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Recently, the use of uncrewed aerial vehicles (UAVs) in joint sensing and communication applications has received a lot of attention. However, integrating UAVs in current cellular systems presents major challenges related to trajectory optimization and interference management among others. This paper considers a multi-cell network including a UAV, which senses and forwards the sensory data from different events to the central base station. Particularly, the current manuscript covers how to design the UAV's ({i} ) 3D trajectory, (ii) power allocation, and (iii) sensing scheduling such that (a) a set of events are sensed, (b) interference to neighboring cells is kept at bay, and (c) the amount of energy required by the UAV is minimized. The resulting nonconvex optimization problem is tackled through a combination of ({i} ) low-complexity binary optimization, (ii) successive convex approximation, and (iii) the Lagrangian method. Simulation results over a range of various key parameters have shown the merits of our approach, which consumes 33%-200% less energy compared to different benchmarks.

The uptake of low-carbon domestic heating systems is a significant strategy towards global targets of reducing greenhouse emissions and mitigating climate change. Pre-1900 hard-to-treat houses will still be existing in the next 25 years, and they have the greatest potential for improved energy-efficiency. This study investigates the potential of fabric retrofit to prepare an older, hard-to-treat house type for heat pump use. The house type was modelled in DesignBuilder and validated using the Ulster University test house. The wall, loft and floor insulation, as well as glazing upgrades can yield up to 50% reduction in heating demand for a hard-to-treat house type, thereby preparing it for heat pump installation. Additionally, upgrading insulation and glazing in line with the current building standards was cost-effective, with a net present value of approximately GBP 12,000. [ABSTRACT FROM AUTHOR]

Fog networking has become an established architecture addressing various applications with strict latency, jitter, and bandwidth constraints. Fog Nodes (FNs) allow for flexible and effective computation offloading and content distribution. However, the transmission of computational tasks, the processing of these tasks, and finally sending the results back still incur energy costs. We survey the literature on fog computing, focusing on energy consumption. We take a holistic approach and look at energy consumed by devices located in all network tiers from the things tier through the fog tier to the cloud tier, including communication links between the tiers. Furthermore, fog network modeling is analyzed with particular emphasis on application scenarios and the energy consumed for communication and computation. We perform a detailed analysis of model parameterization, which is crucial for the results presented in the surveyed works. Finally, we survey energy-saving methods, putting them into different classification systems and considering the results presented in the surveyed works. Based on our analysis, we present a classification and comparison of the fog algorithmic models, where energy is spent on communication and computation, and where delay is incurred. We also classify the scenarios examined by the surveyed works with respect to the assumed parameters. Moreover, we systematize methods used to save energy in a fog network. These methods are compared with respect to their scenarios, objectives, constraints, and decision variables. Finally, we discuss future trends in fog networking and how related technologies and economics shall trade their increasing development with energy consumption. [ABSTRACT FROM AUTHOR]

This paper proposes an improved dual-phased framework for energy-efficient, cluster-based routing in Wireless Sensor Networks (WSNs). It addresses the critical challenge of balancing energy consumption with reliable network performance. Cluster-based routing is a crucial parameter for WSN operational efficiency, especially in applications demanding minimal energy use and dependable data transmission. The proposed framework integrates two advanced meta-heuristic algorithms: Sailfish Optimization (SFO) and Spotted Hyena Optimization (SHO). This combined approach leverages SFO's rapid exploration for efficient clustering and optimal Cluster Head (CH) selection. Additionally, SHO's refined exploitation capabilities optimize efficient routing paths. This innovative methodology significantly improves network performance metrics like energy efficiency, network lifetime, and Packet Delivery Ratio (PDR). The originality of this work lies in the dual-phased optimization strategy. It distinctively outperforms traditional single-algorithm based approaches by employing a unique hybrid optimization approach, offering greater originality and value. Experimental simulations demonstrate that the proposed framework outperforms several popular algorithms in terms of key performance metrics. This makes it a valuable contribution to the field and an efficient solution for diverse applications. [ABSTRACT FROM AUTHOR]

Improving energy efficiency in the transportation system is an important goal in facing the challenges of climate change and limited energy resources. Electric trains are a promising alternative for reducing greenhouse gas emissions and dependence on fossil fuels. Speed profile optimization has been a significant research focus on achieving maximum energy efficiency in electric trains. This paper aims to provide insight into energy efficiency using speed profile optimization. Several issues were discussed in this paper including energy consumption modeling methods, speed profile optimization methods, and integration of speed profile optimization with schedules and regenerative braking. This study concluded that the most frequently used energy consumption modeling is the deterministic model using the Davis equation. There are two classifications in optimization: classical and modern methods (heuristics). Classical optimization methods are often used on problems with simple constraints, and modern methods are often used on problems with more complex constraints or variables.

