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The research addresses the pressing need to understand wind conditions for optimal wind energy utilization, a crucial aspect of achieving global energy sustainability. While previous studies have focused on assessing wind energy potential and terrain roughness, gaps persist in understanding how wind and spatial conditions affect blade geometry. Therefore, this study aims to investigate the impact of local wind conditions on wind turbine blade geometry, integrating nonuniform wind inflow due to wind shear into the theoretical model. Through the paper, it explores blade design for varying inflow conditions and analyses the relationship between terrain type and blade designs. A novel approach to wind turbine blade design, utilizing a theoretical model that combines the local momentum theorem with insights from propeller vortex theory was adopted. Through the analysis, the authors demonstrate that variations in terrain significantly influence blade geometry, underscoring the necessity for tailored turbine designs based on local conditions. The findings reveal discrepancies in blade geometry across different turbine capacities and terrain types, offering novel insights into wind turbine performance optimization. By tailoring blade geometry to specific locations enhances resource utilization, offering insights for energy policymakers. The research provides validated methods for optimizing turbine geometric parameters, opening avenues for increased local investment in wind energy and promoting distributed energy development. Illustrated with wind plants examples, the paper concludes by outlining future research directions in this field. [ABSTRACT FROM AUTHOR]

Summary: By incorporating on‐demand resources, software and data for collaborative services through the Internet, the conventional Information Technology enterprise has been transformed by cloud computing. Based on the pay‐per‐use approach, Infrastructure, platform or Software resources and servers located across data centres are among the several types of resources offered to consumers in cloud computing. Data centres handle these resources. These resources are constantly provisioned to users based on their availability, demand, and quality requirements. Cloud computing systems are known as one of the largest utilisers of energy resources all over the world. Also, power consumption has become a crucial aspect as most cloud computing systems work on traditional nonrenewable resources of energy. In order to make data centres environment‐friendly, there is a need for optimal approaches to reduce energy consumption and their hazardous effects on the environment. To analyse different available strategies for building and maintaining an energy‐efficient cloud is the main objective of this paper. The paper will comprehensively review several energy‐efficient resource provisioning methods and provide a graphical comparative study of Quality of Service (QoS) Metrics in cloud computing. Moreover, the present study identifies the areas of study that need to be further improved to increase the energy efficiency of cloud computing systems. [ABSTRACT FROM AUTHOR]

This paper explores the economic, social, and technical dynamics affecting municipal solid waste (MSW) per capita in organisation for economic co‐operation and development (OECD) countries from 2003 to 2018. Static and dynamic panel data methods are followed for empirical analysis. According to the estimation results, there is an inverted‐U‐shaped relationship between economic growth and MSW. The turning point income level, where technological and structural effects occur, is calculated in the range of $20,000–$34,000. Human development and energy efficiency have mitigating effects on MSW. Energy efficiency and human development are critical for sustainable MSW management. Credit expansion has an increasing effect on MSW generation. Financial development draws attention as a negative indicator in sustainable waste management targets. All models confirm that income inequality has an increasing effect on MSW generation. In addition, poverty reduction is associated with an increase in MSW generation. Thus, income inequality and poverty reduction are a major constraint for sustainable MSW management. After all, the empirical findings of this paper can guide authorities in designing waste management policies. [ABSTRACT FROM AUTHOR]

Vectorized instructions were introduced to improve the performance of applications. However, they come at the cost of an increase in the power consumption. As a consequence, processors are designed to limit their frequency when such instructions are used in order to respect the thermal design power limit. In this paper, we study and compare the impact of thermal design power and Simple Instruction Multiple Data (SIMD) instructions on performance, power and energy consumption of processors and memory. The study is performed on three different architectures providing different characteristics and four applications with different profiles (including one application with different phases, each phase having a different profile). The study shows that, because of processor frequency, performance and power consumption are strongly related to thermal design power. It also shows that AVX512 has unexpected behavior regarding processor power consumption, while DRAM power consumption is impacted by SIMD instructions because of the generated memory throughput. Finally, this paper tackles the impact of turboboost which shows equivalent to better performance for all the studied cases while not always decreasing energy consumption. [ABSTRACT FROM AUTHOR]

The platform economy is growing fast in the world. Its expansion, exemplified by industry giants like Amazon, Airbnb, and Uber, is reshaping societal lifestyles and commercial businesses. However, its environmental impacts are uncertain. Currently, the relationship between electronic commerce (e‐commerce) activities and environmental pollution has attracted a lot of attention. This paper attempts to construct a comprehensive theoretical model to investigate the nexus between e‐commerce and regional environmental pollution, with a particular focus on the effects of air pollution and waste pollution. Fixed effect models and China's provincial data during the period from 2008 to 2018 are employed to examine the roles of energy efficiency and resource consumption in such a nexus. The findings suggest that the development of e‐commerce is linked to a reduction in regional air pollution through the channel of improving transportation energy efficiency. The results also show that e‐commerce is associated with an increase in regional waste pollution due to the mechanism of resource consumption in the express packaging industry. As a result, the relationship between e‐commerce and environmental pollution is quite complex at the regional level, transcending a binary distinction of solely positive or negative impacts. This paper contributes to a better understanding of the complex environmental impacts of e‐commerce and helps governments design more efficient and targeted policies to reduce regional pollution. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the performance of the Energy Aware Scheduling Algorithm (EASA) in a 5G green communication system. 5G green communication systems rely on EASA to manage resource sharing. The aim of the proposed model is to improve the efficiency and energy consumption of resource sharing in 5G green communication systems. The main objective is to address the challenges of achieving optimal resource utilization and minimizing energy consumption in these systems. To achieve this goal, the study proposes a novel energy‐aware scheduling model that takes into consideration the specific characteristics of 5G green communication systems. This model incorporates intelligent techniques for optimizing resource allocation and scheduling decisions, while also considering energy consumption constraints. The methodology used involves a combination of mathematical analysis and simulation studies. The mathematical analysis is used to formulate the optimization problem and design the scheduling model, while the simulations are used to evaluate its performance in various scenarios. The proposed EASM reached a 91.58% false discovery rate, a 64.33% false omission rate, a 90.62% prevalence threshold, and a 91.23% critical success index. The results demonstrate the effectiveness of the proposed model in terms of reducing energy consumption while maintaining a high level of resource utilization. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network (WSN) is frequently being used for increasing the data collection and monitoring critical parameters. Major challenge in WSN design is achieving the better energy efficiency and latency considering the dynamic sink location. Lots of research has been done for improving the performance of WSN systems in wide range of applications. Prime goal of this paper is to evaluate the performance of WSN routing under the grid‐based dynamic sink locations. Later the performance of various existing critical protocols is compared to route data over centralized server. Various applications of WSNs are tabulated along with the sensor utilization. The paper contributed in three passes. Initial contribution of this research is to identify the issues and limitations of present WSN communication technologies. The paper contributed to present a case study to discover the implications of dynamic sink routing for building protocol to extend network's lifetime. The effectiveness of several clustering‐based WSN routing algorithms is first compared in this paper. After that, the protocol with the longest network life is put to the test under a grid‐based dynamic sink routing scenario. The significance of clustering‐based protocols for improving lifetime of the network is evaluated as case study. The clustering‐based energy efficient protocol like low energy adaptive clustering hierarchy (LEACH) and its different variants, PEGASIS, SEP, DEC, their variants, and so on, are compared in this paper. Various performance metrics like network delays, throughput, latency, energy consumption and network lifetime are defined for studying routing protocols. Significance of dynamic sink locations performance is evaluated for Enhanced Distributed Energy Efficient Clustering (EDEEC) protocol. Overall paper covers and reviewed many critical design aspects for WSN networks. [ABSTRACT FROM AUTHOR]

