A Tabu Search Algorithm for Cluster Building in Wireless Sensor Networks

Recent studies have shown that the use of mobile sinks and Network Coding (NC) and determining the Sink Optimal Route (SOR) in wireless sensor networks (WSNs) reduces the energy consumption. The purpose of this paper is to determine the multicast SOR to move mobile sinks at specific deadline using NC and modeling and problem formulating based on a Mixed Integer Linear Programming (MILP) in WSNs. In this paper, we first show that finding the SOR is NP-hard, and then for determining the SOR, several convex optimization models are presented using Support Vector Regression (SVR). Solving these models in a polynomial time is not possible due to various parameters and limited resources of WSNs. To solve this problem in polynomial time, a Tabu Search algorithm is proposed to reduce runtime and energy consumption. Simulation results show that optimization models and proposed Tabu Search algorithm significantly reduce energy consumption and required time for computing than non-NC methods.

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An Energy Efficient Routing Approach to Enhance Coverage for Application Specific Wireless Sensor Networks using Genetic Algorithm

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191210144821 ◽

2019 ◽

Vol 13 ◽

Author(s):

Amandeep Kaur Sohal ◽

Ajay Kumar Sharma ◽

Neetu Sood

Keyword(s):

Genetic Algorithm ◽

Wireless Sensor Networks ◽

Sensor Networks ◽

Data Transmission ◽

Energy Efficient ◽

Search Algorithm ◽

Cluster Formation ◽

Green Computing ◽

Wireless Sensor ◽

Energy Efficient Routing

Background: An information gathering is a typical and important task in agriculture monitoring and military surveillance. In these applications, minimization of energy consumption and maximization of network lifetime have prime importance for green computing. As wireless sensor networks comprise of a large number of sensors with limited battery power and deployed at remote geographical locations for monitoring physical events, therefore it is imperative to have minimum consumption of energy during network coverage. The WSNs help in accurate monitoring of remote environment by collecting data intelligently from the individual sensors. Objective: The paper is motivated from green computing aspect of wireless sensor network and an Energy-efficient Weight-based Coverage Enhancing protocol using Genetic Algorithm (WCEGA) is presented. The WCEGA is designed to achieve continuously monitoring of remote areas for a longer time with least power consumption. Method: The cluster-based algorithm consists two phases: cluster formation and data transmission. In cluster formation, selection of cluster heads and cluster members areas based on energy and coverage efficient parameters. The governing parameters are residual energy, overlapping degree, node density and neighbor’s degree. The data transmission between CHs and sink is based on well-known evolution search algorithm i.e. Genetic Algorithm. Conclusion: The results of WCEGA are compared with other established protocols and shows significant improvement of full coverage and lifetime approximately 40% and 45% respectively.

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Energy efficient fixed-cluster architecture for wireless sensor networks

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-192177 ◽

2021 ◽

Vol 40 (5) ◽

pp. 8727-8740

Author(s):

Rajvir Singh ◽

C. Rama Krishna ◽

Rajnish Sharma ◽

Renu Vig

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Data Aggregation ◽

Energy Efficient ◽

Search Algorithm ◽

Cluster Formation ◽

Wireless Sensor ◽

Efficient Operation ◽

Cluster Architecture ◽

Dynamic Cluster

Dynamic and frequent re-clustering of nodes along with data aggregation is used to achieve energy-efficient operation in wireless sensor networks. But dynamic cluster formation supports data aggregation only when clusters can be formed using any set of nodes that lie in close proximity to each other. Frequent re-clustering makes network management difficult and adversely affects the use of energy efficient TDMA-based scheduling for data collection within the clusters. To circumvent these issues, a centralized Fixed-Cluster Architecture (FCA) has been proposed in this paper. The proposed scheme leads to a simplified network implementation for smart spaces where it makes more sense to aggregate data that belongs to a cluster of sensors located within the confines of a designated area. A comparative study is done with dynamic clusters formed with a distributive Low Energy Adaptive Clustering Hierarchy (LEACH) and a centralized Harmonic Search Algorithm (HSA). Using uniform cluster size for FCA, the results show that it utilizes the available energy efficiently by providing stability period values that are 56% and 41% more as compared to LEACH and HSA respectively.

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