Learning automata: an introduction
Learning automata: an introduction
Research challenges in wireless networks of biomedical sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
A new kind of science
Infrastructure tradeoffs for sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Grid Coverage for Surveillance and Target Location in Distributed Sensor Networks
IEEE Transactions on Computers
Data Gathering Algorithms in Sensor Networks Using Energy Metrics
IEEE Transactions on Parallel and Distributed Systems
Power Efficient Topologies for Wireless Sensor Networks
ICPP '02 Proceedings of the 2001 International Conference on Parallel Processing
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Application-specific protocol architectures for wireless networks
Application-specific protocol architectures for wireless networks
Sensor deployment and target localization in distributed sensor networks
ACM Transactions on Embedded Computing Systems (TECS)
Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems
Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems
Estimating Coverage Holes and Enhancing Coverage in Mixed Sensor Networks
LCN '04 Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks
Movement-Assisted Sensor Deployment
IEEE Transactions on Mobile Computing
Maintaining Coverage by Progressive Crystal-Lattice Permutation in Mobile Wireless Sensor Networks
ICSNC '06 Proceedings of the International Conference on Systems and Networks Communication
Mobility Limited Flip-Based Sensor Networks Deployment
IEEE Transactions on Parallel and Distributed Systems
Bidding Protocols for Deploying Mobile Sensors
IEEE Transactions on Mobile Computing
Deploying Wireless Sensor Networks under Limited Mobility Constraints
IEEE Transactions on Mobile Computing
Efficient Placement and Dispatch of Sensors in a Wireless Sensor Network
IEEE Transactions on Mobile Computing
Brief paper: Asynchronous cellular learning automata
Automatica (Journal of IFAC)
Event-Based Motion Control for Mobile-Sensor Networks
IEEE Pervasive Computing
Mobility Control for Complete Coverage in Wireless Sensor Networks
ICDCSW '08 Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems Workshops
Distributed Deployment Schemes for Mobile Wireless Sensor Networks to Ensure Multilevel Coverage
IEEE Transactions on Parallel and Distributed Systems
Cellular learning automata with multiple learning automata in each cell and its applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
J-Sim: a simulation and emulation environment for wireless sensor networks
IEEE Wireless Communications
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
Varieties of learning automata: an overview
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Energy-efficient deployment of Intelligent Mobile sensor networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Sensor planning for elusive targets
Mathematical and Computer Modelling: An International Journal
Scan-Based Movement-Assisted Sensor Deployment Methods in Wireless Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
ELACCA: Efficient Learning Automata Based Cell Clustering Algorithm for Wireless Sensor Networks
Wireless Personal Communications: An International Journal
Adaptive cooperative particle swarm optimizer
Applied Intelligence
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One important problem which may arise in designing a deployment strategy for a wireless sensor network is how to deploy a specific number of sensor nodes throughout an unknown network area so that the covered section of the area is maximized. In a mobile sensor network, this problem can be addressed by first deploying sensor nodes randomly in some initial positions within the area of the network, and then letting sensor nodes to move around and find their best positions according to the positions of their neighboring nodes. The problem becomes more complicated if sensor nodes have no information about their positions or even their relative distances to each other. In this paper, we propose a cellular learning automata-based deployment strategy which guides the movements of sensor nodes within the area of the network without any sensor to know its position or its relative distance to other sensors. In the proposed algorithm, the learning automaton in each node in cooperation with the learning automata in the neighboring nodes controls the movements of the node in order to attain high coverage. Experimental results have shown that in noise-free environments, the proposed algorithm can compete with the existing algorithms such as PF, DSSA, IDCA, and VEC in terms of network coverage. It has also been shown that in noisy environments, where utilized location estimation techniques such as GPS-based devices and localization algorithms experience inaccuracies in their measurements, or the movements of sensor nodes are not perfect and follow a probabilistic motion model, the proposed algorithm outperforms the existing algorithms in terms of network coverage.