A heading adjustment method in wireless directional sensor networks

Li, Wei; Huang, Changxin; Xiao, Chao; Han, Songchen

doi:10.1016/j.comnet.2018.01.031

Cited by 12 publications

(7 citation statements)

References 18 publications

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“…To maximize their coverage relative to their neighbors and achieve area coverage optimization. In the literature [ 21 ], an improved coverage method based on Delaunay triangulation to adjust sensor tilt was proposed to strengthen the coverage of weak spots. Each sensor independently makes a decision on the rotation direction of the sensor based on the local information of the neighboring sensor.…”

Section: Related Workmentioning

confidence: 99%

Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Cheng

Huangfu

2021

Sensors

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With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

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Section: Related Workmentioning

confidence: 99%

Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Cheng

Huangfu

2021

Sensors

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“…The available studies about coverage problem can be categorized into four main types [27]: target-based coverage, areabased coverage, sensor deployment, and minimizing energy consumption. And a set of heuristic algorithm [28][29][30] has been proposed to find a nearly-optimal solution under a specific setting, as most of the problems are proved NP-hard. Recently, with the advances of machine learning, Mohamadi, et al adopt learning-based methods [31,32] for maximizing network lifetime in wireless sensor networks.…”

Section: Related Workmentioning

confidence: 99%

Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks

Xu¹,

Zhong²,

Wang³

2020

Preprint

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Maximum target coverage by adjusting the orientation of distributed sensors is an important problem in directional sensor networks (DSNs). This problem is challenging as the targets usually move randomly but the coverage range of sensors is limited in angle and distance. Thus, it is required to coordinate sensors to get ideal target coverage with low power consumption, e.g. no missing targets or reducing redundant coverage. To realize this, we propose a Hierarchical Targetoriented Multi-Agent Coordination (HiT-MAC), which decomposes the target coverage problem into two-level tasks: targets assignment by a coordinator and tracking assigned targets by executors. Specifically, the coordinator periodically monitors the environment globally and allocates targets to each executor. In turn, the executor only needs to track its assigned targets. To effectively learn the HiT-MAC by reinforcement learning, we further introduce a bunch of practical methods, including a self-attention module, marginal contribution approximation for the coordinator, goal-conditional observation filter for the executor, etc. Empirical results demonstrate the advantage of HiT-MAC in coverage rate, learning efficiency, and scalability, comparing to baselines. We also conduct an ablative analysis on the effectiveness of the introduced components in the framework. 1 https://playsight.com/automatic-production/ 34th Conference on Neural Information Processing Systems (NeurIPS 2020),

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“…3D-DAOA. We design related algorithms to solve two core issues encountered with directional sensor nodes: node rotation and mobility in [29]. We now describe a dynamic adjustment optimisation algorithm for 3D DSNs based on spherical sector coverage models: 3D-DAOA.…”

Section: Definitions Of Vfamentioning

confidence: 99%

Dynamic Adjustment Optimisation Algorithm in 3D Directional Sensor Networks Based on Spherical Sector Coverage Models

2019

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In directional sensor networks research, target event detection is currently an active research area, with applications in underwater target monitoring, forest fire warnings, border areas, and other important activities. Previous studies have often discussed target coverage in two-dimensional sensor networks, but these studies cannot be extensively applied to three-dimensional networks. Additionally, most of the previous target coverage detection models are based on a circular or omnidirectional sensing model. More importantly, if the directional sensor network does not design a better coverage algorithm in the coverage-monitoring process, its nodes’ energy consumption will increase and the network lifetime will be significantly shortened. With the objective of addressing three-dimensional target coverage in applications, this study proposes a dynamic adjustment optimisation algorithm for three-dimensional directional sensor networks based on a spherical sector coverage model, which improves the lifetime and coverage ratio of the network. First, we redefine the directional nodes’ sensing model and use the three-dimensional Voronoi method to divide the regions where the nodes are located. Then, we introduce a correlation force between the target and the sensor node to optimise the algorithm’s coverage mechanism, so that the sensor node can accurately move to the specified position for target coverage. Finally, by verifying the feasibility and accuracy of the proposed algorithm, the simulation experiments demonstrate that the proposed algorithm can effectively improve the network coverage and node utilisation.

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A heading adjustment method in wireless directional sensor networks

Cited by 12 publications

References 18 publications

Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks

Dynamic Adjustment Optimisation Algorithm in 3D Directional Sensor Networks Based on Spherical Sector Coverage Models

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