Multi-access mobile edge computing (MEC), which enables mobile users (MUs) to offload their computation-workloads to the computation-servers located at the edge of cellular networks via multi-access ...radio access, has been considered as a promising technique to address the explosively growing computation-intensive applications in mobile Internet services. In this paper, by exploiting non-orthogonal multiple access (NOMA) for improving the efficiency of multi-access radio transmission, we study the NOMA-enabled multi-access MEC. We aim at minimizing the overall delay of the MUs for finishing their computation requirements, by jointly optimizing the MUs' offloaded workloads and the NOMA transmission-time. Despite the non-convexity of the formulated joint optimization problem, we propose efficient algorithms to find the optimal offloading solution. For the single-MU case, we exploit the layered structure of the problem and propose an efficient layered algorithm to find the MU's optimal offloading solution that minimizes its overall delay. For the multi-MU case, we propose a distributed algorithm (in which the MUs individually optimize their respective offloaded workloads) to determine the optimal offloading solution for minimizing the sum of all MUs' overall delay. Extensive numerical results have been provided to validate the effectiveness of our proposed algorithms and the performance advantage of our NOMA-enabled multi-access MEC in comparison with conventional orthogonal multiple access enabled multi-access MEC.
•We formulate a battery purchasing and charging problem for battery swap.•We use a dynamic model to capture the time-varying energy price and demand.•A fluid approach is used to address the curse of ...dimensionality of the model.•Robust optimization is applied to examine the impact of demand uncertainty.•We investigate the impact of energy price and demand patterns on system cost.
A battery swap station (BSS) is a facility where electric vehicle owners can quickly exchange their depleted battery for a fully-charged one. In order for battery swap to be economically sound, the BSS operator must make a long-term decision on the number of charging bays in the facility, a medium-term decision on the number of batteries in the system, and short-term decisions on when and how many batteries to recharge. In this paper, we introduce a periodic fluid model to describe charging operations at a BSS facing time-varying demand for battery swap and time-varying prices for charging empty batteries, with the objective of finding an optimal battery purchasing and charging policy that best trades off battery investment cost and operating cost including charging cost and cost of customer waiting. We consider a two-stage optimization problem: An optimal amount of battery fluid is identified in the first stage. In the second stage, an optimal charging rule is determined by solving a continuous-time optimal control problem. We characterize the optimal charging policy via Pontryagin’s maximum principle and derive an explicit upper bound for the optimal amount of battery fluid which allows us to quantify the joint effect of demand patterns and electricity prices on battery investment decisions. In particular, fewer batteries are needed when the peaks and the troughs of these periodic functions occur at different times.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Fog computing, complementary to cloud computing, has recently emerged as a new paradigm that extends the computing infrastructure from the center to the edge of the network. This article explores the ...design of a fog computing orchestration framework to support IoT applications. In particular, we focus on how the widely adopted cloud computing orchestration framework can be customized to fog computing systems. We first identify the major challenges in this procedure that arise due to the distinct features of fog computing. Then we discuss the necessary adaptations of the orchestration framework to accommodate these challenges.
A battery charging station (BCS) is a charging facility that supplies electric energy for recharging electric vehicles' depleted batteries (DBs). A BCS has a certain number of charging bays and ...maintains a dynamic inventory of fully charged batteries (FBs). This paper studies a BCS scheduling (BCSS) problem whose target is to schedule the charging processes of the charging bays such that the charging cost is minimized while satisfying the FB demand. Specifically, the BCSS problem has two types of operations: 1) loading DBs into the charging bays and then unloading them to the FB inventory when they are fully charged and 2) controlling the charging rate of each charging bay. We formulate the BCSS problem as a mixed-integer program with quadratic battery degradation cost. A generalized benders decomposition algorithm is then developed to solve the problem efficiently. The salience of the developed algorithm is that: 1) each charging bay can solve its own subproblem separately and 2) each subproblem can be further partitioned into multiple independent and identically structured quadratic programming problems, and thus the algorithm facilitates an efficient parallel implementation. We perform extensive real data simulation to validate the optimization model and demonstrate the efficiency of the proposed algorithm.
Motivated by the urgent demand for the electric vehicle (EV) fast refueling technologies, battery swapping and charging stations (BSCSs) are envisioned as a promising solution to provide timely EV ...refueling services. However, inappropriate battery charging operation in BSCSs cannot only incur unnecessary high charging cost but also threaten the reliability of the power grid. In this paper, we aim at obtaining an optimal charging operation policy for a single BSCS to minimize its charging cost while ensuring its quality-of-service. Leveraging the novel queueing network model, we propose to formulate the charging operation problem as a constrained Markov decision process and derive the optimal policy by the standard Lagrangian method and dynamic programming. To avoid the curse of dimensionality in practical large-scale systems, we further analyze the structure of the optimal policy and transform the dynamic programming procedure into an equivalent threshold optimization problem with a discrete separable convex objective function. Numerical results validate our theoretical analysis and the computational efficiency of our proposed algorithms. This paper also shows the impact of the system parameters (e.g., numbers of batteries and chargers) on the average cost under the optimal charging policy, which gives rich insights into the infrastructure planning of future BSCS networks.
