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Spin-Transfer-Torque (STT) MRAM is configured with high access speed, readily hybrid integration and guaranteed endurance/retention, which becomes one of the most promising candidates for next generation memory. However, the major challenge of STT-MRAM is its high switching energy consumption, accompanied with unreliable writing operation. This work exploits two emerging design paradigms entitled approximation and speculation MRAM. A novel design scheme with timing speculation and approximate storage is proposed to improve energy efficiency in MRAM write operation by reducing write time. Simulation results show that latency and energy loss in entire write operation can be significantly reduced. The proposed technique obtains 84%/57.4% in the symmetric and unidirectional writing operations. Meanwhile, timing speculative method improves the failure/reliability in writing by re-write strategy. Related MRAM macro specification is applied to image processing, which obtains 10% (58.8%-67.2%) of the improvement of precision in image and higher image quality is obtained under the same accuracy rate.

Evaluations of energy efficiency programs reveal that realized savings consistently fall short of projections. We decompose this “performance wedge” using data from the Illinois Home Weatherization Assistance Program (IHWAP) and a machine learning-based event study research design. We find that bias in engineering models can account for up to 41% of the wedge, primarily from overestimated savings in wall insulation. Heterogeneity in workmanship can also account for a large fraction (43%) of the wedge, while the rebound effect can explain only 6%. We find substantial heterogeneity in energy-related benefits from IHWAP projects, suggesting opportunities for better targeting of investments.

AI edge devices require local intelligence for the concerns of latency and privacy. Given the accuracy and energy constraints, low-power convolutional neural networks (CNNs) are gaining popularity. To alleviate the high memory access energy and computational cost of large CNN models, prior works have proposed promising approaches including in-memory-computing (IMC) [1], mixed-signal multiply-and-accumulate (MAC) calculation [2], and reduced resolution network –[4]. With weights and activations restricted to ±1, binary neural network (BNN) combining with IMC greatly improves the storage and computation efficiency, making it well-suited for edge-based applications, and has demonstrated state-of-the-art energy efficiency in image classification problems [5]. However, compared to full resolution network, BNN requires larger model thus more operations (OPs) per inference for a certain accuracy. To address such challenge, we propose a mixed-signal ternary CNN based processor featuring higher energy efficiency than BNN. It confers several key improvements: 1) the proposed ternary network provides 1.5-b resolution (0/+1/-1), leading to 3.9x OPs/inference reduction than BNN for the same MNIST accuracy; 2) a 1.5b MAC is implemented by V CM -based capacitor switching scheme, which inherently benefits from the reduced signal swing on the capacitive DAC (CDAC); 3) the V CM -based MAC introduces sparsity during training, resulting in lower switching rate. With a complete neural network on chip, the proposed design realizes 97.1% MNIST accuracy with only 0.18uJ per classification, presenting the highest power efficiency for comparable MNIST accuracy.

Thanks to the increasing number and massive coverage, delivery drones, equipped with various sensors, have demonstrated significant but unexplored potentials for large-scale and low-cost urban sensing during package delivery. In this paper, we propose novel studies on the reutilization of such delivery drone resources to fill this void in urban crowdsensing. Accounting for interdependency between flying/sensing and drone delivery weight, we jointly optimize route selection, sensing time, and delivery weight allocation, to maximize delivery and sensing utility under drones energy constraints. This problem is formulated as a non-convex mixed integer non-Linear programming problem, which is proved to be NP-hard. To address this intricate problem, we propose near-optimal algorithms that leverage the equivalent objective function construction, the local search scheme, and the alternating iteration technique. Theoretical analysis indicates that our algorithms can achieve the 1/(4+ -approximation ratio (where is an arbitrarily small positive parameter and the convergence guarantee in polynomial time, for the scenarios of fixed and adjustable delivery weights, respectively. Extensive trace-based simulations and field experiments demonstrate that ours can significantly improve the delivery & sensing utility by 124.7% and the energy utilization rate by 72.2% on average, compared with the drone delivery without reusing.

Since 2012, the United States has witnessed explosive growth in mandatory energy efficiency disclosure policies, which aim to address market failures in housing. I examine one such policy, comparing prices before and after the policy’s introduction for homes with different levels of energy efficiency features. I find increased capitalization of energy efficiency features. Effects are larger for difficult-to-observe features, suggesting the results are driven by information and not changing preferences for energy efficiency over time. This highlights the potential for disclosure policies to promote long-run energy efficiency investment by increasing the returns homeowners expect on these investments when they sell.