Today, Internet of things (IoT), Cloud and Fog networks have spread out around the world. The more these networks grow, the more their energy consumption comes to attention. Many efforts have been made during recent years to decrease this energy consumption, mainly focused on utilizing low‐power devices. Green algorithms are recently proposed to reduce energy consumption by modifying the structure of many algorithms employed in the network and its protocols. This paper proposes a new green reliability algorithm for Transmission Control Protocol/Internet Protocol (TCP/IP protocol) in Fog computing. The proposed algorithm does not require extensive TCP/IP protocol changes or relevant hardware. It is based on transferring less number of packets in the network by using the advantage of differences between TCP and User Datagram Protocol (UDP). TCP and User Datagram Protocol (UDP) are different in nature as the number of total packets in UDP is half that of TCP. As a result, the number of complete packets in UDP is half that of TCP. The proposed method is built around the loss of some packets in applications, such as voice and online video, does not severely degrade the end results. Therefore, the UDP protocol can substitute TCP in such situations. The criterion to switch between the two is the minimum acceptable Quality of Service (QoS) of the overall network. In other words, the UDP protocol will be used as long as QoS requirements are met. The switching process between UDP and TCP is dynamic, optimized by estimating network noise in the period. Additionally, we evaluated the proposed method based on several QoS functions, including delay, throughput, and energy usage. [ABSTRACT FROM AUTHOR]

In recent years, the mining of cryptocurrencies such as Bitcoin has attracted much global attention due to the simplicity and high profitability of this business, especially in countries with low electricity rates. The process of cryptocurrency mining using cryptocurrency mining devices requires a significant amount of computing power. This industry, which is increasingly growing in energy and carbon emissions, can cause environmental concerns. Common ways to reduce this amount of energy consumption are to prohibit mining and charge these loads at a higher electricity rate; however, these solution methods are not feasible and applicable in many countries. This paper presents an energy efficiency program to persuade the customers to mine cryptocurrency using high‐efficient cryptocurrency mining devices (CMDs), including informative, regulation, and financial policy instruments. This energy efficiency approach suggests a standard, if implemented, would ban the application of inefficient CMDs, and would encourage the cryptocurrency miners to improve the profitability of their business with high‐efficient CMDs through a penalty‐reward strategy. The performance of this energy efficiency program is studied through an analysis of data gathered from different CMDs used in Iran. The effectiveness of this suggested program is proved through its sufficient incentives for miners using high‐efficient CMDs. [ABSTRACT FROM AUTHOR]

The paper contributes towards filling a 'blind spot' in the field of climate finance by investigating the role of climate finance for behavioural insights in developing countries. The evidence review synthesises 71 high‐quality studies and focuses on clean fuel adoption and household energy‐saving behaviour in developing countries. The synthesis finds that there is a need for climate finance from developed countries that is more targeted towards interventions and measures that support household decision‐making in developing countries by engaging with stakeholders that understand local attitudes, constraints and knowledge levels governing the perception of health and environmental risks associated with energy‐consuming technologies. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic made people reevaluate their energy consumption and energy efficiency. It held up a mirror to humanity's opportunistic and ruthless deployment of energy sources. As the city streets went empty and the international air traffic stopped altogether, people witnessed the significant drop in CO2 emissions, realized their dependence upon energy sources, and became aware of global climate change. In addition, with lockdowns and home offices, the demand for energy and the usual peak hours shifted offering a new perspective in energy consumption. The pandemic delivered a devastating blow to the human belief in having the constant and everlasting supply of energy that can be relied upon in everything. All these attracted the attention of researchers focusing on energy consumption and efficiency during the COVID‐19 pandemic. As a result, the vast body of academic literature on the topic burgeoned in the past four years yielding many exciting perspectives and viewpoints. Our paper conducts a comprehensive review of this body of literature employing the bibliometric network analysis of 12960 publications indexed in Web of Science database. It demonstrates the potential benefits and challenges associated with implementing energy‐saving technologies, altering energy consumption behavior, and implementing novel ICT solutions in a post‐COVID world. In addition, it highlights the importance of energy efficiency measures and examines novel technologies that can contribute to a sustainable and resilient energy future. Our findings emphasize the need for robust policies, technological advancements, and public engagement for fostering energy efficiency and mitigating environmental impacts of post‐pandemic energy consumption. [ABSTRACT FROM AUTHOR]

Summary Even with existing methodologies, dynamic resource allocation, service inconsistency, operational costs, and excess energy/power consumption in optical access networks remain the key challenges for the next‐generation passive optical network (NG‐PON) design and development. This paper introduces a novel method for analyzing energy/power consumption in time and wavelength division multiplexed (TWDM) passive optical networks (PONs) with a ring‐based topology. We propose subscriber‐based channel allocation models (CAMs) for on‐demand services, allocate bandwidth dynamically, and evaluate their downstream power consumption. To determine the necessary optical devices for the various CAMs, we investigate subscriber take‐up rates by optimizing the utilization of optical components based on active subscribers. Implementing a novel Poisson distribution‐based network traffic model addresses energy‐related concerns and explores optimization techniques to minimize power and improve energy efficiency. With a take‐up rate of 64 active subscribers, simulation results show significant power consumption reductions ranging from 62.2 to 67.7%, achieved by the proposed subscriber‐based architecture models with varying subscriber counts, when compared with the conventional model. The model with the most subscribers (2048 subscribers) saves the most energy by reducing power consumption to 292 W from the conventional model's (2048 subscribers) 440 W. Furthermore, the model with the most subscribers saves 33.4% of energy, outperforming other subscriber‐based architecture models without affecting QoS even during peak hours of the day. These numerical results show that TWDM‐PONs with the most subscribers exhibit improved energy efficiency than the conventional and other proposed subscriber‐based architecture models. [ABSTRACT FROM AUTHOR]

