. 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).

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Abstract-Networked unmanned aerial vehicles (UAVs) have found an increasing number of applications in recent years. In this work, we provide an analytical method to evaluate the sensor coverage performance of a UAV network, where the individual UAVs can work independently or cooperatively, respectively, to achieve a common goal. More specifically, we propose a stochastic model in terms of a Markov chain including approximations for its parameters. Studying several scenarios using the Markov chain as well as simulations, we investigate the impact of network size and area size on the achieved coverage. While the Markovbased analysis is an approximation, the results are still in good agreement with the simulations.

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In this paper, controlled mobility is used in a novel fashion in order to achieve the best placement for prolonging the lifetime and for maximizing the energy efficiency of the wireless sensor network. Basically, a new source of energy consumption is introduced when the mobility of nodes is exploited. We design a new mechanism for the nodes, which, by virtualizing the movement phase, makes nodes reach the best placement and, at the same time, it counterbalances the effects of energy wasting caused by the movement. We validated the results by extensive simulation campaigns where different network parameters vary. In all the cases, the performance of the network, in terms of lifetime and energy efficiency, has been found fairly superior to the schemes existing in literature.

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