Showing papers on "Efficient energy use published in 2016"

Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices

Abstract: Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost , which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm is proposed, namely, the Lyapunov optimization-based dynamic computation offloading algorithm, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the current system state without requiring distribution information of the computation task request, wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to corroborate the theoretical analysis as well as validate the effectiveness of the proposed algorithm.

Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks

Abstract: Deep convolutional neural networks (CNNs) are widely used in modern AI systems for their superior accuracy but at the cost of high computational complexity. The complexity comes from the need to simultaneously process hundreds of filters and channels in the high-dimensional convolutions, which involve a significant amount of data movement. Although highly-parallel compute paradigms, such as SIMD/SIMT, effectively address the computation requirement to achieve high throughput, energy consumption still remains high as data movement can be more expensive than computation. Accordingly, finding a dataflow that supports parallel processing with minimal data movement cost is crucial to achieving energy-efficient CNN processing without compromising accuracy.In this paper, we present a novel dataflow, called row-stationary (RS), that minimizes data movement energy consumption on a spatial architecture. This is realized by exploiting local data reuse of filter weights and feature map pixels, i.e., activations, in the high-dimensional convolutions, and minimizing data movement of partial sum accumulations. Unlike dataflows used in existing designs, which only reduce certain types of data movement, the proposed RS dataflow can adapt to different CNN shape configurations and reduces all types of data movement through maximally utilizing the processing engine (PE) local storage, direct inter-PE communication and spatial parallelism. To evaluate the energy efficiency of the different dataflows, we propose an analysis framework that compares energy cost under the same hardware area and processing parallelism constraints. Experiments using the CNN configurations of AlexNet show that the proposed RS dataflow is more energy efficient than existing dataflows in both convolutional (1.4× to 2.5×) and fully-connected layers (at least 1.3× for batch size larger than 16). The RS dataflow has also been demonstrated on a fabricated chip, which verifies our energy analysis.

Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks

Abstract: Mobile edge computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading (EECO) mechanisms for MEC in 5G heterogeneous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogeneous network, we then design an EECO scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme.

Estimating the global waste heat potential

Abstract: The process chain of energy conversion from primary energy carriers to final energy use is subject to several losses. Especially in end use, vast amounts of converted energy occur as waste heat, which is often released to the environment. In terms of raising energy efficiency and reducing the energy consumption, such waste heat needs to be used. To date, some studies or investigations about industrial waste heat of selected countries have been carried out, but other sectors like commerce were not considered. Therefore, this work presents a novel top-down approach for the estimation of waste heat potential of the most common sectors of end use (transportation, industrial, commercial and residential) including electricity generation on a global scale. It also deals with the temperature distribution of this unused energy. The evaluation reveals that 72% of the global primary energy consumption is lost after conversion. In further detail, 63% of the considered waste heat streams arise at a temperature below 100 °C in which electricity generation has the largest share followed by transportation and industry.

Smart home energy management systems: Concept, configurations, and scheduling strategies

Abstract: With the arrival of smart grid era and the advent of advanced communication and information infrastructures, bidirectional communication, advanced metering infrastructure, energy storage systems and home area networks would revolutionize the patterns of electricity usage and energy conservation at the consumption premises. Coupled with the emergence of vehicle-to-grid technologies and massive distributed renewable energy, there is a profound transition for the energy management pattern from the conventional centralized infrastructure towards the autonomous responsive demand and cyber-physical energy systems with renewable and stored energy sources. Under the sustainable smart grid paradigm, the smart house with its home energy management system (HEMS) plays an important role to improve the efficiency, economics, reliability, and energy conservation for distribution systems. In this paper, a brief overview on the architecture and functional modules of smart HEMS is presented. Then, the advanced HEMS infrastructures and home appliances in smart houses are thoroughly analyzed and reviewed. Furthermore, the utilization of various building renewable energy resources in HEMS, including solar, wind, biomass and geothermal energies, is surveyed. Lastly, various home appliance scheduling strategies to reduce the residential electricity cost and improve the energy efficiency from power generation utilities are also investigated.

A review on modeling and simulation of building energy systems

Abstract: Buildings consume about 40% of the overall energy consumption, worldwide and correspondingly are also responsible for carbon emissions. Since, last decade efforts have been made to reduce this share of CO2 emissions by energy conservation and efficient measures. Scientist across the world is working on energy modeling and control in order to develop strategies that would result in an overall reduction of a building׳s energy consumption. Development of control strategies asks for a computationally efficient energy model of a building under study. This paper presents a review of all the significant modeling methodologies which have been developed and adopted to model the energy systems of buildings. Attention is majorly focused on the works which involved development of the control strategies by modeling the building energy systems. Models reviewed are presented categorically as per the modeling approach adopted by the researchers. Simulation programs and softwares available for building energy modeling are also presented.

