Energy Efficiency Considerations Research Articles

Crossover Boosted Grey Wolf Optimizer‐based framework for leader election and resource allocation in Intrusion Detection Systems for MANETs

AbstractMobile Ad hoc Networks (MANETs) is a self‐organizing networks without having a fixed infrastructure for making them susceptible to security threats. Intrusion Detection Systems (IDS) promotes security in MANETs by identifying malicious activities. Leader election is a fundamental aspect of IDS deployment, impacting resource allocation and system efficiency. This article presents a novel approach, the Crossover Boosted Grey Wolf Optimizer (CBGWO), for leader election and resource allocation in MANET‐based IDS. The proposed CBGWO algorithm integrates the Grey Wolf Optimizer (GWO) with innovative crossover operators that have an ability to enhance the capabilities of exploration and exploitation in the optimization process. The leader election problem is solved through applying multi‐objective optimization by considering energy consumption, reputation, and communication overhead. Objective functions are defined to maximize energy efficiency while maintaining a balanced distribution of leadership roles. Extensive simulations are conducted, varying network densities and the percentage of selfish nodes. Results demonstrate the effectiveness of the CBGWO‐based model in balancing energy consumption, prolonging network lifespan, and enhancing intrusion detection by comparing different state‐of‐the‐art models such as PCA‐FELM, CTAA‐MPSO, FLS‐RE, LEACH, DCAIDS, WOA‐GA, and VOELA. The proposed model achieved an energy consumption of 4.31 J, network lifetime of 560.482 ms, and average intrusion detection latency of 0.12 s, respectively. The proposed model outperforms than existing random and connectivity‐based leader election methods that is evaluated by taking main consideration of energy efficiency and network survivability. This research contributes to the field by introducing a robust algorithm for leader election in MANET‐based IDS, addressing challenges posed by network dynamics and resource constraints. The CBGWO‐based approach showcases its potential to achieve effective leader election and efficient resource allocation, thereby enhancing the security and sustainability of MANETs.

Envelope retrofits for enhancing thermal comfort in detached houses in hot semi-arid climatic conditions: A year-long monitoring and simulation study

Retrofitting residential buildings could substantially reduce the sector's energy and carbon footprint and provide significant economic benefits in the long run. Building retrofits must be urgently developed and promoted in India, where most residences are constructed without any consideration of energy efficiency due to the lack of a mandatory residential energy code. However, building retrofits must be devised considering the local construction practices and climatic conditions since an improper retrofit can deteriorate the indoor environment and even increase the building's energy consumption. Therefore, through year-long monitoring and simulations of the comfort conditions of a room inside a detached single-story house, this investigation evaluated different envelope retrofit options for improving it in India's hot semi-arid climate.The results showed that the room's indoor environment (without the retrofit) was comfortable for only 26–43 % of the year, depending on the room's configuration, with the roof being the major contributor to the heat gain and the floor and external walls largely contributing to heat losses. The comfort duration increased by 6–21 percentage points annually by applying cool/super-cool roof paints since they significantly reduce the roof's heat gain. Similar comfort enhancements could also be obtained by insulating the roof or the entire envelope, mainly due to reduced roof heat gains. However, insulating the walls alone deteriorated the comfort conditions since it became difficult for the heat to escape the room. Overall, applying cool/super-cool roof paints was recommended over other studied options since they provide substantial comfort benefits with low cost and easy implementation.

A new tool for energy conservation in operating room: The role of machine learning models in enhancing airflow control

Maintaining high ventilation rates in operating rooms (ORs) is critical to ensure the safety of healthcare professionals and patients. Yet, the significant energy consumption associated with ventilation systems is a growing global concern. While traditional approaches have predominantly focused on optimizing ventilation design, limited attention has been given to operational control strategies. This study aims to control ceiling air supply speed by exposure aerosol concentrations exhaled by the patient. It achieves this by integrating empirical data with machine learning (ML) models, effectively balancing the need for maintaining optimal ventilation conditions and ensuring a safe aerosol exposure concentration. Experimental results from a full-scale OR setup, manipulating ceiling air supply speed, respiratory flow rate, and surgical bed height, generated 81 datasets. These datasets served as the basis for assessing the performance of 11 ML models. Notably, the extra trees (ET) and multi-layer perceptron (MLP) models outperformed the others, exhibiting impressive coefficient of determination (R2) values of 0.954 and 0.904, respectively. Moreover, the models were further enhanced by assessing their robustness with Gaussian noise and interval prediction. This study presents a novel approach that holds significant relevance for the practical control of ventilation systems, particularly with regard to health and energy efficiency considerations.