Geothermal energy with abundance and large quantity can partially cover building heating/cooling loads and promote the carbon-neutrality transitions. Shallow geothermal ventilation (SGV) system, with a little initial investment cost, is one of promising technologies to partly replace the conventional air-conditioning system for air pre-cooling/pre-heating. This paper reviews applications of SGV system for improving thermal performance over latest two decades, which mainly includes the reclassification of SGV system, coupling with other advanced energy-saving technologies, application potentials for building cooling/heating under various weather conditions. Heat transfer mechanism and mathematical modelling techniques have been reviewed, together with in-depth analysis on current research trends, existing limitations, and recommendations of SGV system. Phase change materials, with considerable latent energy density, can stabilize the thermal performance with high reliability. The review identifies that optimization designs and advanced approaches need to be investigated to address the existing urgent issues of SGV system (e.g., large land occupation, difficulty in centralized collection of condensate water timely for horizontal buried pipe, bacteria growth, polluted supply air, and high construction cost for vertical buried pipe). A plenty of studies show that the SGV system could greatly expand the application scope and improve system energy efficiency by combining with other energy-saving technologies. This paper will provide some guidelines for the scientific researchers and engineers to keep track on recent advancements and research trends of SGV system for the building thermal performance enhancement and pave path for future research works.

Mobile crowdsensing (MCS) by unmanned aerial vehicles (UAVs) servicing delay-sensitive applications becomes popular by navigating a group of UAVs to take advantage of their equipped high-precision sensors and durability for data collection in harsh environments. In this paper, we aim to simultaneously maximize collected data amount, geographical fairness, and minimize the energy consumption of all UAVs, as well as to guarantee the data freshness by setting a deadline in each timeslot. Specifically, we propose a centralized control, distributed execution framework by decentralized deep reinforcement learning (DRL) for delay-sensitive and energy-efficient UAV crowdsensing, called "DRL-eFresh". It includes a synchronous computational architecture with GRU sequential modeling to generate multi-UAV navigation decisions. Also, we derive an optimal time allocation solution for data collection while considering all UAV efforts and avoiding much data dropout due to limited data upload time and wireless data rate. Simulation results show that DRL-eFresh significantly improves the energy efficiency, as compared to the best baseline DPPO, by 14% and 22% on average when varying different sensing ranges and number of PoIs, respectively.

In order to pursue good crushing load uniformity and enchance energy absorption efficiency of conventional honeycombs, a kind of bio-inspired hierarchical honeycomb model is proposed by mimicking the arched crab shell structures. Three bio-inspired hierarchical honeycombs (BHHs) with different topologies are designed by replacing each vertex of square honeycombs with smaller arc-shaped structures. The effects of hierarchical topologies and multi-material layout on in-plane dynamic crushings and absorbed-energy capacities of the BHHs are explored based on the explicit finite element (FE) analysis. Different deformation modes can be observed from the BHHs, which mainly depend upon hierarchical topologies and impact velocities. According to energy efficiency method and one-dimensional (1D) shock theory, calculation formulas of densification strains and plateau stresses for the BHHs are derived to characterize the dynamic bearing capacity, which is consistent well with FE results. Compared with conventional honeycombs, the crushing load efficiency and energy absorption capacity of the BHHs can be improved by changing the proper hierarchical topology and multi-material layout. These researches will provide theoretical guidance for innovative design and dynamic response performance controllability of honeycombs.

Secure, Clean, and Efficient Energy is one of the great societal challenges of our time. Electricity as a sustainable energy carrier plays a central role in the most effective transition scenarios toward sustainability. To harness this potential, the current electricity infrastructure needs to be rigorously re-engineered into an integrated and intelligent electricity system: the smart grid. Key elements of the smart grid vision are the coordination mechanisms. In such a system, vast numbers of devices, currently just passively connected to the grid, will become actively involved in system-wide and local coordination tasks. In this light, transactive energy (TE ) is emerging as a strong contender for orchestrating the coordinated operation of so many devices.

This study aims to estimate and explore the experience of introducing renewable energy use in the context of the world’s smart cities. In this regard, the study points out that the use of green energy is an important part of sustainable development. Environmental problems are a matter of global concern. Hence sustainable development is one of the approaches to end the harmful anthropogenic impact. The work includes quantitative assessment methods, for example, statistics, quantitative analysis, analogy, and synthesis. As a result, the analysis confirms that the effective development of a smart green city is impossible without the introduction of several renewable energy sources, the integrated use of which will reduce the likelihood of problems with the city’s energy supply. Likewise, the outcome accentuates that the desire to fully switch to renewable energy sources (RES) can be accompanied by several problems as the creation of RES technologies does not always take the risk of abnormal situations into account. In conclusion, the research findings are recommended to be taken into consideration by researchers in the field of smart and sustainable cities development, as well as urbanists and economists for designing future smart green cities based on renewable energy sources.

Enhancing the energy efficiency of structures has been a staple of energy policies. The key goal is to slash electricity usage in order to minimize the footprint of houses. This goal is sought by putting restrictions on the design specifications with respect to the properties of the raw materials and components as well as the exploitation of sustainable sources of energy. These facts for the basis for zero-energy building (ZEB) being established. This novel technology has faced several obstacles impeding its commercialization and future advancement. This investigation therefore holistically explored and evaluated the state of zero energy building and factors impeding their commercialization. The review further proposed some suggestion in terms of technology that can be considered by the sector to augment existing technologies. Similarly, the investigation touched on the effect of occupant's character in zero energy structures. Policies in terms of government subsidies and tax rebates were recommended to encourage more investors into the sector. Finally, the perception of zero energy building being more expensive compared to the traditional structures can equally be curbed via efficient and effective public sensitization.

