Energy Aware Heuristic Approach for Cluster Head Selection in Wireless Sensor Networks

Wireless sensor networks idea is envisioned and defined as self-deployed, error prone, long living inexpensive communication devices that are densely deployed to collect data from physical space. Sensor nodes communicate with each other to detect events depending on the application, to collect and process data, and to transmit the sensed information to the base station by hopping the data from node to node. Major challenge in wireless network is energy consumption, since the sensor devices are deployed on unattended wide areas and replacing their batteries is not very feasible. Therefore, designing energy-aware algorithms becomes a major challenge for extending the lifetime of sensors. The lifetime of the network can be maximized by selecting the most optimal cluster head. In this paper, a heuristic decision making approach is proposed for producing energy-aware clusters with optimal selection of cluster head. This helps to improve the efficiency and accuracy and overcome the drawbacks like getting trapped at a local extreme in the optimization process

Energy Aware Heuristic approach for Cluster Head Selection in Wireless Sensor Networks

Wireless sensor networks idea is envisioned and defined as self-deployed, error prone, long living inexpensive communication devices that are densely deployed to collect data from physical space. Sensor nodes communicate with each other to detect events depending on the application, to collect and process data, and to transmit the sensed information to the base station by hopping the data from node to node. Major challenge in wireless network is energy consumption, since the sensor devices are deployed on unattended wide areas and replacing their batteries is not very feasible. Therefore, designing energy-aware algorithms becomes a major challenge for extending the lifetime of sensors. The lifetime of the network can be maximized by selecting the most optimal cluster head. In this paper, a heuristic decision making approach is proposed for producing energy-aware clusters with optimal selection of cluster head. This helps to improve the efficiency and accuracy and overcome the drawbacks like getting trapped at a local extreme in the optimization process

Multiple solutions based particle swarm optimization for cluster-head-selection in wireless-sensor-network

Wireless sensor network (WSN) has a significant role in wide range of scientific and industrial applications. In WSN, within the operation area of sensor nodes the nodes are randomly deployed. The constraint related to energy is considered as one of the major challenges for WSN, which may not only affect the sensor nodes efficiency but also influences the operational capabilities of the network. Therefore, numerous attempts of researches have been proposed to counter this energy problem in WSN. Hierarchical clustering approaches are popular techniques that offered the efficient consumption of the energy in WSN. In addition to this, it is understood that the optimum choice of sensor as cluster head can critically help to reduce the energy consumption of the sensor node. In recent years, metaheuristic optimization is used as a proposed technique for the optimal selection of cluster heads. Furthermore, it is noteworthy here that proposed techniques should be efficient enough to provide the optimal solution for the given problem. Therefore, in this regard, various attempts are made in the form of modified versions or new metaheuristic algorithms for optimization problems. The research in the paper offered a modified version of particle-swarm-optimization (PSO) for the optimal selection of sensor nodes as cluster heads. The performance of the suggested algorithm is experimented and compared with the renowned optimization techniques. The proposed approach produced better results in the form of residual energy, number of live nodes, sum of dead nodes, and convergence rate

Bioinspired Principles for Large-Scale Networked Sensor Systems: An Overview

Biology has often been used as a source of inspiration in computer science and engineering. Bioinspired principles have found their way into network node design and research due to the appealing analogies between biological systems and large networks of small sensors. This paper provides an overview of bioinspired principles and methods such as swarm intelligence, natural time synchronization, artificial immune system and intercellular information exchange applicable for sensor network design. Bioinspired principles and methods are discussed in the context of routing, clustering, time synchronization, optimal node deployment, localization and security and privacy

An energy-efficient cluster head selection in wireless sensor network using grey wolf optimization algorithm

Clustering is considered as one of the most prominent solutions to preserve theenergy in the wireless sensor networks. However, for optimal clustering, anenergy efficient cluster head selection is quite important. Improper selectionofcluster heads(CHs) consumes high energy compared to other sensor nodesdue to the transmission of data packets between the cluster members and thesink node. Thereby, it reduces the network lifetime and performance of thenetwork. In order to overcome the issues, we propose a novelcluster headselection approach usinggrey wolf optimization algorithm(GWO) namelyGWO-CH which considers the residual energy, intra-cluster and sink distance.In addition to that, we formulated an objective function and weight parametersfor anefficient cluster head selection and cluster formation. The proposedalgorithm is tested in different wireless sensor network scenarios by varyingthe number of sensor nodes and cluster heads. The observed results conveythat the proposed algorithm outperforms in terms of achieving better networkperformance compare to other algorithms

EODC: An Energy Optimized Dynamic Clustering Protocol for Wireless Sensor Network using PSO approach

Wireless Sensor Network comprises of a number of small wireless nodes whose role is to sense, gather, process and communicate. One of the primary concerns of the network is to optimize the energy consumption and extend the network lifespan. Sensor nodes can be clustered to increase the network lifespan. This is done by selecting the cluster head for every cluster and by performing data fusion on the cluster head. The proposed system is using an energy efficient hierarchical routing protocol named Energy Optimized Dynamic Clustering (EODC) for clustering large ad-hoc WSN and route the data towards the sink. The sink receives the data collected from the set of cluster heads after every round. The cluster head was selected using Particle Swarm Optimization (PSO) approach and the cluster members are allocated based on Manhattan distance. The metrics used to find the fitness function are location, link quality, energy of active node and energy of inactive node. The system employs shortest path approach to communicate between the cluster heads till it reaches the base station. By this, we have increased the energy efficiency and lifetime of the network. The analysis and outcomes show that the EODC was found to outperform the existing protocol which compares with this algorithm

A Network Topology Control and Identity Authentication Protocol with Support for Movable Sensor Nodes

It is expected that in the near future wireless sensor network (WSNs) will be more widely used in the mobile environment, in applications such as Autonomous Underwater Vehicles (AUVs) for marine monitoring and mobile robots for environmental investigation. The sensor nodes’ mobility can easily cause changes to the structure of a network topology, and lead to the decline in the amount of transmitted data, excessive energy consumption, and lack of security. To solve these problems, a kind of efficient Topology Control algorithm for node Mobility (TCM) is proposed. In the topology construction stage, an efficient clustering algorithm is adopted, which supports sensor node movement. It can ensure the balance of clustering, and reduce the energy consumption. In the topology maintenance stage, the digital signature authentication based on Error Correction Code (ECC) and the communication mechanism of soft handover are adopted. After verifying the legal identity of the mobile nodes, secure communications can be established, and this can increase the amount of data transmitted. Compared to some existing schemes, the proposed scheme has significant advantages regarding network topology stability, amounts of data transferred, lifetime and safety performance of the network