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Workload Allocation in Mobile edge Computing Empowered Internet of Things

Research Area:  Edge Computing

Abstract:

   In edge computing, the communications latency critically affects the response time of IoT user requests. Owing to the dynamic distribution of IoT users (i.e.,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; 2) the traffic loads in the network should be allocated among macro BSs and DBSs to avoid instigating traffic congestion.
   Therefore, a Traffic Load balancing (TALL) scheme in such drone-assisted fog network is proposed to minimize the wireless latency of IoT users. In the scheme, the problem is decomposed into two sub-problems, two algorithms are designed to optimize the DBS placement and user association, respectively. Extensive simulations have been set up to validate the performance of the proposed scheme.

Name of the Researcher:  Qiang Fan

Name of the Supervisor(s):  Nirwan Ansari

Year of Completion:  2019

University:  The State University of New Jersey

Thesis Link:   Home Page Url