ArticleSensors (Basel, Switzerland)2020
Priority-Based Data Collection for UAV-Aided Mobile Sensor Network.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Data Collection Method for Mobile Wireless Sensor Networks Based on Improved Dragonfly Algorithm.Computational intelligence and neuroscience · 2022Article
- Flight Planning Optimization of Multiple UAVs for Internet of Things.Sensors (Basel, Switzerland) · 2021Article
- Unfairness of Random Access with Collision Avoidance in Industrial Internet of Things Networks.Sensors (Basel, Switzerland) · 2021Article
- Optimization and Communication in UAV Networks.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors.
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Abstract
In this work, we study data collection in multiple unmanned aerial vehicle (UAV)-aided mobile wireless sensor networks (WSNs). The network topology is changing due to the mobility of the UAVs and the sensor nodes, so the design of efficient data collection protocols is a major concern. We address such high dynamic network and propose two mechanisms: prioritized-based contact-duration frame selection mechanism (PCdFS), and prioritized-based multiple contact-duration frame selection mechanisms (PMCdFS) to build collision-free scheduling and balance the nodes between the multi-UAV respectively. Based on the two mechanisms, we proposed a Balance algorithm to conduct the collision-free communication between the mobile nodes and the multi-UAVs. Two key design ideas for a Balance algorithm are: (a) no need of higher priority for those nodes that have lower transmission rate between them and the UAV and (b) improve the communication opportunity for those nodes that have shorter contact duration with the UAVs. We demonstrate the performance of proposed algorithms through extensive simulations, and real experiments. These experiments using 15 mobile nodes at a path with 10 intersections and 1 island, present that network fairness is efficiently enhanced. We also confirm the applicability of proposed algorithms in a challenging and realistic scenario through numerous experiments on a path at Tongji campus in Shanghai, China.
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