An improved protocol for Energy efficient communication in Wireless Sensor Network

Transcription

1 International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN An improved protocol for Energy efficient communication in Wireless Sensor Network Aman Deep Singh 1, Mr Nirendra Kumar 2 1 MTech (CS) BSACET MATHURA, UP, INDIA 2 ASSISTANT PROFESSOR (CS) BSACET MATHURA, UP, INDIA ABSTRACT The popularity of Wireless Sensor Networks has increased extremely due to the huge potential of the sensor networks to join the physical world with the virtual world. Since these devices depend on battery power and may be placed in hostile environments replacing them becomes a tedious task. Thus, increasing the energy of these networks becomes important. Our paper provides methods for clustering and cluster head selection to WSN to improve energy efficiency. It provides a comparative study of the different methods on the basis of the network lifetime. It provides a modified approach for cluster head selection with good performance and less computational complexity. In addition it also proposes modified communication protocol as an algorithm for clustering of WSN which would result in improved performance with faster convergence. Keywords: Battery lifetime, cluster head, sensor nodes, leach, throughput. 1. INTRODUCTION The WSN system has witnessed a tremendous upsurge coming along with numerous applications, e.g., battle field surveillance, environment monitoring, disaster detection and rescue, biodiversity and habitat monitoring, precise and intelligent agriculture, medicine and health care, environment-friendly buildings, and logistics. WSN can prevent grape plants from overheating or freezing and thus considerably increase the plant value and the wine quality [4]. Another WSN application serves livestock farming by attaching to each livestock a sensor node which measures and reports the animal s health status, e.g., the body temperature and any pest infection, farmers can be alerted to react in time to any potential disease outbreak in the herd. In view of application diversity, it is difficult to set up a one-for-all node hardware framework and a standard communication protocol stacks that application. [6] WSN have attracted tremendous attention of the research community in recent years and a vast amount of research work has been conducted solve the practical and theoretical issues. This has resulted in a surge of civil and military applications over the last few years. Today, most deployed WSNs measure scalar physical phenomenon line temperature, pressure, humidity, or location of objects [5]. In general, most sensor networks are designed for delay-tolerant and low-bandwidth applications. For this reason most research on sensor networks has concentrated on this low-power and delaytolerant network paradigm, which is referred as terrestrial sensor networks. WSNs were originally motivated by military applications, which range from large-scale acoustic surveillance systems for ocean surveillance to small networks of unattended ground sensors for ground target detection [1]. However, the availability of low-cost sensors and wireless communication has promised the development of a wide range of applications in both civilian and military fields. 1.1 PROTOCOLS FOR WSN Low Energy Adaptive Clustering Hierarchy () (Low Energy Adaptive Clustering Hierarchy) is made for sensor networks where an end user desires to remotely monitor the environment. [2] In such a condition, the data from the individual nodes must be sent to a central base station, often placed far from the sensor network, through which the end-user can retrieve the data. There are several desirable properties for protocols on these networks: Use 1's - 1's of nodes Maximize system lifetime Maximize network coverage Use uniform, battery-operated nodes Conventional network protocols, such as direct transmission, minimum transmission energy, multi-hop

2 International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN routing and clustering all have shortcomings that don't allow them to get all the desirable properties. has distributed cluster formation, local processing to decrease global communication, and randomized rotation of the cluster-heads. Together, these features permits to get the desired properties. Initial simulations depicts that is an energy-efficient protocol that makes larger system lifetime. Hybrid Energy-Efficient Distributed Clustering (HEED) Nodes in independently resolve to become cluster heads. While this approach demands no communication overhead, it has the flaws of not guaranteeing that the cluster head nodes are well distributed all the way through the network.[9] While the -C protocol finds an answer to this problem, it is a centralized approach that can t scale to very big numbers of sensors. HEED applies an iterative cluster formation algorithm, where sensors assigns themselves a cluster head probability that is a function of their residual energy and a communication cost that is a function of neighbor proximity. Linked cluster algorithm In the Linked Cluster Algorithm, a node becomes the cluster-head if it has the highest identity among all within one hop of itself or among all nodes within one hop of one of its neighbors [6]. This algorithm was improved by the LCA2 algorithm, which generates a smaller number of clusters. The LCA2 algorithm elects as a cluster-head the node with the lowest id among all nodes that are neither a cluster-head nor are within 1-hop of the already chosen cluster-heads. Sensor Protocol for Information via Negotiation (SPIN) SPIN is a protocol that was designed to enable datacentric information dissemination in sensor networks. Rather than blindly broadcasting sensor data all the way through the network, nodes receiving or generating data first advertise this data through short ADV messages. The ADV messages simply composed of an applicationspecific meta-data description of the data itself. This meta-data can describe such aspects as the type of data and the location of its origin. Energy-Efficient Hierarchical Clustering (EEHC) Another significant probabilistic clustering algorithm was earlier proposed in Ref. [1](Energy Efficient Hierarchical Clustering EEHC). The main objective of this algorithm was to address the shortcomings of onehop random selection algorithms such as by extending the cluster architecture to multiple hops.[8] It is a distributed, k-hop hierarchical clustering algorithm aiming at the maximization of the network lifetime. Initially, each sensor node is elected as a CH with probability p and announces its election to the neighboring nodes within its communication range. The above CHs are now called the volunteer CHs. Next, all the nodes that are within k -hops distance from a volunteer CH, are supposed to receive the election message either directly or through intermediate forwarding. Consequently, any node that receives such CH election message and is not itself a CH, becomes a member of the closest cluster. Additionally, a number of forced CHs are elected from nodes that are neither CHs nor belong to a cluster. Specifically, if the election messages do not reach a node within a preset time interval t, the node becomes a forced CH assuming that it is not within k hops of all volunteer CHs. 2. ALGORITHM OPERATION The operation of is broken up into, where each round starts with a setup phase, when the clusters are arranged systematically, followed by a steady state phase, when data moves to the base station occur. In order to reduce overhead, the steady-state phase is long compared to the set-up phase. The process of it is shown in Figure 1 At the stage of cluster forming, a node randomly picks a number between to 1, compared this number to the threshold values t(n) if the number is less than t(n) then it become cluster head in this round, else it become common node. Threshold t(n) is determined by the following: Where p is the percentage of the cluster head nodes in all nodes, r is the number of the round; G is the collections

