Indirect Direct Learning Coverage Control for Wireless Sensor and Mobile Robot Networks
Abstract : This article proposes indirect/direct learning control schemes for wireless sensor and mobile robot networks to cover an environment according to the density function, which is the distribution of an important quantity within the environment. When stationary sensors cooperate with mobile robots, the density estimation can be enhanced by using nonstationary basis functions to relax the assumption of matching conditions in the previous approach. To improve the density function estimation, this study employs an expectation-maximization algorithm and log-likelihood, which maximizes the similarity between the proposed normalized density and normalized coverage function. Subsequently, the adaptive weighting algorithm is combined with the proposed indirect coverage control for tunable basis centers and the weighting of the basis functions. For direct coverage control, mobile robots are driven to cover the regions of higher importance while simultaneously estimating the density function utilizing a sensory model function. We prove that the Lloyd algorithm is a special case of the direct method when the density function and Voronoi partitions are available. The efficiency of the proposed methods is confirmed in numerical examples and semiexperiments.
? Their work underlines that most existing topology control techniques are divided into two categories depending on which metric drives the whole process: network coverage or network connectivity.
? With respect to this classification there is no existing work matching exactly our hypotheses and objectives. However, our contributions are related to several areas highlighted by the authors of the survey.
? It exists several methods to control sensors movements keeping connectivity from a fully connected initial state of the network.
? The authors propose a reinforcement of the network by forcing the network to be bi-connected. So there exists always two different links between any pair of sensors.
? The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes.
? This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed.
? The coverage problem is one of the fundamental problems in WSNs as it has a direct impact on the sensors energy consumption and the network lifetime. The coverage problem can generally refer to how to monitor the network field effectively.
? However, to the best of our knowledge, none of the existing studies analyze, review and provide a clear description of all features that cover all factors as well as classify the coverage problems in its entirety.
• The proposed method is based on the decentralized management of the sensors moves and states regarding their battery level and their neighbors positions.
• They conclude their review giving some insights for improving global WSN performances and, balancing coverage and connectivity appears as one of the proposed design guidelines.
• The proposed method is also centralized. This is also the objective of the SPAN algorithm except that the latter is a decentralized one.
• Further to this work, these authors also proposed a Grid-Quorum algorithm with Vorono¨i diagram and cascaded movement to identify and relocate redundant sensors by minimizing the message complexity and the energy consumption.
? Moreover, the performance of these protocols is limited by the challenges on determining an accurate radio model for the sensor nodes in the network.
? We found that the performance of these protocols is mainly limited by challenges related to determining a more realistic coverage model for the sensor nodes in the networks.
? In, the authors conducted empirical measurements of the packet delivery performance of various sensor platforms.
? However, performance results have shown that NSGA-II outperforms MOEA/D in terms of many optimization criteria.
? Experimental results shown that CCP performance degrades with the location errors increase, sensing signal irregularity and packet losses.
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