Future smart grid (SG) has been considered a complex and advanced power system, where energy consumers are connected not only to the traditional energy retailers (e.g., the utility companies), but ...also to some local energy networks for bidirectional energy trading opportunities. This paper aims to investigate a hybrid energy trading market that is comprised of an external utility company and a local trading market managed by a local trading center (LTC). The existence of local energy market provides new opportunities for the energy consumers and the distributed energy sellers to perform the local energy trading in a cooperative manner such that they all can benefit. This paper first quantifies the respective benefits of the energy consumers and the sellers from the local trading and then investigates how they can optimize their benefits by controlling their energy scheduling in response to the LTC's pricing. Two different types of the LTC are considered: 1) the nonprofit-oriented LTC, which solely aims at benefiting the energy consumers and the sellers; and 2) the profit-oriented LTC, which aims at maximizing its own profit while guaranteeing the required benefit for each consumer and seller. For each type of the LTC, the optimal trading problem is formulated and the associated algorithm is further proposed to efficiently find the LTC's optimal price, as well as the optimal energy scheduling for each consumer and seller. Numerical results are provided to validate the benefits of the hybrid energy trading market and the performance of the proposed algorithms.
Ambient backscatter communication (AmBC) leverages the existing ambient radio frequency (RF) environment to implement communication with battery-free devices. The key challenge in the development of ...AmBC is the very weak RF signals backscattered by the AmBC Tag. To overcome this challenge, we propose the use of orthogonal space-time block codes (OSTBC) by incorporating multiple antennas at the Tag as well as at the Reader. Our approach considers both coherent and non-coherent OSTBC so that systems with and without channel state information can be considered. To allow the application of OSTBC, we develop an approximate linearized and normalized multiple-input multiple-output (MIMO) channel model for the AmBC system. This MIMO channel model is shown to be accurate for a wide range of useful operating conditions. Two coherent detectors and a non-coherent detector are also provided based on the proposed AmBC channel model. Simulation results show that enhanced bit error rate performance can be achieved, demonstrating the benefit of using multiple antennas at the Tag as well as the Reader.
We study energy-efficient spectrum sensing and transmission for Cognitive Radio (CR) which jointly determines its sensing and transmission durations. Our results quantify the impact of different ...power consumption components (i.e., sensing, transmission, and idling) on SU's optimal sensing and transmission durations. Our results also show that with a limited power capacity, SU has to strike a balance in energy consumption between sensing and transmission via appropriate idling.
Electric vehicles (EVs) have been well recognized as a deferrable load with the flexibility to shift their energy demands over time. Although this one-dimensional flexibility has been extensively ...investigated both by research and industrial implementations, the expanding energy demand and the associated uncertainties still make the integration of a large population of EVs into power system reliably and economically greatly challenging. In this paper, we design an auction scheme via mechanism design to elicit two additional flexibilities from EVs, namely energy flexibility and deadline flexibility. An offline mechanism is firstly designed as a benchmark based on the famous Vickrey-Clark-Groves mechanism. Then based on the primal-dual approach, we propose an online auction, in which all bids are truthful, the loss of social welfare is bounded by competitive ratio, and the mechanism can be implemented in polynomial time. By the numerical results, we quantitatively show that both the power system operators and individual EVs can benefit from the integration of the multi-dimensional flexibilities through our proposed mechanisms.
Multiple-input multiple-output (MIMO) ambient backscatter communication (AmBC) systems are investigated in order to develop approaches to achieve power, multiplexing, and diversity gains. These ...results can be utilized to motivate the development of MIMO AmBC by providing performance bounds on the MIMO AmBC gains. Our approach to the investigation is to reformulate the MIMO AmBC channel model as an accurate linear MIMO channel model. Using this model, we show that the MIMO AmBC received signal is real-valued, hence the dimension of the received signal is halved. In addition, we show that MIMO AmBC has a per antenna constant modulus transmit signal constraint. Therefore, increases in antennas at the Tag provide a power gain in contrast to conventional MIMO systems. Under these constraints, when channel state information at the Reader (CSIR) is available, we provide estimates of channel capacity. Assuming channel state information at the Tag (CSIT) is also available, we use a fixed-point iteration algorithm to maximize channel capacity. With CSIT, beamforming design and the corresponding majorization-minimization (MM) algorithm are proposed to find the optimal transmit and receive beamformers so that diversity gain can be leveraged. It is also shown that in the low signal-to-noise ratio (SNR) operating region, beamforming design maximizes channel capacity. In addition, we provide numerical results for the diversity-multiplexing tradeoff (DMT). Utilizing comparisons between AmBC and conventional MIMO, we highlight the unique characteristics of MIMO AmBC. These approaches to maximize power, capacity, and diversity gains demonstrate the potential of MIMO AmBC.
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