This paper proposes an improved dual-phased framework for energy-efficient, cluster-based routing in Wireless Sensor Networks (WSNs). It addresses the critical challenge of balancing energy consumption with reliable network performance. Cluster-based routing is a crucial parameter for WSN operational efficiency, especially in applications demanding minimal energy use and dependable data transmission. The proposed framework integrates two advanced meta-heuristic algorithms: Sailfish Optimization (SFO) and Spotted Hyena Optimization (SHO). This combined approach leverages SFO's rapid exploration for efficient clustering and optimal Cluster Head (CH) selection. Additionally, SHO's refined exploitation capabilities optimize efficient routing paths. This innovative methodology significantly improves network performance metrics like energy efficiency, network lifetime, and Packet Delivery Ratio (PDR). The originality of this work lies in the dual-phased optimization strategy. It distinctively outperforms traditional single-algorithm based approaches by employing a unique hybrid optimization approach, offering greater originality and value. Experimental simulations demonstrate that the proposed framework outperforms several popular algorithms in terms of key performance metrics. This makes it a valuable contribution to the field and an efficient solution for diverse applications.

In the fog computing-based Internet of Things (IoT) architecture, the sensor devices represent the basic elements needed to sense the surrounding environment. They gather and send a huge amount of data to the fog gateway and then to the cloud due to their use in various real-world IoT applications. This would lead to high data traffic, increased energy consumption, and slow decisions at the fog gateway. Therefore, it is important to reduce the transmitted data to save energy and provide an accurate decision regarding the safety and health of the building’s environment. This paper suggests an energy-aware data transmission approach with decision-making (EDaTAD) for Fog Computing-based IoT applications. It works on two-level nodes in the fog computing-based TI architecture: sensor devices and fog gateways. The EDaTAD implements a Lightweight Redundant Data Removing (LiReDaR) algorithm at the sensor device level to lower the gathered data before sending it to the fog gateway. In the fog gateway, a decision-making model is proposed to provide suitable decisions to the monitoring staff in remote monitoring applications. Finally, it executes a Data Set Redundancy Elimination (DaSeRE) approach to discard the repetitive data sets before sending them to the cloud for archiving and further analysis. EDaTAD outperforms other methods in terms of transmitted data, energy consumption, and data accuracy. Furthermore, it assesses the risk efficiently and provides suitable decisions while decreasing the latency time. [ABSTRACT FROM AUTHOR]

Increasing the adoption of energy-efficiency measures (EEM) by households is one of the practical strategies to increase energy efficiency and reduce greenhouse gas emissions. For the 3.24 million low-income households in Australia, partly due to their potentially disproportionately large waste of usable energy, there is a gap between the potential for energy efficiency through existing EEM and the actual energy-efficiency level. Many measures increase energy efficiency in buildings; however, barriers persist for such households to adopt these measures, of which limited research has yet to be conducted. In response, this study examines the relationships involved by a questionnaire survey of 212 low-income households in Australia, showing that financial barriers have the greatest influence followed by split incentive barriers, with the provision of information having no significant role to play. This paper contributes to filling the gap of limited energy-efficiency research involving low-income households, revealing the effect of energy-efficiency barriers on their adoption of EEM. [ABSTRACT FROM AUTHOR]

Effective emission control technologies and eco-friendly propulsion systems have been developed to decrease exhaust particle emissions. However, more work must be conducted on non-exhaust traffic-related sources such as tyre wear. The advent of automated vehicles (AVs) enables researchers and automotive manufacturers to consider ways to further decrease tyre wear, as vehicles will be controlled by the system rather than by the driver. In this direction, this work presents the formulation of an optimal control problem for the trajectory optimisation of automated articulated vehicles for tyre wear minimisation. The optimum velocity profile is sought for a predefined road path from a specific starting point to a final one to minimise tyre wear in fixed time cases. Specific boundaries and constraints are applied to the problem to ensure the vehicle's stability and the feasibility of the solution. According to the results, a small increase in the journey time leads to a significant decrease in the mass loss due to tyre wear. The employment of articulated vehicles with low powertrain capabilities leads to greater tyre wear, while excessive increases in powertrain capabilities are not required. The conclusions pave the way for AV researchers and manufacturers to consider tyre wear in their control modules and come closer to the zero-emission goal. [ABSTRACT FROM AUTHOR]

