Search

A substantial amount of research exists in the area of energy efficiency in communications and networking. In recognition of this fact, the IEEE Communications Society has decided to publish a series on Green Communications and Networking as three issues of the IEEE Journal On Selected Areas in Communications. The first issue of the Series was published in December 2015 with 39 papers. This second issue of the Series has 52 papers, covering a wide selection of topics. These topics can be characterized in five categories. The first category in this issue is cellular networks and consists of 15 papers. [ABSTRACT FROM AUTHOR]

One of the key design problems of wireless sensor networks is to reduce energy consumption and improve node survival. Because the battery in sensor nodes is difficult to replace and the energy is limited, the design of energy-saving routing protocol becomes very important. Aiming at the problems of limiting node energy and shortening network lifetime in wireless sensor networks, a WSNs routing protocol based on improved archimedes optimization algorithm (IAOAR) is proposed in this paper. Firstly, in the cluster establishment phase, the protocol defines different fitness functions according to the energy, number and distance of sensor nodes to select the appropriate initial cluster. Then the fitness value of the node is calculated, and the weight is dynamically updated according to the number of iterations to improve the optimization ability of AOA algorithm. Virtual force is introduced to adjust the selected cluster head position to ensure the selection of optimal cluster heads, reduce the transmission energy consumption of common sensor nodes. In the data transmission stage, combined with the improved ant colony algorithm, the shortest transmission path to the sink node is constructed to reduce the energy consumption of long-distance data transmission at the cluster head. The simulation results showed that compared with LEACH, E-LEACH, LEACH-ANT and MAXLEACH protocols, the network life cycle of the protocol proposed in this paper was improved by 140.55%, 107.09%, 99.24% and 96.76% respectively. It effectively reduced the speed of node death, balances the network energy consumption and prolonged the network lifetime. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for solving the optimal merging control problem for hybrid electric vehicles (HEVs) under a connected environment. To achieve a reduction in energy consumption and save travel time, this paper focuses on deriving a decentralized feedback control law that provides not only the optimal velocity trajectory for merging but also a torque distribution strategy for the HEV powertrain. For this purpose, a distance domain-based optimal control problem is first proposed to avoid a free end-time cost function formulation that usually arises due to considering the minimization of traveling time. Then, the vehicle dynamics take into account the constraint of the optimization problem to evaluate the energy consumption at the power device level instead of the acceleration, unlike the common method used for reducing energy consumption in previous studies of merging control with linear models. The proposed optimization problem is solved by Pontryagin’s maximum principle, and a traffic-in-the-loop powertrain simulation platform with a real-world emulated traffic scenario and high-fidelity HEV powertrain model is constructed to eliminate the randomly generated merging scenario. Finally, the simulation results obtained on the platform are demonstrated to validate the effectiveness of the proposed decentralized merging control law. [ABSTRACT FROM AUTHOR]

In this paper, a traction motor for maximum power 76kW electrical vehicle (EV) was designed, and the design results were verified through experiments. One of the important design objectives of the traction motor for EV is to improve energy efficiency. The energy efficiency of a traction motor affects the mileage of electric vehicles (EVs). Among the design factors influencing energy efficiency, shape parameters include torque per rotor volume (TRV), torque density (TD) and shape ratio (SR). These shape parameters have an impact on the motor's phase back EMF, inductance, winding resistance and core loss resistance, eventually affecting the energy efficiency in the entire operating range of the motor. In this paper, a New European Driving Cycle (NEDC) was applied to calculate the energy efficiency of the traction motor for EV. Changes in motor parameters such as phase back electromotive force (EMF), inductance, winding resistance and core loss resistance were identified according to variations in shape parameters. In addition, the motor characteristics according to the changes of these parameters were confirmed, and shape parameters to improve energy efficiency were selected. Since the traction motor designed in this paper adopted a water cooling method, thermal analysis was performed by reflecting the water cooling method in the thermal equivalent circuit (TEC). In order to confirm the reliability of the analysis method and design, a load test was conducted on the entire operating range to calculate energy efficiency, and a temperature test was performed to compare the test results with the analysis results. [ABSTRACT FROM AUTHOR]

The presence of condensate inside the dryer cylinders produces a significant increase in the power requirements during starting, putting at risk the capacity of the drive to complete a successful start. In past papers, authors developed estimation algorithms for the detection of condensate during section starting. This paper proposes and evaluates a new strategy that will complement the previous algorithms. This strategy will detect the presence of condensate from the condensate collapsing during the section shutdown. The proposed correlations are derived through a complete experimental evaluation in two experimental cylinders covering rim thicknesses up to 55 mm. The estimation algorithms are split into a low-/moderate-condensate-load zone and a high-condensate-load zone. Results confirm the robustness and accuracy of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

