A New Sensing Direction Rotation Approach to Area Coverage Optimization in Directional Sensor Network

2020 ◽  
Vol 24 (2) ◽  
pp. 206-213 ◽  
Author(s):  
Song Peng ◽  
◽  
Yonghua Xiong
Keyword(s):  
Area Coverage ◽  
Sensor Nodes ◽  
Target Area ◽  
Coverage Problem ◽  
Directional Sensor Networks ◽  
High Coverage ◽  
Coverage Ratio ◽  
Verification Algorithm ◽  
Grid Points ◽  
Directional Sensor

Coverage is a crucial issue in directional sensor networks (DSNs), and a high coverage ratio ensures a good quality of service (QoS). However, a DSN encounters various problems because they use directional sensor nodes, which are characterized by directionality and a definite sensing angle. To address the area coverage problem of DSNs, this paper proposes a new sensing direction rotation approach to optimize coverage. First, we conduct grid partitioning in the target area and propose a coverage verification algorithm to justify the coverage situation of the grid points. Then, we utilize particle swarm optimization (PSO) to find an optimal sensing direction group of the directional sensor nodes to maximize the coverage ratio. Extensive simulation experiments were conducted to prove the effectiveness and reliability of our proposed approach. The results show that the approach improves the area coverage ratio of DSNs in various scenarios.

Optimization of the Virtual Potential Field Based on Coverage-Enhancing Algorithm for Directional Sensor Networks

2014 ◽  
Vol 651-653 ◽  
pp. 1882-1887
Author(s):  
Jun Zhu ◽  
Chen Shi ◽  
Shan Shan Zhu ◽  
Jun Zhang
Keyword(s):  
Sensor Networks ◽  
Potential Field ◽  
Boundary Region ◽  
Sensor Nodes ◽  
Target Area ◽  
Virtual Node ◽  
Directional Sensor Networks ◽  
Coverage Ratio ◽  
Coverage Holes ◽  
Directional Sensor

After directional sensor nodes are randomly thrown into target area, coverage ratio often less than the anticipant value, in order to improve the coverage, sensor nodes should turn from overlapping regions to coverage holes by a much faster way. In this paper, we improved the existing potential field based coverage-enhancing algorithm (PFCEA), presented a optimization of the virtual potential field based on coverage-enhancing algorithm for directional sensor networks (OPFCEA), we introducing a new-style virtual node to enhance the coverage of boundary region and a new-style control for the rotation angle. By these ways, we can improve network’s performance. This algorithm enhanced the coverage ratio of the network, the simulation results show the effectiveness of the algorithm.

An Improved Greedy Algorithm for Coverage in Directional Sensor Networks

2013 ◽  
Vol 711 ◽  
pp. 440-445
Author(s):  
Xiang Fu ◽  
Chun Ping Lu ◽  
Hao Li
Keyword(s):  
Greedy Algorithm ◽  
Sensor Nodes ◽  
Network Coverage ◽  
Simulation Experiments ◽  
Directional Sensor Networks ◽  
Coverage Area ◽  
Priority Level ◽  
Coverage Ratio ◽  
Processing Order ◽  
Directional Sensor

DGreedy (distributed greedy) algorithm evaluates the priority level in view of remaining energy of terminals, and the relationships between neighbor nodes are not considered. At the same time, the adjustable sensing orientations of sensors are limited. Therefore, the network coverage ratio of DGreedy is affected usually by the processing order of sensor nodes. In this paper, an improved Greedy algorithm for the coverage in directional sensor network is proposed based on the principle of global greedy. The single coverage area of nodes is considered as priority. The direction of node with maximum single coverage area is deployed firstly. Thereby it reduces the sensing overlapping regions and accomplishes coverage enhancement of the networks. Meanwhile, in order to improve the network coverage ratio, the sensing orientations of sensors are adjustable continuously, so the best sensing orientation of node can be selected by considering the deployment of neighbor nodes. Simulation experiments show that the proposed algorithm can improve the coverage area effectively.

scholarly journals An Area Coverage and Energy Consumption Optimization Approach Based on Improved Adaptive Particle Swarm Optimization for Directional Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1192 ◽  
Author(s):  
Song Peng ◽  
Yonghua Xiong
Keyword(s):  
Energy Consumption ◽  
Cluster Head ◽  
Area Coverage ◽  
Optimization Approach ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Ratio ◽  
Energy Consumption Optimization ◽  
Set Up ◽  
Directional Sensor

Coverage is a vital indicator which reflects the performance of directional sensor networks (DSNs). The random deployment of directional sensor nodes will lead to many covergae blind areas and overlapping areas. Besides, the premature death of nodes will also directly affect the service quality of network due to limited energy. To address these problems, this paper proposes a new area coverage and energy consumption optimization approach based on improved adaptive particle swarm optimization (IAPSO). For area coverage problem, we set up a multi-objective optimization model in order to improve coverage ratio and reduce redundancy ratio by sensing direction rotation. For energy consumption optimization, we make energy consumption evenly distribute on each sensor node by clustering network. We set up a cluster head selection optimization model which considers the total residual energy ratio and energy consumption balance degree of cluster head candidates. We also propose a cluster formation algorithm in which member nodes choose their cluster heads by weight function. We next utilize an IAPSO to solve two optimization models to achieve high coverage ratio, low redundancy ratio and energy consumption balance. Extensive simulation results demonstrate the our proposed approach performs better than other ones.

A learning automata-based algorithm to solve imbalanced k-coverage in visual sensor networks

2020 ◽  
Vol 39 (3) ◽  
pp. 2817-2829
Author(s):  
Ahmad Javan Bakht ◽  
Homayun Motameni ◽  
Hosein Mohamadi
Keyword(s):  
Sensor Networks ◽  
Learning Automata ◽  
Coverage Problem ◽  
Visual Sensor ◽  
Directional Sensor Networks ◽  
Algorithm Performance ◽  
Visual Sensor Networks ◽  
Directional Sensors ◽  
Minimum Number ◽  
Directional Sensor

One of the most important problems in directional sensor networks is k-coverage in which the orientation of a minimum number of directional sensors is determined in such a way that each target can be monitored at least k times. This problem has been already considered in two different environments: over provisioned where the number of sensors is enough to cover all targets, and under provisioned where there are not enough sensors to do the coverage task (known as imbalanced k-coverage problem). Due to the significance of solving the imbalanced k-coverage problem, this paper proposes a learning automata (LA)-based algorithm capable of selecting a minimum number of sensors in a way to provide k-coverage for all targets in a balanced way. To evaluate the efficiency of the proposed algorithm performance, several experiments were conducted and the obtained results were compared to those of two greedy-based algorithms. The results confirmed the efficiency of the proposed algorithm in terms of solving the problem.

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