Summary: Wireless body area networks (WBANs) are becoming widely considered in remote healthcare monitoring applications. However, WBAN nodes have limited resources; therefore, effective and reliable routing protocols are pivotal research challenges. Furthermore, balancing the traffic load among sensor nodes is also highly required to increase network stability. In recent years, many interesting routing solutions have been proposed for WBANs; however, the significant feature in terms of stability and energy in these solutions has not been sufficiently addressed. Therefore, in this context, the Hot‐Sspot Aaware Multicost‐based Energy‐efficient Routing (H‐SAMER) protocol is proposed in this paper. The suggested protocol used multieffective cost function for next‐hop node selection based on the data type, where the patient's data are categorized into three classes: normal data, on‐demand data, and emergency data. Furthermore, the H‐SAMER protocol adds awareness to the transmission of control packets by adding the cost value to the RREQ packets. Thus, the simulation scenario shows that the H‐SAMER saves energy 8.57%, 88%, 98%, 97%, and 100% greater than E‐HARP, EH‐RCP, CO‐LAEEBA, EECBSR, and ELR‐W, respectively. Moreover, the first node death of the proposed H‐SAMER is delayed to round number 7500, which proves H‐SAMER to be a stable and reliable effective solution for WBANs. [ABSTRACT FROM AUTHOR]

Summary Internet of things–enabled wireless sensor networks face challenges like inflexibility, poor scalability, suboptimal cluster head selection, and energy inefficiencies. This is due to the faster data transmission rates between cluster nodes during data packet routing. This creates unnecessary energy consumption burdens for those actively transmitting nodes. Conceptually, an effective cluster formation phase supports better data routing mechanisms, while sustaining the energy efficiency of individual nodes. This paper proposes a Neuro‐Fuzzy based Cluster Formation (NFCF) scheme to facilitate adaptive and energy‐efficient cluster topologies. NFCF utilizes fuzzy logic and neural networks to identify optimal super nodes for flexible cluster formations. This approach enables configurable cluster sizes along with inclusion/exclusion criteria for member nodes based on energy thresholds. Parameters evaluated for node selection include the degree of super node, expected energy per cluster, energy variance, and residual energy. Nodes not meeting the thresholds are excluded. The neural network updates fuzzy rules to guide optimal clustering decisions based on anticipated energy dynamics under different conditions. The performance of the proposed NFCF scheme is evaluated based on objective function changes related to data transmission, individual node energy variation, energy variance before and after transmissions, and averaged end‐to‐end delay across transmission cycles. Results are compared against genetic fuzzy clustering, fuzzy energy‐aware clustering, fuzzy‐based distributed clustering, fuzzy logic‐based multi‐hop clustering, and fuzzy weighted k‐means clustering. [ABSTRACT FROM AUTHOR]

Image binarization algorithms in document image analysis divide pixel values into two groups, including white as background and black as foreground. Among others, the local contrast and mean (LCM)‐based thresholding algorithm offers excellent performance in processing degraded documents. This algorithm, however, is susceptible to noise and requires significant hardware resources. In this paper, an energy‐efficient and fault‐tolerant architecture is proposed for implementing the LCM algorithm in stochastic computing (SC). Leveraging correlated input bitstreams, this architecture saves energy and improves the fault tolerance of the implementation. Experimental results show that the proposed LCM stochastic architecture significantly outperforms the stochastic implementation of the Sauvola algorithm in terms of both binarization accuracy and hardware overhead and energy consumption. Even using 16‐bit streams, the proposed circuit produces an error rate lower than 5%. The stochastic implementation of the LCM algorithm using a 16‐bit length FSM‐based LD sequence is 22 times less in area, 26 times less in total power, 28 times less in energy consumption and more fault‐tolerant than the conventional 8‐bit bit‐width weighted binary with the same frequency constraints. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, research based on mobile ad hoc network's (MANET) performance improvement has drawn a particular concentration from scientists worldwide. The shortest path selection among destination and source nodes determines the network's throughput in MANET. In addition, power management and energy consumption are also the major difficulties that MANET suffers. The conventional routing protocols are intended for static bonding, but that protocol does not satisfy the stability in ad hoc networks by means of regularly changing connectivity. This shows the routing problem in MANETs. Therefore, to overcome the routing issues and maintain energy efficiently, the Type‐2 fuzzy‐based Starling Murmuration Optimizer (SMO)‐War Strategy Optimization (WSO) (T2FSMO‐WSO) routing protocol is proposed in this paper. MANET takes into account four input parameters: route length, active link ratio to global traffic, network lifetime, and control packet ratio. The SMO algorithm is employed to choose the relay node with low energy consumption. The trapezoidal membership functions are used to describe the fuzzy sets for these parameters. The content‐based routing among source and destination nodes is determined using the WSO algorithm. The simulation outcomes reveal that the proposed method discovers highly stable paths with minimum distance and minimized the consumption of energy. [ABSTRACT FROM AUTHOR]

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Summary: File sharing is one of the leading Internet applications of P2P technology. Given the high number of computer nodes involved in peer‐to‐peer networks, reducing their aggregate energy consumption is an important challenge to be faced. In this paper, we show how the sleep‐and‐wake energy saving approach can be exploited to reduce energy consumption in BitTorrent, one of the most popular file sharing peer‐to‐peer networks. We describe BitTorrentSW, a sleep‐and‐wake approach for BitTorrent networks that allows seeders (ie, peers that hold complete files) to cyclically switch between wake and sleep modes to save energy while ensuring good file sharing performance. The decision to switch to sleep mode is taken independently by each seeder based on local information about the composition of the peer‐to‐peer network. BitTorrentSW has been evaluated through PeerSim using real BitTorrent traces. The simulation results show that, in all the configurations under analysis, the percentage of energy saved by BitTorrentSW is much higher than the percentage of increase in download time. For instance, in a network with 50% of seeders, about 20% of energy is saved using BitTorrentSW, with an increase of only 7% of the average time needed to complete a file download compared to a standard BitTorrent network in which all seeders are always powered on. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a multi‐hop cognitive radio (CR) enabled device‐to‐device (D2D) network is considered to extend the communication from a functional area (FA) to a non‐functional area (NFA). A D2D user utilizes a cellular user (CU) band in the cognitive mode for information transmission while maintaining the service quality of CUs by satisfying a collision constraint. It transmits with full power in overlay mode, while the power is controlled in underlay mode. We derive the outage probability of the considered D2D network. The main challenge in CR communications lies in the system robustness to uncertainties. In this paper, we also show the impact of the noise power uncertainty in a multi‐hop D2D network. The expressions for throughput and energy efficiency are also developed under collision constraint and noise uncertainty. The outage probability, throughput, and energy efficiency are investigated for several network parameters such as collision probability, sensing time, noise uncertainty, etc. It is observed that the collision constraint and noise uncertainty have significant impacts on network performance. A simulation test‐bed is developed in MATLAB, and the results obtained in the simulation are verified with the analytical results. [ABSTRACT FROM AUTHOR]

