Energy-Efficient Architecture and Technologies for Device to Device(D2D) Based Proximity Service

Considering that modern mobile terminals possess the capability to detect users' proximity,and offer means to directly communicate and share content with the people in close area,Device-to-Device(D2D) based Proximity Services(ProSe) have recently witnessed great development,which enable users to seek for and utilize relevant value in their physical proximity,and are capable to create numerous new mobile service opportunities.However,without a breakthrough in battery technology,the energy will be the biggest limitation for ProSe.Through incorporating the features of ProSe(D2D communication technologies,abundant built-in sensors,localization-dependent,and context-aware,etc.),this paper thoroughly investigates the energy-efficient architecture and technologies for ProSe from the following four aspects:underlying networking technology,localization,application and architecture features,context-aware and user interactions.Besides exploring specific energy-efficient schemes pertaining to each aspect,this paper offers a perspective for research and applications.In brief,through classifying,summarizing and optimizing the multiple efforts on studying,modeling and reducing energy consumption for ProSe on mobile devices,the paper would provide guide for developers to build energy-efficient ProSe.

Real-Time Pricing for Smart Grid with Multiple Companies and Multiple Users Using Two-Stage Optimization

In this paper, we focus on the real-time interactions among multiple utility companies and multiple users and formulate real-time pricing(RTP) as a two-stage optimization problem. At the first stage, based on cost function, we propose a continuous supply function bidding mechanism to model the utility companies’ profit maximization problem, by which the analytic expression of electricity price is further derived. At the second stage, considering that individually optimal solution may not be socially optimal, we employ convex optimization with linear constraints to model the price anticipating users’ daily payoff maximum. Substitute the analytic expression of electricity price obtained at the first stage into the optimization problem at the second stage. Using customized proximal point algorithm(C-PPA), the optimization problem at the second stage is solved and electricity price is obtained accordingly. We also prove the existence and uniqueness of the Nash equilibrium in the mentioned twostage optimization and the convergence of C-PPA. In addition, in order to make the algorithm more practical, a statistical approach is used to obtain the function of price only through online information exchange, instead of solving it directly. The proposed approach offers RTP, power production and load scheduling for multiple utility companies and multiple users in smart grid. Statistical approach helps to protect the company’s privacy and avoid the interference of random factors, and C-PPA has an advantage over Lagrangian algorithm because the former need not obtain the objection function of the dual optimization problem by solving an optimization problem with parameters. Simulation results show that the proposed framework can significantly reduce peak time loading and efficiently balance system energy distribution.