Showing papers on "Efficient energy use published in 2018"

A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems

Abstract: The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. On the other hand, UAVs can operate as flying mobile terminals within a cellular network. Such cellular-connected UAVs can enable several applications ranging from real-time video streaming to item delivery. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as three-dimensional deployment, performance analysis, channel modeling, and energy efficiency are explored along with representative results. Then, open problems and potential research directions pertaining to UAV communications are introduced. Finally, various analytical frameworks and mathematical tools such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems.

Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication

Abstract: The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements, subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to $300\%$ higher energy efficiency, in comparison with the use of regular multi-antenna amplify-and-forward relaying.

Energy-Efficient Data Collection in UAV Enabled Wireless Sensor Network

Abstract: In wireless sensor networks, utilizing the unmanned aerial vehicle (UAV) as a mobile data collector for the sensor nodes (SNs) is an energy-efficient technique to prolong the network lifetime. In this letter, considering a general fading channel model for the SN-UAV links, we jointly optimize the SNs’ wake-up schedule and UAV’s trajectory to minimize the maximum energy consumption of all SNs, while ensuring that the required amount of data is collected reliably from each SN. We formulate our design as a mixed-integer non-convex optimization problem. By applying the successive convex optimization technique, an efficient iterative algorithm is proposed to find a sub-optimal solution. Numerical results show that the proposed scheme achieves significant network energy saving as compared to benchmark schemes.

On the Total Energy Efficiency of Cell-Free Massive MIMO

Abstract: We consider the cell-free massive multiple-input multiple-output (MIMO) downlink, where a very large number of distributed multiple-antenna access points (APs) serve many single-antenna users in the same time-frequency resource. A simple (distributed) conjugate beamforming scheme is applied at each AP via the use of local channel state information (CSI). This CSI is acquired through time-division duplex operation and the reception of uplink training signals transmitted by the users. We derive a closed-form expression for the spectral efficiency taking into account the effects of channel estimation errors and power control. This closed-form result enables us to analyze the effects of backhaul power consumption, the number of APs, and the number of antennas per AP on the total energy efficiency, as well as, to design an optimal power allocation algorithm. The optimal power allocation algorithm aims at maximizing the total energy efficiency, subject to a per-user spectral efficiency constraint and a per-AP power constraint. Compared with the equal power control, our proposed power allocation scheme can double the total energy efficiency. Furthermore, we propose AP selections schemes, in which each user chooses a subset of APs, to reduce the power consumption caused by the backhaul links. With our proposed AP selection schemes, the total energy efficiency increases significantly, especially for large numbers of APs. Moreover, under a requirement of good quality-of-service for all users, cell-free massive MIMO outperforms the colocated counterpart in terms of energy efficiency.

The CO2 economy: Review of CO2 capture and reuse technologies

Abstract: The continuously increasing share of Renewable Energy Sources (RES) and EU targets for CO2 reduction and energy efficiency necessitate significant changes both on technical and regulatory level. Environmental challenges of CO2 emissions are assessed in a review of CO2 capture and utilisation technologies, offering new opportunities in CO2 economy. Commercial applications in the thermal power and industrial sector for pre and post combustion capture as well as the potential of direct air CO2 capture are reviewed. The potential of Carbon Capture and Utilisation (CCU) is assessed focusing on the use of CO2 for fuel as well as for combined heat and power production. Combining CCU with energy storage as an evolutionary measure for balancing RES with thermal power under the power to fuel concept presents high market potentials for fuel and chemical production. Moreover, the recent progress in supercritical CO2 cycles for combined heat and power production is reported.

Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach

Abstract: Unmanned aerial vehicles (UAVs) can be used to serve as aerial base stations to enhance both the coverage and performance of communication networks in various scenarios, such as emergency communications and network access for remote areas. Mobile UAVs can establish communication links for ground users to deliver packets. However, UAVs have limited communication ranges and energy resources. Particularly, for a large region, they cannot cover the entire area all the time or keep flying for a long time. It is thus challenging to control a group of UAVs to achieve certain communication coverage in a long run, while preserving their connectivity and minimizing their energy consumption. Toward this end, we propose to leverage emerging deep reinforcement learning (DRL) for UAV control and present a novel and highly energy-efficient DRL-based method, which we call DRL-based energy-efficient control for coverage and connectivity (DRL-EC3). The proposed method 1) maximizes a novel energy efficiency function with joint consideration for communications coverage, fairness, energy consumption and connectivity; 2) learns the environment and its dynamics; and 3) makes decisions under the guidance of two powerful deep neural networks. We conduct extensive simulations for performance evaluation. Simulation results have shown that DRL-EC3 significantly and consistently outperform two commonly used baseline methods in terms of coverage, fairness, and energy consumption.

