Nodes self-deployment for coverage maximization in mobile robot networks using an evolving neural network

Abstract

. Nodes self-deployment for coverage maximization in mobile robot networks using an evolving neural network. Computer Communications, Elsevier, 2012, 35 (9) AbstractThere 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 objective and/or QoS parameters. In fact, it is sufficient to consider a different input for the neural network to aim to a different objective. All things considered, we propose a new method for programming a WSRN and we will 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).

Multiple point of interest discovery and coverage with mobile wireless sensors

“…WSN have received a lot of attention over the last decade above all in improving the deployment quality [14,25], self-organization [11,31], energy efficiency [19], communication aspects [34,35], and the overall reliability and security [7]. A typical application of WSN is environmental monitoring.…”

The integration of mobile (tele) robotics and wireless sensor networks: A survey

“…A more distributed approach to the deployment of MRNs is studied in [92]. Here the authors propose using a neural network to control the locations of the robots in order to maximize coverage.…”

“…Autonomous robots and SLAM to localize and map the sensed area [12]. Controlled mobility and a neural network [92], or virtual forces [93].…”

Sensor Network Deployment under Distance Uncertainty with Robust Optimization

“…According to the method adopted, approaches for sensor deployment can be classified into three categories: virtual force based [4,5], grid-based [6,7] and computational geometry based [8,9]. In many cases, non-uniform detection requirements must be considered according to the importance of the surveillance area.…”