Role of Industry 5.0 for driving sustainability in the manufacturing sector: an emerging research agenda

PurposeWith ever-increasing global concerns over environmental degradation and resource scarcity, the need for sustainable manufacturing (SM) practices has become paramount. Industry 5.0 (I5.0), the latest paradigm in the industrial revolution, emphasizes the integration of advanced technologies with human capabilities to achieve sustainable and socially responsible production systems. This paper aims to provide a comprehensive analysis of the role of I5.0 in enabling SM. Furthermore, the review discusses the integration of sustainable practices into the core of I5.0.Design/methodology/approachThe systematic literature review (SLR) method is adopted to: explore the understanding of I5.0 and SM; understand the role of I5.0 in addressing sustainability challenges, including resource optimization, waste reduction, energy efficiency and ethical considerations and propose a framework for effective implementation of the I5.0 concept in manufacturing enterprises.FindingsThe concept of I5.0 represents a progressive step forward from previous industrial revolutions, emphasizing the integration of advanced technologies with a focus on sustainability. I5.0 offers opportunities to optimize resource usage and minimize environmental impact. Through the integration of automation, artificial intelligence (AI) and big data analytics (BDA), manufacturers can enhance process efficiency, reduce waste and implement proactive sustainability measures. By embracing I5.0 and incorporating SM practices, industries can move towards a more resource-efficient, environmentally friendly and socially responsible manufacturing paradigm.Research limitations/implicationsThe findings presented in this article have several implications including the changing role of the workforce, skills requirements and the need for ethical considerations for SM, highlighting the need for interdisciplinary collaborations, policy support and stakeholder engagement to realize its full potential.Originality/valueThis article aims to stand on an unbiased assessment to ascertain the landscape occupied by the role of I5.0 in driving sustainability in the manufacturing sector. In addition, the proposed framework will serve as a basis for the effective implementation of I5.0 for SM.

Improving efficiency of DNN-based relocalization module for autonomous driving with server-side computing

The substantial computational demands associated with Deep Neural Network (DNN)-based camera relocalization during the reasoning process impede their integration into autonomous vehicles. Cost and energy efficiency considerations may dissuade automotive manufacturers from employing high-computing equipment, limiting the adoption of advanced models. In response to this challenge, we present an innovative edge cloud collaborative framework designed for camera relocalization in autonomous vehicles. Specifically, we strategically offload certain modules of the neural network to the server and evaluate the inference time of data frames under different network segmentation schemes to guide our offloading decisions. Our findings highlight the vital role of server-side offloading in DNN-based camera relocation for autonomous vehicles, and we also discuss the results of data fusion. Finally, we validate the effectiveness of our proposed framework through experimental evaluation.

Energy efficiency considerations in software‐defined wireless body area networks

AbstractWireless body area networks (WBAN) provide remote services for patient monitoring which allows healthcare practitioners to diagnose, monitor, and prescribe them without their physical presence. To address the shortcomings of WBAN, software‐defined networking (SDN) is regarded as an effective approach in this prototype. However, integrating SDN into WBAN presents several challenges in terms of safe data exchange, architectural framework, and resource efficiency. Because energy expenses account for a considerable portion of network expenditures, energy efficiency has to turn out to be a crucial design criterion for modern networking methods. However, creating energy‐efficient systems is difficult because they must balance energy efficiency with network performance. In this article, the energy efficiency features are discussed that can widely be used in the software‐defined wireless body area network (SDWBAN). A comprehensive survey has been carried out for various modern energy efficiency models based on routing algorithms, optimization models, secure data delivery, and traffic management. A comparative assessment of all the models has also been carried out for various parameters. Furthermore, we explore important concerns and future work in SDWBAN energy efficiency.

An Ultra-Energy-Efficient Reversible Quantum-Dot Cellular Automata 8:1 Multiplexer Circuit

Energy efficiency considerations in terms of reduced power dissipation are a significant issue in the design of digital circuits for very large-scale integration (VLSI) systems. Quantum-dot cellular automata (QCA) is an emerging ultralow power dissipation approach, distinct from traditional, complementary metal-oxide semiconductor (CMOS) technology, for building digital computing circuits. Developing fully reversible QCA circuits has the potential to significantly reduce energy dissipation. Multiplexers are fundamental elements in the construction of useful digital circuits. In this paper, a novel, multilayer, fully reversible QCA 8:1 multiplexer circuit with ultralow energy dissipation is introduced. The power dissipation of the proposed multiplexer is simulated using the QCADesigner-E version 2.2 tool, describing the microscopic physical mechanisms underlying the QCA operation. The results show that the proposed reversible QCA 8:1 multiplexer consumes 89% less energy than the most energy-efficient 8:1 multiplexer circuit previously presented in the literature.