3 Energy Dead Percent of Allive Nodes International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN of the nodes that have not yet been head nodes in the first 1/P. Using this threshold, all nodes will be able to be head nodes after 1/P. The analysis is as follows: Each node becomes a cluster head with probability p when the round begins, the nodes which have been head nodes in this round will not be head nodes in the next 1/P, because the number of the nodes which is capable of head node will gradually reduce, so, for these remain nodes, the probability of being head nodes must be increased. After 1/P-1 round, all nodes which have not been head nodes will be selected as head nodes with probability 1, when 1/P finished, all nodes will return to the same starting line. When clusters have formed, the nodes start to transmit the inspection data. Cluster heads receive data was sent to the gateway after fused.. This is a frame data transmission. In order to reduce unnecessary energy cost, steady stage is composed of multiple frames and the steady stage is much longer than the set-up stage. New improved algorithm vs : (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that minimizes energy dissipation in sensor networks was developed, there after we modified the clustering head selection and faster convergence is achieved 3. Simulation and Results for usage over multiple nodes, the cluster-head nodes are not fixed; rather, this position is self-elected at different time intervals. Thus a set C of nodes might elect themselves cluster-heads at time t1, but at time t1 + d a new set C of nodes elect themselves as cluster-heads. The decision to become a cluster-head depends on the amount of energy left at the node. In this way, nodes with more energy remaining will perform the the energy-intensive functions of the network The above figure is the comparison between Leach and Modified Leach that we proposed, the no. of nodes will alive for the more time in comparison to leach and the proposed method improves the efficiency of the battery of the sensor nodes which eventually results in improved life time of the sensor nodes.being cluster-head drains the battery of that node The above figure is the comparison between Leach and Modified Leach that we proposed, the no. of nodes will dead for the lesser time in comparison to leach and the proposed method improves the efficiency of the battery of the sensor nodes which eventually results in improved life time of the sensor nodes.being cluster-head drains the battery of that node. In order to spread this energy 5 The above figure shows the amount of energy dissipated using Modified versus as the network diameter is increased and the electronics energy varies.

4 Throughput International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN This figure shows the large energy savings achieved using for most of the parameter space. In addition to reducing energy waste, successfully distributes energy-usage among the nodes in the network such that the nodes die randomly and at the same rate. The above figure is the Energy Efficiency between Leach and Modified Leach that we proposed, the no. of nodes in modified leach will alive for the more time in comparison to leach and the proposed method improves the efficiency of the battery of the sensor nodes which eventually results in improved life time of the sensor nodes.being cluster-head drains the battery of that node. In order to spread this energy usage over multiple nodes, the cluster-head nodes are not fixed; rather, this position is self-elected at different time intervals 1 x The above figure shows that Modified more than doubles the useful system lifetime compared with the alternative approaches. While these simulations do not account for the setup time to configure the dynamic clusters (nor do they account for any necessary routing start-up costs or updates as nodes die), they give a good first order approximation of the lifetime extension we can achieve using. 4. CONCLUSION AND FUTURE SCOPE Our paper work included the study of clustering, cluster head selection and other energy efficient communication protocols for WSN, since it was earlier put forward that clustering improves the network lifetime. We used modified approach for cluster head choosing and proposed a new method for cluster head selection having less computational complexity. It was also found that the modified approach has identical performance to that of the fuzzy logic based approach. For the purpose of clustering a WSN GA, K-Means algorithms were used and a new method of clustering WSN was proposed. We used as a reference to compare the performance of each of the clustering methods. In this work, we divide the network into logical regions. Each region use different communication hierarchy. Two regions use direct communication. In this work, we divide the network into logical regions. Each region use different communication hierarchy. Two regions use direct communication topology and two regions are further sub-divided into clusters and use multi-hop communication hierarchy. Each node in a region elects itself as a CH independent of other region. This technique encourages better distribution of CHs in the network. Simulation results show that our proposed protocol performs well compared to. In this work, we study the three performance metrics: Network lifetime, throughput and residual energy. Limitation: Wireless Sensor Network cannot be implemented where large power is required to drive the system, also the charging of the nodes is difficult as it is driven by the battery and can be utilized once to get the information from the field rest we need to replace all the sensor nodes with new to again getting the information from the field. Future Scope The modified algorithm for clustering of WSN has the scope of giving better results if the algorithm parameters are chosen suitably. The modified cluster head selection technique may give better results if implemented with other clustering techniques which have not been discussed in the thesis (eg: Fuzzy C-Mean clustering).the network lifetime may also be improved if the clustering algorithms are made distributed as in. In all of the methods discussed above the energy parameter is taken into consideration only during cluster head selection considering energy as as a parameter during clustering itself. Also in future it can be possible to deploy the solar cells in order to increase the energy of the system and eventually the network lifetime will also improve. REFERENCES [1] H. Karl and A. Willig,25, Protocols and architectures for wireless sensor networks, NJ: Wiley, pp. 3-8, 18-36, , , [2] C.S. Raghavendra, K. M. Sivalingam, and T. Znatil, 24,Wireless sensor networks, Boston Academic Publishers, pp.5-7,

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