Microarray Gene Expressions (MGE) are vital in critical disease diagnosis and remote health monitoring. Industry 4.0 for smart health monitoring using MGE needs efficient data collection and routing solutions at the edge layer. Energy efficiency clustering-based routing is crucial for edge-layer communications in Industry 4.0. This study presents a novel routing protocol based on clustering as a solution to this issue called the Lightweight Clustering protocol for Industrial 4.0-enabled Precision Health (LCIPH). LCIPH considers energy efficiency, processing efficiency, and QoS performance across long distances to processes and transmits MGE data. The Bacterial Foraging Algorithm (BFA) optimizes CH selection using the fitness function. A fitness score model using direct and indirect body sensor properties is used as the fitness function in the BFA. The proposed fitness function selects the best Cluster Head (CH) and reliable data forwarder. We set the clustering and routing objective function using the fitness function to decrease energy consumption and communication overhead and improve network performance. The lightweight route discovery method leverages periodic fitness ratings to reliably establish MGE data transfer routes between and within clusters without specialized processing. The simulation results demonstrate that the performance of the LCIPH protocol shows enhancements compared to the available clustering methods. Throughput and PDR performances of the LCIPH protocol are improved by 6.61% and 6.34% compared to the state-of-the-art. Communication delay, energy consumption, and communication overhead are reduced by 8.07%, 7.29%, and 6.56%, respectively. [ABSTRACT FROM AUTHOR]

As the environmental impact of computing worsens, transitioning to more sustainable computing practices, also known as green computing, has become crucial. This literature review examines the concept of green computing, focusing on the environmental impacts of software languages and how millions of developers worldwide interact with popular yet energy-intensive languages such as Python and Perl daily without realizing their negative environmental consequences. The review elucidates how such impacts are measured and the potential advantages and drawbacks of adopting more green programming languages as new tools instead of energy-intensive ones. The paper further argues that green computing, while challenging to implement now, is critical for creating a more sustainable future for our society. [ABSTRACT FROM AUTHOR]

Smart grid leverages distribution networks and intelligent transmission techniques to advance the delivery of electricity, with a primary focus on enhancing reliability, security, and efficiency. This advancement is realized through bidirectional data exchange for consumption monitoring and operation optimization within the electrical system. Smart grid infrastructures commonly depend on precise monitoring of electrical system parameters to enhance the reliability and robustness of the grid. It is crucial to underscore the significance of maintaining data integrity while disseminating these measurements across diverse grid stakeholders over expansive network infrastructures. In this paper, we propose a lightweight privacy-enhanced secure data sharing scheme for the smart grid, which not only ensures that only authorized users in the smart grid can efficiently access the smart grid data, but also prevents sensitive information leakage from the access policies revealing about the protected data, data owners, or recipients in smart grid. Besides, we also present strict security proofs to prove the resistance to chosen-ciphertext attacks (CCA). Finally, some efficiency analyses are made to indicate the most prominent energy-efficiency our scheme enables. [ABSTRACT FROM AUTHOR]

Underwater Wireless Sensor Networks (UWSNs) are essential for a number of environmental and oceanographic monitoring applications. However, they face different and more complex challenges than terrestrial wireless sensor networks (TWSNs). The main challenges faced by UWSNs are limited include high propagation delays, poor bandwidth, low throughput, and high energy consumption. Replacing sensor batteries in such networks becomes extremely difficult as they are usually deployed in remote areas where limited human interaction is possible. The unbalanced and inefficient usage of energy by various network nodes poses another issue, as it may reduce the applicability and feasibility of the network. Therefore, proposing Energy-Efficient Routing Protocols (E-ER-Ps) is crucial to improve the performance and lifespan of these networks. Due to the challenges mentioned earlier, this research presents an extensive analysis of several different E-ER-Ps intended for UWSNs. We compare contemporary approaches that use machine learning (ML) with conventional protocols, as ML-based approaches have shown significant potential in resolving the intricate challenges faced by UWSNs. This paper aims to present a critical review of different E-ER-Ps from various prospects for UWSNs. To better comprehend the structure and uses of these protocols, we provide an innovative taxonomy for their classification. While ML-based protocols are evaluated for their flexibility, predictive power, and overall efficiency advancements, traditional protocols are evaluated based on their routing tactics and energy-efficiency improvements. A thorough comparative analysis highlights the advantages, disadvantages, and possible uses for different protocols. Furthermore, a critical analysis of ML’s function, incorporating intelligent and adaptive routing approaches, is presented, highlighting the technology’s potential to completely alter UWSN management. To formulate and implement E-ER-Ps for UWSNs, the article concludes by highlighting the present obstacles, including the need for real-time flexibility, resilience to environmental alters, and interaction with pre-existing network infrastructures. The development of ML-based approaches, hybrid approaches that combine conventional and ML-based methodologies, and the design of protocols that can adapt dynamically to the changing circumstances of underwater habitats are highlighted as future research objectives. This research provides the foundation for future advancements in this crucial field by presenting a comprehensive overview of the state-of-the-art UWSN E-ER-Ps.

