Ruilong Deng

Energy-Efficient Cooperative Spectrum Sensing by Optimal Scheduling in Sensor-Aided Cognitive Radio Networks

A promising technology that tackles the conflict between spectrum scarcity and underutilization is cognitive radio (CR), of which spectrum sensing is one of the most important functionalities. The use of dedicated sensors is an emerging service for spectrum sensing, where multiple sensors perform cooperative spectrum sensing. However, due to the energy constraint of battery-powered sensors, energy efficiency arises as a critical issue in sensor-aided CR networks. An optimal scheduling of each sensor active time can effectively extend the network lifetime. In this paper, we divide the sensors into a number of nondisjoint feasible subsets such that only one subset of sensors is turned on at a period of time while guaranteeing that the necessary detection and false alarm thresholds are satisfied. Each subset is activated successively, and nonactivated sensors are put in a low-energy sleep mode to extend the network lifetime. We formulate such problem of energy-efficient cooperative spectrum sensing in sensor-aided CR networks as a scheduling problem, which is proved to be NP-complete. We employ Greedy Degradation to degrade it into a linear integer programming problem and propose three approaches, namely, Implicit Enumeration (IE), General Greedy (GG), and λ-Greedy (λG), to solve the subproblem. Among them, IE can achieve an optimal solution with the highest computational complexity, whereas GG can provide a solution with the lowest complexity but much poorer performance. To achieve a better tradeoff in terms of network lifetime and computational complexity, a brand new λG is proposed to approach IE with the complexity comparable with GG. Simulation results are presented to verify the performance of our approaches, as well as to study the effect of adjustable parameters on the performance.

Residential Energy Consumption Scheduling: A Coupled-Constraint Game Approach

This paper investigates the residential energy consumption scheduling problem, which is formulated as a coupled-constraint game by taking the interaction among users and the temporally-coupled constraint into consideration. The proposed solution consists of two parts. Firstly, dual decomposition is applied to transform the original coupled-constraint game into a decoupled one. Then, Nash equilibrium of the decoupled game is proven to be achievable via best response, which is computed by gradient projection. The proposed solution is also extended to an online version, which is able to alleviate the impact of the price prediction error. Numerical results demonstrate that the proposed approach can effectively shift the peak-hour demand to off-peak hours, enhance the welfare of each user, and minimize the peak-to-average ratio. The scalability of the approach and the impact of the user number are also investigated.

Sensing-Performance Tradeoff in Cognitive Radio Enabled Smart Grid

Smart grid is widely considered to be the next generation of power grid, where power generation, management, transmission, distribution, and utilization are fully upgraded to improve agility, reliability, efficiency, security, economy, and environmental friendliness. Demand response management (DRM) is recognized as a control unit of the smart grid, with the attempt to balance the real-time load as well as to shift the peak-hour load. Communications are critical to the accuracy and optimality of DRM, and hence at the core of the control performance of the smart grid. In this paper, we introduce cognitive radio into the smart grid to improve the communication quality. By means of spectrum sensing and channel switching, smart meters can decide to transmit data on either an original unlicensed channel or an additional licensed channel, so as to reduce the communication outage. Considering the energy cost taxed by spectrum sensing together with the control performance degradation incurred by imperfect communications, we formulate the sensing-performance tradeoff problem between better control performance and lower communication cost, paving the way towards a green smart grid. The impact of the communication outage on the control performance of DRM is also analyzed, which reduces the profit of power provider and the social welfare of the smart grid, although it may not always decrease the profit of power consumer. By employing the energy detector, we prove that there exists a unique optimal sensing time which yields the maximum tradeoff revenue, under the constraint that the licensed channel is sufficiently protected. Numerical results are provided to validate our theoretical analysis.

Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption

Mobile users typically have high demand on localized and location-based information services. To always retrieve the localized data from the remote cloud, however, tends to be inefficient, which motivates fog computing. The fog computing, also known as edge computing, extends cloud computing by deploying localized computing facilities at the premise of users, which prestores cloud data and distributes to mobile users with fast-rate local connections. As such, fog computing introduces an intermediate fog layer between mobile users and cloud, and complements cloud computing toward low-latency high-rate services to mobile users. In this fundamental framework, it is important to study the interplay and cooperation between the edge (fog) and the core (cloud). In this paper, the tradeoff between power consumption and transmission delay in the fog-cloud computing system is investigated. We formulate a workload allocation problem which suggests the optimal workload allocations between fog and cloud toward the minimal power consumption with the constrained service delay. The problem is then tackled using an approximate approach by decomposing the primal problem into three subproblems of corresponding subsystems, which can be, respectively, solved. Finally, based on simulations and numerical results, we show that by sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.

Load scheduling with price uncertainty and temporally-coupled constraints in smart grids

Summary form only given. Recent years have witnessed the significant growth in electricity consumption. The emerging smart grid aims to address the ever-increasing load through appropriate scheduling, i.e., to shift the energy demand from peak to off-peak periods by pricing tariffs as incentives. Under the real-time pricing environment, due to the uncertainty of future prices, load scheduling is formulated as an optimization problem with expectation and temporally-coupled constraints. Instead of resorting to stochastic dynamic programming that is generally prohibitive to be explicitly solved, we propose dual decomposition and stochastic gradient to solve the problem. That is, the primal problem is firstly dually decomposed into a series of separable subproblems, and then the price uncertainty in each subproblem is addressed by stochastic gradient based on the statistical knowledge of future prices. In addition, we propose an online approach to further alleviate the impact of price prediction error. Numerical results are provided to validate our theoretical analysis.

