Ferry based data gathering in Wireless Sensor Networks

Vanarotti, Gurudevi C.; Kulkarni, Umesh M.; Kenchannavar, Harish H.

doi:10.1109/icatcct.2016.7911985

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…As a key technology for future networks, the internet of things (IoT) can meet the transparent and seamless integration of massive heterogeneous nodes at any time, any place, and any interconnection mode [1,2] and can provide service support for information exchange and content sharing between nodes [3].…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation in UAV-Assisted Wireless Powered Communication Networks for Urban Monitoring

Lyu

Zhang

2022

Wireless Communications and Mobile Computing

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In this paper, we studied the unmanned aerial vehicle-assisted urban monitoring network, in which unmanned aerial vehicle (UAV) with wireless power transmission provides energy transmission and data collection services for the network. Considering the density of the urban monitoring network, we use the k -means algorithm to cluster the monitoring nodes and reduce the complexity of the UAV services. The UAVs are serviced using a fly-hover-communication protocol. During hovering, the UAV works in the full-duplex mode, collecting data from cluster head nodes on one side and recharging nodes in coverage on the other side. We propose a multiobjective joint optimization problem that considers maximizing the amount of data collection and energy transfer and minimizing the energy consumption of the UAV during the service period. In the optimization process, there is a partial conflict between the three objectives. For this reason, the importance of the optimization objectives is considered and described by weighting parameters. A multiobjective joint deep deterministic policy gradient algorithm is proposed for the multiobjective control policy of UAVs. Numerical results show that the proposed algorithm can achieve the joint optimization of multiple objectives and is compared with other algorithms to verify the superiority of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Resource Allocation in UAV-Assisted Wireless Powered Communication Networks for Urban Monitoring

Lyu

Zhang

2022

Wireless Communications and Mobile Computing

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Path Planning for UAV Communication Networks: Related Technologies, Solutions, and Opportunities

Luo

Wang

Xia³

et al. 2023

ACM Comput. Surv.

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Path planning has been a hot and challenging field in unmanned aerial vehicles (UAV). With the increasing demand of society and the continuous progress of technologies, UAV communication networks (UAVCN) are also flourishing. The mobility of UAV nodes allows for flexible network deployment, but some challenges are brought, such as power constraints, throughput, cost, and time efficiency. Therefore, path planning is significant for UAVCN. This paper presents a review of UAVCN path planning. We first introduce the network structure and performance evaluation of UAVCN. We then investigate the generic UAV path planning algorithms and the path planning algorithms in UAVCN. In this paper, the advantages and disadvantages of each path planning algorithm and the functional problems. The challenges faced in path planning for UAVCN, the solutions, state-of-the-art, and representative results are also presented. In addition, we illustrate future research directions for UAVCN path planning as well, which can provide some help to researchers.

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Ferry based data gathering in Wireless Sensor Networks

Cited by 2 publications

References 2 publications

Resource Allocation in UAV-Assisted Wireless Powered Communication Networks for Urban Monitoring

Resource Allocation in UAV-Assisted Wireless Powered Communication Networks for Urban Monitoring

Path Planning for UAV Communication Networks: Related Technologies, Solutions, and Opportunities

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