I am pleased to announce the Best Paper for the TRANSACTIONS published in 2013. [ABSTRACT FROM AUTHOR]

A new approach to evaluate personalized energy consumption is presented in this paper. The method consists of identifying driver–vehicle dynamics using the probability weighted autoregressive model, which is one of the multi-mode ARX models, and then of reproducing the driver–vehicle behavior in a vehicle-following task. The energy consumption of the vehicle is estimated from the velocity profile calculated by using the driver–vehicle model. In this paper, driving simulator and real-world driving data were recorded to identify the driver–vehicle model in various situations. As a result, real-world energy consumption could be reproduced in a variety of situations with an average error of 1.9% and a standard deviation within 1.5%. Several promising applications of the energy consumption evaluation are introduced in this paper, such as an online energy consumption prediction, a powertrain choice-assistance system for car buyers, and a solution to estimate the macroscopic energy consumption of aggregated vehicles in a traffic flow. [ABSTRACT FROM PUBLISHER]

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE. [ABSTRACT FROM AUTHOR]

Although smart home has received wide attention in recent years, numerous scholars focus more on energy optimization strategy than energy dispatch hardware device (named energy router). Meanwhile, this energy router should have several features, i.e., high renewable energy utilization, energy multi-port and low volume. Thus, this paper designs a nine-port energy router regarding smart home and proposes a multimode hierarchical management strategy for this energy router. First, for the multi-port demand of wind, solar, storage and utilization, this paper presents a nine-port energy router to improve the renewable energy consumption and power supply flexibility. In addition, to reduce the volume of the energy router, a non-isolated AC/DC hybrid topology is constructed through embedding the integrated power electronic converters, which achieves the miniaturization of the energy router. In order to improve the renewable energy utilization rate, the decentralized module control is proposed for the components of energy router to provide the voltage and frequency support for system, and realizes the power sharing of distributed generations (DGs). Furthermore, the power exchange control with three-mode switching is proposed to guarantee the global energy flow balance under complex conditions. Eventually, the feasibility of the energy router is verified by the simulation and experiments. [ABSTRACT FROM AUTHOR]

The opening up of the railway to competition in Europe has been accompanied by many technical challenges never before seen in the railway field. One of these challenges concerns precise energy metering. As European railways have been a monopoly until now, the current systems can only measure the energy consumption of a single operator. In accordance with European law, the installation of an energy meter has been mandatory in new or refurbished rolling stocks since 2014. Nevertheless, these energy meters are still not fully reliable, with misreadings and missing readings being common occurrences. To address the problem of energy metering data reliability, this paper presents a method for validating the reading measurement based on a physical model and validates it in two different case studies. For the issue of energy metering data availability, different estimators are developed, and their performance are shown with the aim of calculating the missing consumptions for both case studies. Finally, the paper suggests how these methods may be implemented in other types of trains. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) is the key to the performance of fuel cell / battery hybrid system. At present, reinforcement learning (RL) has been introduced into this field and has gradually become the focus of research. However, traditional EMSs only take the energy consumption into consideration when optimizing the operation economy, and ignore the cost caused by power source degradations. It would cause the problem of poor operation economy regarding Total Cost of Ownership (TCO). On the other hand, most studied RL algorithms have the disadvantages of overestimation and improper way of restricting battery SOC, which would lead to relatively poor control performance as well. To solve these problems, this paper establishes a TCO model including energy consumption, equivalent energy consumption and degradation of power sources at first, then adopt the Double Q-learning RL algorithm with state constraint and variable action space to determine the optimal EMS. Finally, using hardware-in-the-loop platform, the feasibility, superiority and generalization of proposed EMS is proved by comparing with the optimal dynamic programming and traditional RL EMS and equivalent consumption minimum strategy (ECMS) under both training and unknown operating conditions. Results prove that the proposed strategy has high global optimality and excellent SOC control ability regardless of training or unknown conditions. [ABSTRACT FROM AUTHOR]