Wide voltage design has been widely used to achieve power reduction and energy efficiency improvement. The consequent increasing number of PVT corners poses severe challenges to timing analysis in terms of accuracy and efficiency, especially for low voltage corners. In this paper, a learning-based timing prediction framework is proposed to predict multi-corner path delays across wide voltage range by exploiting both the correlations of circuit topology information and timing analysis results from known corners, which are respectively captured by LSTM (Long Short-Term Memory) and MMoE (Multi-gate Mixture-of-Experts) networks. Experimental results demonstrate that with the proposed framework, the path delays at low voltages could be predicted across wide voltage range with rRMSE of less than 2.9% and 4.7% for seen and unseen circuits respectively. Compared with the learning-based models used in prior works, the proposed framework achieves significant prediction error reduction by up to 19.1×.

Energy management strategy is gaining much popularity due to the involvement of conventional/non-conventional power sources with an efficient energy storing solution to offer un-interruptible power demand. Thus, in this work a simpler logic control circuit based nonisolated bi-directional DC-DC converter with both battery and super capacitor topology is proposed. For the proposed topology, first its mathematical modelling in ideal-case with parasitic in every mode of actions is presented. To control the bidirectional DC-DC converter topology, its small signal model for individual buck and boost operation is obtained by using averaging and linearization technique. Then, a unified logic circuit-based controller is designed for the obtained systems to analyze the performance of the converter in the buck and boost mode operation. The performance of the proposed topology is verified through MATLAB/Simulink environment.

In Vehicular Edge Computing (VEC) systems, the computing resources of connected Electric Vehicles~(EV) are used to fulfill the low-latency computation requirements of vehicles. However, local execution of heavy workloads may drain a considerable amount of energy in EVs. One promising way to improve the energy efficiency is to share and coordinate computing resources among connected EVs. However, the uncertainties in the future location of vehicles make it hard to decide which vehicles participate in resource sharing and how long they share their resources so that all participants benefit from resource sharing. In this paper, we propose VECMAN, a framework for energy-aware resource management in VEC systems composed of two algorithms: (i) a resource selector algorithm that determines the participating vehicles and the duration of resource sharing period; and (ii) an energy manager algorithm that manages computing resources of the participating vehicles with the aim of minimizing the computational energy consumption. We evaluate the proposed algorithms and show that they considerably reduce the vehicles computational energy consumption compared to the state-of-the-art baselines. Specifically, our algorithms achieve between 7\% and 18\% energy savings compared to a baseline that executes workload locally and an average of 13\% energy savings compared to a baseline that offloads vehicles workloads to RSUs.

Mobile edge computing is a key enabler for ultra-low latency in heterogeneous dense cellular networks in the 5G era and beyond, by deploying services at the network edge. Due to high user mobility, the services are usually migrated to follow the users by predicting the user trajectory to achieve a balance between energy consumption and service latency. However, service migration for multi-user heterogeneous dense cellular networks is challenging because the user trajectory prediction, which is crucial for service migration, becomes intractable with a large number of users, and making service migration decisions for each user independently is subjected to interference among the users. In this study, we formulated the service migration of all the users in MEC-enabled heterogeneous dense cellular networks as an optimization problem, with the objective of minimizing the average energy consumption while satisfying the service latency requirements, taking into account the interference among different users. Next, we developed an efficient energy-efficient online algorithm based on the Lyapunov and particle swarm optimizations, called EGO, to resolve the original problem without predicting the trajectories of the users. Finally, simulations based on real-world mobility traces of vehicles were conducted to establish the superiority of the EGO algorithm over state-of-the-art solutions.

The aim of this study is to investigate the ongoing energy management and energy efficiency practices in the industries of Peshawar division of Khyber Pakhtunkhwa, Pakistan. The outcomes of this research shows that Khazana Sugar Mills, Caliph Pharmaceutical, Naguman Flour Mill have no or low awareness of energy management practices (awareness level mainly includes barriers to energy efficiency and drivers for energy efficiency) except FF steel mill. The major paces observed in lacking the awareness are (1) “understanding level of staff and workers about energy savings due to energy management practices” (2) “interest level of top managers for energy management implementation” (3) “awareness level regarding energy efficiency” (4) “lack of technical competence” (5) “use of standardized procedures”. When the industry managers were inquired about the relationship with energy service providers it is found that there is absolute absence of energy engineers, relationship with energy consultancies and long-term energy strategy in all the industries. It was also found that there is lack of using renewable energy technologies in all industries as they were relying upon using the generators during the blackout hours. In terms of percentage, the energy saving factor of 36 kWh/day has also observed with the replacement of fluorescent tube lights with light emitting diode (LED) tube lights. Proper maintenance and energy management practices in the heating, ventilation and air conditioning system (HVAC) would also save energy in a significant amount. The study shows that there is an overall improvement factor of 4% to 8% if the inclusion of energy management and energy efficiency practices would have been applied in all the stated industries.

