0000000000042571
AUTHOR
Zhenyu Zhou
Robust Energy Scheduling in Vehicle-to-Grid Networks
The uncertainties brought by intermittent renewable generation and uncoordinated charging behaviors of EVs pose great challenges to the reliable operation of power systems, which motivates us to explore the integration of robust optimization with energy scheduling in V2G networks. In this article, we first introduce V2G robust energy scheduling problems and review the stateof- the art contributions from the perspectives of renewable energy integration, ancillary service provision, and proactive demand-side participation in the electricity market. Second, for each category of V2G applications, the corresponding problem formulations, robust solution concepts, and design approaches are describ…
Energy Efficient Optimization for Computation Offloading in Fog Computing System
In this paper, we investigate the energy efficient computation offloading scheme in a multi-user fog computing system. We consider the users need to make the decision on whether to offload the tasks to the fog node nearby, based on the energy consumption and delay constraint. In particular, we utilize queuing theory to bring a thorough study on the energy consumption and execution delay of the offloading process. Two queuing models are applied respectively to model the execution processes at the mobile device (MD) and fog node. Based on the theoretical analysis, an energy efficient optimization problem is formulated with the objective to minimize the energy consumption subjects to execution…
Energy Efficient Resource Allocation for Wireless Power Transfer Enabled Collaborative Mobile Clouds
In order to fully enjoy high rate broadband multimedia services, prolonging the battery lifetime of user equipment is critical for mobile users, especially for smartphone users. In this paper, the problem of distributing cellular data via a wireless power transfer enabled collaborative mobile cloud (WeCMC) in an energy efficient manner is investigated. WeCMC is formed by a group of users who have both functionalities of information decoding and energy harvesting, and are interested for cooperating in downloading content from the operators. Through device-to-device communications, the users inside WeCMC are able to cooperate during the downloading procedure and offload data from the base sta…
Incentive Mechanism for Edge-Computing-Based Blockchain
Blockchain has been gradually applied to different Internet of Things (IoT) platforms. As the efficiency of the blockchain mainly depends on the network computing capability, how to make sure the acquisition of the computational resources and participation of the devices would be the driving force. In this work, we focus on investigating incentive mechanism for rational miners to purchase the computational resources. A edge computing-based blockchain network is considered, where the edge service provider (ESP) can provide computational resources for the miners. Accordingly, we formulate a two-stage Stackelberg game between the miners and ESP. The aim is to investigate Stackelberg equilibriu…
Energy Efficient Resource Allocation for OFDMA Two-Way Relay Networks with Channel Estimation Error
In this work, we consider the practical issues of resource allocation problem in OFDMA two-way relay networks: the inaccuracy of channel-state information (CSI) available to the transmitters. Instead, only the estimated channel status is known by the transmitters. In this context, a joint optimization of subcarrier pairing and allocation, relay selection and transmit power allocation is formulated in the OFDMA two-way amplify-and-forward relay networks. Moreover, to ensure the Quality of Service (QoS) requirement, the energy consumption must be minimized without compromising the QoS. Therefore, by applying convex optimization techniques, energy efficient algorithms are developed with the ob…
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…
Towards Service-oriented 5G: Virtualizing the Networks for Everything-as-a-Service
It is widely acknowledged that the forthcoming 5G architecture will be highly heterogeneous and deployed with a high degree of density. These changes over the current 4G bring many challenges on how to achieve an efficient operation from the network management perspective. In this article, we introduce a revolutionary vision of the future 5G wireless networks, in which the network is no longer limited by hardware or even software. Specifically, by the idea of virtualizing the wireless networks, which has recently gained increasing attention, we introduce the Everything-as-a-Service (XaaS) taxonomy to light the way towards designing the service-oriented wireless networks. The concepts, chall…
Energy-Efficient Edge Computing Service Provisioning for Vehicular Networks: A Consensus ADMM Approach
In vehicular networks, in-vehicle user equipment (UE) with limited battery capacity can achieve opportunistic energy saving by offloading energy-hungry workloads to vehicular edge computing nodes via vehicle-to-infrastructure links. However, how to determine the optimal portion of workload to be offloaded based on the dynamic states of energy consumption and latency in local computing, data transmission, workload execution and handover, is still an open issue. In this paper, we study the energy-efficient workload offloading problem and propose a low-complexity distributed solution based on consensus alternating direction method of multipliers. By incorporating a set of local variables for e…