This paper presents the two level optimization algorithms: a centralized day-ahead and decentralized minute-ahead algorithm for energy management in an integrated mainline railway system. All energy players, such as trains, infrastructure facilities, wayside storages, and distributed energy resources, are considered in the simulation. The algorithms are developed to demonstrate the railway energy management system architecture. This paper demonstrates the validity of the algorithms and analyzes the simulation results in offline and online real case studies. In the online case study, the developed system for minute ahead optimization and real-time operation was tested on the Malaga–Fuengirola line (Spanish railway) for a few hours. The optimization is done regarding three different objectives: cost optimization or energy consumption optimization or power demand optimization. [ABSTRACT FROM AUTHOR]

The memristor-based memory cell design is getting renewed attention in recent years due to its high speed and low power consumption. This paper aims at designing a novel hybrid memory cell incorporating a minimum number of transistors for a rapid bidirectional write operation. The read stability is one of the key elements for high efficiency and superior performance in memory. The memory cell in this paper was designed undertaking adequate measures for maintaining the stability in successive reads without any refresh operation. Extensive simulation experiments were conducted using 32-nm predictive technology model transistors and the results demonstrate superb performance and sound stability of the proposed memory cell in terms of read/write time as well as switching power consumptions. [ABSTRACT FROM PUBLISHER]

The multi-hop protocols are proved effective in the railway disaster wireless monitoring system. However, farther transmission distance with the larger data will decline the valid lifetime and reliability of the system. Most existing studies focused primarily on the communication protocols optimization, and some works tried to utilize the limited computation ability at the network-level or node-level, which are insufficient for the stiff disaster information monitoring demands. This paper presents an adaptive hybrid computation and communication strategy to fully taking advantage of the sensor processing ability, and improve the energy efficiency at the link-level. Furthermore, an adaptive optimization model is designed to meet the different monitoring demands of the system, and the valid lifetime is improved accordingly. Numerical examples with various operational scenarios are developed to demonstrate the superiority and practicality of the proposed protocol in the lifetime improvement, energy consumption minimization and equalization compared with other outstanding protocols. [ABSTRACT FROM AUTHOR]

This paper proposes a cooperative optimal power split (COPS) method for a group of intelligent electric vehicles with battery/supercapacitor hybrid energy storage systems (HESSs). To achieve good performance, the proposed COPS method is made up of the upper and lower layers: the upper layer aims at obtaining the optimal power demand sequence and sending it to the lower layer; the lower layer attempts to optimize the power split for HESS. Firstly, to ensure the performance of the proposed method in practice, the resistance of the battery and supercapacitor packs as well as the DC/DC converter efficiency are not assumed to be constants, but modeled well in the paper. Secondly, to achieve the optimal power demand sequence, an upper power demand sequence optimization problem is formulated based on distributed model predictive control (DMPC), in which energy demand is incorporated into the cost function. Thirdly, after receiving the optimal power demand sequence, a hybrid power split optimization strategy is presented to obtain the optimal power split, in which the lower HESS power split optimization problem is formulated based on DMPC to decrease battery aging process and energy consumption. Finally, an improved particle swarm optimization algorithm with multiple dynamic populations is used to solve the formulated upper and lower optimization problems. Simulation results demonstrate that, compared with the benchmark, the proposed COPS method can significantly extend battery lifespan and slightly decrease energy consumption. [ABSTRACT FROM AUTHOR]

Variable leakage flux (VLF) interior permanent-magnet synchronous machines (IPMSMs), which use intentional magnet leakage flux paths cross-coupled with the q-axis flux paths to reduce losses over driving cycles, have been introduced recently, but self-sensing has not been discussed for these machines. This paper focuses on the saliency angular offset attributes that are the primary metric for the well-established injection-based self-sensing methods. This paper begins by evaluating the effect of the intentionally designed cross-coupling in the VLF-IPMSM with rotor surface bridges on the saliency angular offset. This paper then proposes alternate rotor designs with side-loop structure that cause relatively linear and well-behaved cross-coupling that improves the load-dependent saliency angular offset characteristics. The effect of VLF properties on power conversion and driving cycle loss reduction capability is evaluated for the proposed designs. This paper also applies the concept of VLF design to conventional flux-weakening interior permanent-magnet machine rotor design and presents the loss reduction capability of such designs. [ABSTRACT FROM AUTHOR]

The increasing use of induction motors (IMs) with variable frequency drives for industrial process control and automation has necessitated the need to evaluate their energy efficiency in the presence of voltage and current harmonics. As the determination of IM efficiency is becoming mandatory in many countries, there is a need to develop a testing approach that eliminates inaccuracies due to the operator. This paper presents an automated efficiency testing procedure developed to mitigate the adverse effect of varying technical skill and competence levels of testing personnel on efficiency test results. This is a particularly useful development considering the recently proposed IEC/TS 60034-2-3 methodology which specifies identical tests between sinusoidal supply and converter operation. A comparison of several manual and automated efficiency tests on a 55-kW IM highlighted improvements in repeatability, correlation, and temperature variation due to the latter. In addition, the advanced data acquisition and computation capability of the measurement device used in the developed test rig provides an excellent platform to investigate the dynamic operation of IM drives. [ABSTRACT FROM AUTHOR]