Summary: The solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation. [ABSTRACT FROM AUTHOR]

Summary: Millimeter‐wave (mmWave) massive MIMO and non‐orthogonal multiple access (NOMA) are promising ways for improving energy efficiency of integrated terrestrial‐satellite networks. In this paper, we investigate the performance of integrated terrestrial‐satellite networks, in which MIMO‐NOMA‐based terrestrial networks and satellite network cooperatively provide ubiquitous services for ground users while reusing the entire bandwidth. Both base stations (BSs) and satellite are equipped with multiple antennas, and beamforming technology is utilized to serve multiple users simultaneously. First, a user selection scheme based on channel gain ratio is proposed to select satellite users. Then, we design a user clustering algorithm for reducing the intra‐beam and inter‐beam interference of BSs users. Analog and digital beamforming techniques are also adopted to further increase beam gain and improve system energy efficiency. The terrestrial networks and satellite networks energy efficiency are separately optimized through beamforming design and power allocation scheme, and then, a joint iterative algorithm is proposed to maximize the system energy efficiency. In addition, we introduce the interference temperature threshold to limit the satellite interference to BSs users. Finally, the performance of the proposed algorithm is simulated in comparison with the existing algorithm. The results indicate that the presented algorithm has high superiority in system energy efficiency and it can be applied to integrated terrestrial‐satellite networks. [ABSTRACT FROM AUTHOR]

Summary: The combination of non‐orthogonal multiple access in millimeter‐wave communication called as mmWave‐NOMA is capable of supporting more data streams with a larger number of users to cope up with the increasing traffic demand. In this paper, an energy‐efficient scheme for downlink mmWave‐NOMA systems with joint user grouping, scheduling, and optimal power allocation with the objective of energy efficiency maximization is proposed. Enhanced k‐means clustering for user grouping which can handle the possible simultaneous access of network to enhance the sum throughput is proposed. Weighted sum algorithm‐based scheduling where weights are calculated based on the service requirement of the users to maximize the sum rate and a proportional‐based scheme of power allocation modeled as a resource allocation problem considering the beam gain and discrimination weight of each user for energy‐efficient power allocation is discussed. The simulation results show that the proposed scheme is energy efficient than the conventional mmWave orthogonal multiple access systems. [ABSTRACT FROM AUTHOR]

Summary: In a high‐speed vehicular scenario, due to vehicle penetration loss (VPL) and fast signal fluctuations, frequent handovers occur, which results in more battery consumption. So power saving is a critical concern in high‐speed wireless networks. In the past, moving relay (MR)‐based heterogeneous network (HetNet) models were proposed and studied to overcome the effect of VPL, but the issue of power saving has not been studied till now for vehicular HetNet. In this paper, we propose an energy‐efficient algorithm that selects the best number of cooperative MRs among multiple MRs installed on the top of the train for efficient utilization of transmission power in a NOMA‐based wireless network. A half‐duplex amplify‐and‐forward relaying protocol is used for the investigation. We define the optimization problem of energy efficiency for cooperative MR. The problem of energy maximization is stated as an optimization problem, and an energy‐efficient MR selection algorithm is proposed as the solution to the optimization problem. The simulation results show that the proposed algorithm enhances the energy efficiency of vehicular and nonvehicular users and it also decreases the outage probability at fixed spectral efficiency in comparison with the fixed MR‐based selection combining and maximum ratio combining diversity techniques. This result will be beneficial in vehicular communication to achieve maximum energy efficiency. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network (WSN) is a well‐developed domain suitable in the optimal collection and processing of information needed for the present world. For processing and transmitting the correct data by the network, it is affected by many disrupting factors like interference, battery life, distance between the nodes, and redundant data. There have been many methods proposed in the past. Among all the disrupting factors, routing has been the most studied problem in the WSN literature, whereas packet length sizing has been the most untouched topic compared to routing. Sizing the packet lengths to transmit it through the network is important to get error‐free data and run an energy‐efficient network. In this paper, we propose a sling‐shot spider optimization (S2SO) algorithm for packet length optimization in WSN and Internet of Things (IoT)‐based networks. The S2SO algorithm is developed based on the spider's unique behavior of catching prey. The proposed algorithm is implemented in three types of networks: 1‐hop, 2‐hop, and multi‐hop networks. A mathematical model for the communication channel, energy efficiency, and energy consumption is developed for all three types of networks. The proposed packet length optimization model is simulated in MATLAB software and compared with conventional bio‐inspired algorithms. The results show that the proposed algorithm is very fast in finding the optimal results and transmits optimal packet size with low error rate of 0.2 p.u. and a high efficiency of 98%. [ABSTRACT FROM AUTHOR]

This paper deals with load extrapolation methods and algorithms for the estimation of the energy efficiency of induction motors, starting from the electrical data generally available after parameter identification. The main aim of this paper is to help the readers move from the theoretical analysis to the experimental verification, which is suitable to be included in its own testing. For this reason, step‐by‐step algorithms are included in this paper. The first part deals with the determination of energy efficiency based on an original load extrapolation. Finally, experimental verification of the high‐power and high‐voltage induction motors is provided. Comparison between the values of the in–out method and load extrapolation method confirms the validity of the proposed algorithms. Compared to the direct in–out method, the error of the load extrapolation proposed in this paper is less than 1%.The load extrapolation method is mainly used for the high‐voltage and high‐power asynchronous motor. The high‐voltage and high‐power motor can be used to drive a low‐power motor to achieve coaxial twin trawling in the absence of a suitable power supply and matched load. The energy efficiency of a high‐power motor under different loads are measured and estimated using a mathematical fitting method. The estimated energy efficiency curves in the whole load range can be used to evaluate the high‐power and high‐voltage induction motor. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing has transformed service delivery through its pay‐per‐use model, supporting diverse users with multiple heterogeneous Virtual Machines (VMs). However, energy consumption has emerged as a critical concern, necessitating cloud resource optimization for environment‐friendly practices. This research paper presents an innovative energy‐efficient threshold‐based sender‐initiated load‐balancing strategy (e‐STLB) to address this concern. The approach employs threshold values to trigger task migration between VMs, ensuring optimal performance while maximizing energy efficiency. The proposed strategy significantly reduces Makespan and increases Resource Utilization in an energy‐conscious manner. Experimental evaluations using Cloudsim 3.0 demonstrate that the e‐STLB outperforms other state‐of‐the‐art solutions, offering a compelling approach to sustainable cloud computing. [ABSTRACT FROM AUTHOR]