A Study on DDoS Attack Detection Technique Prior to Machine Learning in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are integral to a wide range of critical applications, including environmental monitoring, healthcare, and industrial control systems. Then delves into DDoS attacks, elucidating the various attack types, motives, and their detrimental effects on WSNs. The core of the study investigates DDoS detection techniques, encompassing signature-based, anomaly-based, and hybrid approaches. Optimization mechanisms for DDoS detection in WSNs are explored in-depth, including resource-efficient algorithms and scalability solutions. Consideration is given to striking a balance between detection accuracy and resource consumption, essential in the energy-limited world of WSNs. Machine learning and artificial intelligence's role in DDoS detection is elucidated, with a focus on feature selection, dimensionality reduction, and the application of various supervised and unsupervised learning algorithms. Cross-layer approaches, coordinating physical, data-link, and network layers, are discussed to enhance DDoS detection's robustness against sophisticated attacks. Energy efficiency considerations are integral, with strategies outlined to prolong sensor nodes' lifespans while ensuring reliable DDoS detection. The study culminates in the presentation of real-world experiments and simulations, offering insights into the practical performance of DDoS detection and optimization mechanisms. Various evaluation metrics are employed, such as detection rate, false positives, and resource utilization.

Genetic Algorithm-Based Secure Routing Protocol for Wireless Sensor Networks

In a wireless sensor network (WSN), security threats are prevalent as a result of the network's distributed nature and resource limitations. This paper proposes a secure routing protocol for WSNs based on Genetic Algorithms (GA). The protocol utilizes GA to optimize secure route selection while considering network lifetime and energy efficiency. To showcase the efficiency of the suggested protocol, we present a case study, provide a detailed algorithmic representation, and evaluate its performance through extensive simulations. The results showcase the protocol's ability to enhance security and improve network performance, including increased packet delivery ratio, reduced energy consumption, and robustness against attacks. The proposed GA-based secure routing protocol offers a promising approach to address the unique security challenges of WSNs, contributing to the development of secure and efficient communication frameworks for various applications. Additionally, we highlight the significance of incorporating genetic algorithms into secure routing protocols as a means to optimize route selection in WSNs. By leveraging the evolutionary nature of genetic algorithms, our proposed protocol adapts to dynamic network conditions and effectively balances security requirements with energy efficiency and network lifetime considerations. The case study, algorithmic representation, and comprehensive simulation results validate the protocol's ability to enhance security while maintaining robust network performance. This study contributes to the advancement of secure routing in WSNs, offering a viable solution to mitigate security threats and ensure reliable communication in resource-constrained environments.

Calculating the optimum window-to-wall ratio according to daylight factor and thermal performance in Mediterranean climate

Energy is a significant part of socio-economic development of modern societies. Increasing fossil fuel consumption is almost the main source of energy throughout the world. Thus, it is essential to search for more sustainable alternatives or a method to decrease this huge amount of usage. On the other hand, the building industry is known as one of the biggest energy consumers. Among building energy efficiency measures, openings are playing a key role in declining energy consumption, especially in the hot summers of Cyprus. Therefore, this study carries out a field measurement of various opening sizes in the case study in order to make a satisfactory situation both from energy efficiency and visual comfort considerations. Meanwhile, a studio in a faculty of architecture, Famagusta, North Cyprus, has been selected as an experimental examination to show the effectiveness of the method. In order to analyze its energy performance, the calculation simplified method is chosen. Outcomes are intended to illustrate the benefits of the calculation method and to authorize opening size comparisons to display the differences in energy conservation measures inherent in the various compliance methods allowable by the regulations. Finally, the results obviously display that by decreasing the window-to-wall ratio, the heat loss significantly reduces. But, by considering the daylight factor (DF) in the standard defined range and applying 750lux as an essential lighting requirement for the studio, finding the minimum WWR seems more meaningful. Therefore, according to the mentioned criteria in this special case, an optimum amount of WWR can be considered in the range of 10 to 20%, which gives architects some flexibility to apply in their designs.

Retracted: Energy Efficiency and Reliability Considerations in Wireless Body Area Networks: A Survey.

[This retracts the article DOI: 10.1155/2022/1090131.].