The use of base station (BS) sleep modes is one of the most studied approaches for the reduction of the energy consumption of radio access networks (RANs). Many papers have shown that the potential energy saving of sleep modes is huge, provided the future behavior of the RAN traffic load is known. This paper investigates the effectiveness of sleep modes combined with machine learning (ML) approaches for traffic forecast. A portion of an RAN is considered, comprising one macro BS and a few small cell BSs. Each BS is powered by a photovoltaic (PV) panel, equipped with energy storage units, and a connection to the power grid. The PV panel and battery provide green energy, while the power grid provides brown energy. This paper examines the impacts of different prediction models on the consumed energy mix and on QoS. Numerical results show that the considered ML algorithms succeed in achieving effective trade-offs between energy consumption and QoS. Results also show that energy savings strongly depend on traffic patterns that are typical of the considered area. This implies that a widespread implementation of these energy saving strategies without the support of ML would require a careful tuning that cannot be performed autonomously and that needs continuous updates to follow traffic pattern variations. On the contrary, ML approaches provide a versatile framework for the implementation of the desired trade-off that naturally adapts the network operation to the traffic characteristics typical of each area and to its evolution. [ABSTRACT FROM AUTHOR]

This paper examines the problem of predictive gear scheduling for fuel consumption minimization in connected/automated heavy trucks. The literature highlights the fuel economy benefits of such predictive scheduling, but there is a need to optimize such scheduling online, in real time. To address this need, we begin by using dynamic programming (DP) to schedule gear shifting offline, in a manner that achieves a globally optimal Pareto tradeoff between the conflicting objectives of minimizing fuel consumption and shift frequency. The computational cost of DP is unfavorable for online implementation, but we present two algorithms addressing this challenge. Both algorithms rely on the fact that in the Pareto limit where fuel consumption minimization is the sole objective, DP furnishes a simple static shift map. Our first algorithm trains a recurrent neural network to prune the shift schedule generated by this map. The second algorithm performs this pruning in a direct manner tailored to reduce the schedule's rain flow count. We simulate these algorithms for different drive cycles. Both algorithms achieve a reasonable tradeoff between fuel consumption and gear shift frequency. However, the rain flow count algorithm is both more effective in approaching the DP-based Pareto front and more computationally efficient. [ABSTRACT FROM AUTHOR]

This paper constructs an integrated virtual reconfiguration model that can simultaneously group workstations, schedule virtual cells, and select energy consumption levels. If managers prefer the physical proximity of machines in a certain virtual cell, the material handling cost in it can be reduced. However, the distances among machines in other virtual cells are probably large, which may cause greater material handling cost. If a virtual cell for a certain product type has priority to be created, the backorder cost of that type can be decreased or even avoided. Nevertheless, the creation of virtual cells for other product types may be delayed, perhaps leading to the higher backorder cost of other product types. In addition, managers can choose a high energy consumption level of a machine to expedite its production to reduce backorder cost at the expense of more energy consumption cost. To minimize the total operational cost, we develop a novel Discrete Imperialist Competitive Algorithm with a Priority rule-based heuristic (DICAP). It includes a colony movement strategy, a competition strategy, a collapse mechanism, a development strategy, and a sufficient convergence policy. Numerical experiments and t-test are conducted to validate that the proposed DICAP outperforms genetic algorithm and simulated annealing. Note to Practitioners—Virtual cellular manufacturing systems can create an expectation of improving machine utilization and productivity, reducing reconfiguration cost, and adaptation to product specification changes in reality. However, managers often feel difficult to make appropriate decisions for three interrelated issues, i.e., workstation grouping, virtual cell creation and release, and energy consumption options. Workstation grouping depends on the availability of machines and workers, and the availability may be related to the creation and release time of some virtual cells. Whether a virtual cell is created in time has an influence on the selection of energy consumption levels, because managers need to consider both backorder cost and energy consumption cost. In addition, there often exists a bottleneck workstation in each virtual cell. Therefore, it is essential and worthwhile to select appropriate energy consumption levels of machines to smooth the production efficiency of grouped workstations. To effectively address the issues, this paper presents an integrated virtual reconfiguration model. A novel Discrete Imperialist Competitive Algorithm with a Priority rule-based heuristic is developed to minimize the total operational cost. Numerical experiments and t-test results indicate that the proposed algorithm outperforms two commonly used ones, i.e., genetic algorithm and simulated annealing in solution quality. It is suitable for virtual reconfiguration problem with industrial size in practice. [ABSTRACT FROM AUTHOR]

