In this paper, we consider a smart power model, where some subscribers share several energy providers and there are some malicious users in this power grid. The energy providers are managed by a Power Market Scheduling Center (PMSC), which broadcasts electricity price to subscribers and energy providers. The energy providers and subscribers update their capacities and energy consumption requirements respectively according to the electricity prices received. In order to identify the malicious users and the unstable energy providers, the Mechanism of Identification and Processing (MIP) for the malicious users and unstable energy providers is proposed. By integrating the MIP, we proposed a heuristic algorithm named as Dynamic Pricing Algorithm with Malicious users and Unstable energy providers (DPAMU) to get the optimal electricity price as well as the optimal power requirement and the load capacity. Finally, the simulation results show that the proposed DPAMU has good convergence performance and can shave and clip the peak load effectively.Index Terms-Power market scheduling center, demand response, real-time pricing, malicious user, unstable energy provider, smart grid.

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This paper studies an unmanned aerial vehicle (UAV)-enabled mobile edge network for Cyber-Physical System (CPS), where UAV with fixed-wing or rotary-wing is dispatched to provide communication and mobile edge computing (MEC) services to ground terminals (GTs). To minimize the energy consumption so as to extend the endurance of the UAV, we intend to jointly optimize its 3D trajectory and the task-cache strategies among GTs to save the energies spent on flight propulsion and GT tasks. Such joint trajectorytask-cache problem is difficult to be optimally solved, as it is non-convex and involves multiple constraints. To tackle this problem, we reformulate the optimizing of task offloading and cache into two tractable linear program (LP) problems, and the optimizing of UAV trajectory into three convex Quadratically Constrained Quadratically Program (QCQP) problems on horizontal trajectory, vertical trajectory and flight time of the UAV respectively. Then a block coordinate descent algorithm is proposed to iteratively solve the formed subproblems through a successive convex optimization (SCO) process. A high-quality sub-optimal solution to the joint problem then will be obtained, after the algorithm converging to a prescribed accuracy. The numerical results show the proposed solution significantly outperforms the baseline solution. INDEX TERMS Unmanned aerial vehicle, Internet of Thing, mobile edge computing, 3D trajectory design, cache deployment.

SUMMARYWireless nodes operating on batteries are always assumed to be selfish to consume their energy solely to maximize their own benefits. Thus, the two network objectives, that is, system efficiency and user fairness should be considered simultaneously. To this end, we propose two game theoretic mechanisms, that is, the signal‐to‐noise ratio (SNR) game and the data‐rate game to stimulate cooperation among selfish user nodes for cooperative relaying. Considering one node could trade its transmission power for its partner's relaying directly, the strategy of a node is defined as the amount of power that it is willing to contribute for relaying purpose. In the SNR game, selfish nodes are willing to achieve SNR increases at their receivers, while in the data‐rate game the nodes are willing to achieve data‐rate gains. We prove that each of the games has a unique Nash bargaining solution. Simulation results show that the Nash bargaining solution lead to fair and efficient resource allocation for both the cooperative partner nodes in the Pareto optimal sense, that is, both the nodes could experience better performance than they work independently and the degree of cooperation of a node only depends on how much contribution its partner can make to improve its own performance. Copyright © 2012 John Wiley & Sons, Ltd.

Following the wired network virtualization, virtualization of wireless networks becomes the next step aiming to provide network or infrastructure providers with the ability to manage and control their networks in a more dynamic fashion. The benefit of the wireless mobile network virtualization is a more agile business model where virtual mobile network operators (MNOs) can request and thus pay physical MNOs in a more pay-as-you-use manner. This paper presents some resource allocation algorithms for joint network virtualization and resource allocation of wireless networks. The overall algorithm involves the following two major processes: firstly, to virtualize a physical wireless network into multiple slices, each representing a virtual network, and secondly, to carry out physical resource allocation within each virtual network (or slice). In particular, the paper adopts orthogonal frequency division multiplexing (OFDM) as its physical layer to achieve more efficient resource utilization. Therefore, the resource allocation is conducted in terms of sub-carriers. Although the motivation and algorithm design are based on IEEE 802.16 or WiMAX networks, the principle and algorithmic essence are also applicable to other OFDM access-based wireless networks. The aim was to achieve the following design goals: virtual network isolation and resource efficiency. The latter is measured in terms of network throughput and packet delivery ratio. The simulation results show that the aforementioned goals have been achieved.