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 semi experiments.
? A significant parameter that has an effect on the final positions of the sensor nodes is the existence of points of interest (POI) which require k- coverage.
? The initial population on the chromosomes was created in a random way so that the bounds and the linear constrains were satisfied, provided that they existed.
? PSO proposes the existence of a group of particles, where each of them represents a potential solution to the problem.
? Grid based methods consider grid points in order to determine the location of sensor nodes and calculate the existent area coverage as the ratio of grid points covered to the total number of grid points.
? 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.
? 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 algorithm combines the virtual force (VF) algorithm with particle swarm optimization, where VF uses both attractive and repulsive forces to determine virtual motion paths and the rate of movement for sensors.
• The authors in proposed a novel algorithm in order to optimize the coverage in a WSN using the PSO algorithm along with Voronoi diagrams.
• In, a novel bidding protocol, which is applied in an area where a mixture of static and mobile nodes has been deployed, is proposed.
• A virtual force algorithm (VFA) is proposed in, as a sensor deployment methodology in order to improve the coverage area.
? The sensors deployment model is an important design criterion for designing energy-efficient coverage protocols in WSNs.
? Moreover, the performance of these protocols is limited by the challenges on determining an accurate radio model for the sensor nodes in the network.
? In, the authors conducted empirical measurements of the packet delivery performance of various sensor platforms.
? 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.
? We give a thorough discussion on the open issues associated with the design of realistic energy-efficient coverage protocols for WSNs.
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