concentration of nodes & residual energy of the nodes are as SELECTION PHASE: Proximity to sink - distance from the base station, designated by the fuzzy variable proximity_to_sink.(low means away from sink and high means close to the sink). Random Number - any random number, fuzzy variable is random_number. The fuzzy rules of secondary phase are listed below: proximity_to_bs random_number Chance of Selection Low Low Medium Low Avg Less Low High Less Avg Low Less Avg Avg Medium Avg High Medium High Low Medium High Avg Large High High Large The surface view shows the chances of the nodes to be Selected as cluster heads which are shown below:

4 SIMULATIONS RESULTS OF PROPOSED SCHEME We are using a 200 * 200 network of 100 sensor nodes for simulation using MATLAB. The sink is present at (200,100). The nodes are randomly chosen in each simulation. COMPARITIVE GRAPHS with LEACH The comparison graphs between leach and proposed model on the basis of energy dissipation and power variation per round is shown below: The proposed model will work as per the following flow diagram. ENERGY DISSIPATED PER ROUND NETWORK PARAMETERS The following network and node parameters values are considered during implementation: Network size 200 * 200 m 2 Number of nodes 100 Base Station location (200,100) Initial network energy Initial Energy of each node 50J 0.5 J Threshold prob. to become CH ETX(transmission energy) 50* ERX(receiving energy) 50* POWER VARIANCE PER ROUND The comparison graphs between leach and proposed model on the basis of alive nodes and number of CHs per round is shown below:

5 REFERENCES:- [1] Heinzelman WR, Chandrakasan A, Balakrishnan H. Energy-efficient communication protocol for wireless microsensor networks. Proc. of the 33rd Annual Hawaii International Conference on System Sciences, Maui; p [2] Younis O, Fahmy S. Heed: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing 2004; 3: ALIVE NODES PER ROUND [3] F. Zhao, J. Liu, J. Liu, L. Guibas, J. Reich, Collaborative signal and information processing: an information-directed approach, Proceedings of IEEE 91 (8) (2003) [4] D. Dardari, A. Conti, C. Buratti, R. Verdone, Mathematical evaluation of environmental monitoring estimation error through energy-efficient wireless sensor networks, IEEE Transactions on Mobile Computing 6 (7) (2007) [5] Qing Bian, Yan Zhang, Yanjuan Zhao Research on Clustering Routing Algorithms in Wireless Sensor Networks 2010 International Conference on Intelligent Computation Technology and Automation. CHs SELECTED PER ROUND COMPARATIVE ANALYSIS On the basis of above graphs, simulation results show that the proposed scheme enhances the lifetime of the network with decreased energy consumption in comparison with LEACH. The results are analysed with 800 rounds. The results are analysed in terms of energy dissipated per round and power variance per round. We also analyse the number of dead and alive nodes in each round, in both models. V CONCLUSION AND FUTURE WORK In our approach, a two level fuzzy based technique is used in cluster head selection. In first level, the SNs are nominated for CH selection by using two parameters, i.e. residual energy and node concentration. In second level i.e. selection phase, two parameters i.e. proximity to sink and one random parameter are used to finally elect the cluster heads. The simulation of the proposed approach results in decreased energy consumption with lifetime maximization when compared with LEACH. One of the possible future works is to investigate how we can best control the number of associated cluster members in every cluster, to achieve a relative load balance in terms of number of nodes among all clusters formed. This would give better uniformity in their respective energy usage, eventually leading to further prolonged effective network lifetime. [6] Indranil Gupta, Denis Riordan, Srinivas Sampalli Cluster-head Election using Fuzzy Logic for Wireless Sensor Networks. [7] Ashutosh Kumar Singh, Sandeep Goutele, S.Verma and N. Purohit An Energy Efficient Approach for Clustering in WSN using Fuzzy Logic International Journal of Computer Applications ( ) Volume 44 No18, April 2012 [8] Puneet Azad and Vidushi Sharma Cluster Head Selection in Wireless Sensor Networks under Fuzzy Environment Hindawi Publishing Corporation, ISRN Sensor Networks,Volume 2013, Article ID , 8 pages, 2013 [9] Rogaia Mhemed, Nauman Aslam, William Phillips and Frank Comeau, An Energy Efficient Fuzzy Logic Cluster Formation Protocol in Wireless Sensor Networks 3rd International Conference on ANT 2011 Published by Elsevier. [10] Partha Pratim Bhattacharya,Anita Garhwal Fuzzy Logic Controlled Cluster Head Selection for Wireless Sensor Networks International Journal of Electronics and Computer Science Engineering ISSN [11] Ranjita Sannamani, Manoj Challa, Dr. Jitendranath Mungara Multilayer Fuzzy Interface Design for Cluster Head Selection in Single Sink Wireless Ad-hoc sensor network International Journal of Science and Advanced Technology (ISSN ) Volume 2 No 5 May 2012.