0000000000684610
AUTHOR
Haijun Liao
Energy Harvesting Enabled Energy Efficient Cognitive Machine-to-Machine Communications
Energy harvesting based cognitive machine-to-machine (EH-CM2M) communication has been introduced to overcome the problem of spectrum scarcity and limited battery capacity by enabling M2M transmitters (M2M-TXs) to harvest energy from ambient radio frequency signals, as well as to reuse the resource blocks (RBs) allocated to CUs in an opportunistic manner. However, the complex interference scenarios and the stringent QoS requirements pose new challenges on resource allocation optimization. In this chapter, we consider how to maximize the energy efficiency of M2M-TXs via the joint optimization of channel selection, peer discovery, power control, and time allocation.
Dynamic Computation Offloading Scheme for Fog Computing System with Energy Harvesting Devices
Fog computing is considered as a promising technology to meet the ever-increasing computation requests from a wide variety of mobile applications. By offloading the computation-intensive requests to the fog node or the central cloud, the performance of the applications, such as energy consumption and delay, are able to be significantly enhanced. Meanwhile, utilizing the recent advances of social network and energy harvesting techniques, the system performance could be further improved. In this paper, we take the social relationships of the energy harvesting MDs into the design of computational offloading scheme in fog computing. With the objective to minimize the social group execution cost…
Energy-Efficient M2M Communications in for Industrial Automation
M2M communication with autonomous data acquisition and exchange plays a key role in realizing the “control”-oriented tactile Internet (TI) applications such as industrial automation. In this chapter, we develop a two-stage access control and resource allocation algorithm. In the first stage, we introduce a contract-based incentive mechanism to motivate some delay-tolerant machine-type communication (MTC) devices to postpone their access demands in exchange for higher access opportunities. In the second stage, a long-term cross-layer online resource allocation approach is based on Lyapunov optimization, which jointly optimizes rate control, power allocation, and channel selection without pri…
Energy-Efficient Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT
Edge computing provides a promising paradigm to support the implementation of industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resource-limited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this chapter, we consider the optimization of channel selection which is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to long-term constraints of energy budget and service reliability. We propose a learning-based channel selection fr…
Software Defined Machine-to-Machine Communication for Smart Energy Management in Power Grids
The successful realization of smart energy management relies on ubiquitous and reliable information exchange among millions of sensors and actuators deployed in the field with little or no human intervention. This motivates us to introduce a unified communication framework for smart energy management by exploring the integration of software-defined networking (SDN) with M2M communication. In this chapter, the overall design of the software-defined M2M (SD-M2M) framework is presented, with an emphasis on its technical contributions to cost reduction, fine granularity resource allocation, and end-to-end QoS guarantee. Then, a case study is conducted for a electric vehicle energy management sy…
Energy-Efficient Resource Allocationin for D2D Enabled Cellular Networks
Energy-efficiency (EE) is critical for D2D enabled cellular networks due to limited battery capacity and severe co-channel interference. In this chapter, we address the EE optimization problem by adopting a stable matching approach. The NP-hard joint resource allocation problem is formulated as a one-to-one matching problem under two-sided preferences, which vary dynamically with channel states and interference levels. A game-theoretic approach is employed to analyze the interactions and correlations among user equipments (UEs), and an iterative power allocation algorithm is developed to establish mutual preferences based on nonlinear fractional programming. We then employ the Gale–Shapley …
Licensed and Unlicensed Spectrum Management for Energy-Efficient Cognitive M2M
Edge computing has emerged as a promising solution for relieving the tension between resource-limited MTDs and computational-intensive tasks. To realize successful task offloading with limited spectrum, we focus on the cognitive machine-to-machine (CM2M) paradigm which enables a massive number of MTDs to either opportunistically use the licensed spectrum that is temporarily available, or to exploit the under-utilized unlicensed spectrum. We formulate the channel selection problem with both licensed and unlicensed spectrum as an adversarial multi-armed bandit (MAB) problem, and combine the exponential-weight algorithm for exploration and exploitation (EXP3) and Lyapunov optimization to devel…