Technical, Economic, and Environmental Review of Waste to Energy Technologies from Municipal Solid Waste

Global municipal solid waste production and electricity demand gradually increased as a result of urbanization, population increase, and economic growth. The appropriate selection of Waste to energy (WTE) technologies needs consideration of energy efficiency, financial, and environmental aspects. This article discusses the technical, financial, and environmental side of existing WTE technologies. Waste-to-energy (WTE) technologies include thermal, physical, biochemical, and bio-electrochemical technology. Pyrolysis, gasification, and incineration are thermal technology used to generate energy from waste in the form of heat and syn-gas. Anaerobic digestion and landfill are biochemical technology to to generate energy from waste in the form of biogas. Physical technology is used to to generate energy from waste in the form of refuse-derived fuel (RDF). Microbial fuel cells (MFC) and microbial electrolysis cells (MEC) are the most recent WTE technology that produces electricity and hydrogen fuel. The results of the assessment of existing technology show that anaerobic digestion and landfill are low-cost WTE technology but have a low potential for energy generation. Plasma gasification is WTE technology with a high potential for energy generation, cold gas efficiency (CGE), carbon conversion efficiency (CCE), and H2/CO ratio, low CO2 emissions, and high operating costs. MEC has a high H2-potential for energy generation, low CO2 emissions, and the highest capital cost. Incineration is a common conversion technology with a low potential for energy generation, high CO2 emissions, and high capital costs. The selection of WTE technologies is influenced by technical, economic, and environmental factors. 

Economic model predictive control with terminal set dynamic programming for tracking control

AbstractThis paper focuses on vehicle tracking control issues with consideration of energy efficiency, ride comfort and tracking performance. To address this issue, an economic model predictive control (EMPC) framework is proposed by taking average performance into account and introducing a time‐varying terminal set. We integrate an economic model predictive controller and a terminal controller with a feedback gain related to the time‐varying terminal set into the proposed EMPC framework. An acceleration‐dependent average constraint is designed and incorporated into the MPC optimization problem to facilitate the convergence of the actual acceleration to the desired one, which enables the vehicle to implement a smooth driving style that boosts energy economy. A tuning factor in the average constraint has the capacity to balance energy consumption and control performance. Under the premise of invariance constraints, a dynamic terminal set is constructed by solving a dynamic programming problem online, which enables an arbitrary location and scale of the terminal set at each iteration by taking the current and predicted tracking errors into account. This allows a reduction in the conservativeness of an economic model predictive controller in the sense of both the region of attraction and the cost bound. Recursive feasibility of the MPC optimization problem and the terminal set dynamic programming is ensured, and average performance is not worse than the performance with an optimal steady‐state operation. Moreover, the convergence of the closed‐loop tracking error to the optimal steady‐state is guaranteed by using the dissipativity theory. The effectiveness of the proposed algorithm is verified by numerical comparisons with different controller parameters and standard EMPC.

Delay-Aware and Energy-Efficient Resource Allocation for Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surfaces (RISs) have emerged as one of the most promising techniques for future 6G wireless networks. However, the energy efficiency and the latency of RIS-based communications have not received enough attention. This letter proposes a novel framework that addresses this gap through the consideration of the effective energy efficiency (EEE) metric. EEE maximization is performed in a RIS-based multi-carrier link, with respect to the transmit powers and RIS phase shifts. Numerical results show that the proposed procedure improves the performance of the considered system.

Joint Optimization of Energy Conservation and Privacy Preservation for Intelligent Task Offloading in MEC-Enabled Smart Cities

Internet of Thing-based mobile devices (MDs) make the vision of smart cities become reality. Nevertheless, MDs are subjected to some shortcomings and cannot effectively handle the explosive growth of applications. Fortunately, the performance of MDs can be augmented by offloading latency-critical tasks to edge service providers (ESPs). Nevertheless, there is a competitive relationship among MDs as the resources of ESPs are limited. Moreover, there is a certain risk of privacy leakage during computation offloading. In view of this, we study the computation offloading and resource allocation which is formulated as a Stackelberg game with the aims of maximizing the utilities of MDs and the profits of ESPs under the consideration of energy efficiency by optimizing the strategies of prices, computation offloading and the privacy investment. Additionally, both the cooperation scenario and non-cooperation among ESPs are investigated. Besides, the social effect of MDs on privacy concerns is also considered. Technically, the Stackelberg equilibrium is solved by utilizing the distributed Alternating Direction Method of Multipliers algorithm in a distributed manner. Numerous simulation results have illustrated that the method is effective and also has fast convergence and high scalability.

