Voronoi-Based Improved Algorithm for Connected Coverage Problem in Wireless Sensor Networks

2005 ◽  
pp. 224-233 ◽  
Author(s):  
Jie Jiang ◽  
Zhen Song ◽  
Heying Zhang ◽  
Wenhua Dou
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Connected Coverage ◽  
Improved Algorithm

scholarly journals M-Connected Coverage Problem in Wireless Sensor Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
S. Mini ◽  
Siba K. Udgata ◽  
Samrat L. Sabat
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Target Coverage ◽  
Coverage Problem ◽  
Adaptive Clustering ◽  
Connected Coverage ◽  
Target Coverage Problem

Solving coverage problem alone is not adequate in a wireless sensor network, since data has to be transmitted to the base station. This leads to the lookout for an energy efficient method to solve connected coverage problem. This paper addresses M-connected (each sensor node will have at least M other sensor nodes within its communication range) target coverage problem in wireless sensor networks, where the required level of connectivity and coverage may be high or low as required. We propose a heuristic for M-connected target coverage problem, where initially a cover is decided and later on it is checked for M-connectivity. M-connectivity for simple coverage, k-coverage, and Q-coverage is focussed on in this paper. We use a Low-Energy Adaptive Clustering Hierarchy (LEACH) inspired model, where a cluster is considered as a set of sensor nodes satisfying M-connectivity and required level of coverage. It is enough if one among these nodes transmits the monitored information to the base station. When the required level of coverage are high, chances of nodes being connected in the cover is high. Simulation results show that our proposed method can achieve better results than Communication Weighted Greedy Cover (CWGC).

scholarly journals Proficient QoS-Based Target Coverage Problem in Wireless Sensor Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 74315-74325 ◽  
Author(s):  
Manju ◽  
Samayveer Singh ◽  
Sandeep Kumar ◽  
Anand Nayyar ◽  
Fadi Al-Turjman ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Target Coverage ◽  
Coverage Problem ◽  
Target Coverage Problem

The critical-square-grid coverage problem in wireless sensor networks is NP-Complete

2011 ◽  
Vol 55 (9) ◽  
pp. 2209-2220 ◽  
Author(s):  
Wei-Chieh Ke ◽  
Bing-Hong Liu ◽  
Ming-Jer Tsai
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Square Grid ◽  
Np Complete

scholarly journals Simulated Annealing Application to Maximum Lifetime Coverage Problem in Wireless Sensor Networks

10.29007/gl61 ◽  
2018 ◽  
Author(s):  
Antonina Tretyakova ◽  
Franciszek Seredynski
Keyword(s):  
Wireless Sensor Networks ◽  
Simulated Annealing ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Maximization Problem ◽  
Main Task ◽  
Coverage Problem ◽  
Wsn Lifetime ◽  
Efficient Performance ◽  
Hard Problems

Energy optimization problem in Wireless Sensor Networks (WSN) is a backbone of efficient performance of sensor network consisting of small devices with limited and non-recovering battery. WSN lifetime maximization problem under assumption of that the coverage is main task of the network is known as Maximal lifetime coverage problem (MLCP).This problem belongs to a class of NP-hard problems. In this paper we propose a novel simulated annealing (SA) algorithm to solve MLCP. The proposed algorithm is studied for high dense WSN instances under different parameter setup.

scholarly journals A Novel Bi-Tuning SSO Algorithm for Optimizing the Budget-Limited Sensing Coverage Problem in Wireless Sensor Networks

2021 ◽  
Vol 11 (21) ◽  
pp. 10197
Author(s):  
Wenbo Zhu ◽  
Chia-Ling Huang ◽  
Wei-Chang Yeh ◽  
Yunzhi Jiang ◽  
Shi-Yi Tan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Smart Grids ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Swarm Optimization ◽  
Coverage Ratio ◽  
Sensing Coverage ◽  
Limited Sensing ◽  
Novel Concept

The wireless sensor network (WSN) plays an essential role in various practical smart applications, e.g., smart grids, smart factories, Internet of Things, and smart homes, etc. WSNs are comprised and embedded wireless smart sensors. With advanced developments in wireless sensor networks research, sensors have been rapidly used in various fields. In the meantime, the WSN performance depends on the coverage ratio of the sensors being used. However, the coverage of sensors generally relates to their cost, which usually has a limit. Hence, a new bi-tuning simplified swarm optimization (SSO) is proposed that is based on the SSO to solve such a budget-limited WSN sensing coverage problem to maximize the number of coverage areas to improve the performance of WSNs. The proposed bi-tuning SSO enhances SSO by integrating the novel concept to tune both the SSO parameters and SSO update mechanism simultaneously. The performance and applicability of the proposed bi-tuning SSO using seven different parameter settings are demonstrated through an experiment involving nine WSN tests ranging from 20, 100, to 300 sensors. The proposed bi-tuning SSO outperforms two state-of-the-art algorithms: genetic algorithm (GA) and particle swarm optimization (PSO), and can efficiently accomplish the goals of this work.

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