5G and beyond wireless networks are the upcoming evolution for the current cellular networks to provide the essential requirement of future demands such as high data rate, low energy consumption, and low latency to provide seamless communication for the emerging applications. Heterogeneous cloud radio access network (H-CRAN) is envisioned as a new trend of 5G that uses the advantages of heterogeneous and cloud radio access networks to enhance both spectral and energy efficiency. In this paper, building on the notion of effective capacity (EC), we propose a framework in orthogonal frequency division multiple access (OFDMA)- non-orthogonal multiple access (NOMA) based H-CRAN to meet these demands simultaneously. Our proposed approach is to maximize the effective energy efficiency (EEE) while considering spectrum and power cooperation between a macro base station (MBS) and radio remote heads (RRHs). To solve the formulated problem and to make it more tractable, we transform the original problem into an equivalent subtractive form via Dinkelbach algorithm. Afterward, the combinational framework of distributed stable matching and successive convex algorithm (SCA) is then adopted to obtain the solution of the equivalent problem. Hereby, we propose an efficient resource allocation scheme to maximize energy efficiency while maintaining the delay quality of service (QoS) requirements for all users. The simulation results show that the proposed algorithm can provide a non-trivial trade-off between delay and energy efficiency in OFDMA-NOMA based H-CRAN systems in terms of EC and EEE and the spectrum and power cooperation improves EEE of the proposed network. Moreover, our proposed solution complexity is much lower than the optimal solution and it suffers a very limited gap compared to the optimal method.

Cloud computing has become one of the most important platforms for various applications like artificial intelligence, big data, the Internet of Things, and others, especially demand for cloud computing has exploded in recent years. Because of the increased demand for cloud computing, it has become more complex, which can lead to software or hardware failure. Due to the advanced infrastructure, failure may not be detected and repaired at a given timeline and will end up costing higher. In addition, the advent of cloud computing has another advantage over the massive spread of scientific work. Scientific workflow refers to a series of computations that enable data analysis in a distributed and systematic way; because this work flow has a large number of works, energy consumption is a major problem. Besides these issues, it also suffers from system reliability; in the response to these issues, several researchers have designed their mechanism, however they failed to understand cloud complex environment. Therefore, here we have designed a mechanism that efficiently reduces energy consumption, improve the fault tolerance to achieve reliability, performs operations in very less time, and optimize the cost in the workflow model. We have also demonstrated efficient energy optimization techniques by reducing task loads.

In this paper, we investigate the energy-efficient multi-access mobile edge computing with secrecy provisioning. Specifically, we first investigate the scenario of one wireless device's (WD's) multi-access offloading subject to a malicious node's eavesdropping. By characterizing the WD's secrecy based throughput in its offloading transmission, we formulate a joint optimization of the WD's multi-access computation offloading, secrecy provisioning, and offloading-transmission duration, with the objective of minimizing the WD's total energy consumption, while providing a guaranteed secrecy-outage during offloading and a guaranteed overall-latency in completing the WD's workload. Despite the non-convexity of this joint optimization problem, we exploit its layered structure and propose an efficient algorithm for solving it. Based on the study on the single-WD scenario, we further investigate the scenario of multiple WDs, in which a group of WDs sequentially execute the multi-access computation offloading, while subject to a malicious node's eavesdropping. Taking the coupling effect among different WDs into account, we propose a swapping-heuristic based algorithm (that uses our proposed single-WD algorithm as a subroutine) for finding the ordering of the WDs to execute the multi-access computation offloading, with the objective of minimizing all WDs' total energy consumption. Extensive numerical results are provided to validate the effectiveness and efficiency of our proposed algorithms.

Multi-camera networks are increasingly becoming pervasive in many monitoring and surveillance applications, and have attracted much attention in distributed systems with collaborative, real-time decision-making capabilities. While in-network data compression brings significant energy savings in camera nodes, signal representation using sparse approximations and overcomplete dictionaries have been shown to outperform traditional compression methods. In this work, an end-to-end and real-time solution is designed and implemented to enable energy-efficient and robust dictionary learning in distributed camera networks by leveraging the spatial correlation of the collected multimedia data. Traditional distributed dictionary learning relies on consensus-building algorithms, which involve communicating with neighboring nodes until convergence is achieved. Existing methods, however, do not exploit spatial correlations in camera networks for improved energy efficiency. In contrast, low-computational-complexity metrics are employed in this work to quantify and exploit the spatial correlation across camera nodes in a wireless network for efficient distributed dictionary learning and in-network image compression. The performance of the proposed approach is validated through extensive simulations on public datasets as well as via real-world experiments on a testbed composed of Raspberry Pi nodes.

Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms but also energy-efficient computational models when implemented in very-large-scale integration (VLSI) circuits. In this article, we propose a novel supervised learning algorithm for SNNs based on temporal coding. A spiking neuron in this algorithm is designed to facilitate analog VLSI implementations with analog resistive memory, by which ultrahigh energy efficiency can be achieved. We also propose several techniques to improve the performance on recognition tasks and show that the classification accuracy of the proposed algorithm is as high as that of the state-of-the-art temporal coding SNN algorithms on the MNIST and Fashion-MNIST datasets. Finally, we discuss the robustness of the proposed SNNs against variations that arise from the device manufacturing process and are unavoidable in analog VLSI implementation. We also propose a technique to suppress the effects of variations in the manufacturing process on the recognition performance.

