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.

Solving target coverage problem in directional sensor networks with ability to adjust sensing range using continuous learning automata

2021 ◽  
pp. 1-14
Author(s):  
Azam Qarehkhani ◽  
Mehdi Golsorkhtabaramiri ◽  
Hosein Mohamadi ◽  
Meisam Yadollahzadeh Tabari
Keyword(s):  
Sensor Networks ◽  
Learning Automata ◽  
Target Coverage ◽  
Continuous Learning ◽  
Coverage Problem ◽  
Directional Sensor Networks ◽  
Sensing Range ◽  
Decrease Energy Consumption ◽  
Target Coverage Problem ◽  
Directional Sensor

Directional sensor networks (DSNs) are classified under wireless networks that are largely used to resolve the coverage problem. One of the challenges to DSNs is to provide coverage for all targets in the network and, at the same time, to maximize the lifetime of network. A solution to this problem is the adjustment of the sensors’ sensing ranges. In this approach, each sensor adjusts its own sensing range dynamically to sense the corresponding target(s) and decrease energy consumption as much as possible through forming the best cover sets possible. In the current study, a continuous learning automata-based method is proposed to form such cover sets. To assess the proposed algorithm’s performance, it was compared to the results obtained from a greedy algorithm and a learning automata algorithm. The obtained results demonstrated the superiority of the proposed algorithm regarding the maximization of the network lifetime.

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