Social Network-Based Content Delivery in Device-to-Device Underlay Cellular Networks Using Matching Theory
With the popularity of social network-based services, the unprecedented growth of mobile date traffic has brought a heavy burden on the traditional cellular networks. Device-to-device (D2D) communication, as a promising solution to overcome wireless spectrum crisis, can enable fast content delivery based on user activities in social networks. In this paper, we address the content delivery problem related to optimization of peer discovery and resource allocation by combining both the social and physical layer information in D2D underlay networks. The social relationship, which is modeled as the probability of selecting similar contents and estimated by using the Bayesian nonparametric models…
Learn to Cache: Machine Learning for Network Edge Caching in the Big Data Era
The unprecedented growth of wireless data traffic not only challenges the design and evolution of the wireless network architecture, but also brings about profound opportunities to drive and improve future networks. Meanwhile, the evolution of communications and computing technologies can make the network edge, such as BSs or UEs, become intelligent and rich in terms of computing and communications capabilities, which intuitively enables big data analytics at the network edge. In this article, we propose to explore big data analytics to advance edge caching capability, which is considered as a promising approach to improve network efficiency and alleviate the high demand for the radio resou…
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…
Service Provisioning with Multiple Service Providers in 5G Ultra-dense Small Cell Networks
In this work, a game theoretical approach for addressing the virtual network service providers (NSPs), small cell provider (SCP) and user interaction in heterogenous small cell networks is presented. In particular, we consider the users can select the services of different NSPs based on their prices. The NSPs have no dedicated hardware and need to rent from the SCP in term of radio resources, e.g., small cell base stations (SBSs) in order to provide satisfied services to the users. Due to the fact that the selfish parties involved aim at maximizing their own profits, a hierarchical dynamic game framework is presented to address interactive decision problem. In the lower-level, a Stackelberg…
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 …
Stable-Matching-Based Energy-Efficient Context-Aware Resource Allocation for Ultra-Dense Small Cells
Implementing caching to ultra-densely deployed small cells provides a promising solution for satisfying the stringent quality of service (QoS) requirements of delay-sensitive applications with limited backhaul capacity. With the rapidly increasing energy consumption, in this chapter, the authors investigate the NP-hard energy-efficient context-aware resource allocation problem and formulate it as a one-to-one matching problem. The preference lists in the matching are modeled based on the optimum energy efficiency (EE) under specified matching, which can be obtained by using an iterative power allocation algorithm based on nonlinear fractional programming and Lagrange dual decomposition. Nex…
User-cell association in heterogenous small cell networks: A context-aware approach
Deploying small cells to complement the traditional cellular networks is foreseen as a major feature of the next generation of wireless networks. In this paper, a novel approach for addressing the cell association problem in heterogenous small cell networks is proposed. The presented approach exploits different types of information extracted from the user devices and environment to improve the way in which users are assigned to their serving small cell base stations (SBSs). We formulate the problem within the framework of matching theory, where the players, namely the users and SBSs, have interdependent preferences over the members of the opposite set. We also utilize the fuzzy synthetic ev…
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…
SAGECELL: Software-Defined Space-Air-Ground Integrated Moving Cells
Ultra-dense networks (UDNs) provide an effective solution to accommodate the explosively growing data traffic of multimedia services and real-time applications. However, the densification of large numbers of static small cells faces many fundamental challenges, including deployment cost, energy consumption and control, and so on. This motivates us to develop software-defined space-air-ground integrated moving cells (SAGECELL), a programmable, scalable, and flexible framework to integrate space, air, and ground resources for matching dynamic traffic demands with network capacity supplies. First, we provide a comprehensive review of state-of-the-art literature. Then the conceptual architectur…