Energy conservation is a practice of reducing the energy used, which may be achieved by efficient use of energy. The existing old buildings generally consume more energy than the new buildings because of their unavoidable stagnations in physical performances. Distribution side load management of buildings play a vital role in the field of energy management. And 30% of this distribution side loads are Heating, Ventilation and Air Conditioning (HVAC) loads. So the accurate estimation of heating load (HL) and cooling load (CL) of HVAC system in a building is very much important. It has been studied that the building design parameters optimization approach alone can provide up to 62.1% electrical energy consumption reduction compared to buildings that are not designed for efficient energy usage. Recent research has demonstrated that a combination of existing HVAC technologies can offer effective solutions for energy conservation and thermal comfort. So in this study various energy saving strategies for HVAC systems are investigated and different measures on further energy and cost savings in buildings are discussed.

Mobile Ad-hoc networks (MANET) is a collection of nodes of wireless connectivity. In Mobile Ad hoc Network (MANET) all the mobile nodes are requiring restricted battery capacity. Because of the changing nature of network the sudden communication link breakage happens. Hence the possibility of data loss is more. Efficient utilization of energy and lossless transmission are the important issues in MANET. This paper proposes a new Trust based Efficient Energy Balanced Less Loss Routing (TER) Protocol for MANETS. This protocol improves the network performance by properly choosing the intermediate forwarding nodes. The Source Node picks only single of its neighbors as its next hop node by using Efficient Parameter (EP). The EP is calculated from the residual energy, distance, Occupied Queue space, the velocity of that node. The node using maximum EP is nominated as the succeeding node. This protocol reduces the number of channeling messages and hence reduces the path-finding Overhead of the network. It lessens the chance of data loss and hence improves throughput and Packet Delivery Ratio. This protocol adds trust to the selected node. In this paper, we do the simulation in ns-2 simulator and the performance is measured by with performance parameters like Routing Overhead, Packet Delivery Ratio, Throughput, End to End Delay and Average energy consumption.

Author(s): Mathew, Paul; Farmer, Alision; Werner, Jacob | Abstract: Purpose and Audience A wide spectrum of laboratory owners, ranging from universities to federal agencies, have explicit goals for energy efficiency and greenhouse gas reductions in their facilities. For example, new federal buildings and major renovations of existing buildings are to reduce fossil fuel-generated energy consumption by 90% in 2025, and 100% in 2030, compared with a 2003 baseline (FEMP n.d.). Minnesota SB2030 standard requires achieving an 80% reduction from the average building baseline for commercial, institutional, and industrial buildings (SB2030 n.d). A laboratory—new or existing—is much more likely to meet energy efficiency goals if quantitative metrics and targets are explicitly specified and tracked over the life cycle of the building, from design through construction, commissioning, operations, and renovations. If efficiency targets are not explicitly and properly defined, any additional capital costs or design time associated with attaining higher efficiencies can be difficult to justify relative to other priorities. The purpose of this guide is to provide guidance on how to specify and compute energy efficiency metrics and benchmarks for laboratories, at the whole-building as well as the system level. The information in this guide can be used to incorporate quantitative metrics and targets into new construction or retrofit of existing facilities. For information on strategies and technologies to achieve energy efficiency, the reader is referred to I2SL resources, including technology best practice guides and case studies.

Downlink mobile edge computing (MEC) networks are required to serve increasing large number of Internet of Things (IoT) devices with limited battery capacity. In order to serve massive user equipments with low power consumption requirements, in this paper, we propose an energy-efficient multi-carrier non-orthogonal multiple access (MC-NOMA) design which allows more than two IoT devices to multiplex and access the same subcarrier band. With the aim to minimize the total transmit energy while meeting the demands of each IoT device such as the low latency, in our design, we firstly derive the optimal successive interference cancellation (SIC) policy and minimum power allocated to every IoT device. Then we propose an optimal greedy algorithm to allocate the frequency blocks, and formulate the optimization of the computational resource allocation as a min-max problem. Subsequently, we characterize the MC-NOMA network with the potential game model, and present a scheduling scheme to manage massive IoT devices. Simulation results demonstrate that our proposed scheme can consume 3-10 dB less energy in a MEC network deployed with 256 IoT devices compared with the conventional orthogonal multiple access (OMA) scheme and non-orthogonal multiple access (NOMA) scheme.

Intelligent reflecting surface (IRS) has received widespread attention because it can significantly improve the performance of wireless networks. The X-duplex is a more flexible duplex mode, which can decrease a small sum rate in exchange for a higher quality-of-service performance. In this article, an IRS transmission scheme based on X-duplex is investigated to enhance the flexibility and freedom of communication design. We present the X-duplex IRS with the simultaneous wireless information and power transfer, and establish a power consumption model based on real application scenarios. An alternating optimization algorithm is proposed to maximize the energy efficiency (EE) of a multi-input single-output (MIMO) network assisted by the X-duplex IRS. As for the X-duplex relay, we explore an algorithm based on the golden section to optimize the EE. Simulation results comprehensively evaluate the performance of the two auxiliary transmission methods. The X-duplex IRS has higher EE and weighted sum rate (WSR) than the X-duplex relay when sufficient transmission power is available. However, the IRS cannot entirely replace the traditional relay. The X-duplex relay with self-interference energy harvesting achieves a higher WSR and EE when the access point energy is limited or the transmission distance is short.

