Electronic Thesis and Dissertation Repository

Thesis Format

Monograph

Degree

Doctor of Philosophy

Program

Electrical and Computer Engineering

Supervisor

Xianbin Wang

2nd Supervisor

Hongbo Zhu

Affiliation

Nanjing University of Posts and Telecommunications

Co-Supervisor

Abstract

Along with rapidly evolving communications technologies and data analytics, Internet of Things (IoT) systems interconnect billions of smart devices to gather, exchange, analyze data, and perform tasks autonomously, which poses a huge pressure on IoT devices' computing capabilities. Taking advantage of collaborative computing enabled by cloud computing and edge computing technologies, IoT devices can offload computation tasks to idle computing devices and remote servers, thus alleviating their pressure. However, scheduling resources effectively to realize collaborative computing remains a severe challenge due to diverse application objectives, limited distributed resources, and unpredictable environments. To overcome the above challenges, this thesis aims to design effective resource scheduling for collaborative computing in edge-assisted IoT systems.

First of all, horizontal collaboration amongst IoT devices is a promising way of balancing computing tasks within the device layer. However, engaging idle computing devices for sharing could be difficult as computation offloading potentially affects their local computing tasks. As an economic methodology, game theory-based methods contribute to revenue generation; thus, it is regarded as a suitable tool for addressing incentive problems. To incentivize horizontal collaborations, a hierarchical game model is first proposed in smart buildings to obtain maximum utilities for the building management systems (BMS) and idle computing devices (ICDs). The Stackelberg game model is built to analyze interactions between the BMS and ICDs, and the Cournot game model is presented to formulate internal competitions among multiple ICDs. Under the premise of the subgame perfect Nash equilibrium (SPNE), the BMS can quote the optimal pricing strategy, and ICDs can share the corresponding optimal amount of computing resources. Furthermore, to deal with unpredictability in emergency communication networks, an incomplete information-based two-tier game model is estimated. Depending on what the emergency management systems (EMS) and ICDs know, the Bayesian Nash equilibrium (BNE) is obtained under incomplete information that achieves better performances in terms of computation latency and participants' utilities. Finally, a new computational latency-based pricing scheme is designed from the perspective of the quality-of-experience (QoE) performance, where the computing offloading price varies dynamically with data processing rates. The interactive behaviors between the centralized computing sharing platform (CSP) and ICDs are modeled as the Stackelberg game, seeking out SPNE through the dynamic pricing mechanism, the computation workload selection, and the CPU frequency control. Through this scheme, the pressure of imbalanced computing capabilities in the device layer of IoT systems can be effectively relieved.

Furthermore, utilizing resources in the edge layer can effectively enhance the performance of IoT systems as edge systems generally have more powerful computing capabilities. However, due to the concurrent dynamics of application requirements, available resources, and network conditions, meeting the increasingly diverse requirements of IoT applications remains an ultimate challenge in vertical collaboration between edge systems and IoT devices. Towards this end, a device-specific QoE enhancement resource scheduling scheme is proposed through jointly optimizing communication and computation resources. Specifically, a three-layer QoE assessment model is first constructed to describe the general relationship between resource provisioning and device-specific QoE performance. Then, a two-stage resource scheduling scheme is proposed to realize simultaneous optimization of IoT devices and the edge system, where an online learning approach is designed on the edge system to schedule communication bandwidth and optimize computational rate. Based on the proposed two-stage resource scheduling scheme, IoT device-specific QoE performance can be effectively enhanced in edge-assisted IoT systems.

Summary for Lay Audience

Along with rapidly evolving communications technologies and data analytics, Internet of Things (IoT) systems interconnect billions of smart devices for performing tasks autonomously, which poses a huge pressure on IoT devices' computing capabilities. Taking advantage of collaborative computing enabled by cloud computing and edge computing technologies, IoT devices can offload computation tasks to idle computing devices and remote servers, thus alleviating their pressure. However, scheduling resources effectively to realize collaborative computing remains a severe challenge due to diverse application objectives, limited distributed resources, and unpredictable environments. To overcome the above challenges, this thesis aims to design effective resource scheduling for collaborative computing in edge-assisted IoT systems.

To incentivize horizontal collaboration amongst IoT devices, a hierarchical game model is first proposed in smart buildings to obtain maximum utilities for the building management systems and idle computing devices (ICDs), which jointly combines the Stackelberg game and the Cournot game. Under the premise of the subgame perfect Nash equilibrium (SPNE), the BMS can quote the optimal pricing strategy, and ICDs can share the corresponding optimal amount of computing resources. Then, to deal with unpredictability in emergency communication networks, an incomplete information-based two-tier game model is estimated for analyzing the interactions between the emergency management systems (EMS) and ICDs. The Bayesian Nash equilibrium (BNE) is obtained depending on what the EMS and ICDs know. Furthermore, a new computational latency-based pricing scheme is designed from the perspective of the quality-of-experience (QoE) performance, where the computing offloading price varies dynamically with data processing rates. The interactive behaviors between the centralized computing sharing platform (CSP) and ICDs are modeled as the Stackelberg game, seeking out SPNE through the dynamic pricing mechanism, the computation workload selection, and the CPU frequency control. Finally, to meet the increasingly diverse requirements of IoT applications in vertical collaboration between edge systems and IoT devices, a device-specific QoE enhancement resource scheduling scheme is designed, where an online learning approach is proposed on the edge system to schedule communication bandwidth and computational rate simultaneously.

Download

Share

COinS