Integrated coverage and connectivity configuration in wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Sensor deployment and target localization in distributed sensor networks
ACM Transactions on Embedded Computing Systems (TECS)
Worst and Best-Case Coverage in Sensor Networks
IEEE Transactions on Mobile Computing
Efficient Deployment Algorithms for Ensuring Coverage and Connectivity ofWireless Sensor Networks
WICON '05 Proceedings of the First International Conference on Wireless Internet
Handbook On Theoretical And Algorithmic Aspects Of Sensor, Ad Hoc Wireless, and Peer-to-Peer Networks
Movement-Assisted Sensor Deployment
IEEE Transactions on Mobile Computing
Connected sensor cover: self-organization of sensor networks for efficient query execution
IEEE/ACM Transactions on Networking (TON)
Redundancy and coverage detection in sensor networks
ACM Transactions on Sensor Networks (TOSN)
A Delaunay Triangulation based method for wireless sensor network deployment
Computer Communications
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
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
Genetic Evolution of a Neural Network for the Autonomous Control of a Four-Wheeled Robot
MICAI '07 Proceedings of the 2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session
A Short-Range Infrared Communication for Swarm Mobile Robots
ICSPS '09 Proceedings of the 2009 International Conference on Signal Processing Systems
IEEE Transactions on Robotics
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There are many critical issues arising in wireless sensor and robot networks (WSRN). Based on the specific application, different objectives can be taken into account such as energy consumption, throughput, delay, coverage, etc. Also many schemes have been proposed in order to optimize a specific quality of service (QoS) parameter. With the focus on the self-organizing capabilities of nodes in WSRN, we propose a movement-assisted technique for nodes self-deployment. Specifically, we propose to use a neural network as a controller for nodes mobility and a genetic algorithm for the training of the neural network through reinforcement learning [27]. This kind of scheme is extremely adaptive, since it can be easily modified in order to consider different objectives and QoS parameters. In fact, it is sufficient to consider a different kind of input for the neural network to aim for a different objective. All things considered, we propose a new method for programming a WSRN and we show practically how the technique works, when the coverage of the network is the QoS parameter to optimize. Simulation results show the flexibility and effectiveness of this approach even when the application scenario changes (e.g., by introducing physical obstacles).