Document Type


Date of Award

Spring 5-31-2019

Degree Name

Doctor of Philosophy in Mathematical Sciences - (Ph.D.)


Mathematical Sciences

First Advisor

Nirwan Ansari

Second Advisor

MengChu Zhou

Third Advisor

Qing Gary Liu

Fourth Advisor

Ali Mili

Fifth Advisor

Roberto Rojas-Cessa


In the past few years, a tremendous number of smart devices and objects, such as smart phones, wearable devices, industrial and utility components, are equipped with sensors to sense the real-time physical information from the environment. Hence, Internet of Things (IoT) is introduced, where various smart devices are connected with each other via the internet and empowered with data analytics. Owing to the high volume and fast velocity of data streams generated by IoT devices, the cloud that can provision flexible and efficient computing resources is employed as a smart "brain" to process and store the big data generated from IoT devices. However, since the remote cloud is far from IoT users which send application requests and await the results generated by the data processing in the remote cloud, the response time of the requests may be too long, especially unbearable for delay sensitive IoT applications. Therefore, edge computing resources (e.g., cloudlets and fog nodes) which are close to IoT devices and IoT users can be employed to alleviate the traffic load in the core network and minimize the response time for IoT users.

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 congestions. 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.

Meanwhile, various IoT applications can be run in cloudlets to reduce the response time between IoT users (e.g., user equipments in mobile networks) and cloudlets. Considering the spatial and temporal dynamics of each application's workloads among cloudlets, the workload allocation among cloudlets for each IoT application affects the response time of the application's requests. To solve this problem, an Application awaRE workload Allocation (AREA) scheme for edge computing based IoT is designed to minimize the response time of IoT application requests by determining the destination cloudlets for each IoT user's different types of requests and the amount of computing resources allocated for each application in each cloudlet. In this scheme, both the network delay and computing delay are taken into account, i.e., IoT users' requests are more likely assigned to closer and lightly loaded cloudlets. The performance of the proposed scheme has been validated by extensive simulations.

In addition, the latency of data flows in IoT devices consist of both the communications latency and computing latency. When some BSs and fog nodes are lightly loaded, other overloaded BSs and fog nodes may incur congestion. Thus, a workload balancing scheme in a fog network is proposed to minimize the latency of IoT data in the communications and processing procedures by associating IoT devices to suitable BSs. Furthermore, the convergence and the optimality of the proposed workload balancing scheme has been proved. Through extensive simulations, the performance of the proposed load balancing scheme is validated.



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