Tao Han

Bio: Tao Han is an academic researcher from University of North Carolina at Charlotte. The author has contributed to research in topics: Energy consumption & Cellular network. The author has an hindex of 24, co-authored 104 publications receiving 2416 citations. Previous affiliations of Tao Han include University of North Carolina at Chapel Hill & Wilmington University.

On Optimizing Green Energy Utilization for Cellular Networks with Hybrid Energy Supplies

Abstract: Green communications has received much attention recently. For cellular networks, the base stations (BSs) account for more than 50 percent of the energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to achieve green cellular networks. With the development of green energy technologies, BSs are able to be powered by green energy in order to reduce the on-grid energy consumption, thus reducing the CO2 footprints. In this paper, we envision that the BSs of future cellular networks are powered by both on-grid energy and green energy. We optimize the energy utilization in such networks by maximizing the utilization of green energy, and thus saving on-grid energy. The optimal usage of green energy depends on the characteristics of the energy generation and the mobile traffic, which exhibit both temporal and spatial diversities. We decompose the problem into two sub-problems: the multi-stage energy allocation problem and the multi-BSs energy balancing problem. We propose algorithms to solve these sub-problems, and subsequently solve the green energy optimization problem. Simulation results demonstrate that the proposed solution achieves significant on-grid energy savings.

Powering mobile networks with green energy

Abstract: Explosive mobile data demands are driving a significant growth in energy consumption in mobile networks, and consequently a surge of carbon footprints Reducing carbon footprints is crucial in alleviating the direct impact of greenhouse gases on the earth environment and the climate change With advances of green energy technologies, future mobile networks are expected to be powered by green energy to reduce their carbon footprints This article provides an overview on the design and optimization of green energy enabled mobile networks, discusses the energy models for the analysis and optimization of the networks, and lays out basic design principles and research challenges on optimizing the green energy powered mobile networks

On Green-Energy-Powered Cognitive Radio Networks

Abstract: A green-energy-powered cognitive radio (CR) network is capable of liberating the wireless access networks from spectral and energy constraints. The limitation of the spectrum is alleviated by exploiting cognitive networking in which wireless nodes sense and utilize the spare spectrum for data communications, whereas dependence on the traditional unsustainable energy is assuaged by adopting energy harvesting through which green energy can be harnessed to power wireless networks. Green-energy-powered CR increases the network availability and thus extends emerging network applications. Designing green CR networks is challenging. It requires not only the optimization of dynamic spectrum access but also the optimal utilization of green energy. This paper surveys the energy-efficient CR techniques and the optimization of green-energy-powered wireless networks. Existing works on energy-aware spectrum sensing, management, and sharing are investigated in detail. The state of the art of the energy-efficient CR-based wireless access network is discussed in various aspects, such as relay and cooperative radio and small cells. Envisioning green energy as an important energy resource in the future, network performance highly depends on the dynamics of the available spectrum and green energy. As compared with the traditional energy source, the arrival rate of green energy, which highly depends on the environment of the energy harvesters, is rather random and intermittent. To optimize and adapt the usage of green energy according to the opportunistic spectrum availability, we discuss research challenges in designing CR networks that are powered by energy harvesters.

On Green Energy Powered Cognitive Radio Networks

Abstract: Green energy powered cognitive radio (CR) network is capable of liberating the wireless access networks from spectral and energy constraints. The limitation of the spectrum is alleviated by exploiting cognitive networking in which wireless nodes sense and utilize the spare spectrum for data communications, while dependence on the traditional unsustainable energy is assuaged by adopting energy harvesting (EH) through which green energy can be harnessed to power wireless networks. Green energy powered CR increases the network availability and thus extends emerging network applications. Designing green CR networks is challenging. It requires not only the optimization of dynamic spectrum access but also the optimal utilization of green energy. This paper surveys the energy efficient cognitive radio techniques and the optimization of green energy powered wireless networks. Existing works on energy aware spectrum sensing, management, and sharing are investigated in detail. The state of the art of the energy efficient CR based wireless access network is discussed in various aspects such as relay and cooperative radio and small cells. Envisioning green energy as an important energy resource in the future, network performance highly depends on the dynamics of the available spectrum and green energy. As compared with the traditional energy source, the arrival rate of green energy, which highly depends on the environment of the energy harvesters, is rather random and intermittent. To optimize and adapt the usage of green energy according to the opportunistic spectrum availability, we discuss research challenges in designing cognitive radio networks which are powered by energy harvesters.

An Edge Network Orchestrator for Mobile Augmented Reality

Abstract: Mobile augmented reality (MAR) involves high complexity computation which cannot be performed efficiently on resource limited mobile devices. The performance of MAR would be significantly improved by offloading the computation tasks to servers deployed with the close proximity to the users. In this paper, we design an edge network orchestrator to enable fast and accurate object analytics at the network edge for MAR. The measurement-based analytical models are built to characterize the tradeoff between the service latency and analytics accuracy in edge-based MAR systems. As a key component of the edge network orchestrator, a server assignment and frame resolution selection algorithm named FACT is proposed to mitigate the latency-accuracy tradeoff. Through network simulations, we evaluate the performance of the FACT algorithm and show the insights on optimizing the performance of edge-based MAR systems. We have implemented the edge network orchestrator and developed the corresponding communication protocol. Our experiments validate the performance of the proposed edge network orchestrator.

A Survey on Mobile Edge Computing: The Communication Perspective

Abstract: Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

A Survey on Mobile Edge Computing: The Communication Perspective

Abstract: Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

A Survey on Wireless Security: Technical Challenges, Recent Advances, and Future Trends

Abstract: Due to the broadcast nature of radio propagation, the wireless air interface is open and accessible to both authorized and illegitimate users. This completely differs from a wired network, where communicating devices are physically connected through cables and a node without direct association is unable to access the network for illicit activities. The open communications environment makes wireless transmissions more vulnerable than wired communications to malicious attacks, including both the passive eavesdropping for data interception and the active jamming for disrupting legitimate transmissions. Therefore, this paper is motivated to examine the security vulnerabilities and threats imposed by the inherent open nature of wireless communications and to devise efficient defense mechanisms for improving the wireless network security. We first summarize the security requirements of wireless networks, including their authenticity, confidentiality, integrity, and availability issues. Next, a comprehensive overview of security attacks encountered in wireless networks is presented in view of the network protocol architecture, where the potential security threats are discussed at each protocol layer. We also provide a survey of the existing security protocols and algorithms that are adopted in the existing wireless network standards, such as the Bluetooth, Wi-Fi, WiMAX, and the long-term evolution (LTE) systems. Then, we discuss the state of the art in physical-layer security, which is an emerging technique of securing the open communications environment against eavesdropping attacks at the physical layer. Several physical-layer security techniques are reviewed and compared, including information-theoretic security, artificial-noise-aided security, security-oriented beamforming, diversity-assisted security, and physical-layer key generation approaches. Since a jammer emitting radio signals can readily interfere with the legitimate wireless users, we also introduce the family of various jamming attacks and their countermeasures, including the constant jammer, intermittent jammer, reactive jammer, adaptive jammer, and intelligent jammer. Additionally, we discuss the integration of physical-layer security into existing authentication and cryptography mechanisms for further securing wireless networks. Finally, some technical challenges which remain unresolved at the time of writing are summarized and the future trends in wireless security are discussed.

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Abstract: In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.