Mingxiong Zhao

Bio: Mingxiong Zhao is an academic researcher from Yunnan University. The author has contributed to research in topics: Mobile edge computing & Resource allocation. The author has an hindex of 6, co-authored 24 publications receiving 127 citations. Previous affiliations of Mingxiong Zhao include South China University of Technology.

Collaborative offloading for UAV-enabled time-sensitive MEC networks

Abstract: Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV are rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ( $$\mathbf {P}_{\mathbf {T}}$$ ), resource allocation at UAV ( $$\mathbf {P}_{\mathbf {R}}$$ ) and offloading decisions at IoT devices ( $$\mathbf {P}_{\mathbf {O}}$$ ) and then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

Energy-Aware Task Offloading and Resource Allocation for Time-Sensitive Services in Mobile Edge Computing Systems

Abstract: Mobile Edge Computing (MEC) is a promising architecture to reduce the energy consumption of mobile devices and provide satisfactory quality-of-service to time-sensitive services. How to jointly optimize task offloading and resource allocation to minimize the energy consumption subject to the latency requirement remains an open problem, which motivates this paper. When the latency constraint is taken into account, the optimization variables, including offloading ratio, transmission power, and subcarrier and computing resource allocation, are strongly coupled. To address this issue, we first decompose the original problem into three subproblems named as offloading ratio selection, transmission power optimization, and subcarrier and computing resource allocation. Then, we propose an iterative algorithm to deal with them in a sequence. To be specific, we derive the closed-form solution of offloading ratios, employ the equivalent parametric convex programming to obtain the optimal power allocation policy, and deal with subcarrier and computing resource allocation by the primal-dual method. Simulation results demonstrate that the proposed algorithm can save 20%–40% energy compared with the reference schemes, and can converge to local optimal solutions.

Joint Power Splitting and Secure Beamforming Design in the Multiple Non-Regenerative Wireless-Powered Relay Networks

Abstract: The physical-layer security issue in the multiple non-regenerative wireless-powered relay (WPR) networks is investigated in this letter, where the idle relay is treated as a potential eavesdropper. To guarantee secure communication, the destination-based artificial noise is sent to degrade the receptions of eavesdroppers, and it also becomes a new source of energy powering relays to forward the information with power splitting (PS) technique. We propose an efficient algorithm ground on block-wise penalty function method to jointly optimize PS ratio and beamforming to maximize the secrecy rate. Despite the nonconvexity of the considered problem, the proposed algorithm is numerically efficient and is proved to converge to the local optimal solution. Simulation results demonstrate that the proposed algorithm outperforms the benchmark method.

Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning

Abstract: The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration; therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this revolution. The intellectualization of industrial information is an important part of industry 4.0, and we can efficiently integrate multisource heterogeneous industrial data and realize the intellectualization of information through the powerful semantic association of knowledge graphs. Knowledge graphs have been increasingly applied in the fields of deep learning, social network, intelligent control and other artificial intelligence areas. The objective of this present study is to combine traditional NLP (natural language processing) and deep learning methods to automatically extract triples from large unstructured Chinese text and construct an industrial knowledge graph in the automobile field.

Fairness-Aware Task Scheduling and Resource Allocation in UAV-Enabled Mobile Edge Computing Networks

Abstract: Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged to provide data processing and caching in the infrastructure-less areas. However, the limited battery capacity of UAV constrains its endurance time, and makes energy efficiency one of the top priorities in implementing UAV-enabled MEC architecture. In this backdrop, we aim to minimize the UAV’s energy consumption by jointly optimizing its trajectory and resource allocation, and task decision and bits scheduling of users considering fairness. The problem is formulated as a mix-integer nonlinear programming problem with strongly coupled variants, and further transformed into three more tractable subproblems: 1) Trajectory optimization PT, 2) Task Decision and Bits Scheduling PS, 3) Resource allocation PR. Then, we propose an iterative algorithm to deal with them in a sequence, and further design a penalty methodbased algorithm to reduce computation complexity when the branch-and-bound (B&B) algorithm incurs a high complexity to solve PS. Simulation results demonstrate that our proposed algorithm can efficiently reduce the energy consumption of UAV, and help save 17.7% -54.6% and 78.9% -91.9% energy compared with Equal Resource Allocation and Random Resource Allocation. Moreover, it reduces more than 88% running time and achieves relatively satisfactory performance compared with B&B.