Summary: The continuous growth of wireless connectivity and the emergence of the concept of the Internet of Everything in the future sixth‐generation (6G) network require a new communication paradigm. Different from the prior works that focus on performance optimization for communication networks, in this paper, we attempt to analyze the energy efficiency of unmanned aerial vehicles (UAVs) communication aided by reconfigurable intelligent surfaces (RIS) which is a new disruptive technology, for the reason that UAV has the line‐of‐sight (LoS) link transmission and flexibility and RIS has the low power for improving the communication reliability and network coverage. At firstly, when central limit theorem (CLT) assumption is used and the number of RIS reflecting elements is large, we derive the closed‐form expression of the average signal‐to‐interference‐plus‐noise ratio (SINR) of each user based on the new BS‐RIS‐user communication link and RIS‐user/BS association. Following this, we further gain the energy efficiency (EE) in closed‐form based on random access between BS and user. Finally, we further show the performance of EE under certain assumptions. In particular, the offered results demonstrate that the use of the RIS can significantly improve EE due to RIS assist in improving the SINR and has very low transmission power; meanwhile, the UAV equipped with RIS can also improve EE for the reason that RIS can be used as a mobile relay to take advantage of the LoS link transmission. [ABSTRACT FROM AUTHOR]

Summary: Providing the appropriate coverage is quite essential for the effective functioning of many applications in Wireless Sensor Networks. Therefore, the efficiency of node deployment algorithms to supply the requested coverage is of high significance. In this paper, inspired by the equilibrium of molecules, a novel node deployment algorithm, called Smart Self‐organising Node Deployment (SSND), is proposed to provide maximum coverage. Despite other proposed algorithms, which provide coverage based on the collective movement of nodes with massive energy consumption, SSND moves one sensor in every neighbourhood at each step to reduce the sensor nodes' movement and hence the energy consumption. The chosen sensor nodes at each time step are distributedly determined by an eligibility function to reduce the non‐essential movements while improving the accuracy of the reported locations of neighbours. Our extensive simulation study shows that SSND can achieve up to 30% coverage improvement compared to those of other algorithms in most scenarios and provides an adequate trade‐off between coverage and energy consumption. [ABSTRACT FROM AUTHOR]

Summary: Wireless visual sensor networks (WVSN) have vital roles in surveillance applications. In these networks, wireless visual sensors include camera and transceiver module and collect visual information. However, energy consumption and coverage of the tracked targets are important challenges in WVSNs since increasing the coverage leads to increasing energy consumption. Therefore, energy optimization and satisfying the quality of experience (QoE) of the tracked targets are essential issues in these networks. In fact, the appropriate focal length setting leads to increasing the target coverage and quality of the captured targets' images and energy consumption. Therefore, selection of the suitable visual sensors and setting their focal length can overcome the energy consumption challenges and improve QoE of the tracked targets. In this case, compressive sensing is also expected to overcome the battery constraints of the WVSN resources. In this paper, the problem is to minimize the energy consumption of the multi‐target tracking with high reliability, while the coverage and also the quality of the received image of the targets are satisfied by selection of the proper visual sensors and the focal length adjustment. The convex optimization method is used to solve the problem. Also, based on the Karush‐Kuhn‐Tucker conditions, the optimal solution for the problem is obtained. Simulation results validate the efficiency of the proposed method in comparison with the other bench mark algorithms. [ABSTRACT FROM AUTHOR]

Summary: Convolutional neural network (CNN) inference usually runs on high‐performance graphic processing units (GPUs). Since GPU is a high power consumption unit, that makes the energy consumption increases sharply due to the deep learning tasks. The energy efficiency of CNN inference is not only related to the software and hardware configurations, but also closely related to the application requirements of inference tasks. However, it is not clear on GPUs at present. In this paper, we conduct a comprehensive study on the model‐level and layer‐level energy efficiency of popular CNN models. The results point out several opportunities for further optimization. We also analyze the parameter settings (i.e., batch size, dynamic voltage and frequency scaling) and propose a revenue model to allow an optimal trade‐off between energy efficiency and latency. Compared with the default settings, the optimal settings can improve revenue by up to 15.31×. We obtain the following main findings: (i) GPUs do not exploit the parallelism from the model depth and small convolution kernels, resulting in low energy efficiency. (ii) Convolutional layers are the most energy‐consuming CNN layers. However, due to the cache, the power consumption of all layers is relatively balanced. (iii) The energy efficiency of TensorRT is 1.53× than that of TensorFlow. [ABSTRACT FROM AUTHOR]

The recent liberalization of the energy industry provides a challenge for understanding the decision-making processes of the agents involved in their markets. In this new environment, it is of particular interest to investigate consumers' decisions with respect to energy efficiency for a range of purposes that include the assessment of government policy and traders' strategy intents. The neo-classic methodologies reported in the literature generally make strong, and not evident, assumptions with respect to the decision-making processes of end-users, including: complete information, full rationality and lack of risk perception. In this paper, the concept of bounded rationality is incorporated, seeking alternative grounds to the traditional approaches. Against this background, we examine different sets of assumptions based on the concept of rationally bounded energy use and propose policies that may reduce some of the apparent market failures. However, both these claims, as well as the proposed system dynamics models that intend to account for bounded rationality, need to be clearly justified. Here, we do this by indicating, explicitly and implicitly, how bounded rationality operates and what policies and strategies might be appropriate to remove some of the consumers' barriers when confronting decisions. The system dynamics models presented in this paper incorporate consumers' behaviour and alternative policies to assess their likely impact on society. Examples related to the penetration of fuels and lighting appliances are provided, to illustrate the approach. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The Paris Agreement goals require net‐zero CO2 emissions by mid‐century. The European Commission in its recent proposal for climate and energy strategy for 2050 indicated the need for more intensified actions to substantially improve the energy performances of buildings. With the rate of new construction in Europe, the challenge is to increase both the pace and depth of building energy renovations. Several barriers inhibit the wide uptake of comprehensive energy renovations, including the inability or inertia to finance upfront costs of energy renovations. Despite various policies implemented to address some of these barriers, current investments in buildings remain at suboptimal levels. The paper reviews current financing practices for energy renovations and investigates some innovative instruments with a special focus on their applicability to residential buildings. In addition to "traditional" financial schemes such as subsidies, tax incentives, and loans, the paper assesses innovative financing schemes: On property tax and on‐bill financing, energy efficiency mortgages, and energy efficiency feed‐in tariffs. The paper also investigates the concept of one‐stop shops for building renovations and crowdfunding. The paper offers an assessment of the characteristics, benefits, and challenges of each analyzed financing instrument and provides policy recommendations for their successful implementation. In general, as financing instruments involve different stakeholders and due to complex nature of the sector, there is no single solution to accelerate energy renovation investment in buildings. The emerging financial models offer the potential to address the long‐standing barriers to investment in energy efficiency. This article is categorized under:Energy Efficiency > Economics and PolicyEnergy Efficiency > Climate and EnvironmentEnergy and Climate > Economics and Policy [ABSTRACT FROM AUTHOR]