Overview of energy storage systems in distribution networks: Placement, sizing, operation, and power quality

Abstract: The deployment of energy storage systems (ESSs) is a significant avenue for maximising the energy efficiency of a distribution network, and overall network performance can be enhanced by their optimal placement, sizing, and operation. An optimally sized and placed ESS can facilitate peak energy demand fulfilment, enhance the benefits from the integration of renewables and distributed energy sources, aid power quality management, and reduce distribution network expansion costs. This paper provides an overview of optimal ESS placement, sizing, and operation. It considers a range of grid scenarios, targeted performance objectives, applied strategies, ESS types, and advantages and limitations of the proposed systems and approaches. While batteries are widely used as ESSs in various applications, the detailed comparative analysis of ESS technical characteristics suggests that flywheel energy storage (FES) also warrants consideration in some distribution network scenarios. This research provides recommendations for related requirements or procedures, appropriate ESS selection, smart ESS charging and discharging, ESS sizing, placement and operation, and power quality issues. Furthermore, this study identifies future research opportunities in relation to challenges for optimal ESS placement planning, development and implementation issues, optimisation techniques, social impacts, and energy security.

Graphene aerogels for efficient energy storage and conversion

Abstract: Concerns over air quality reduction resulting from burning fossil fuels have driven the development of clean and renewable energy sources. Supercapacitors, batteries and solar cells serve as eco-friendly energy storage and conversion systems vitally important for the sustainable development of human society. However, many diverse elements influence the performance of energy storage and conversion systems. The overall efficiency of systems depends on the specific structure and properties of incorporated functional materials. Carbon materials, such as graphene, are especially promising for materials development in the energy storage and conversion fields. Graphene, a two-dimensional (2D) carbon material only a single atom thick, has massless Dirac fermions (electron transport is governed by Dirac's equation), displays outstanding electrical conductivity, superior thermal conductivity and excellent mechanical properties. 2D free-standing graphene films and powders have paved the way for promising energy applications. Recently, much effort has been spent trying to improve the number of active sites in electrode materials within 3D network/aerogel structures derived from graphene. This is because graphene aerogels are promising materials for energy systems due to their porous hierarchical structure which affords rapid electron/ion transport, superior chemical and physical stability, and good cycle performance. This review aims to summarize the synthetic methods, mechanistic aspects, and energy storage and conversion applications of novel 3D network graphene, graphene derivatives and graphene-based materials. Areas of application include supercapacitors, Li-batteries, H2 and thermal energy storage, fuel cells and solar cells.

Scaling for edge inference of deep neural networks

Abstract: Deep neural networks offer considerable potential across a range of applications, from advanced manufacturing to autonomous cars. A clear trend in deep neural networks is the exponential growth of network size and the associated increases in computational complexity and memory consumption. However, the performance and energy efficiency of edge inference, in which the inference (the application of a trained network to new data) is performed locally on embedded platforms that have limited area and power budget, is bounded by technology scaling. Here we analyse recent data and show that there are increasing gaps between the computational complexity and energy efficiency required by data scientists and the hardware capacity made available by hardware architects. We then discuss various architecture and algorithm innovations that could help to bridge the gaps. This Perspective highlights the existence of gaps between the computational complexity and energy efficiency required for the continued scaling of deep neural networks and the hardware capacity actually available with current CMOS technology scaling, in situations where edge inference is required; it then discusses various architecture and algorithm innovations that could help to bridge these gaps.

Soft magnetic materials for a sustainable and electrified world

Abstract: Soft magnetic materials are key to the efficient operation of the next generation of power electronics and electrical machines (motors and generators). Many new materials have been introduced since Michael Faraday's discovery of magnetic induction, when iron was the only option. However, as wide bandgap semiconductor devices become more common in both power electronics and motor controllers, there is an urgent need to further improve soft magnetic materials. These improvements will be necessary to realize the full potential in efficiency, size, weight, and power of high-frequency power electronics and high-rotational speed electrical machines. Here we provide an introduction to the field of soft magnetic materials and their implementation in power electronics and electrical machines. Additionally, we review the most promising choices available today and describe emerging approaches to create even better soft magnetic materials.

Plasma Technology: An Emerging Technology for Energy Storage

Abstract: Plasma technology is gaining increasing interest for gas conversion applications, such as CO2 conversion into value-added chemicals or renewable fuels, and N2 fixation from the air, to be used for the production of small building blocks for, e.g., mineral fertilizers. Plasma is generated by electric power and can easily be switched on/off, making it, in principle, suitable for using intermittent renewable electricity. In this Perspective article, we explain why plasma might be promising for this application. We briefly present the most common types of plasma reactors with their characteristic features, illustrating why some plasma types exhibit better energy efficiency than others. We also highlight current research in the fields of CO2 conversion (including the combined conversion of CO2 with CH4, H2O, or H2) as well as N2 fixation (for NH3 or NOx synthesis). Finally, we discuss the major limitations and steps to be taken for further improvement.

