HEEL: A new clustering method to improve wireless sensor network lifetime

In wireless sensor networks, some resources such as memory and energy are limited. In recent years, there has been an increasing interest in improving network lifetime. Node energy plays an important role in the network lifetime. Along with this remarkable growth in wireless sensor networks, however, there is an increasing concern over network lifetime. The principal purpose of this study is to develop an understanding of the effects of other parameters on selecting a cluster head. The methodological approach taken in this study is a mixed methodology typically based on the node's energy. The authors have operated four parameters to select the cluster head: Node energy, the energy of the node's neighbours, number of hops and number of links to neighbours. Each of these parameters has an impact in selecting the cluster head. They accurately observed hop size, energy of each sensor node, average energy of sensor neighbours, links to sensor nodes (HEEL) has better improvements in comparison of Node ranked Low Energy Adaptive Clustering Hierarchy (Nr-LEACH), Modified Low Energy Adaptive Clustering Hierarchy (ModLEACH), Low Energy Adaptive Clustering Hierarchy-B (LEACH-B), Low Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Gathering in Sensor Information System (PEGASIS), energy-aware clustering scheme with transmission power control for sensor networks (EACLE) and hybrid energy efficient distributed clustering (HEED) algorithms in possible case of network lifetime and throughput

Improved Estimation Performance of Sensor in Wireless Sensor Network Using Suboptimal Technique

ABSTRACT--This paper presents a novel network lifetime extension technique. In order to collect information more efficiently, wireless sensor networks (WSNs) are partitioned into clusters. Clustering provides an effective way to prolong the lifetime of WSNs. Current clustering approaches often use two methods: selecting cluster heads with more residual energy, and rotating cluster head periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, most of the previous algorithms have not considered the expected residual energy, only consider the estimation performance. In this paper we propose a probabilistic based transmission using clustering algorithm. Probabilistic transmission control at which is to minimize the mean squared error of estimation by increasing the packet transmission success probability of only sensors having high observation SNR. These newly available sensors are partitioned into several sensor sets select the cluster head to maintain the same estimation performance. The simulation results show that the proposed approach is more efficient than other distributed algorithms. It is believed that the technique presented in this paper could be further applied to large-scale wireless sensor networks

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Overlapping Multi-hop Clustering for Wireless Sensor Networks

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Traditionally, clustering algorithms aim at generating a number of disjoint clusters that satisfy some criteria. In this paper, we formulate a novel clustering problem that aims at generating overlapping multi-hop clusters. Overlapping clusters are useful in many sensor network applications, including inter-cluster routing, node localization, and time synchronization protocols. We also propose a randomized, distributed multi-hop clustering algorithm (KOCA) for solving the overlapping clustering problem. KOCA aims at generating connected overlapping clusters that cover the entire sensor network with a specific average overlapping degree. Through analysis and simulation experiments we show how to select the different values of the parameters to achieve the clustering process objectives. Moreover, the results show that KOCA produces approximately equal-sized clusters, which allows distributing the load evenly over different clusters. In addition, KOCA is scalable; the clustering formation terminates in a constant time regardless of the network size