Learning automata with changing number of actions
IEEE Transactions on Systems, Man and Cybernetics
Learning automata: theory and applications
Learning automata: theory and applications
Review: Coverage and connectivity issues in wireless sensor networks: A survey
Pervasive and Mobile Computing
Wireless sensor network survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Data aggregation in sensor networks using learning automata
Wireless Networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
A memetic algorithm for extending wireless sensor network lifetime
Information Sciences: an International Journal
Coverage problems in sensor networks: A survey
ACM Computing Surveys (CSUR)
The Journal of Supercomputing
Maximizing Lifetime of Target Coverage in Wireless Sensor Networks Using Learning Automata
Wireless Personal Communications: An International Journal
The Journal of Supercomputing
A learning automata-based solution to the target coverage problem in wireless sensor networks
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
Wireless Personal Communications: An International Journal
| Hi-index | 0.00 |
Wireless sensor networks (WSNs) have been widely used in different applications. One of the most significant issues in WSNs is developing an efficient algorithm to monitor all the targets and, at the same time, extend the network lifetime. As sensors are often densely deployed, employing scheduling algorithms can be considered a promising approach that is able ultimately to result in extending total network lifetime. In this paper, we propose three learning automata-based scheduling algorithms for solving target coverage problem in WSNs. The proposed algorithms employ learning automata (LA) to determine the sensors that should be activated at each stage for monitoring all the targets. Additionally, we design a pruning rule and manage critical targets in order to maximize network lifetime. In order to evaluate the performance of the proposed algorithms, extensive simulation experiments were carried out, which demonstrated the effectiveness of the proposed algorithms in terms of extending the network lifetime. Simulation results also revealed that by a proper choice of the learning rate, a proper trade-off could be achieved between the network lifetime and running time.