In B5G heterogeneous cellular networks, a rapid increase in the number of small cell base stations (SBSs) to support a massive number of devices tends to waste a considerable amount of energy. Therefore, intelligent management of SBSs’ power consumption is one of the most important research issues. We herein propose quasi-distributed Q-learning-based cell breathing (QD-QCB) considering full and partial SBS collaborations for maximizing network energy efficiency. Also, the concept of an aggregated active SBS set based on regional user distributions is proposed for computing- and energy-efficient operation. Through intensive simulations, we show that the proposed QD-QCB algorithm can achieve optimal energy efficiency, and improve the network energy efficiency significantly compared with conventional algorithms such as no transmit power control, random cell breathing, and greedy cell breathing algorithms.

Usage of plastics in the form of personal protective equipment, medical devices, and common packages has increased alarmingly during these pandemic times. Though they have served as an excellent protection source in minimizing the coronavirus disease (COVID-19) spreading, they have still emerged as major environmental pollutants nowadays. These non-degradable COVID-19 plastic wastes (CPW) were treated through incineration and landfilling process, which may lead to either the release of harmful gases or contaminating the surrounding environment. Further, they can cause numerous health hazards to the human and animal populations. These plastic wastes can be efficiently managed through thermochemical processes like pyrolysis or gasification, which assist in degrading the plastic waste and also effectively convert them into useful energy-yielding products. The pyrolysis process promotes the formation of liquid fuels and chemicals, whereas gasification leads to syngas and hydrogen fuel production. These energy-yielding products can help to compensate for the fossil fuels depletion in the near future. There are many insights explained in terms of the types of reactors and influential factors that can be adopted for the pyrolysis and gasification process, to produce high efficient energy products from the wastes. In addition, advanced technologies including co-gasification and two-stage gasification were also reviewed., Graphical abstract Image 1

The safety and energy efficiency of autonomous vehicles in the vehicle-following driving scenario is seriously affected by the decision-making framework. This paper proposes a safe and energy-saving decision-making framework for autonomous vehicles to avoid the potential rear-end collision and improve the energy efficiency in vehicle-following driving scenario. To realize this framework, the vehicle longitudinal dynamics model is first presented, and the autonomous vehicles requirements from the safety and energy efficiency perspectives are comprehensively analyzed in the vehicle-following driving scenario. Then, a two-stage decision-making framework is designed. The first stage determines the safety region in the powertrain control domain, and the second stage determines the optimal energy efficiency in the safety region. Based on the two-stage decision-making framework, the perspective of safety first principle can be enforced by simultaneously considering the driving safety and energy efficiency. Finally, the experiment is conducted to demon-strate the advantages of the proposed method by com-paring it with two other state-of-art methods.

Wireless Sensor Networks include a substantial number of nodes with limited battery power that is used for gathering and sending the data to the Base station. Here most energy is consumed in data transfer. Hence a foremost problem is the maximization of network lifetime through minimization of energy consumption in the nodes. To resolve this issue, the clustering method is involved to achieve energy-efficient data transmission. A cluster head is elected which uses an energy distribution mechanism to conserve the remaining power, thereby extending network lifetime. Various present methodologies are employed but every algorithm had notable constraints individually. In this paper, a hybrid Sparrow Search Algorithm with Differential Evolution algorithm is intended to solve the energy efficiency issue by cluster head selection in Wireless Sensor Networks. The proposed algorithm uses the high-level search efficiency of the Sparrow Search Algorithm and the lively potential of Differential Evolution that enhances the lifetime of nodes. The performance of this hybrid model seems to be exploiting the count of alive nodes and dead nodes, throughput, and residual energy. The proposed Improved Sparrow search algorithm using the Differential evolution model for choosing the best possible cluster head shows a development in residual power and throughput than other compared algorithms.

Cloud-fog based industries are entailing today greedy energy costs, given the wide multiplication of their AI models and distributed BD frameworks implementations. This paper conducts a newsworthy inquiry purposing to study at which extent IT community are conscious about energy evaluation of their hardware and software implementations, and whether they are in line with sustainable implications toward efficient AI and BD deployments. Through an analysis of responses to the first inquiry questions, we were able to address the residing interoperability between AI models, distributed BD frameworks, and cloud-fog systems. Unfortunately, only 10% of respondents were adopting energy metrics when evaluating their implementations. Even worse, multi-level energy consumption measurement techniques were not evident to most respondents. Accordingly, we provided a useful guideline of various multi-level energy and power estimation approaches. Hereafter, we devoted in accordance with both inquiry and literature results some essential parts for analyzing emerging efficient DNNs and distributed BD implementations. These endeavors were mainly manifested in the form of efficient reconfigurable accelerators designs based on Processing-In-Memory and Processing-Near-Memory architectures. To serve eventually IT community with other tangible solutions, we proposed two roadmaps opening up to the possibility of investigating sustainable actions covering hardware, software, and data levels.