Accelerator-based chip multiprocessors (ACMPs), which combine application-specific HW accelerators (ACCs) with host processor core(s), are promising architectures for high-performance and power-efficient computing. However, ACMPs with many ACCs have scalability limitations. The ACCs’ performance benefits can be overshadowed by bottlenecks on shared resources of processor core(s), communication fabric/DMA, and on-chip memory. Primarily, this is rooted in the ACCs’ data access and the orchestration dependency. Due to very loosely defined ACC communication semantics, and relying on general architectures, the resources bottlenecks hamper performance. This paper explores and alleviates the scalability limitations of ACMPs. To this end, this paper first proposes ACMPerf, an analytical model to capture the impact of the resources bottlenecks on the achievable ACCs’ benefits. Then, this paper identifies and formalizes ACC communication semantics which paves the path toward a more scalable integration of ACCs. The semantics describe four primary aspects: 1) data access; 2) data granularity; 3) data marshalling; and 4) synchronization. Finally, this paper proposes a novel architecture of transparent self-synchronizing accelerators (TSS). TSS efficiently realizes our identified communication semantics of direct ACC-to-ACC connections often occurring in streaming applications. TSS delivers more of the ACCs’ benefits than conventional ACMP architectures. Given the same set of ACCs, TSS has up to $130 \times $ higher throughput and $78 \times $ lower energy consumption, mainly due to reducing the load on shared architectural resources by $78.3 \times $. [ABSTRACT FROM AUTHOR]

In this paper, we explore the different per-user bandwidths available for each tier of heterogeneous networks (HetNets) and the corresponding downlink range expansion (RE, a key measure to boost small cell association). Based on the practical case of tier-specific path loss exponents, we derive the local delay of HetNets under the random discontinuous transmission scheme (DTX, employed to manage the interference and reduce energy consumption). Using such a new expression for local delay, we then analyze the corresponding energy efficiency (EE). These delay and EE expressions reveal that there exists an optimal value for both the RE bias factor and for the DTX mute probability. A closed-form optimum solution for the RE bias factor is also derived for the low-rate regime. [ABSTRACT FROM AUTHOR]

The resistance of lithium-ion cells increases at subzero temperatures reducing the cells’ power availability. One way to improve a cell’s performance in these adverse operation conditions is to proactively heat them. This paper considers the scenario in which a cell is heated from both inside and outside; a current is drawn from the cell to power a convective heater and Joule heating warms the cell from inside. A method to derive the time-limited energy-optimal current policy is presented, analyzed, and numerically solved. It is proven that the optimal current policy is a sequence of constant voltage, constant current, and rest phases. Using this observation, two rule-based approximations of the optimal solution are presented and their relative performance is compared to conclude that the inclusion of the rest phase has the potential to decrease energy consumption by ~9%. To build and compare these approximations, new tools related to the estimation of the time-limited backward reachable set of nonlinear systems are presented. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical smart home control system, which controls the residential appliances. In this system, there are a supervisory controller, a secondary controller, and some smart plugs. The supervisory controller is the power utility, which produces the instantaneous or multi-time electricity tariffs and the appropriate load shedding commands. The secondary controller, which is central controller of the smart home connected to a smart power meter, produces ON/OFF commands to control the residential appliances optimally and automatically, which are connected to the smart plugs. The smart plugs apply the central controller commands to the appliances. The central controller considers tariff signal and load shedding commands to reduce the power consumption and costs while maintaining the consumer satisfaction. In this control system, the optimum time for customers to use each appliance is considered. Then, the best answer to the optimization using the brute-force search algorithm is selected and implemented in compliance with all constraints. In addition, conservation voltage reduction is considered and implemented in this system. The simulation results illustrate the performance of the hierarchical control system. The whole system is developed and implemented in a pilot home and is introduced in this paper. [ABSTRACT FROM AUTHOR]

