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

International audienceThere 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)

Multi-Objective Evolving Neural Network supporting SDR Modulations Management

International audienceThis paper proposes a distributed Neural/Genetic algorithm able to compute both the more suitable positioning and transmission modulation schemes for fixed/mobile wireless nodes equipped with software defined radio abilities. Devices considered in this work are able to move towards new positions by applying the concept of controlled mobility. The selection of the more suitable modulation scheme is realized through the SDR (Software Defined Radio) paradigm. The synergistic combination of controlled mobility and SDR in a totally distributed way, allows to obtain a high degree of self-configurability; moreover, the extreme adaptability to the network conditions and application level constraints in terms of coverage and guaranteed connectivity, make the proposed approach well suited for quite different communication scenarios such as classical monitoring or disaster recovery. The obtained results, validated throughout an intensive simulation campaign, show how the controlled mobility paradigm applied to the wireless devices and the intrinsic re-configuring SDR capabilities, increase the performance of the network both in terms of coverage and connectivity respect to other algorithms

Performance Evaluation of Novel Distributed Coverage Techniques for Swarms of Flying Robots

International audienceThis paper focuses on the coverage of specific Zones of Interest that can change dynamically over time by using a swarm of flying robots. The mobility of the flying devices is achieved by the design of two distributed and local algorithms. The first algorithm is based on Particle Swarm Optimization (PSO) and Virtual Forces Algorithm (VFA). We modify the classical PSO approach to propose a totally distributed algorithm, which only requires the flying robots to receive local information from the neighbors to update their velocity and trajectory (PSO-S). This new distributed version of the PSO is combined with a distributed version of the Virtual Forces Algorithm. The second algorithm is a distributed implementation of the VFA (VFA-D). To the best of our knowledge, these two approaches are novel in their distributed character, scalability and implementability on resource-constrained devices. We show that the proposed algorithms are reactive, i.e. able to capture in an effective fashion the events happening within the field even if the position of the events changes over time. To show the effectiveness of the proposed techniques, we perform extensive simulations to compare both the PSO-S and the VFA-D schemes with a centralized version of the VFA. Simulations show the good performance in terms of coverage and traveled distance as well as the high reactivity of both PSO-S and VFA-D when the ZoI changes

Navigation control of an automated mobile robot robot using neural network technique

Over recent years, automated mobile robots play a crucial role in various navigation operations. For any mobile device, the capacity to explore in its surroundings is essential. Evading hazardous circumstances, for example, crashes and risky conditions (temperature, radiation, presentation to climate, and so on.) comes in the first place, yet in the event that the robot has a reason that identifies with particular places in its surroundings, it must discover those spots. There is an increment in examination here due to the requisition of mobile robots in a solving issues like investigating natural landscape and assets, transportation tasks, surveillance, or cleaning. We require great moving competencies and a well exactness for moving in a specified track in these requisitions. Notwithstanding, control of these navigation bots get to be exceptionally troublesome because of the exceedingly unsystematic and dynamic aspects of the surrounding world. The intelligent reply to this issue is the provision of sensors to study the earth. As neural networks (NNs) are described by adaptability and a fitness for managing non-linear problems, they are conceived to be useful when utilized on navigation robots. In this exploration our computerized reasoning framework is focused around neural network model for control of an Automated motion robot in eccentric and unsystematic nature. Hence the back propagation algorithm has been utilized for controlling the direction of the mobile robot when it experiences by an obstacle in the left, right and front directions. The recreation of the robot under different deterrent conditions is carried out utilizing Arduino which utilizes C programs for usage

Self-organizing technique for improving coverage in connected mobile objects networks

Despite the multiple benefits offered today by connected mobile objects networks (CMONs), some constraints continue to limit their development and to degrade their applications and services’ performance. Given their limited energy, some or many objects may stop functioning which leads to the deterioration of network functionalities such as monitoring, detection and transfer of data. It is in this context that our work is situated, namely the improvement of applications performance and the quality of service (QoS) within CMONs, by exploiting some communication environment parameters and geometry techniques.We propose a new technique called self-organization area coverage (SOAC) for CMONs which aims to ensure maximum coverage in the network while optimizing the exploited resources. SOAC has been evaluated and compared not only to the network without improvement but to two other solutions proposed in the literature. The obtained results show a clear improvement in terms of network coverage and several QoS parameters

Neural Networks and SDR Modulation schemes for wireless mobile nodes: a synergic approach

International audienceIn this paper, we envisage the possibility to exploit, in a synergic way, the Software Defined Radio (SDR) capability and the mobility support for wireless devices to dynamically compute the most suitable modulation scheme and the best position in order to improve both the coverage and connectivity in a specific area. The combined approach is based on a Neural/Genetic technique and wireless nodes are able to self-organize in a totally distributed way by using only local information. The extreme adaptability to the network conditions and application level constraints makes the proposed approach well suited for different communication scenarios such as standard monitoring or disaster recovery. The system performance has been evaluated by dealing a suite of simulation tests to show as the controlled mobility paradigm, coupled with the intrinsic re-configuring SDR capabilities of such wireless devices, allows to increase the network performances both in terms of coverage and connectivity by dynamically adapting the modulation schemes to the specific communication scenario