In wireless sensor networks, sensor nodes are deployed to collect data, perform calculations, and forward information to either other nodes or sink nodes. Recently, geographic routing has become extremely popular because it only requires the locations of sensor nodes and is very efficient. However, the local minimum phenomenon, which hinders greedy forwarding, is a major problem in geographic routing. This phenomenon is attributed to an area called a hole that lacks active sensors, which either prevents the packet from being forwarded to a destination node or produces a long detour path. In order to solve the hole problem, mechanisms to detect holes and determine landmark nodes have been proposed. Following the proposed mechanisms, landmark-based routing was developed in which the source node first sends a packet to the landmark node, and the landmark node then sends the packet to the destination. However, this approach often creates a constant node sequence, causing nodes that perform routing tasks to quickly run out of energy thus producing larger holes. In this paper, a new approach is proposed in which two virtual ellipses are created with the source, landmark, and destination nodes. Forwarding is then guided along virtual ellipses. Furthermore, a recursive algorithm is designed to ensure a shortcut even if there are multiple holes or a hole has multiple landmarks. Thus, the proposed approach improves both geographic routing and energy efficiency routing. Simulation experiments show that the proposed approach increases the battery life of sensor nodes, lowers the end-to-end delay, and generates a short path.

Manufacturers are pursuing energy-efficient production in response to the fluctuating energy price, growing global competition, rigorous international laws, and severe environmental crisis. This article proposes to boost the energy efficiency of production systems by controlling the production. It extends the existing energy-saving control research by presenting integrated modeling, analyzing, and controlling approaches. The work starts from the modeling of the production systems and establishes an analytical model to systematically quantify the production loss resulted from energy-saving control and the various disruptions. A dynamic control algorithm is proposed to reduce energy consumption and maintain desirable productivity. Simulation studies are utilized to demonstrate the application of the proposed method and verify its effectiveness.

With the surging energy cost and environmental impacts, strategies to achieve energy-efficient production have attracted increasing concerns of the manufacturing enterprises. For the fact that most manufacturing systems operate in a dynamic and nondeterministic environment, rescheduling strategies may be beneficial as it serves for adaption of initial schedule to dynamic events. Besides that, the development of modern information technology in manufacturing practice enables the flexibility of production toward alternative process plans. However, very little research has focused on the rescheduling problem integrated with process planning for energy saving. Hence, this work undertakes this challenge by proposing rescheduling decision mechanisms in response to two typical dynamic events with alternative process plans for energy-efficient flexible job shops. More specifically, by modeling the energy consumption of the flexible manufacturing system, the problem is first formulated as a mixed-integer programming optimization model. Rescheduling mechanisms for both new job arrivals and machine tool breakdowns are then designed, based on which a rescheduling algorithm is proposed in the form of a heuristic framework. The significance of the proposed algorithm is exemplified by a comparative case study under various scenarios.

Approximate computing is a promising alternative to improve energy efficiency for IoT devices on the edge. This work proposes a piecewise-linearly-approximated and unbiased floating-point approximate multiplier with run-time configurability. We provide a theoretically sound formulation that turns multiplication approximation to an optimization problem. With the formulation and findings, a multi-level architecture is proposed to easily incorporate run-time configurability and module execution parallelism. Finally, the proposed multiplier is further optimized to reduce the circuit implementation complexity, making the multiplier linearly dependent on the precision requirement, instead of quadratically or exponentially as in prior work. When compared to the prior state-of-the-art approximate floating-point multiplier, ApproxLP [1], the proposed multiplier outperforms in all the aspects including accuracy, area, and delay. By replacing a full-precision floating-point multiplier in GPU, the proposed design can improve the energy efficiency for various edge computing tasks. Even with Level 1 approximation, the proposed multiplier improves energy efficiency up to 20x for machine learning on CIFAR-10, with almost negligible accuracy loss.

Multi-FPGA architecture has gained great interests in accelerating large-scale graph processing with great potential on high throughput and energy efficiency. As a beneficial complement, work stealing functions effectively to balance the computational workload on different FPGAs dynamically. Unfortunately, existing graph partitioning schemes originally designed in distributed settings potentially mismatch the work stealing-enabled multi-FPGA situations, where the computation is balanced while the communication overhead is unprecedentedly significant over otherwise. In this paper, we present a 2-dimension balanced graph partitioning for work stealing assisted graph system on multi-FPGAs, which can reduce communication overhead while preserving the optimal performance of work stealing. Our approach is novel by 1) exploring the tradeoff between load balance dimension and communication dimension in work-stealing-enabled graph processing system for the optimal performance, and 2) optimizing the memory access sequences to improve the granularity of graph partitioning for high-throughput graph analytics. Our experimental results show that our approach achieves 1.63∼2.56x speedups compared with state-of-the-art FPGA-based systems.