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

Abstract: In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted.

Energy saving potentials of connected and automated vehicles

Abstract: Connected and automated vehicles (CAV) are marketed for their increased safety, driving comfort, and time saving potential. With much easier access to information, increased processing power, and precision control, they also offer unprecedented opportunities for energy efficient driving. This paper is an attempt to highlight the energy saving potential of connected and automated vehicles based on first principles of motion, optimal control theory, and a review of the vast but scattered eco-driving literature. We explain that connectivity to other vehicles and infrastructure allows better anticipation of upcoming events, such as hills, curves, slow traffic, state of traffic signals, and movement of neighboring vehicles. Automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and save energy. Opportunities for cooperative driving could further increase energy efficiency of a group of vehicles by allowing them to move in a coordinated manner. Energy efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles.

Energy Efficient Multi-User MISO Communication Using Low Resolution Large Intelligent Surfaces

Abstract: We consider a multi-user Multiple-Input Single-Output (MISO) communication system comprising of a multiantenna base station communicating in the downlink simultaneously with multiple single-antenna mobile users. This communication is assumed to be assisted by a Large Intelligent Surface (LIS) that consists of many nearly passive antenna elements, whose parameters can be tuned according to desired objectives. The latest design advances on these surfaces suggest cheap elements effectively acting as low resolution (even 1-bit resolution) phase shifters, whose joint configuration affects the electromagnetic behavior of the wireless propagation channel. In this paper, we investigate the suitability of LIS for green communications in terms of Energy Efficiency (EE), which is expressed as the number of bits per Joule. In particular, for the considered multi-user MISO system, we design the transmit powers per user and the values for the surface elements that jointly maximize the system's EE performance. Our representative simulation results show that LIS-assisted communication, even with nearly passive 1-bit resolution antenna elements, provides significant EE gains compared to conventional relay-assisted communication.

Google Workloads for Consumer Devices: Mitigating Data Movement Bottlenecks

Abstract: We are experiencing an explosive growth in the number of consumer devices, including smartphones, tablets, web-based computers such as Chromebooks, and wearable devices. For this class of devices, energy efficiency is a first-class concern due to the limited battery capacity and thermal power budget. We find that data movement is a major contributor to the total system energy and execution time in consumer devices. The energy and performance costs of moving data between the memory system and the compute units are significantly higher than the costs of computation. As a result, addressing data movement is crucial for consumer devices. In this work, we comprehensively analyze the energy and performance impact of data movement for several widely-used Google consumer workloads: (1) the Chrome web browser; (2) TensorFlow Mobile, Google's machine learning framework; (3) video playback, and (4) video capture, both of which are used in many video services such as YouTube and Google Hangouts. We find that processing-in-memory (PIM) can significantly reduce data movement for all of these workloads, by performing part of the computation close to memory. Each workload contains simple primitives and functions that contribute to a significant amount of the overall data movement. We investigate whether these primitives and functions are feasible to implement using PIM, given the limited area and power constraints of consumer devices. Our analysis shows that offloading these primitives to PIM logic, consisting of either simple cores or specialized accelerators, eliminates a large amount of data movement, and significantly reduces total system energy (by an average of 55.4% across the workloads) and execution time (by an average of 54.2%).

User Scheduling and Resource Allocation in HetNets With Hybrid Energy Supply: An Actor-Critic Reinforcement Learning Approach

Abstract: Densely deployment of various small-cell base stations in cellular networks to increase capacity will lead to heterogeneous networks (HetNets), and meanwhile, embedding the energy harvesting capabilities in base stations as an alternative energy supply is becoming a reality. How to make efficient utilization of radio resource and renewable energy is a brand-new challenge. This paper investigates the optimal policy for user scheduling and resource allocation in HetNets powered by hybrid energy with the purpose of maximizing energy efficiency of the overall network. Since wireless channel conditions and renewable energy arrival rates have stochastic properties and the environment’s dynamics are unknown, the model-free reinforcement learning approach is used to learn the optimal policy through interactions with the environment. To solve our problem with continuous-valued state and action variables, a policy-gradient-based actor-critic algorithm is proposed. The actor part uses the Gaussian distribution as the parameterized policy to generate continuous stochastic actions, and the policy parameters are updated with the gradient ascent method. The critic part uses compatible function approximation to estimate the performance of the policy and helps the actor learn the gradient of the policy. The advantage function is used to further reduce the variance of the policy gradient. Using the numerical simulations, we demonstrate the convergence property of the proposed algorithm and analyze network energy efficiency.