The urgent need to combat global warming and environmental pollution underscores the importance of developing energy-efficient systems that effectively reduce emissions. One of the possible strategies for achieving energy efficiency consists in diminishing the mass of mechanical systems since mass reduction implies decreased inertia, lower fuel and energy consumption, and enhanced transportation capabilities, particularly in industries such as aerospace, nautical, and automotive. The key design principle must involve the use of lightweight materials in optimized structures, manufactured through suitable methods like additive manufacturing. Design activity must be conducted with a focus on ensuring structural integrity and durability of systems under various loading and environmental conditions. This article introduces a pivotal method that includes mathematical modelling, numerical simulation and experimental verification. Based on these three approaches, it aims to provide a comprehensive assessment framework for ensuring the integrity and durability of energy-efficient systems, covering topics such as fatigue, impact damage, coating deposition effects, and material selection. The advantages of using metamaterials are presented. Real-world case studies are examined to offer practical strategies for researchers and engineers engaged in the design and assessment of energy-efficient components, structures, and systems, contributing to a sustainable future.

Energy efficiency and behavioural changes are among the key pillars of decarbonization of the global energy system. Residential sector is responsible for a large share of fossil fuels final consumption, therefore the growth of its energy efficiency can bring a valuable impact on decarbonization speed and scale. In countries with growing economies residential energy consumption can increase dramatically due to the desire of the population to improve their standard of living, therefore striking a balance between economic growth and energy efficiency improvement is a complex policy challenge. The purpose of this study is to analyze attitudes and assess barriers of energy efficient behavior in Republic of Armenia as a country with growing economy and standards of living. The paper contributes to the literature by providing new empirical evidence of consumers’ attitude toward different forms of energy efficient behavior in Armenia and reveals how general pro-environmental consciousness and social conditions influence on it. The results of the study prove that the level of general environmental consciousness of the population has a direct impact on the frequency of practical application of various patterns of energy-efficient behavior. As a policy application for emerging economies, our study proposes that the main direction of energy efficiency policy can be education of the population and raising its level of environmental self-awareness.

An Electrocardiogram (ECG) is the first diagnostic tool a medical practitioner uses to measure the electrical and mucular fitness of an individual heart. The use of ECG is so important because heart-related diseases are silent killers. In recent times, advancement and the further development of wearable devices and ECG sensors have made it possible to continuously measure and analyze the electrocardiogram and myocardium signals of the heart. However, it requires significant training and adeptness to interpret the recorded ECG correctly and effectively. An energy-efficient wireless sensor infrastructure for improved operation of cardiovascular problems in the development economy using the concept of fog technology was presented. The ECG data was collected from Federal Medical Center Umuahia Abia State, Nigeria. Discrete Wavelet Transform (DWT) at first to perform preprocessing of ECG data to eliminate noise from motion artifacts, power line interference, and high frequency sources, followed by the undecimated Wavelet Transform (UWT) at first to extract relevant features, which are of high interest to a cardiologist. The proposed system classifies a recorded heartbeat into four classes, namely Normal Beat, Premature Ventricular Contraction (PVC), Premature Atrial Contraction (PAC), and Myocardial Infarction. The study found that processing and analyzing health data at the fog resulted in total energy savings of 36% and 52% when compared to conventional processing. [ABSTRACT FROM AUTHOR]