Building energy load forecasting using Deep Neural Networks

Abstract: Ensuring sustainability demands more efficient energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent decision making requires accurate predictions of future energy demand/load, both at aggregate and individual site level. Thus, energy load forecasting have received increased attention in the recent past. However, it has proven to be a difficult problem. This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically, Long Short Term Memory (LSTM) algorithms. The presented work investigates two LSTM based architectures: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer. Both architectures were trained and tested on one hour and one-minute time-step resolution datasets. Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data. It was shown that S2S architecture performed well on both datasets. Further, it was shown that the presented methods produced comparable results with the other deep learning methods for energy forecasting in literature.

Caching at the wireless edge: design aspects, challenges, and future directions

Abstract: Caching at the wireless edge is a promising way to boost spectral efficiency and reduce energy consumption of wireless systems. These improvements are rooted in the fact that popular contents are reused, asynchronously, by many users. In this article we first introduce methods to predict the popularity distributions and user preferences, and the impact of erroneous information. We then discuss the two aspects of caching systems, content placement and delivery. We expound the key differences between wired and wireless caching, and outline the differences in the system arising from where the caching takes place (e.g., at base stations or on the wireless devices themselves). Special attention is paid to the essential limitations in wireless caching, and possible trade-offs between spectral efficiency, energy efficiency, and cache size.

Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer

Abstract: Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability , under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases the computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control.

A review on hybrid electric vehicles architecture and energy management strategies

Abstract: Faced with environmental issues caused by fossil fuel burning in the industrial and transportation sectors, innovations towards cleaner solutions to replace the ever diminishing fossil fuels have been the focus of not only researchers but governments all around the world. The hybrid electric vehicle (HEV) technology is the result of the desire to have vehicles with a better fuel economy and lower tailpipe emissions to meet the requirements of environmental policies as well as to absorb the impact of rising fuel prices. The objectives are met by combining a conventional internal combustion engine (ICE) with one or more electric motors powered by a battery pack that can be charged using an on-board generator and the regenerative braking technology to power the transmission. The challenge is to develop an efficient energy management strategy (EMS) to satisfy the objectives while not having a reduced vehicle performance. In this paper, EMSs that are proposed and developed in the recent years are revisited and reviewed. Additionally, the Plug-in HEV is discussed in a new perspective from the EMS point of view. The through-the-road (TtR) HEV with in-wheel motors (IWM) is a fairly new concept in the HEV design that features less complicated configuration with reduced hardware requirements and lower cost. Recent research findings are evaluated throughout this paper leading to a hypothetical TtR HEV materialization. A thorough discussion is made encompassing the advantages and disadvantages of the concept, its performance compared to conventional HEVs and the way forward.

Green Industrial Internet of Things Architecture: An Energy-Efficient Perspective

Abstract: The Internet of Things (IoT) can support collaboration and communication between objects automatically. However, with the increasing number of involved devices, IoT systems may consume substantial amounts of energy. Thus, the relevant energy efficiency issues have recently been attracting much attention from both academia and industry. In this article we adopt an energy-efficient architecture for Industrial IoT (IIoT), which consists of a sense entities domain, RESTful service hosted networks, a cloud server, and user applications. Under this architecture, we focus on the sense entities domain where huge amounts of energy are consumed by a tremendous number of nodes. The proposed framework includes three layers: the sense layer, the gateway layer, and the control layer. This hierarchical framework balances the traffic load and enables a longer lifetime of the whole system. Based on this deployment, a sleep scheduling and wake-up protocol is designed, supporting the prediction of sleep intervals. The shifts of states support the use of the entire system resources in an energy-efficient way. Simulation results demonstrate the significant advantages of our proposed architecture in resource utilization and energy consumption.

Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing

Abstract: Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich cloud. However, how to achieve energy-efficient computation offloading under the hard constraint for application completion time remains a challenge issue. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into the energy-efficiency cost (EEC) minimization problem while satisfying the task-dependency requirements and the completion time deadline constraint. To solve the optimization problem, we then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. More importantly, we find that the computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, our experimental results in a real testbed demonstrate that the eDors algorithm can effectively reduce the EEC by optimally adjusting the CPU clock frequency of SMDs based on the dynamic voltage and frequency scaling (DVFS) technique in local computing, and adapting the transmission power for the wireless channel conditions in cloud computing.

