S. S. Bacanli and D. Turgut

Energy-efficient unmanned aerial vehicle scanning approach with node clustering in opportunistic networks


Cite as:

S. S. Bacanli and D. Turgut. Energy-efficient unmanned aerial vehicle scanning approach with node clustering in opportunistic networks. Computer Communications, 161:76–85, September 2020. DOI: 10.1016/j.comcom.2020.07.010

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Abstract:

The opportunistic networks are challenging due to their inherent characteristics of intermittent and unreliable communication between nodes. In order to alleviate the communication issues, the unmanned aerial vehicles (UAVs) can be used for delivering packets within the opportunistic networks. This paper investigates how to leverage the UAVs in Unmanned Aerial Vehicle aided Opportunistic Networks (UAON). The UAVs are considered responsible for relaying the messages generated by the nodes on the ground. The simulation study is conducted on the real-world datasets of the nodes moving around Orlando and Korea Advanced Institute of Science \& Technology (KAIST). Our proposed approach, State-based Campus Routing (SCR) with Density-based spatial clustering of applications with noise (DBSCAN), meander, random, and random spiral scanning approaches, as well as SCR and Epidemic protocols without UAV usage, have been evaluated on both datasets. The simulation metrics included the success rate, the message delay, the number of packets sent, and the distance traveled by the UAVs. SCR with DBSCAN and meander scan approaches were also tested with two UAVs using the Orlando dataset. Furthermore, spiral density and message creation frequency parameters were evaluated for SCR with DBSCAN protocol on North Carolina State University (NCSU) dataset. The simulation results showed improvements in terms of message delay and success rate when the UAVs were used in an opportunistic network setting. The proposed approach showed around 12\% less total number of packets sent by the UAVs and the nodes. Similarly, the message delay distributions of the SCR with the DBSCAN achieve 90\% of the message delay results, whereas the message delay distributions of random scanning form only 70\% in less than an hour.

BibTeX:

@article{Bacanli-2020-ComCom,
   author = "S. S. Bacanli and D. Turgut",
   title = "Energy-efficient unmanned aerial vehicle scanning approach with node clustering in opportunistic networks",
   journal = "Computer Communications",
   volume = "161",
   pages = "76-85",
   month = "September",
   year = "2020",
   note = "DOI: 10.1016/j.comcom.2020.07.010",
   abstract = {The opportunistic networks are challenging due to their inherent characteristics of intermittent and unreliable communication between nodes. In order to alleviate the communication issues, the unmanned aerial vehicles (UAVs) can be used for delivering packets within the opportunistic networks. This paper investigates how to leverage the UAVs in Unmanned Aerial Vehicle aided Opportunistic Networks (UAON). The UAVs are considered responsible for relaying the messages generated by the nodes on the ground. The simulation study is conducted on the real-world datasets of the nodes moving around Orlando and Korea Advanced Institute of Science \& Technology (KAIST). Our proposed approach, State-based Campus Routing (SCR) with Density-based spatial clustering of applications with noise (DBSCAN), meander, random, and random spiral scanning approaches, as well as SCR and Epidemic protocols without UAV usage, have been evaluated on both datasets. The simulation metrics included the success rate, the message delay, the number of packets sent, and the distance traveled by the UAVs. SCR with DBSCAN and meander scan approaches were also tested with two UAVs using the Orlando dataset. Furthermore, spiral density and message creation frequency parameters were evaluated for SCR with DBSCAN protocol on North Carolina State University (NCSU) dataset. The simulation results showed improvements in terms of message delay and success rate when the UAVs were used in an opportunistic network setting. The proposed approach showed around 12\% less total number of packets sent by the UAVs and the nodes. Similarly, the message delay distributions of the SCR with the DBSCAN achieve 90\% of the message delay results, whereas the message delay distributions of random scanning form only 70\% in less than an hour.},
}

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