Summary: The energy efficiency sustenance in wireless sensor networks completely depends on the process of clustering to prolong network lifetime. The success of this clustering process completely depends on the cluster head (CH) selection process as potential CH selection prevents frequent clustering and aids in maintaining maximized energy in the network. In this paper, an improved Game Theory‐Gray Wolf Optimization algorithm (GAGWO)‐based efficient CH selection strategy is improved for maintaining the energy in the network through the construction of equal size clusters. This GWOA mimicked the hunting and attacking characteristics of gray wolves and facilitated better CH selection, which completely prevented worst sensor nodes from being selected as CH. It is proposed for establishing a better trade‐off between the local and global search for the objective of preventing re‐clustering that unnecessarily drains network energy. The simulation results of the proposed GAGWO confirmed better performance in terms of network lifetime, energy stability, mean throughput, and packet delivery ratio, on an average by 23.18%, 21.86%, 19.84%, and 20.98%, compared to the baseline CH approaches used for investigation. [ABSTRACT FROM AUTHOR]

This paper focuses on a mobile edge‐computing‐enabled heterogeneous network. A battery level‐aware task‐scheduling framework is proposed to improve the energy efficiency and prolong the operating hours of battery‐powered mobile devices. The formulated optimization problem is a typical mixed‐integer nonlinear programming problem. To solve this nondeterministic polynomial (NP)‐hard problem, a decomposition‐based task‐scheduling algorithm is proposed. Using an alternating optimization technology, the original problem is divided into three subproblems. In the outer loop, task offloading decisions are yielded using a pruning search algorithm for the task offloading subproblem. In the inner loop, closed‐form solutions for computational resource allocation subproblems are derived using the Lagrangian multiplier method. Then, it is proven that the transmitted power‐allocation subproblem is a unimodal problem; this subproblem is solved using a gradient‐based bisection search algorithm. The simulation results demonstrate that the proposed framework achieves better energy efficiency than other frameworks. Additionally, the impact of the battery level‐aware scheme on the operating hours of battery‐powered mobile devices is also investigated. [ABSTRACT FROM AUTHOR]

Summary: Heterogeneous networks (HetNets) have been a trending topic of interest for researchers in 5G technology. A HetNet structure comprises a macro cell network assisted by small cell networks such as pico cells and femto cells. This additional hardware ensures distribution of the user equipment (UE) load of the main base station (MBS) at the cost of a surge in the overall system power consumption. Optimized power consumption coupled with enhanced cell throughput by dynamic small cell ON/OFF strategy improves the energy efficiency (EE) in dense HetNets. This paper proposes two algorithms to switch the small cell ON/OFF based on cell throughput contribution rate (CTCR). CTCR is the ratio of actual cell throughput to the maximum cell throughput with full utilization of the allotted bandwidth. In the first method, the threshold to decide small cell ON/OFF has been carefully defined considering two important factors—the MBS‐SBS distance and the ratio of small cell density to UE density such that less loaded small cells that are closer to the MBS are the candidates chosen for sleep mode. In the second method, a correction factor in the computation of CTCR is introduced. It is a logarithmic function of the relative distance of the small base station (SBS) to the radius of macro cell coverage. This steps up the threshold for SBS that are close to MBS. They are more suitable sleep state candidates as their UEs can be served directly by the MBS and enable large user throughput. Simulation results show that the EE of the proposed amended CTCR method is 8% more than proposed CTCR method and 30.66% better relative to conventional load‐based sleep control method. [ABSTRACT FROM AUTHOR]

Summary: In 5G cloud computing, the most notable and considered design issues are the energy efficiency and delay. The majority of the recent studies were dedicated to optimizing the delay issue by leveraging the edge computing concept, while other studies directed its efforts towards realizing a green cloud by minimizing the energy consumption in the cloud. Active queue management‐based green cloud model (AGCM) as one of the recent green cloud models reduced the delay and energy consumption while maintaining a reliable throughput. Multiaccess edge computing (MEC) was established as a model for the edge computing concept and achieved remarkable enhancement to the delay issue. In this paper, we present a handoff scenario between the two cloud models, AGCM and MEC, to acquire the potential gain of such collaboration and investigate its impact on the cloud fundamental constraints; energy consumption, delay, and throughput. We examined our proposed model with simulation showing great enhancement for the delay, energy consumption, and throughput over either model when employed separately. [ABSTRACT FROM AUTHOR]

Summary: To design a reliable and energy efficient medium access control (MAC) protocol for underwater wireless sensor networks (UWSNs) is an active research area due to its variety of applications. There are many issues associated with underwater acoustic channels including long and variable propagation delay, attenuation, and limited bandwidth which pose significant challenges in the design of MAC protocol. The available sender‐initiated asynchronous preamble‐based MAC protocols for UWSNs are not reliable and energy‐efficient. This is due to the problems caused by transmission of preambles for longer duration and collision of preambles from hidden nodes in sender‐initiated preamble‐based MAC protocols. To resolve these issues, the paper proposed an asynchronous receiver‐initiated preamble‐based MAC protocol named Receiver Preambling with Channel Polling MAC (RPCP‐MAC) protocol for shallow underwater monitoring applications with high data rates. The protocol is proposed to resolve data packet collision and support reliability in an energy‐efficient way without using any transmission schedule. The proposed protocol is based on the following mechanisms. Firstly, receiver preambling mechanism is adopted to reduce idle listening. Secondly, channel polling mechanism is used to determine missing data frame during its sleeping period and to minimize the active time of node and reduces energy wastage. Finally, a back‐off mechanism is applied to resolve collision when preambles are received simultaneously. In addition, performance analysis through Markov chain together with its validation with simulation‐based studies is reported in the paper. Both the analytical and simulation results have demonstrated the reliability achievable with RPCP‐MAC while providing good energy efficiency. [ABSTRACT FROM AUTHOR]

