TY - JOUR
T1 - Towards traffic load balancing in drone-assisted communications for IoT
AU - Fan, Qiang
AU - Ansari, Nirwan
N1 - Funding Information:
Manuscript received October 20, 2018; revised November 21, 2018; accepted December 11, 2018. Date of publication December 25, 2018; date of current version May 8, 2019. This work was supported in part by the National Science Foundation under Grant CNS-1814748. (Corresponding author: Qiang Fan.) The authors are with the Advanced Networking Laboratory, Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA (e-mail: qf4@njit.edu; nirwan.ansari@njit.edu). Digital Object Identifier 10.1109/JIOT.2018.2889503
Publisher Copyright:
© 2014 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - Edge computing enables data collected by Internet of Things (IoT) devices to be stored in and processed by local fog nodes as well as allows IoT users to access IoT applications via these nodes at the same time. In this case, the communications latency critically affects the response time of IoT user requests. Owing to the dynamic distribution of IoT users [i.e., user equipments (UEs)], drone base station (DBS), which can be flexibly deployed for hotspot areas, can potentially improve the wireless latency of IoT users by mitigating the heavy traffic loads of macro BSs. Drone-based communications poses two major challenges: 1) the DBS should be deployed in suitable areas with heavy traffic demands to serve more UEs and 2) the traffic loads in the network should be allocated among macro BSs and DBSs to avoid instigating traffic congestions. Therefore, we propose a traffic load balancing scheme in such drone-assisted fog network to minimize the wireless latency of IoT users. In the scheme, we divide the problem into two subproblems and design two algorithms to optimize the DBS placement and user association, respectively. Extensive simulations have been set up to validate the performance of the proposed scheme.
AB - Edge computing enables data collected by Internet of Things (IoT) devices to be stored in and processed by local fog nodes as well as allows IoT users to access IoT applications via these nodes at the same time. In this case, the communications latency critically affects the response time of IoT user requests. Owing to the dynamic distribution of IoT users [i.e., user equipments (UEs)], drone base station (DBS), which can be flexibly deployed for hotspot areas, can potentially improve the wireless latency of IoT users by mitigating the heavy traffic loads of macro BSs. Drone-based communications poses two major challenges: 1) the DBS should be deployed in suitable areas with heavy traffic demands to serve more UEs and 2) the traffic loads in the network should be allocated among macro BSs and DBSs to avoid instigating traffic congestions. Therefore, we propose a traffic load balancing scheme in such drone-assisted fog network to minimize the wireless latency of IoT users. In the scheme, we divide the problem into two subproblems and design two algorithms to optimize the DBS placement and user association, respectively. Extensive simulations have been set up to validate the performance of the proposed scheme.
KW - Drone base station (DBS)
KW - Internet of Things (IoT)
KW - edge computing
KW - traffic load allocation
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U2 - 10.1109/JIOT.2018.2889503
DO - 10.1109/JIOT.2018.2889503
M3 - Article
AN - SCOPUS:85059274495
SN - 2327-4662
VL - 6
SP - 3633
EP - 3640
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 2
M1 - 8588325
ER -