Coverage Adaptive Optimization Algorithm of Static‐Sensor Networks for Target Discovery

Xiao, Shuo; Li, Tianxu; Tang, Chaogang; Chen, Yuan

doi:10.1049/cje.2018.02.009

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…Xiao et al focused on analysing the target discovery ability of static sensor networks [40], and investigating the target discovery probability and discovery delay with different nodes density, sensing range and duty cycle. Based on that, they gave a coverage adaptive optimization algorithm that can significantly improve the lifetime of WSNs.…”

Section: B Lifetime Of Wsnsmentioning

confidence: 99%

A Novel Mobile-Coverage Scheme for Hybrid Sensor Networks

Huang

Liu

et al. 2020

IEEE Access

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In this paper, a novel Mobile-Coverage scheme is proposed to improve the coverage and lifetime of hybrid sensor networks, and thus the target region can be completely covered with minimum number of mobile sensor nodes and the energy consumption can be reduced in hybrid sensor networks at the same time. Our scheme is applied to a hybrid network comprising of both static and mobile sensor nodes, which are randomly deployed in a rectangle area. We derived the minimum number of sensor nodes as well as the mobile nodes required to cover the target region completely. Compared to the existing works, the minimum number of mobile sensor nodes required in this paper is significantly smaller, which implies that the movement distance and energy consumption of mobile nodes in our scheme is reduced. Moreover, we proved that the construction of our scheme based on the Virtual Force and Voronoi Graph (VFVG) is correct, and then derived the optimal trade-off between the coverage of the target region and energy consumption of mobile nodes. Our scheme can achieve the maximum coverage which is 98.7% by minimum number of mobile sensor nodes. Meanwhile, a connectivity rate of 76.84% can also be achieved by our connectivity evaluation method. Finally, a new topology construction algorithm-Topology Construction and Maintenance based on Trust (TCMT) is created to further increase the lifetime of the sensor network. We also evaluated the performance of our scheme in terms of coverage and lifetime, and carried out extensive experiments to compare the coverage and energy consumption in our scheme and several previous schemes. The experimental results showed that our scheme is more efficient than the previous schemes simulated. INDEX TERMS Topology construction and maintain, Minimum numbers of mobile sensor, Voronoi graph, Virtual force, TCMT.

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Section: B Lifetime Of Wsnsmentioning

confidence: 99%

A Novel Mobile-Coverage Scheme for Hybrid Sensor Networks

Huang

Liu

et al. 2020

IEEE Access

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“…For example, in the context of UAV-based collaborative beamforming [13], UAVs can realize collaborative beamforming for establishing a virtual antenna array to generate a beam pattern with a sharp main lobe and low sidelobe levels to enhance the antenna gain, reduce interference and improve the signal-to-noise ratio of the received signal and focused transmitted signal [14]. Similarly, in collaborative multiple-target tracking [15], the natural characteristics of flexible mobility of UAVs can play a significant role in sensing and tracking mobile targets at a large scale, leading to advanced disaster monitoring, damage assessment, manufacturing safety, and border security [16], [17]. At the same time, collaborative UAV routing also offers efficient ways for tasks offloading in a distributed manner, such as localization, actuation-based task assignments and optimal path selection from source to destination for different product delivery and disaster relief applications [18], [19].…”

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confidence: 99%

Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

Javaid¹,

Saeed²,

Qadir³

et al. 2023

Preprint

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The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs.

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Nodes Deployment Algorithm Based on Data Fusion and Evidence Theory in Wireless Sensor Networks

Liu

2021

Wireless Pers Commun

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Coverage Adaptive Optimization Algorithm of Static‐Sensor Networks for Target Discovery

Cited by 7 publications

References 15 publications

A Novel Mobile-Coverage Scheme for Hybrid Sensor Networks

A Novel Mobile-Coverage Scheme for Hybrid Sensor Networks

Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

Nodes Deployment Algorithm Based on Data Fusion and Evidence Theory in Wireless Sensor Networks

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