Towards Real-Time Data Delivery in oneM2M Platform for UAV Management System

Choi, Sung-Chan; Ahn, Il-Yeop; Park, Jong-Hong; Kim, Jae-Ho

doi:10.23919/elinfocom.2019.8706417

Cited by 13 publications

(10 citation statements)

References 5 publications

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“…Simulation results showed that their algorithm performs close to the optimal solution obtained by using Integer Linear Programming (ILP). To support the drone service scenarios such as surveillance, remote sensing and mapping, Choi et al [15] designed oneM2M standard platform architecture which supports realtime data delivery for drone management system. The system improves end-to-end delay performance in different network environments.…”

Section: B Charging Technology For Dronesmentioning

confidence: 99%

“…In general, a drone needs to fly close to a sensor node issuing a charging request in order to charge it. Due to the limitation of the drone flight distance [15], after charging a sensor node, the residual energy of a drone must ensure it can reach the nearest pad to replenish energy. Without the need to consider any charging request, we set d max as the maximum flight distance for every drone.…”

Section: Deployment Of Padsmentioning

confidence: 99%

“…When v = 0, equation 14is simplified to (15). Moreover, when v <0, equation 14is equivalent to (16).…”

Section: G Analysis Of Disk Cover Schemementioning

confidence: 99%

“…1, a drone can fly directly from node A to node B, then to node C, and so forth. Moreover, since the speed of a drone can reach 100 to 289 miles per hour [15], by adopting drones instead of vehicles, one can alleviate the traveling speed limitation within the network. Evidently, the effective deployment of pads, especially geographically, becomes a significant issue when establishing the new charging system.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Wireless Charging Pad Deployment in Wireless Rechargeable Sensor Networks

Chen

Ouyang

2020

IEEE Access

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The rapid development of wireless power transfer technology brings forth innovative vehicle energy solutions and breakthroughs utilizing wireless sensor networks (WSNs). In most existing schemes, wireless rechargeable sensor networks (WRSNs) are generally equipped with one or more wireless charging vehicles (vehicles) to serve sensor nodes (SNs). These schemes solve the energy issue to some extent; however, due to off-road and speed limitations of vehicles, some SNs still cannot be charged in time, negatively affecting the lifetime of the networks. Our work proposes a new WRSN model equipped with one wireless charging drone (drone) with a constrained flight distance coupled with several wireless charging pads (pads) deployed to charge the drone when it cannot reach the subsequent stop. Our model solves this charging issues effectively and overcomes energy capacity limitations of the drone. Thus, a wireless charging pad deployment problem is formulated, which aims to apply the minimum number of pads so that at least one feasible routing path can be established for the drone to reach every SN in a given WRSN. Four feasible heuristics, three based on graph theory and one on geometry, are proposed for this problem. In addition, a novel drone scheduling algorithm, the shortest multi-hop path algorithm, is developed for the drone to serve charging requests with the assistance of pads. We examine the proposed schemes through extensive simulations. The results compare and demonstrate the effectiveness of the proposed schemes in terms of network density, region size and maximum flight distance. INDEX TERMS Wireless power transfer, wireless charging drone, wireless charging pad, sensor node, wireless rechargeable sensor networks. WEN OUYANG was born in Hsinchu, Taiwan. She received the Ph.D. degree in computer science from The University of Texas at Dallas, in 1993. From then on, she worked as a Member of Scientific Staff and a Senior Member of Scientific Staff with Bell Northern Research, later became Nortel Networks, where she was involved in large-scale telecommunication systems research and development.

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Section: B Charging Technology For Dronesmentioning

confidence: 99%

Section: Deployment Of Padsmentioning

confidence: 99%

“…When v = 0, equation 14is simplified to (15). Moreover, when v <0, equation 14is equivalent to (16).…”

Section: G Analysis Of Disk Cover Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Wireless Charging Pad Deployment in Wireless Rechargeable Sensor Networks

Chen

Ouyang

2020

IEEE Access

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“…Due to convenience and flexibility, the unmanned aerial vehicle (UAV) perfectly replaces the rescue team to perform the SAR mission [43]. Once finding a victim, the UAV will communicate with the ground rescue, thereby they can immediately reach the target location [44]. Hence, the UAV undertaking the SAR mission will lead role in future rescues (e.g.…”

Section: A the Background And Main Featurementioning

confidence: 99%

The Novel Mobility Models Based on Spiral Line for Aerial Backbone Networks

Sun

Shi

2020

IEEE Access

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With the convenience and flexibility, the UAV has received more attention in the search and rescue. Due to inherent mobility of UAV, the aerial backbone network maybe suffers some problems of dynamics of connectivity, such as uncertain delay jitter or occasional connection interruption. However, the existing mobility model could not accurately capture aerial mobility without the smooth trajectory and the capacity of rapid coverage. In this paper, we propose a novel mobility model based on spiral line (SLMM) for aerial backbone network. Firstly, we describe the background and related work. Then, we propose the basic concept and advantages of SLMM. In addition, we investigate the mathematical feature of SLMM. Besides, we propose some novel mobility metrics and analyze quantitatively the mobility feature of SLMM through simulation. Finally, we evaluate the network performance under the SLMM. The result show that the SLMM has the smooth trajectory and the uniform distribution of spatial nodes at steady state. In addition, covering the zone of area of 16pi the aerial node merely take almost 27s under the SLMM. Meanwhile, the convergence time could be adjusted by changing the internal parameters. Moreover, the aerial backbone network could approach the throughput of 0.43Mb/s, the end-to-end delay of 4.42ms and the packet loss rate of nearly 0.00% • under the SLMM, and the best choice of routing strategy is the AODV protocol. We believe that the SLMM as a significant supplement of mobility model can provide the helpful guideline in the design and analysis of aerial backbone network. INDEX TERMS Aerial backbone network, mobility model, rapid coverage, smooth trajectory.

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Multiple Access Towards Non-terrestrial Networks

Yang

et al. 2022

Multiple Access Technology Towards Ubiquitous Networks

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Towards Real-Time Data Delivery in oneM2M Platform for UAV Management System

Cited by 13 publications

References 5 publications

Efficient Wireless Charging Pad Deployment in Wireless Rechargeable Sensor Networks

Efficient Wireless Charging Pad Deployment in Wireless Rechargeable Sensor Networks

The Novel Mobility Models Based on Spiral Line for Aerial Backbone Networks

Multiple Access Towards Non-terrestrial Networks

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