Based on the Petri net (PN) models of the flexible manufacturing systems (FMSs), this paper focuses on solving the scheduling problem of minimizing the total energy consumption of FMSs. In the view of different energy consumption rates of resources under different working statuses, two energy consumption functions are considered. The dynamic programming (DP) models of the scheduling problems based on PNs are established, where a reachable marking of a PN model, start processing time vector, route vector, and the transition sequence leading to the reachable marking from the initial one, is regarded as a state, and the Bellman equation is based on transition firing. For small-size scheduling problems, their optimal solutions can be obtained by the presented DP algorithm. However, it is difficult to solve larger-scale ones since the number of explored states increases exponentially with the problem size, which makes DP computationally infeasible. To obtain an optimal or suboptimal schedule in an acceptable time, modified DP (MDP) algorithm is proposed, in which fewer states are explored. In MDP, two ways are introduced through which only the most promising states are explored. One is keeping only one transition sequence for each marking through an evaluation function. Another is selecting the most promising states in each stage for further exploration through a heuristic function. To guarantee that the generated states are safe, a deadlock controller is applied in the recursion procedure of MDP. Experimental results on manufacturing systems and comparisons with existing works are provided to show the effectiveness of MDP. Note to Practitioners—This paper is motivated by the need of optimizing the energy consumption of manufacturing systems, considering the increasing energy cost and environmental concerns. Existing studies on energy optimization rarely focus on flexible manufacturing systems (FMSs) which exhibit a high degree of resource sharing and route flexibility and can be highly adaptable to various production plans and goals. Scheduling becomes more challenging when facing deadlock-prone FMSs. This paper provides a method to solve this complex scheduling problem efficiently. In this paper, dynamic programming (DP) is formulated for it, and the optimal energy consumption schedules are found. Considering the computational burden of implementing DP in the scheduling of large-size FMSs, a novel scheduling method named modified DP (MDP) is proposed. Experimental tests on an FMS and a stamping system suggest that MDP can successfully find feasible solutions. Besides, it can be applied to other FMS scheduling problems and industrial cases, once their processing time of operations and energy consumption of resources per unit time are known. [ABSTRACT FROM AUTHOR]

Recent advances in resistive random access memory (RRAM) as high density, low power, and faster memory systems drive the need for devising a more lightweight integrity checking system for RRAM. In this paper, we design a new tag generation system for integrity checking of RRAM. A single read operation to a crossbar RRAM in the presence of sneak path currents can output a tag for the memory data that can be used for integrity checking. An analytical approach to model such a tag generation process is described in this paper. Security results predicted by the analytical model provide various design options leading to an optimal system from the perspective of considered security properties. The proposed design is simulated to investigate and verify the security properties of the system for a number of optimal design options predicted by the analytical model. Reliability of the proposed system is also measured for varying conditions of device parameters, operating temperatures, load resistances, and read voltage. Finally, the performance of the proposed system is compared against another existing lightweight tag generation method from the perspective of energy consumption, transistor count, and delay. [ABSTRACT FROM AUTHOR]

Massive Multi-Input Multi-Output (MIMO) and Coordinated MultiPoint (CoMP) technologies in Cloud-RAN (C-RAN) architecture become inevitable trend due to the advent of next-generation mobile applications, which are traffic-intensive, such as ultra high definition (UHD) video. In this paper, we study a joint beam activation and user scheduling problem in a 5G cellular network with massive MIMO and CoMP utilizing orthogonal random beamforming technique. This paper aims to minimize total Remote Radio Heads’ (RRHs’) energy expenditure in a dynamic C-RAN architecture while ensuring finite service time for all user traffic arrivals in the communication coverage. We leverage Lyapunov drift-plus-penalty framework to transform an original long-term average problem into a series of per-slot modified problems. Since the provided per-slot problem is combinatorial and nonlinear optimization problem, we are inspired by a greedy algorithm to design energy and delay guaranteed joint beam activation and user scheduling policy, namely BEANS. We prove that the proposed BEANS ensures finite upper bounds of average RRH energy consumption and average queue backlogs for all traffic arrival rates within constant ratio of capacity region and all energy-delay tradeoff parameters. These proofs are the first attempt to theoretically demonstrate guarantees of energy and queue bounds in a framework consisting of possibly negative submodular objective function and non-matriod constraints. Finally, via extensive simulations, we compare the capacity region and energy-queue backlog tradeoff of BEANS with optimal and existing algorithms, and show that BEANS attains up to 65% of energy saving for the same average queue backlog compared to the algorithms which do not take traffic dynamics and energy consumption into considerations. [ABSTRACT FROM AUTHOR]