Summary: Burst is a common pattern in the user's requirements, which suddenly increases the workload of virtual machines (VMs) and reduces the performance and energy efficiency of cloud computing systems (CCS). Virtualization technology with the ability to migrate VMs attempts to solve this problem. By migration, VMs can be dynamically consolidated to the users' requests. Burst temporarily increases the workload. Ignoring this issue will lead to incorrect decisions regarding the migration of VMs. It increases the number of migrations and Service Level Agreement Violations (SLAVs) due to overload. This may cause waste of resources, increase in energy consumption, and misplaced VMs. Therefore, a burst‐aware method for these issues is proposed in this paper. The method consists of two algorithms: one for determining the migration time and the other for the placement of VMs. We use the PlanetLab real dataset and CloudSim simulator to evaluate the performance of the proposed method. The results confirm the advantages of the method regarding performance compared to benchmark methods. [ABSTRACT FROM AUTHOR]

Summary: Indoor and cell‐edge coverage has been a major issue of concern for predeployed traditional macrocell (MC)–based homogeneous cellular network. Moreover, with the extensive increase of mobile users and developments of smart and highly specified devices, user demands and activities have led to huge cellular traffic. The key solutions to these that include network upgradation, overlaying of small cells (SCs), and scaling of resources have turned out to be the major causes for intercell interference (ICI) and energy‐efficiency degradation in heterogeneous cellular networks (HetCNs). In this paper, authors have tried to analyze the downlink performance metrics of cell boundary users with MCs overlaying SCs for three‐cell circular and seven‐cell sectorized networks through frequency reuse (FR) schemes. This paper also discusses the impact of ICI being encountered by users and the effect of SCs on the energy efficiency of the network. The locations for SCs are perceived where user density is large and demands high data rate such as at hot‐spot (HS) areas, railway stations, shopping malls, working farms, and organization. The performance metrics sum rate, average user throughput, and energy efficiency are compared by employing FR‐1 (full spectrum) and FR‐3 (three subbands) among MCs and deployed SCs. For both scenarios, simulation results and analyses depict that without SCs, utilization of FR‐1 results in performance degradation due to ICI effects. However, the downlink performance of cell boundary user and energy efficiency of the network could be enhanced by overlaying SCs near cell boundaries of preexisting MCs along with the allocation of FR‐1. [ABSTRACT FROM AUTHOR]

Summary: The development of the wireless sensor networks (WSN) being deployed among numerous application for its sensing capabilities is increasing at a very fast tread. Its distributed nature and ability to extend communication even to the inaccessible areas beyond communication range that lacks human intervention has made it even more attractive in a wide space of applications. Confined with numerous sensing nodes distributed over a wide area, the WSN incurs certain limitations as it is battery powered. Many developed routing enhancements with power and energy efficiency lacked in achieving the significant improvement in the performance. So, the paper proposes a machine learning system (capsule network) and technique (data pruning) for WSN involved in the real world observations to have knowledge‐based learning from the experience for an intelligent way of handling the dynamic and real environment without the intervention of the humans. The WSN cluster‐based routing aided with capsule network and data pruning proffered in paper enables the WSN to have a prolonged network lifetime, energy efficiency, minimized delay, and enhanced throughput by reducing the energy usage and extending communication within the limited battery availability. The proposed system is validated in the network simulator and compared with the WSN without ML to check for the performance enhancements of the WSN with ML inclusions in terms of quality of service enhancements, network lifetime, packet delivery ratio, and energy to evince the efficacy of the WSN with capsule network‐based data pruning. [ABSTRACT FROM AUTHOR]

Summary: An unmanned aerial vehicle (UAV)‐assisted communication is a promising technique for enabling ground users in a non‐functional area (NFA) or disaster area to communicate. All the base stations in the disaster area may be partially or fully damaged due to the natural calamity. Device‐to‐device (D2D) communication can be a viable solution for immediate connection between users in an NFA. We propose a UAV‐assisted multi‐hop D2D communication following a hybrid power‐time switching (PTS) in this paper. Moreover, a D2D user of a cluster can communicate with another D2D user in a different cluster through UAVs. However, D2D users can harvest energy from their respective ad hoc energy stations and forward the information to the nearby D2D user following a hybrid PTS‐based strategy. We propose a time frame for the same and show a node‐based energy harvesting strategy. The expressions of outage probability, throughput, end‐to‐end energy consumption, and energy efficiency are developed for the Rician and Nakagami‐m faded channel. The impact of several network parameters such as energy harvesting factor, energy harvesting efficiency, and fading parameter on the network performance is also indicated. [ABSTRACT FROM AUTHOR]

Determining the ripening level of bananas in the environment of banana ripening plays an important role in ensuring the quality of bananas, reducing losses and waste, and improving economic benefits. Traditional monitoring methods face a series of challenges such as inefficiency, high energy consumption, and high cost. This paper aims to develop a flexible wireless in situ optical sensing system (FIS) for banana ripening monitoring with a good prediction effect, low energy consumption, and high economy. When monitoring the ripening process, the ripeness of the bananas can be accurately determined without taking out the bananas. Among them, the prediction effect on soluble solid content and a* is the best, the residual predictive deviation value is above 3, and the classification accuracy is as high as 95%. Compared to other commercially available spectrometers, the device exhibits the lowest power consumption characteristics. The theoretical maximum energy consumption of the FIS is only 627 mW, and a single measurement only consumes 15 J. The overall price of the FIS does not exceed $60. The application of the FIS can effectively reduce the waste of bananas during the ripening process, and greatly alleviate the labor‐intensive and inefficient problems of fruit maturity monitoring, thereby promoting more sustainable and cleaner production in the banana industry. [ABSTRACT FROM AUTHOR]