Rate splitting for MIMO wireless networks: a promising PHY-layer strategy for LTE evolution

Abstract: MIMO processing plays a central part in the recent increase in spectral and energy efficiencies of wireless networks. MIMO has grown beyond the original point-to-point channel and nowadays refers to a diverse range of centralized and distributed deployments. The fundamental bottleneck toward enormous spectral and energy efficiency benefits in multiuser MIMO networks lies in a huge demand for accurate CSIT. This has become increasingly difficult to satisfy due to the increasing number of antennas and access points in next generation wireless networks relying on dense heterogeneous networks and transmitters equipped with a large number of antennas. CSIT inaccuracy results in a multi-user interference problem that is the primary bottleneck of MIMO wireless networks. Looking backward, the problem has been to strive to apply techniques designed for perfect CSIT to scenarios with imperfect CSIT. In this article, we depart from this conventional approach and introduce readers to a promising strategy based on rate-splitting. Rate-splitting relies on the transmission of common and private messages, and is shown to provide significant benefits in terms of spectral and energy efficiencies, reliability, and CSI feedback overhead reduction over conventional strategies used in LTE-A and exclusively relying on private message transmissions. Open problems, the impact on standard specifications, and operational challenges are also discussed.

Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning

Abstract: Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision algorithms. However, they are still rarely deployed on battery-powered mobile devices, such as smartphones and wearable gadgets, where vision algorithms can enable many revolutionary real-world applications. The key limiting factor is the high energy consumption of CNN processing due to its high computational complexity. While there are many previous efforts that try to reduce the CNN model size or amount of computation, we find that they do not necessarily result in lower energy consumption, and therefore do not serve as a good metric for energy cost estimation. To close the gap between CNN design and energy consumption optimization, we propose an energy-aware pruning algorithm for CNNs that directly uses energy consumption estimation of a CNN to guide the pruning process. The energy estimation methodology uses parameters extrapolated from actual hardware measurements that target realistic battery-powered system setups. The proposed layer-by-layer pruning algorithm also prunes more aggressively than previously proposed pruning methods by minimizing the error in output feature maps instead of filter weights. For each layer, the weights are first pruned and then locally fine-tuned with a closed-form least-square solution to quickly restore the accuracy. After all layers are pruned, the entire network is further globally fine-tuned using back-propagation. With the proposed pruning method, the energy consumption of AlexNet and GoogLeNet are reduced by 3.7x and 1.6x, respectively, with less than 1% top-5 accuracy loss. Finally, we show that pruning the AlexNet with a reduced number of target classes can greatly decrease the number of weights but the energy reduction is limited. Energy modeling tool and energy-aware pruned models available at this http URL

D2D Fogging: An Energy-Efficient and Incentive-Aware Task Offloading Framework via Network-assisted D2D Collaboration

Abstract: In this paper, we propose device-to-device (D2D) Fogging, a novel mobile task offloading framework based on network-assisted D2D collaboration, where mobile users can dynamically and beneficially share the computation and communication resources among each other via the control assistance by the network operators. The purpose of D2D Fogging is to achieve energy efficient task executions for network wide users. To this end, we propose an optimization problem formulation that aims at minimizing the time-average energy consumption for task executions of all users, meanwhile taking into account the incentive constraints of preventing the over-exploiting and free-riding behaviors which harm user’s motivation for collaboration. To overcome the challenge that future system information such as user resource availability is difficult to predict, we develop an online task offloading algorithm, which leverages Lyapunov optimization methods and utilizes the current system information only. As the critical building block, we devise corresponding efficient task scheduling policies in terms of three kinds of system settings in a time frame. Extensive simulation results demonstrate that the proposed online algorithm not only achieves superior performance (e.g., it reduces approximately 30% ~ 40% energy consumption compared with user local execution), but also adapts to various situations in terms of task type, user amount, and task frequency.

A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems

Abstract: In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.

Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: A review

Abstract: Energy demand has been increasing worldwide and the building sector represents a large percentage of global energy consumption. Therefore, promoting energy efficiency in buildings is essential. Among all building services, Heating, Ventilation and Air Conditioning (HVAC) systems are significantly responsible for building energy use. In HVAC, ventilation is the key issue for providing suitable Indoor Air Quality (IAQ), while it is also responsible for energy consumption in buildings. Thus, improving ventilation systems plays an important role not only in fostering energy efficiency in buildings, but also in providing better indoor climate for the occupants and decreasing the possibility of health issues in consequence. In the last decades, many energy-efficient ventilation methods are developed by researchers to mitigate energy consumption in buildings. This paper reviews scientific research and reports, as well as building regulations and standards, which evaluated, investigated and reported the development of energy-efficient methods for ventilation in buildings. Besides energy-efficient methods such as natural and hybrid ventilation strategies, occupants’ behaviours regarding ventilation, can also affect the energy demand in buildings. Therefore, the influence of occupants’ behaviour on the energy use and the correlation between ventilation and the occupants’ health and productivity were also considered. The review showed that ventilation is interrelated with many factors such as indoor and outdoor conditions, building characteristics, building application as well as users’ behaviour. Thus, it is concluded that many factors must be taken into account for designing energy-efficient and healthy ventilation systems. Moreover, it should be mentioned that utilizing hybrid ventilation in buildings integrated with suitable control strategies, to adjust between mechanical and natural ventilation, leads to considerable energy savings while an appropriate IAQ is maintained.