Fast Distributed Demand Response With Spatially and Temporally Coupled Constraints in Smart Grid

As the next generation power grid, smart grid is characterized as an informationized system, and demand response is one of its important features to deal with the ever-increasing peak energy usage. However, the supply capacity and required demand make the demand response problem with both spatially and temporally coupled constraints, which, to the best of our knowledge, has not been thoroughly investigated in a distributed manner. The complexity lies in how to guarantee privacy and convergence of distributed algorithms. Aiming at this challenge, in this paper, we first propose a distributed algorithm, which is based on dual decomposition and does not require each user to reveal his/her private information. Then, the convergence analysis is conducted to provide guidance on how to choose the proper step size; through which, we notice that the convergence speed of the subgradient projection method is not fast enough and it is highly dependent on the choice of the step size. Therefore, to increase the convergence rate of the distributed algorithm, we further propose a fast approach based on binary search. Finally, the distributed algorithms are illustrated by numerical simulations and the extensive comparison results validate the better performance of the fast approach.

Distributed Real-Time Demand Response in Multiseller–Multibuyer Smart Distribution Grid

Demand response is a key solution in smart grid to address the ever-increasing peak energy consumption. With multiple utility companies, users will decide from which utility company to buy electricity and how much to buy. Consequently, how to devise distributed real-time demand response in the multiseller-multibuyer environment emerges as a critical problem in future smart grid. In this paper, we focus on the real-time interactions among multiple utility companies and multiple users. We propose a distributed real-time demand response algorithm to determine each users demand and each utility companys supply simultaneously. By applying dual decomposition, the original problem is firstly decoupled into single-seller-multibuyer subsystems; then, the demand response problem in each subsystem can be distributively solved. The major advantage of this approach is that each utility company and user locally solve subproblems to perform energy allocation, instead of requiring a central controller or any third party. Therefore, privacy is guaranteed because no entity needs to reveal or exchange private information. Numerical results are presented to verify efficiency and effectiveness of the proposed approach.

Mobility-Aware Coordinated Charging for Electric Vehicles in VANET-Enhanced Smart Grid

Coordinated charging can provide efficient charging plans for electric vehicles (EVs) to improve the overall energy utilization while preventing an electric power system from overloading. However, designing an efficient coordinated charging strategy to route mobile EVs to fast-charging stations for globally optimal energy utilization is very challenging. In this paper, we investigate a special smart grid with enhanced communication capabilities, i.e., a VANET-enhanced smart grid. It exploits vehicular ad-hoc networks (VANETs) to support real-time communications among road-side units (RSUs) and highly mobile EVs for collecting real-time vehicle mobility information or dispatching charging decisions. Then, we propose a mobility-aware coordinated charging strategy for EVs, which not only improves the overall energy utilization while avoiding power system overloading, but also addresses the range anxieties of individual EVs by reducing the average travel cost. Specifically, the mobility-incurred travel cost for an EV is considered in two aspects: 1) the travel distance from the current position of the EV to a charging station; and 2) the transmission delay for receiving a charging decision via VANETs. The optimal mobility-aware coordinated EV charging problem is formulated as a time-coupled mixed-integer linear programming problem. By solving this problem based on Lagrange duality and branch-and-bound-based outer approximation techniques, an efficient charging strategy is obtained. To evaluate the performance of the proposed strategy, a realistic suburban scenario is developed in VISSIM to track vehicle mobility through the generated simulation traces, based on which the travel cost of each EV can be accurately calculated. Extensive simulation results demonstrate that the proposed strategy considerably outperforms the traditional EV charging strategy without VANETs on the metrics of the overall energy utilization, the average EV travel cost, and the number of successfully charged EVs.

Energy-efficient cooperative spectrum sensing in sensor-aided cognitive radio networks

Cognitive radio has been proposed to make full use of the limited spectrum resources, where spectrum sensing plays an important role to detect the channel state for opportunistic utilization. To address the hidden terminal problem, cooperative spectrum sensing is employed to improve the detection performance. Due to the energy constraint of battery-powered sensor nodes, energy efficiency emerges as a critical issue in sensor-aided cognitive radio networks. By exploiting the abundant sensor nodes and multiuser diversity, a cooperative schedule of each sensor nodes on/off can effectively extend the network lifetime. However, frequent on/off switching of sensor nodes will generate an adverse impact and make the network vulnerable and unreliable. We consider the problem of optimizing the schedule order to reduce the switch frequency. Greedy Heuristic is proposed to approach the minimum node switch with low computational complexity. Simulation results are presented to verify the performance of the proposed algorithm.

Towards power consumption-delay tradeoff by workload allocation in cloud-fog computing

Fog computing, characterized by extending cloud computing to the edge of the network, has recently received considerable attention. The fog is not a substitute but a powerful complement to the cloud. It is worthy of studying the interplay and cooperation between the edge (fog) and the core (cloud). To address this issue, we study the tradeoff between power consumption and delay in a cloud-fog computing system. Specifically, we first mathematically formulate the workload allocation problem. After that, we develop an approximate solution to decompose the primal problem into three subproblems of corresponding subsystems, which can be independently solved. Finally, based on extensive simulations and numerical results, we show that by sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.