The sensor deployment problem of wireless sensor networks (WSNs) is a key issue in the researches and the applications of WSNs. Fewer works focus on the 3D autonomous deployment. Aimed at the problem of sensor deploym...
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ISBN:
(纸本)9783662469811;9783662469804
The sensor deployment problem of wireless sensor networks (WSNs) is a key issue in the researches and the applications of WSNs. Fewer works focus on the 3D autonomous deployment. Aimed at the problem of sensor deployment in three dimensional spaces, the 3D Self-Deployment algorithm (3DSD) in mobile sensor networks is proposed. A 3D virtualforce model is utilized in the 3DSD method. A negotiation tactic is introduced to ensure network connectivity, and a density control strategy is used to balance the node distribution. The proposed algorithm can fulfill the nodes autonomous deployment in 3D space with obstacles. Simulation results indicate that the deployment process of 3DSD is relatively rapid, and the nodes are well distributed. Furthermore, the coverage ratio of 3DSD approximates the theoretical maximum value.
Mobile sensor networks are an important part of modern robotics systems and are widely used in robotics applications. Therefore, sensor deployment is a key issue in current robotics systems research. Since it is one o...
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Mobile sensor networks are an important part of modern robotics systems and are widely used in robotics applications. Therefore, sensor deployment is a key issue in current robotics systems research. Since it is one of the most popular deployment methods, in recent years the virtual force algorithm has been studied in detail by many scientists. In this paper, we focus on the virtual force algorithm and present a corresponding parameter investigation for mobile sensor deployment. We introduce an optimized virtual force algorithm based on the exchange force, in which a new shielding rule grounded in Delaunay triangulation is adopted. The algorithm employs a new performance metric called 'pair-correlation diversion', designed to evaluate the uniformity and topology of the sensor distribution. We also discuss the implementation of the algorithm's computation and analyse the influence of experimental parameters on the algorithm. Our results indicate that the area ratio, phi(s), and the exchange force constant, G, influence the final performance of the sensor deployment in terms of the coverage rate, the convergence time and topology uniformity. Using simulations, we were able to verify the effectiveness of our algorithm and we obtained an optimal region for the (phi(s), G)-parameter space which, in the future, could be utilized as an aid for experiments in robotic sensor deployment.
Wireless Sensor Networks (WSNs) collect and transfer environmental data from a predefined field to a base station to be processed and analyzed. A major problem in designing WSNs is coverage maximization, in which a gi...
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Wireless Sensor Networks (WSNs) collect and transfer environmental data from a predefined field to a base station to be processed and analyzed. A major problem in designing WSNs is coverage maximization, in which a given number of sensor nodes must be deployed in a way that maximizes area coverage of a given network, without violating practical constraints. This is a known NP-hard problem and thus requires metaheuristic approaches for practical problem sizes. Two metaheuristics, namely Genetic algorithm and Particle Swarm Optimization are proposed to tackle this problem. Our new contributions include a partial use of heuristic initialization, new fitness function, modified virtual force algorithm, addition of a uniform deceleration to the calculation of inertia weight and addition of the influence of sub-populations' head individuals. The proposed algorithms are comprehensively experimented and compared with the current state-of-the-art for the equivalent problem without obstacles. Experimental results not only suggest which algorithms should be applied to which cases, but also provide insights into parameter settings, effects of heuristic initialization and effects of virtual force algorithm in each case. These conclusions are meaningful for our future research on obstacles constrained area coverage problems related to connectivity and lifetime of WSNs. (C) 2019 Elsevier B.V. All rights reserved.
Equipped with micro wireless sensor nodes, a unmanned aerial vehicle) cluster can form an emergency communication network, which can have several applications such as environmental monitoring, disaster relief, militar...
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Equipped with micro wireless sensor nodes, a unmanned aerial vehicle) cluster can form an emergency communication network, which can have several applications such as environmental monitoring, disaster relief, military operations and so on. However, situations where there is excessive aggregation and small amount of dispersion of the unmanned aerial vehicle cluster may occur when the network is formed. To mitigate these, a solution based on a 3D virtualforce driven by self-adaptive deployment (named as 3DVFSD) is proposed. As a result, the three virtualforces of central gravity, uniform force, and boundary constraint force are combined to act on each node of the communication network. By coordinating the distance between the nodes, especially the threshold of the distance between the boundary node and the boundary, the centralized nodes can be relatively dispersed. Meanwhile, the nodes can be prevented from being too scattered by constraining the distance from the boundary node to the end. The simulation results show that the 3DVFSD algorithm is superior to the traditional virtualforce-driven deployment strategy in terms of convergence speed, coverage, and uniformity.
The network coverage problem for an area with a randomly deployed wireless sensor network (WSN) in the presence of obstacles can be alleviated through mobile sensor nodes. However, the requirement for the dissipated e...
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The network coverage problem for an area with a randomly deployed wireless sensor network (WSN) in the presence of obstacles can be alleviated through mobile sensor nodes. However, the requirement for the dissipated energy in mobility and sensing has increased. Therefore, energy efficient coverage is a significant issue and has been near-optimally solved by heuristic techniques. Focusing on this issue, this paper introduces an improved competitive swarm optimizer to maximize the coverage area and minimize the network energy simultaneously. The proposed method incorporates the virtual force algorithm (VFA) and the Voronoi diagram (VD) to improve the network performance during the optimization process. The VFA is combined with a boundary mechanism to control the locations of sensors, while the VD is utilized to extract the network information for the decoding process. The superior performance of the model is verified by intensive evaluations against state-of-the-art techniques in terms of the coverage ratio and network energy consumption.
Underwater node coverage is the basis of various applications in underwater wireless sensor network (UWSN). It is easy to cause the coordinates of underwater nodes drift affected by water flow action. Some sparsely de...
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Underwater node coverage is the basis of various applications in underwater wireless sensor network (UWSN). It is easy to cause the coordinates of underwater nodes drift affected by water flow action. Some sparsely deployed underwater nodes may form coverage holes, which makes it impossible to locate underwater targets effectively. Combined with the water flow situation, this paper proposes an improved brain storm optimization integrated with virtual force algorithm (IBSO-VFA) for improving UWSN coverage performance. After analyzing the water jet model and the resultant force along Z axis, the velocity components of east, north and depth directions can be derived, which can reveal the coordinate evolution model of drifted underwater nodes under water flow action. Underwater nodes present non-uniform distribution of partly sparse and partly dense coverage, even with a lot of coverage holes. Inspired by the virtual force algorithm, the drifted underwater nodes are driven to their relative communicable positions. Meanwhile, the brain storm optimization has been improved and applied to avoid falling into local coverage optimum by pure VFA. Based on the node maximum coverage, the dual mapping of signal domain and localizability domain is established in consideration of ranging and coordinate errors. Finally, a comprehensive performance test is conducted to evaluate IBSO-VFA performance in terms of coverage rates, k-coverage and localizability area. The results indicate that the IBSOVFA can maximize the UWSN coverage and localizability performance. The proposed IBSO-VFA can provide a close-to-practical coverage model for drifted underwater nodes, and can provide a theoretical basis for ocean information perception in UWSN.
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