A Survey on Multiple-Antenna Techniques for Physical Layer Security

Abstract: As a complement to high-layer encryption techniques, physical layer security has been widely recognized as a promising way to enhance wireless security by exploiting the characteristics of wireless channels, including fading, noise, and interference. In order to enhance the received signal power at legitimate receivers and impair the received signal quality at eavesdroppers simultaneously, multiple-antenna techniques have been proposed for physical layer security to improve secrecy performance via exploiting spatial degrees of freedom. This paper provides a comprehensive survey on various multiple-antenna techniques in physical layer security, with an emphasis on transmit beamforming designs for multiple-antenna nodes. Specifically, we provide a detailed investigation on multiple-antenna techniques for guaranteeing secure communications in point-to-point systems, dual-hop relaying systems, multiuser systems, and heterogeneous networks. Finally, future research directions and challenges are identified.

Beamforming in Wireless Energy Harvesting Communications Systems: A Survey

Abstract: Wireless energy harvesting (EH) is a promising solution to prolong lifetime of power-constrained networks such as military and sensor networks. The high sensitivity of energy transfer to signal decay due to path loss and fading, promotes multi-antenna techniques like beamforming as the candidate transmission scheme for EH networks. Exploiting beamforming in EH networks has gained overwhelming interest, and lot of literature has appeared recently regarding this topic. The objective of this paper is to point out the state-of-the-art research activity on beamforming implementation in EH wireless networks. We first review the basic concepts and architecture of EH wireless networks. In addition, we also discuss the effects of beamforming transmission scheme on system performance in EH wireless communication. Furthermore, we present a comprehensive survey of multi-antenna EH communications. We cover the supporting network architectures like broadcasting, relay, and cognitive radio networks with the various beamforming deployment within the network architecture. We classify the different beamforming approaches in each network topology according to its design objective such as increasing the throughput, enhancing the energy transfer efficiency, and minimizing the total transmit power, with paying special attention to exploiting the physical layer security. We also survey major advances as well as open issues, challenges, and future research directions in multi-antenna EH communications.

Mobile Data Gathering with Load Balanced Clustering and Dual Data Uploading in Wireless Sensor Networks

Abstract: In this project, a framework which consists of three layers is introduced for the mobile data gathering in the wireless sensor networks. The three layers of the framework are given as the sensor layer, clustered head layer, mobile collector layer (also called as SenCar). This framework is made use of with a LBC-DDU, which is called as the load balanced clustering and the dual data uploading. The main aim of employing this project is to get a good scalability and a long lifetime for the required network and for the lower data gathering recess.

Wireless Powered Cooperative Jamming for Secrecy Multi-AF Relaying Networks

Abstract: This paper studies secrecy transmission with the aid of a group of wireless energy harvesting (WEH)-enabled amplify-and-forward (AF) relays performing cooperative jamming (CJ) and relaying. The source node in the network does simultaneous wireless information and power transfer (SWIPT) with each relay employing a power splitting (PS) receiver in the first phase; each relay further divides its harvested power for forwarding the received signal and generating artificial noise (AN) for jamming the eavesdroppers in the second transmission phase. In the centralized case with global channel state information (CSI), we provide closed-form expressions for the optimal and/or suboptimal AF-relay beamforming vectors to maximize the achievable secrecy rate subject to individual power constraints of the relays, using the technique of semidefinite relaxation (SDR), which is proved to be tight. A fully distributed algorithm utilizing only local CSI at each relay is also proposed as a performance benchmark. Simulation results validate the effectiveness of the proposed multi-AF relaying with CJ over other suboptimal designs.

Wireless Powered Cooperative Jamming for Secrecy Multi-AF Relaying Networks

Abstract: This paper studies secrecy transmission with the aid of a group of wireless energy harvesting-enabled amplify-and-forward (AF) relays performing cooperative jamming (CJ) and relaying. The source node in the network does simultaneous wireless information and power transfer with each relay employing a power splitting receiver in the first phase; each relay further divides its harvested power for forwarding the received signal and generating artificial noise for jamming the eavesdroppers in the second transmission phase. In the centralized case with global channel state information (CSI), we provide the closed-form expressions for the optimal and/or suboptimal AF-relay beamforming vectors to maximize the achievable secrecy rate subject to individual power constraints of the relays, using the technique of semidefinite relaxation (SDR), which is proved to be tight. A fully distributed algorithm utilizing only local CSI at each relay is also proposed as a performance benchmark. Simulation results validate the effectiveness of the proposed multi-AF relaying with CJ over other suboptimal designs.