scholarly journals ACO-Based Sweep Coverage Scheme in Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Peng Huang ◽  
Feng Lin ◽  
Chang Liu ◽  
Jian Gao ◽  
Ji-liu Zhou
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Mobile Sensors ◽  
Wireless Sensor ◽  
Data Delivery ◽  
Network Coverage ◽  
Coverage Problem ◽  
Points Of Interest ◽  
Novel Algorithm

Coverage problem is one of the major issues in wireless sensor networks (WSN). In order to optimize the network coverage, different coverage formulations have been proposed. Recently, a newly emerging coverage scheme in wireless sensor networks, sweep coverage, which uses mobile sensors to monitor certain points of interest (POIs), is proposed. However, the data delivery to sink, an important problem in WSN, is not considered in original sweep coverage and many of the existing works did not consider it yet. In this work, a novel algorithm named ACOSC (ACO-based sweep coverage) to solve the sweep coverage problem considering periodical coverage of POIs and delivery of data simultaneously is proposed. The evaluation results show that our algorithm has better performance than existing schemes.

A Distributed Motion Algorithm for Mobile Sensor in Hybrid Wireless Sensor Networks

2015 ◽  
Vol 719-720 ◽  
pp. 812-817
Author(s):  
Xi Rong Bao ◽  
Yue Huang ◽  
Shi Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Minimum Cost ◽  
Mobile Sensors ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Mobile Sensor ◽  
Coverage Hole ◽  
The Cost ◽  
Static Sensors

Constructing a hybrid wireless sensor networks comprising a mix of static sensors and mobile sensors can achieve a balance between improving coverage and reducing the cost of the network. In order to achieve high network coverage, mobile sensor move from a small to a big size of coverage hole in the hybrid wireless sensor networks. Due to the energy of the mobile sensor is limited, how to reduce the moving distance of the mobile sensor and reduce the energy consumption in the process of moving is a very important issue. This paper proposes a distributed minimum cost matching algorithm (DMMA) to redeploy mobile sensor, which can make the level of network coverage to meet the requirement of the environment, while effectively reducing the number of sensors. In our method, static sensors detect coverage hole by Voronoi diagrams, coverage holing sensors and mobile sensors by using DMMA to excellently heal the large coverage holes. Simulation results show that our method can effectively improve the coverage rate of the WSNs, while save the energy of mobile sensors.

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

scholarly journals Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110181
Author(s):  
Yinggao Yue ◽  
Hairong You ◽  
Shuxin Wang ◽  
Li Cao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Coverage Optimization ◽  
Network Cost ◽  
Whale Optimization ◽  
Heterogeneous Wireless Sensor Networks

Aiming at the problems of node redundancy and network cost increase in heterogeneous wireless sensor networks, this article proposes an improved whale optimization algorithm coverage optimization method. First, establish a mathematical model that balances node utilization, coverage, and energy consumption. Second, use the sine–cosine algorithm to improve the whale optimization algorithm and change the convergence factor of the original algorithm. The linear decrease is changed to the nonlinear decrease of the cosine form, which balances the global search and local search capabilities, and adds the inertial weight of the synchronous cosine form to improve the optimization accuracy and speed up the search speed. The improved whale optimization algorithm solves the heterogeneous wireless sensor network coverage optimization model and obtains the optimal coverage scheme. Simulation experiments show that the proposed method can effectively improve the network coverage effect, as well as the utilization rate of nodes, and reduce network cost consumption.

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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