Experimental study on reliable operation strategy for multi-split backplane cooling system in data centers

The composite air-conditioning system combined heat pipes and vapor compression, which can utilize natural cooling sources effectively, has been widely used in data centers recently. The common energy-saving strategy is to make the best use of the natural cooling sources according to outside ambient temperature. However, existing studies indicate that the cooling failure problem often occurs in the mechanical refrigeration mode due to incorrect control strategy, when the outdoor ambient temperature is lower and natural cooling capacity is insufficient. Thus, taking the multi-split backplane cooling system used in the data center as an object, the reason for the cooling failure under the condition of lower outside ambient temperature is investigated through theoretical analysis and experimental study. Results indicate that system energy consumption will decrease as condensing pressure decreases, but lower condensing pressure may cause insufficient refrigerant problems at the most unfavorable terminal. To solve the problem, a reliable operation control strategy based on variable condensing pressure is proposed and validated by experiments, and results indicate that a maximum of 12% energy consumption can be saved with the decrease of condensing pressure. Finally, the determination for condensing pressure setpoint is provided, and it is pointed out that how to determine optimal condensing pressure setpoint is worthy of further study in the consideration of energy efficiency and cooling effect meantime.

Environmental and Energy-Efficiency Considerations for Selecting Building Envelopes

Life cycle assessments in the construction industry reveal that 70–80% of all CO2 emissions occur precisely at the stage of material production (stages A1–A3 of the life cycle). Therefore, not only does the strength and thermal properties of the material selected for construction have major importance, but also the environmental impact of the material and the amount of energy spent to extract, manufacture and transport the materials. The paper presents the thermal calculations for envelope structures, assessing their environmental impact with respect to three parameters: carbon dioxide emissions, total energy consumption and amount of waste generated during material production. The research method used was an analysis of documents from real manufacturers and calculations of the main environmental parameters. Our investigation has led us to conclude that the largest amount of carbon dioxide emissions is produced by structures containing reinforced concrete, since the weight fraction of concrete is significantly greater than that of all other compared materials. The largest amount of non-renewable energy is consumed by structures containing clay bricks and reinforced concrete, since they consist of natural sources. The largest amount of waste is generated by structures containing expanded polystyrene and reinforced concrete consisting of cement, whose production in turn generates a large amount of waste.

Adaptive Real-Time Object Detection for Autonomous Driving Systems.

Accurate and reliable detection is one of the main tasks of Autonomous Driving Systems (ADS). While detecting the obstacles on the road during various environmental circumstances add to the reliability of ADS, it results in more intensive computations and more complicated systems. The stringent real-time requirements of ADS, resource constraints, and energy efficiency considerations add to the design complications. This work presents an adaptive system that detects pedestrians and vehicles in different lighting conditions on the road. We take a hardware-software co-design approach on Zynq UltraScale+ MPSoC and develop a dynamically reconfigurable ADS that employs hardware accelerators for pedestrian and vehicle detection and adapts its detection method to the environment lighting conditions. The results show that the system maintains real-time performance and achieves adaptability with minimal resource overhead.

Modelling Building Stock Energy Consumption at the Urban Level from an Empirical Study

Quantifying the energy consumption of buildings is a complex and multi-scale task, with the entire process dependent on input data and urban surroundings. However, most urban energy models do not account for the urban environment. This paper employs a physical-based, bottom-up method to predict urban building operating energy consumption, using imported topography to consider shading effects on buildings. This method has proven to be feasible and aligned well with the benchmark. Research also suggests that commercial and transport buildings have the highest energy use intensity, significantly more than residential and office buildings. Specifically, cooling demands far outweigh heating demands for these building types. Therefore, buildings in the commercial and transportation sectors would receive greater consideration for energy efficiency and improvements to the cooling system would be a priority. Additionally, the method developed for predicting building energy demand at an urban scale can also be replicated in practice.

The Tradeoff Power Distribution Strategy Based on the Genetic -Pareto Algorithm for a Dual-Motor Coupling-Propulsion Electric Vehicle

Abstract The dual-motor coupling-propulsion electric vehicle (DMCP-EV) has the merit of favorable energy efficiency due to its multiple operation modes. In the driving process, the performance of the DMCP-EV depends greatly on the power distribution to meet various driving requests. A power distribution coefficient is optimized by using the genetic-Pareto algorithm (GA-Pareto) algorithm. In addition to this effort, a power distribution strategy based on tradeoff optimization with comprehensive consideration of energy efficiency and ride comfort is proposed. Then, the sensitive analysis of optimized performance is conducted to obtain the three appropriate GA-Pareto algorithm parameters. The benchmark strategies are conducted to validate the proposed tradeoff power distribution strategy and the comparisons of three preference strategies with the hardware-in-loop (HIL) validation are performed. Results of the simulation and HIL experiments demonstrate that the proposed control strategies can address the tradeoff between energy-efficient and ride comfort for DMCP-EV.