In the recent years, Wireless Power Transfer (WPT) related technologies experienced a great growth. With the introduction in 2010 of the Qj standard, many wireless rechargeable devices appeared on the market, calling for solution of several classes of problems ranging from EMC, health compliance, and power efficiency, representing a stimulating field of activity for scientists and researchers. Beside this leading application area, great interest is being received by two other application categories: Electric Vehicles (EV) and long range electromagnetic transmission of energy. The first is about to approach the market as testified by recent demonstration activities carried on by leading car makers (mostly from Japan). The second is mostly driven by the Internet of Things (loT) perspective evolution that requires energizing plenty of contactless devices, often non-precisely referred in space. It is clear, even from this very synthetic description of the WPT scenario, how WPT actually is an inclusive, multidiscipline field of different but dependent researches. This special issue aims at evidencing, within this scientific vastness, those aspects more precisely related to RF and microwave technologies in a wide extent (from MHz up to THz). Authors should see http://www.mtt.org/transactions/34-author-information-transactions.html for submission instructions. [ABSTRACT FROM PUBLISHER]

As innovative energy-related technologies are rapidly developing, new elements are populating the electrical power grid, such as unpredictable renewable energies, plug-in electric vehicles (EVs), smart electrical home appliances, energy efficient homes/buildings, and distributed energy storage. All these elements induce a paradigm shift in power grids, which are evolving from producer-controlled structures to large, distributed, and customer-interactive ones, enhancing the coupling between the physical and the information layer. In order to be effectively employed on a large scale, all these elements must rely on automation, as well as on optimization methods, for the efficient management and consumption of energy resources under varying stochastic conditions. Driven by these changes, power grids have become complex and large-scale platforms with growing needs for automation and optimal energy management services, a concept that can be referred to as smart grids. [ABSTRACT FROM AUTHOR]

This paper presents performance improvements of a double excitation synchronous motor by using a reluctance network (RN). The distinguishing feature of the double excitation principle is to use permanent magnets with high energy, while air-gap flux is flexibly controlled by field windings. Therefore, the first contribution of this paper focuses on maximizing air-gap flux range control. Second, an approach for torque ripple reduction is proposed by directly modifying air-gap flux according to the instantaneous torque profile. The achieved resultant torque stays almost constant for a case study. The validity of the RN method is examined by comparisons with 3-D finite element and experimental results for several machines. [ABSTRACT FROM AUTHOR]

The operation of variable-speed ac drives with loss-minimizing flux strategies can produce important energy savings without the need of any investment. This paper proposes algorithms for the evaluation of loss motor components and the flux level that produces the minimum-loss operating condition, covering the whole constant-torque operating region. Both scalar and vector drives are considered, although it is found that the operation of scalar- and vector-controlled drives with optimum flux (or $d$ -axis current) gives very similar results for the same operating points. Evaluation is done using four induction motors of 4, 11, 55, and 110 kW. Results confirm that important savings can be achieved, especially in applications where motors operate with high speed and low to moderate load torque. Results will be used in a follow-up paper to evaluate the expected savings of the use of these strategies in a pulp mill and in a paper mill. [ABSTRACT FROM PUBLISHER]

How to prolong network lifetime has become an important issue in the design of large scale wireless sensor networks (WSNs). In this paper, a novel power saving scheme for conventional WSNs via simultaneous wireless information and power transfer (SWIPT) based relays is proposed. Unlike conventional relays based communications need extra energy supply for data forwarding, the relay applied in this paper works in an energy-free manner with the support of SWIPT. The power saving model with both power splitting (PS) and time switching (TS) based SWIPT are proposed. Then, we formulate the joint power allocation and splitting problems of SWIPT relay assisted WSNs as non-convex constrained optimization problems. Since the formulated problems are non-convex, semi-positive definite programming (SDP) algorithms are proposed to find the joint optimization on power allocation and splitting with low complexity. The simulation results show that the PS model has more advantages than the traditional direct communication model in long distance transmission. Under the same information receiving strategy, the PS model outperforms the TS model. [ABSTRACT FROM AUTHOR]

The heterogeneous fog radio access networks (Fog-RAN), the integration of fog computing, and traditional heterogeneous radio access networks can be implemented through the next-generation wireless communication networks. However, most of the solutions are limited to the spectrum efficiency optimization, and cross-tier interference existing in the fog access points (F-APs) could affect the network performance seriously. In this paper, the user association, resource allocation (including bandwidth and power), and caching deployment are investigated in the heterogeneous Fog-RAN to consider energy efficiency and cross-tier interference mitigation. Specifically, the user association, resource allocation, and caching strategy are formulated as a non-convex optimization problem and then transformed into a convex problem, which can be solved by a proposed algorithm based on the concept of the alternating direction method of multipliers (ADMM). Then an ADMM-based algorithm is proposed to enhance the energy efficiency of the Fog-RAN. Compared with the current solutions, simulation results illustrate the proposed algorithm’s convergence and effectiveness. [ABSTRACT FROM AUTHOR]

