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Xin Wang - One of the best experts on this subject based on the ideXlab platform.

  • joint offloading and computing optimization in wireless powered mobile edge computing systems
    IEEE Transactions on Wireless Communications, 2018
    Co-Authors: Feng Wang, Jie Xu, Xin Wang
    Abstract:

    Mobile-edge computing (MEC) and wireless power transfer (WPT) have been recognized as promising techniques in the Internet of Things era to provide massive low-power wireless devices with enhanced Computation Capability and sustainable energy supply. In this paper, we propose a unified MEC-WPT design by considering a wireless powered multiuser MEC system, where a multiantenna access point (AP) (integrated with an MEC server) broadcasts wireless power to charge multiple users and each user node relies on the harvested energy to execute Computation tasks. With MEC, these users can execute their respective tasks locally by themselves or offload all or part of them to the AP based on a time-division multiple access protocol. Building on the proposed model, we develop an innovative framework to improve the MEC performance, by jointly optimizing the energy transmit beamforming at the AP, the central processing unit frequencies and the numbers of offloaded bits at the users, as well as the time allocation among users. Under this framework, we address a practical scenario where latency-limited Computation is required. In this case, we develop an optimal resource allocation scheme that minimizes the AP’s total energy consumption subject to the users’ individual Computation latency constraints. Leveraging the state-of-the-art optimization techniques, we derive the optimal solution in a semiclosed form. Numerical results demonstrate the merits of the proposed design over alternative benchmark schemes.

  • joint offloading and computing optimization in wireless powered mobile edge computing systems
    International Conference on Communications, 2017
    Co-Authors: Feng Wang, Jie Xu, Xin Wang
    Abstract:

    Integrating mobile-edge computing (MEC) and wireless power transfer (WPT) is a promising technique in the Internet of Things (IoT) era. It can provide massive low-power mobile devices with enhanced Computation Capability and sustainable energy supply. In this paper, we consider a wireless powered multiuser MEC system, where a multi-antenna access point (AP) (integrated with an MEC server) broadcasts wireless power to charge multiple users and each user node relies on the harvested energy to execute latency-sensitive Computation tasks. With MEC, these users can execute their respective tasks locally by themselves or offload all or part of the tasks to the AP based on a time division multiple access (TDMA) protocol. Under this setup, we pursue an energy-efficient MEC-WPT system design by jointly optimizing the transmit energy beamformer at the AP, the central processing unit (CPU) frequencies and the offloaded bits at each user, as well as the time allocation among different users. In particular, we minimize the energy consumption at the AP over a particular time block subject to the Computation latency and energy harvesting constraints per user. By formulating this problem into a convex framework and employing the Lagrange duality method, we obtain its optimal solution in a semi-closed form. Numerical results demonstrate the merits of the proposed joint design over alternative benchmark schemes.

  • joint offloading and computing optimization in wireless powered mobile edge computing systems
    arXiv: Information Theory, 2017
    Co-Authors: Feng Wang, Jie Xu, Xin Wang
    Abstract:

    Mobile-edge computing (MEC) and wireless power transfer (WPT) have been recognized as promising techniques in the Internet of Things (IoT) era to provide massive low-power wireless devices with enhanced Computation Capability and sustainable energy supply. In this paper, we propose a unified MEC-WPT design by considering a wireless powered multiuser MEC system, where a multi-antenna access point (AP) (integrated with an MEC server) broadcasts wireless power to charge multiple users and each user node relies on the harvested energy to execute Computation tasks. With MEC, these users can execute their respective tasks locally by themselves or offload all or part of them to the AP based on a time division multiple access (TDMA) protocol. Building on the proposed model, we develop an innovative framework to improve the MEC performance, by jointly optimizing the energy transmit beamformer at the AP, the central processing unit (CPU) frequencies and the numbers of offloaded bits at the users, as well as the time allocation among users. Under this framework, we address a practical scenario where latency-limited Computation is required. In this case, we develop an optimal resource allocation scheme that minimizes the AP's total energy consumption subject to the users' individual Computation latency constraints. Leveraging the state-of-the-art optimization techniques, we derive the optimal solution in a semi-closed form. Numerical results demonstrate the merits of the proposed design over alternative benchmark schemes.

Byrav Ramamurthy - One of the best experts on this subject based on the ideXlab platform.

  • a survey of security issues in wireless sensor networks
    IEEE Communications Surveys and Tutorials, 2006
    Co-Authors: Yong Wang, Garhan Attebury, Byrav Ramamurthy
    Abstract:

    Wireless Sensor Networks (WSNs) are used in many applications in military, ecological, and health-related areas. These applications often include the monitoring of sensitive information such as enemy movement on the battlefield or the location of personnel in a building. Security is therefore important in WSNs. However, WSNs suffer from many constraints, including low Computation Capability, small memory, limited energy resources, susceptibility to physical capture, and the use of insecure wireless communication channels. These constraints make security in WSNs a challenge. In this article we present a survey of security issues in WSNs. First we outline the constraints, security requirements, and attacks with their corresponding countermeasures in WSNs. We then present a holistic view of security issues. These issues are classified into five categories: cryptography, key management, secure routing, secure data aggregation, and intrusion detection. Along the way we highlight the advantages and disadvantages of various WSN security protocols and further compare and evaluate these protocols based on each of these five categories. We also point out the open research issues in each subarea and conclude with possible future research directions on security in WSNs.

Lei Gao - One of the best experts on this subject based on the ideXlab platform.