An Enhanced PEGASIS Algorithm with Mobile Sink Support for Wireless Sensor Networks

Abstract: Energy efficiency has been a hot research topic for many years and many routing algorithms have been proposed to improve energy efficiency and to prolong lifetime for wireless sensor networks (WSNs). Since nodes close to the sink usually need to consume more energy to forward data of its neighbours to sink, they will exhaust energy more quickly. These nodes are called hot spot nodes and we call this phenomenon hot spot problem. In this paper, an Enhanced Power Efficient Gathering in Sensor Information Systems (EPEGASIS) algorithm is proposed to alleviate the hot spots problem from four aspects. Firstly, optimal communication distance is determined to reduce the energy consumption during transmission. Then threshold value is set to protect the dying nodes and mobile sink technology is used to balance the energy consumption among nodes. Next, the node can adjust its communication range according to its distance to the sink node. Finally, extensive experiments have been performed to show that our proposed EPEGASIS performs better in terms of lifetime, energy consumption, and network latency.

Reinforcement learning in a large-scale photonic recurrent neural network

Abstract: Photonic neural network implementation has been gaining considerable attention as a potentially disruptive future technology. Demonstrating learning in large-scale neural networks is essential to establish photonic machine learning substrates as viable information processing systems. Realizing photonic neural networks with numerous nonlinear nodes in a fully parallel and efficient learning hardware has been lacking so far. We demonstrate a network of up to 2025 diffractively coupled photonic nodes, forming a large-scale recurrent neural network. Using a digital micro mirror device, we realize reinforcement learning. Our scheme is fully parallel, and the passive weights maximize energy efficiency and bandwidth. The computational output efficiently converges, and we achieve very good performance.

A PSO based Energy Efficient Coverage Control Algorithm for Wireless Sensor Networks

Abstract: Wireless Sensor Networks (WSNs) are large-scale and high-density networks that typically have coverage area overlap. In addition, a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area, which leads to coverage holes in WSNs. Thus, coverage control plays an important role in WSNs. To alleviate unnecessary energy wastage and improve network performance, we consider both energy efficiency and coverage rate for WSNs. In this paper, we present a novel coverage control algorithm based on Particle Swarm Optimization (PSO). Firstly, the sensor nodes are randomly deployed in a target area and remain static after deployment. Then, the whole network is partitioned into grids, and we calculate each grid’s coverage rate and energy consumption. Finally, each sensor nodes’ sensing radius is adjusted according to the coverage rate and energy consumption of each grid. Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption.

Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN

Abstract: Energy efficiency is the key requirement to maximize sensor node lifetime. Sensor nodes are typically powered by a battery source that has finite lifetime. Most Internet of Thing (IoT) applications require sensor nodes to operate reliably for an extended period of time. To design an autonomous sensor node, it is important to model its energy consumption for different tasks. Each task consumes a power consumption amount for a period of time. To optimize the consumed energy of the sensor node and have long communication range, Low Power Wide Area Network technology is considered. This paper describes an energy consumption model based on LoRa and LoRaWAN, which allows estimating the consumed power of each sensor node element. The definition of the different node units is first introduced. Then, a full energy model for communicating sensors is proposed. This model can be used to compare different LoRaWAN modes to find the best sensor node design to achieve its energy autonomy.

A review on optimization methods applied in energy-efficient building geometry and envelope design

Abstract: Building envelope parameters and geometric configurations can considerably influence the building energy performance. However, determining the best trade-offs of different building shape and envelope configurations to yield near-optimal design alternatives with respect to their energy performance is not a straight-forward task. Consequently, different methods have been utilized to optimize building envelope parameters and geometric configurations to achieve better energy performance. The objective of this paper is to provide an extensive review of the optimization methods and their application in energy-efficient architectural building design to better identify the potentials and applicability of different optimization methods. This paper reviews the optimization research, where building envelope parameters and geometric configurations are considered remarkably as the optimization independent variable(s) and building energy consumption/demand is included as an objective in the optimization process. The associated derivative-free and derivative-based optimization methods and their application in energy-efficient building design are included in this review. In addition, decision-making approaches are discussed for multi-objective optimizations. Current optimization tools are demonstrated. Finally, crucial considerations, including limitations and suggestions for the related future studies are concluded.

Machine learning for estimation of building energy consumption and performance: a review

Abstract: Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy efficiency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most effective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy efficiency at a very early design stage. On the other hand,efficient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, artificial intelligence (AI) in general and machine learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This paper provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.