This special section of the IEEE Transactions on Automation Science and Engineering (T-ASE) focuses on new models, methods, and technologies for energy efficiency and sustainability in transportation and energy systems. In this section, the focus is thus on articles considering sustainable transportation, such as electric vehicles (EVs), integrated with the smart grid requirements. As guest editors, we are very pleased to present the selected 12 papers, whose topics are specifically related to optimal planning of charging stations (CSs), sustainable transportation and mobility, EVs integration in smart grids, reliability, reduction of consumption, demand response and smart grid modeling, optimal scheduling, routing and charging of fleets of EVs, as well as smart parking. [ABSTRACT FROM AUTHOR]

Fuel economy (FE) improvements for hybrid electric vehicles using a predictive Optimal Energy Management Strategy (Optimal EMS) is an active subject of research. Recent developments have focused on real-time prediction-based control strategies despite the lack of research demonstrating the aspects of prediction that are most important for FE improvements. In this paper, driving-derived nonstochastic prediction errors are applied to a globally optimal control strategy implemented on a validated model of a 2010 Toyota Prius, and the FE results are reported for each type of prediction error. This paper first outlines the real-world drive cycle development, then the baseline model development that simulates a 2010 Toyota Prius, followed by an implementation of dynamic programming (DP) to derive the globally optimal control, and finally the use of the DP solution to evaluate prediction errors. FE comparisons are reported for perfect prediction, prediction errors from 14 alternate drive cycles, and prediction errors from 6 alternate vehicle parameters. The results show that FE improvements from the Optimal EMS are maintained under mispredicted stops, traffic, and vehicle parameters, while route changes and compounded drive cycle mispredictions may result in FE improvements being lost. Taken together, these results demonstrate that implementation of an Optimal EMS can result in a reliable FE improvement. [ABSTRACT FROM AUTHOR]

Reducing energy consumption in the fog computing environment is both a research and an operational challenge for the current research community and industry. There are several industries such as finance industry or healthcare industry that require a rich resource platform to process big data along with edge computing in fog architecture. As a result, sustainable computing in a fog server plays a key role in fog computing hierarchy. The energy consumption in fog servers depends on the allocation techniques of services (user requests) to a set of virtual machines (VMs). This service request allocation in a fog computing environment is a nondeterministic polynomial-time hard problem. In this paper, the scheduling of service requests to VMs is presented as a bi-objective minimization problem, where a tradeoff is maintained between the energy consumption and makespan. Specifically, this paper proposes a metaheuristic-based service allocation framework using three metaheuristic techniques, such as particle swarm optimization (PSO), binary PSO, and bat algorithm. These proposed techniques allow us to deal with the heterogeneity of resources in the fog computing environment. This paper has validated the performance of these metaheuristic-based service allocation algorithms by conducting a set of rigorous evaluations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a training framework for an inverter-based memristive neuromorphic hardware. The framework, which is called PHAX, is a physical characteristics aware one relying on an ex-situ training approach. The considered neuromorphic circuit is highly energy efficient hybrid CMOS-memristive implementation of neuromorphic circuits. To solve the problem of high sensitivity of the training to the mismatches between the high-level mathematical modeling of the neurons and the corresponding physical characteristics, an approach for analytical yet accurate modeling of the memristive crossbar and neuron circuits is suggested. The approach, which is based on SPICE simulations, models the inverter-based neurons using a hyperbolic tangent function. To increase the training efficacy, the backpropagation training algorithm is modified by considering some constraints based on the physical characteristics of the memristive circuit. This modification along with the accurate back-annotation of the physical characteristics considerably improve the effectiveness of the ex-situ training method of the neuromorphic circuit. The results of this paper show an average reduction of $1805{\times }$ in the training runtime compared to that of the in-situ training approach. Furthermore, the results of applying the approach on the kernels of some applications such as image recognition, image processing, and financial analysis reveal that the designed neuromorphic circuits provide an average power saving (speed up) of $1478{\times }$ ( $5.2{\times }$ ) over the ASIC implementation in a 90-nm CMOS technology. [ABSTRACT FROM AUTHOR]