  • joint optimal allocation of wireless resource and mec Computation Capability in vehicular network
    Wireless Communications and Networking Conference, 2020
    Co-Authors: Min Zhu, Yanzhao Hou, Xiaofeng Tao, Tengfei Sui, Lei Gao
    Abstract:

    Numerous applications of vehicles are Computation- intensive and delay-sensitive. In order to deal with the problem caused by limited wireless and Computation Capability in the Mo- bile Edge Computing (MEC) enabled vehicular network, a Joint Optimization of Wireless and Computation Allocation (JOWCA) algorithm is proposed to minimize global delay of MEC-enabled vehicular network. The JOWCA algorithm consists of vehicle- to-vehicle (V2X) matching and MEC Computation Capability allocation. In the V2X matching, a Graph-based Interference Cancellation (Graph-IC) scheme is proposed to allocate Resource Blocks (RBs) for vehicle-to- infrastructure (V2I) links and vehicle- to-vehicle (V2V) links to mitigate co-channel interference. The Graph-IC contains an adaptive interference threshold modified Heuristic Clustering (HC) algorithm and Hungarian algorithm. In the MEC Computation Capability allocation, the optimal solution of V2I link offloading ratio and MEC Computation Capability scheduling are obtained by applying Karush-Kuhn- Tucker (KKT) condition. Simulation shows that the proposed scheme can effectively reduce the global delay of the MEC- enabled vehicular network.

  • WCNC Workshops - Joint Optimal Allocation of Wireless Resource and MEC Computation Capability in Vehicular Network
    2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2020
    Co-Authors: Min Zhu, Yanzhao Hou, Xiaofeng Tao, Tengfei Sui, Lei Gao
    Abstract:

    Numerous applications of vehicles are Computation- intensive and delay-sensitive. In order to deal with the problem caused by limited wireless and Computation Capability in the Mo- bile Edge Computing (MEC) enabled vehicular network, a Joint Optimization of Wireless and Computation Allocation (JOWCA) algorithm is proposed to minimize global delay of MEC-enabled vehicular network. The JOWCA algorithm consists of vehicle- to-vehicle (V2X) matching and MEC Computation Capability allocation. In the V2X matching, a Graph-based Interference Cancellation (Graph-IC) scheme is proposed to allocate Resource Blocks (RBs) for vehicle-to- infrastructure (V2I) links and vehicle- to-vehicle (V2V) links to mitigate co-channel interference. The Graph-IC contains an adaptive interference threshold modified Heuristic Clustering (HC) algorithm and Hungarian algorithm. In the MEC Computation Capability allocation, the optimal solution of V2I link offloading ratio and MEC Computation Capability scheduling are obtained by applying Karush-Kuhn- Tucker (KKT) condition. Simulation shows that the proposed scheme can effectively reduce the global delay of the MEC- enabled vehicular network.

Yan Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Energy Efficiency and Delay Tradeoff for Wireless Powered Mobile-Edge Computing Systems With Multi-Access Schemes
    IEEE Transactions on Wireless Communications, 2020
    Co-Authors: Supeng Leng, Sabita Maharjan, Yan Zhang
    Abstract:

    The integration of Mobile-edge Computing (MEC) and Wireless Energy Transfer (WET) has been recognized as a promising technique to enhance Computation Capability and to prolong battery lifetime of resource-constrained wireless devices in the Internet of Things (IoT) era. However, it is challenging to jointly schedule energy, radio, and Computational resources for coordinating heterogeneous performance requirements in wireless powered MEC systems. To fill this gap, this paper investigates the fundamental tradeoff between Energy Efficiency (EE) and delay in a multi-user wireless powered MEC system. Considering the random channel conditions and task arrivals, we formulate a stochastic optimization problem to study the EE-delay tradeoff, which optimizes network EE subject to network stability, maximum central processing unit frequency, peak transmission power, available communication resource, and energy causality constraints. Further, we propose the online Computation offloading and resource allocation algorithm by transforming the original problem into a series of deterministic optimization problems in each time block based on Lyapunov optimization theory. In addition, theoretical analysis shows that the algorithm achieves the EE-delay tradeoff as [O(1/V), O(V)] and introduces a control parameter V to balance the EE-delay performance. Numerical results verify the theoretical analysis and reveal the impact of various parameters to the system performance.

Sazia Parvin - One of the best experts on this subject based on the ideXlab platform.

  • anomaly detection in wireless sensor networks a survey
    Journal of Network and Computer Applications, 2011
    Co-Authors: Biming Tian, Sazia Parvin
    Abstract:

    Since security threats to WSNs are increasingly being diversified and deliberate, prevention-based techniques alone can no longer provide WSNs with adequate security. However, detection-based techniques might be effective in collaboration with prevention-based techniques for securing WSNs. As a significant branch of detection-based techniques, the research of anomaly detection in wired networks and wireless ad hoc networks is already quite mature, but such solutions can be rarely applied to WSNs without any change, because WSNs are characterized by constrained resources, such as limited energy, weak Computation Capability, poor memory, short communication range, etc. The development of anomaly detection techniques suitable for WSNs is therefore regarded as an essential research area, which will enable WSNs to be much more secure and reliable. In this survey paper, a few of the key design principles relating to the development of anomaly detection techniques in WSNs are discussed in particular. Then, the state-of-the-art techniques of anomaly detection in WSNs are systematically introduced, according to WSNs' architectures (Hierarchical/Flat) and detection technique categories (statistical techniques, rule based, data mining, Computational intelligence, game theory, graph based, and hybrid, etc.). The analyses and comparisons of the approaches that belong to a similar technique category are represented technically, followed by a brief discussion towards the potential research areas in the near future and conclusion.