Summary: Small cell networks can fulfill the increasing demandfor the high data rate of wireless applications. Energy efficiency is an important design parameter of the ultra dense small cell network (UDSCN). The sleeping strategy of small base stations (s‐BSs) is used to enhance the network's energy efficiency. An efficient sleeping strategy of s‐BSs is required while preserving users' quality of service (QoS). The idle s‐BSs can be switched to sleep mode. This paper proposes a blockchain‐enabled solution for the sleeping strategy of s‐BSs. Here, a blockchain‐enabled small cell network is created between the s‐BSs. The network is decentralized, which eliminates the workload of the macro base station (MBS). The proposed network architecture is enabled as a decentralized network through blockchain. The blockchain provides distributed control over the s‐BS operations through a smart contract. Here, smart contracts act as distributed self organizing network features to handle self‐transactions among small cells for switching off s‐BSs in the network. All the software logic required to perform s‐BS operations is written in a smart contract using Ethereum. The proposed solution improves energy efficiency and enables the ultra dense small cell network to be decentralized. [ABSTRACT FROM AUTHOR]

Summary: Deepwater utilization and underwater sound communication with inadequate bandwidth, elongated propagation interruption, signal failure issues, and sensor node malfunction due to ecological circumstances are the limitations of the underwater wireless sensor network. Further, the challenges in underground wireless sensor network (WSN) include superior bandwidth, high power expenses, information handling, and cross‐layer design. On the other hand, cooperative communications facilitate competent employment of communication resources, by permitting nodes in a network to work together in information communication. As wireless sensor networks adapt to multi‐hop transmission in high data traffic, there is a high demand for achieving energy efficiency. Moreover, in the course of proximity nodes to the sink, an energy hole is started for the extended network lifetime. This paper proposes the design of multi‐hoped cooperative communication‐based wireless underground sensor networks that guarantees energy efficiency in a hostile environment. In addition, network coverage and connectivity improvement are also improved by this proposed design. [ABSTRACT FROM AUTHOR]

Summary: In heterogeneous networks (HetNets), sharply increasing data traffic exacerbates the rapid growth in the network energy consumption and at the same time brings tremendous pressure on the backhaul links. Aiming at improving the network energy efficiency (EE) and relieving backhaul data volume traffic pressure, in this paper, we investigate energy efficient resource allocation which restricts small cell data traffic by imposing different kinds of backhaul capacity constraints (BCCs) on small cells, including small cell BCC (SBCC) and macrocell BCC (MBCC). We formulate the joint resource block (RB) and power allocation problem as a fractional programming problem. By using variable relaxation and replacement, we convert this nonconvex optimization problem into a concave–convex fractional programming problem, which can further be converted into a parametric convex problem and solved by convex optimization theory. Based on the Dinkelbach and dual decomposition methods, we propose iterative resource allocation algorithms under different BCCs, where RB and power are jointly allocated among users in each cell. Numerical results reveal that the proposed algorithms have good convergence performance and also evaluates the impact of both SBCC and MBCC on network performance. [ABSTRACT FROM AUTHOR]

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Artificial neural networks and image processing are the error tolerant applications that require massive workloads to be executed within certain power limits. Energy efficient multipliers on an embedded processor are crucial for such applications. In this paper, a highly diminished yet accurate design of an approximate truncated multiplier is presented. A compact error compensation circuit is presented that aims to mitigate the truncation error. To reduce the energy consumption further, the voltage overscaling method is used. The performance of the circuit is optimized by adjusting the two approximation knobs of circuit pruning and voltage scaling. Simulation results for a truncation factor of 8 with an MRED of 0.01 demonstrate an 11.4% reduction in energy consumption compared to that of the 16‐bit radix‐4 exact multiplier. Application based evaluation is performed by using the approximate multipliers on image multiplication application and a high value of PSNR (43.17 dB) is obtained. When evaluated using a handwritten digit recognition application based on convolutional neural networks (CNN), an accuracy of 97.85% is obtained. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) consist of nodes. Issues involved with the use of a sensor network are energy‐saving and the effective use of energy. Clustering in WSNs is a proven technique for energy optimization. The relationship between sensor nodes and cluster heads (CHs) has only been considered prior to cluster‐based routing protocols. However, most clustering algorithms have failed to address the routing overhead and the energy consumption rate between CH nodes and SINK. In this paper, a new framework named as EEC‐MA‐PSOGA ‐ an energy‐efficient (EE) intra‐cluster mobile agent (INC‐MA)‐based particle swarm optimization–genetic algorithm (PSO‐GA), has been presented to initiate the distance communication and place the SINK optimally in WSNs. The cluster members send the collected data towards their respective CHs for aggregation. To find the sink's best position, the PSO‐GA‐based location estimation algorithm is initiated based on the network structure. The limited capability of WSNs makes them more vulnerable to attackers. The prevention mechanism must be less complex to ensure the fair operations in the network. Here, the attack detection ability of the proposed solution has been tested against clone attack. With varied communication range, simulation time, and sensor nodes, the tests are conducted extensively on various scenarios of WSNs. When compared with the prior mechanisms, the framework provides better results and better prevention with less overhead by analyzing the experimental results such as energy consumption, network lifetime, and throughput. [ABSTRACT FROM AUTHOR]

ABSTRACT The interaction between product innovation and companies' activities aimed at improving the energy efficiency of production facilities has been relatively little studied, but is of great relevance to society and companies given the strong focus of governments on grand challenges like climate change, green innovation technologies, and environmental problems in general. This paper utilizes the 2009 European Manufacturing Survey for the Danish sub-sample including 335 manufacturing firms. Through factor analysis, this paper confirms three main areas of focus of new product development: efficiency considerations, market attention, and greening of innovation. Logistic regression analysis demonstrates that while market attention is important for the development of new products, green aspects of innovation and efficiency considerations are important for production companies wanting to improve their energy efficiency. When these models are combined, the results highlight that energy efficiency moderates the effect of market attention to new product development. This paper therefore finds that aligning product innovation and energy efficiency is a complex and intertwined process - focusing on one may have indirect detrimental effects on the other. These results point to the conclusion that researchers and practitioners in innovation management have to consider more carefully the specificities and interactions of different types of products and process innovations and their environmental implications, and must formulate new, more sustainable managerial practices combining energy efficiency and product innovation. Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment [ABSTRACT FROM AUTHOR]

Large‐scale trade reforms in developing countries over the past decades, while boosting economic growth, have raised concerns about deteriorating environmental quality. This paper examines whether participating in export markets affects a firm's energy and emission intensity. Using Vietnamese manufacturing firm‐level data over the period 2009–2014, this study finds that upon entering into the export markets, firms reduce their energy intensity by 27%. Furthermore, along with this energy efficiency gain, there is a decrease in emission intensity by roughly 31%–32%. Moreover, these reductions persist in subsequent periods. A possible mechanism for this is that strict requirements for product quality from buyers in high‐income regions drive firms to use higher‐quality inputs and/or invest in cleaner technology. [ABSTRACT FROM AUTHOR]