A Survey on Energy-Efficient Train Operation for Urban Rail Transit

Abstract: Due to rising energy prices and environmental concerns, the energy efficiency of urban rail transit has attracted much attention from both researchers and practitioners in recent years. Timetable optimization and energy-efficient driving, as two mainly used train operation methods in relation to the tractive energy saving, make major contributions in reducing the energy consumption that has been studied for a long time. Generally speaking, timetable optimization synchronizes the accelerating and braking actions of trains to maximize the utilization of regenerative energy, and energy-efficient driving optimizes the speed profile at each section to minimize the tractive energy consumption. In this paper, we present a fully comprehensive survey on energy-efficient train operation for urban rail transit. First, a general energy consumption distribution of urban rail trains is described. Second, the current literature on timetable optimization and energy-efficient driving is reviewed. Finally, according to the review work, it is concluded that the integrated optimization method jointly optimizing the timetable and speed profile has become a new tendency and ought to be paid more attention in future research.

Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings

Abstract: The building stock in the world consumes approximately 40% of the energy and emits one third of the total greenhouse gases emissions (GHG). Improving the energy efficiency in buildings is vital to address the climate change and achieve energy independence (i.e. to become net-zero energy). Improving energy performance in existing buildings has been receiving significant attention recently, which entails reducing energy demand for building operations, without affecting the health and comfort of its occupants. This approach requires strategies beyond mere technical advancements. However, there is limited published literature which has comprehensively addressed these issues. The aim of this paper is to critically review existing body-of-the-knowledge on improving energy efficiency of operating both commercial and institutional buildings. Peer-reviewed journal articles published from year 2000 to 2014 in reputed journals were reviewed. This review investigated contemporary energy efficiency approaches including technical, organizational, and behavioural changes. Based on the comprehensive literature review, a strategy map was developed as a pathway for achieving better building energy performance. It was noted that even though the existing studies predominately focused on technical advancements, approaches such as building behavioural changes have been largely overlooked. Findings of this study provide an important basis for setting up a national and organization wide strategy for improving the energy efficiency of commercial and institutional buildings.

Cotton-textile-enabled flexible self-sustaining power packs via roll-to-roll fabrication

Abstract: With rising energy concerns, efficient energy conversion and storage devices are required to provide a sustainable, green energy supply. Solar cells hold promise as energy conversion devices due to their utilization of readily accessible solar energy; however, the output of solar cells can be non-continuous and unstable. Therefore, it is necessary to combine solar cells with compatible energy storage devices to realize a stable power supply. To this end, supercapacitors, highly efficient energy storage devices, can be integrated with solar cells to mitigate the power fluctuations. Here, we report on the development of a solar cell-supercapacitor hybrid device as a solution to this energy requirement. A high-performance, cotton-textile-enabled asymmetric supercapacitor is integrated with a flexible solar cell via a scalable roll-to-roll manufacturing approach to fabricate a self-sustaining power pack, demonstrating its potential to continuously power future electronic devices.

Energy efficiency and thermal comfort in historic buildings: A review

Abstract: In recent years, energy efficiency and thermal comfort in historic buildings have become high-interest topics among scholars. Research has demonstrated that retrofitting buildings to current energy efficiency and thermal comfort standards is essential for improving sustainability and energy performance and for maintaining built heritage of historic structures. This study is an extensive overview of the literature surrounding this topic. This paper summarizes the different methods and techniques that have been used around the world to achieve performance refurbishments. Articles are organized based on the different building types used as case studies (residential, religious, academic and palace, museums, libraries and theaters, urban areas, and others). The results reveal that residential, religious and museum building types, especially from the last two centuries, have been most often used as case studies. Moreover, Europe, particularly Italy, is leading the research. The aim of this note is to demonstrate the feasibility of maintaining built heritage values of historic buildings while achieving significant improvements in their energy efficiency and thermal comfort.