Games are energy-intensive applications on mobile devices. Optimizing the energy efficiency of games is hence critical for battery-limited mobile devices. Although the advent of energy-aware scheduling (EAS) integrated in recent devices has provided opportunities for improved energy management, the framework is not specifically tuned for game applications. In this paper, we aim to improve the energy efficiency of game applications running on EAS-enabled mobile devices. To this end, we first analyze the functional characteristics of games, and investigate the source of the energy inefficiency. We then propose a scheme, called System-level Energy-optimization for Game Applications (SEGA), to improve the energy efficiency of games. SEGA governs CPU and GPU power consumption in a tightly coupled manner by employing three key techniques: (1) Gsync-aware GPU DVFS governor, (2) adaptive capacity clamping, and (3) on-demand touch boosting. We implemented SEGA on the latest Android-based smartphones. The evaluation results for 23 popular games showed that SEGA reduced the energy consumption of the Google Pixel 2 XL and Samsung Galaxy S9 Plus smartphones, at the device level, by 6.1-22.3% and 4.0-11.7%, respectively, with a quality of service (QoS) degradation of 1.1% and 0.5%, on average.

Because of COVID-19, factories are facing many difficulties, such as shortage of workers and social alienation. How to improve production performance under limited labor resources is an urgent problem for global manufacturing factories. This work studies an energy-efficient job-shop scheduling problem with limited workers. Those workers can have multiskills. A many-objective model with five objectives, that is: 1) makespan; 2) total tardiness; 3) total idle time; 4) total worker cost; and 5) total energy, is built. To solve this many-objective optimization problem (MaOP), a novel fitness evaluation mechanism (FEM) based on fuzzy correlation entropy (FCE) is adopted. Two construction methods for reference points are proposed to build the bridge between MaOP and a fuzzy set. Based on FCE and cluster methods, an environmental selection mechanism (ESM) is proposed to achieve a balance between solution convergence and diversity. With the proposed FEM and ESM, two many-objective evolutionary algorithms are proposed to solve MaOP. The effect of FCE-based FEM and ESM on the performance of algorithms is verified via experiments. The proposed algorithms are compared with four well-known peers to test their performance. The extensive experimental results show that they are very competitive for the considered many-objective scheduling problem.

Because data centers are energy-intensive industries, data centers have made significant efforts to improve energy efficiency to save energy cost and utilize renewable energy to contribute to environmental sustainability. In addition, data centers have been considered as a potential application of demand response because they can possibly adjust energy consumption to process workload in response to time-of-use prices and intermittent renewable generation. Quality of service (QoS) should be guaranteed at the desired level to respond to users' requests with their satisfaction in an effort to achieve energy-efficient and sustainable data center operations. Given this context, this study intends to develop a two-stage stochastic program that can be used to investigate optimal data center operations based on demand response that results in minimum energy cost with QoS guarantee against the stochastic workloads. Then, an additional chance constraint and risk constraint are considered as means of managing the level of QoS guarantee, and comprehensive numerical experiments are conducted with various parameter settings to evaluates the impact of the QoS guarantee with different levels on overall performance. The results show that the risk associated with the QoS guarantee for data centers needs to be properly managed to improve energy efficiency.

Dynamic consolidation of virtual machines (VMs) is an effective way to improve resource utilization and power efficiency in cloud computing. For example, determining when it is best to relocate VMs from an overloaded host is one aspect of dynamic VM consolidation that directly influences the resource utilization and Quality of Service (QoS) offered by the system. This paper presents a new proposal with an Interval-valued Fuzzy Logic approach to address the uncertainties and inaccuracies in determining resource usage, aiming at energy savings with minimal performance degradation. The results obtained in a simulated cloud computing environment showed improvements for energy efficiency of 2.3% with Inter Quartile Range (IQR). With regard to Service Level Agreement (SLA), results of 10.1% were achieved with Threshold Policy (THR), and finally, IQR obtained 7.9% better results considering the reduction in the number of VM migrations.

Беспроводные сенсорные сети (БСС) представляют собой перспективное направление в области развития телекоммуникационных систем. Данная статья посвящена актуальной на сегодняшний день проблеме энергопотребления в беспроводных сенсорных сетях. Целью исследования является оценка энергоэффективности агрегирования разнородных данных в БСС. В статье рассмотрено влияние качества агрегирования данных на энергопотребление и пропускную способность БСС. Рассмотрены причины возникновения временных и пространственных корреляций данных, возникающих в БСС, определены подходы к их устранению. Предложены новые коэффициенты оценки качества агрегирования данных в БСС: коэффициент энергоэффективности агрегирования и коэффициент размера информационного пакета. Проведен анализ влияния агрегирования данных на БСС произвольной топологии. На основе математического и статистического анализа, а также с применением программных средств математического моделирования в ходе исследования был произведен расчет величин расходуемой энергии и пропускной способности, необходимых для передачи информационных пакетов, построены графики зависимостей этих величин. В результате исследования был разработан способ оценки энергоэффективности агрегирования разнородных данных в беспроводных сенсорных сетях. Представленный материал будет интересен специалистам в сфере телекоммуникаций, студентам и аспирантам технических направлений, а также может служить основой для дальнейших исследований в рассматриваемой области.