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel DILTS algorithm that uses a new approach inspired by the energy efficiency of dragonflies. The algorithm optimizes the energy-harvesting mechanisms in IoT devices, inspired by the way dragonflies use wind energy to fly. A sophisticated algorithm optimizes power consumption during task execution, saving energy and speeding up tasks while maintaining the application throughput. The algorithm leverages lazy task scheduling (LTS) to enhance task execution performance. The proposed algorithm evaluates the energy levels of each task and implements an LTS method. This LTS approach improves performance and task management by streamlining scheduling data and reducing overhead. The LTS model reliably optimizes the energy across microbenchmarks and real-time IoT devices. To assess the efficiency and practicality of our algorithm, we compared it to four alternatives. Our novel algorithm outperformed the others with a chip area of 856 μm2, performance speed of 7.11 ns, scheduling accuracy of 94%, and response time of 2.61 ns. Our simulations showed that our proposed method reduced energy consumption by up to 10.02% compared to existing methods. We evaluated the performance of the algorithms on a Zynq 7000 FPGA using the Xilinx Vivado platform via simulations. Our novel algorithm can improve the energy efficiency of green data centers. [ABSTRACT FROM AUTHOR]

The urgent need to combat global warming and environmental pollution underscores the importance of developing energy-efficient systems that effectively reduce emissions. One of the possible strategies for achieving energy efficiency consists in diminishing the mass of mechanical systems since mass reduction implies decreased inertia, lower fuel and energy consumption, and enhanced transportation capabilities, particularly in industries such as aerospace, nautical, and automotive. The key design principle must involve the use of lightweight materials in optimized structures, manufactured through suitable methods like additive manufacturing. Design activity must be conducted with a focus on ensuring structural integrity and durability of systems under various loading and environmental conditions. This article introduces a pivotal method that includes mathematical modelling, numerical simulation and experimental verification. Based on these three approaches, it aims to provide a comprehensive assessment framework for ensuring the integrity and durability of energy-efficient systems, covering topics such as fatigue, impact damage, coating deposition effects, and material selection. The advantages of using metamaterials are presented. Real-world case studies are examined to offer practical strategies for researchers and engineers engaged in the design and assessment of energy-efficient components, structures, and systems, contributing to a sustainable future. [ABSTRACT FROM AUTHOR]

This paper presents the energy-optimal trajectories for skid-steer rovers on hard ground, without obstacles. We obtain 29 trajectory structures that are sufficient to describe minimum-energy motion, which are enumerated and described geometrically; 28 of these structures are composed of sequences of circular arcs and straight lines; there is also a special structure called whirls consisting of different circular arcs. Our analysis identifies that the turns in the trajectory structures (aside from whirls) are all circular arcs of a particular turning radius, R′, the turning radius at which the inner wheels of a skid-steer rover are not commanded to turn. This work demonstrates its paramount importance in energy-optimal path planning. There has been a lack of analytical energy-optimal trajectory generation for skid-steer rovers, and we address this problem by a novel approach. The equivalency theorem presented in this work shows that all minimum-energy solutions follow the same path irrespective of velocity constraints that may or may not be imposed. This non-intuitive result stems from the fact that with this model of the system the total energy is fully parameterized by the geometry of the path alone. With this equivalency in mind, one can choose velocity constraints to enforce constant power consumption, thus transforming the energy-optimal problem into an equivalent time-optimal problem. Pontryagin's Minimum Principle can then be used to solve the problem. Accordingly, the extremal paths are obtained and enumerated to find the minimum-energy path. Furthermore, our experimental results by using Husky UGV provide the experimental support for the equivalency theorem. [ABSTRACT FROM AUTHOR]

Trust and energy efficiency are key requirements in remote sensing devices, which are frequently deployed in electronic commerce supply chain management. This remote sensing is facilitated by wireless sensor networks (WSNs) which have been integrated into the e-commerce landscape. These WSNs provide various services, including product tracking, monitoring, optimization and design evolution. This integration is evident in Logistics 4.0, whose aim is to enhance adaptability, intelligence and resilience in supply chain management and logistics. However, due to the limited energy, memory, communication and computing power of the sensors deployed in WSNs, numerous energy consumption reduction approaches have been proposed. Nevertheless, these techniques often overlook security and privacy aspects, which are crucial for fostering trust in an e-commerce ecosystem. Consequently, various schemes have been developed to bridge this gap. Unfortunately, these protocols often exhibit vulnerabilities and privacy loopholes that can compromise trust among e-commerce stakeholders. In this paper, we propose a trust-enabled energy efficient protocol for secure remote sensing in supply chain management. Performance evaluation demonstrates that our protocol incurs the lowest energy, computation and communication complexities compared to existing solutions. Additionally, it provides essential security features, including session key agreement, mutual authentication, key secrecy, untraceability and anonymity. These features are critical for trust preservation in e-commerce. Our semantic security analysis confirms the protocol’s resilience against various attacks. In addition, its formal security analysis using the Real-Or-Random (ROR) model validates the security of the negotiated session key.

Unmanned Aerial Vehicles (UAVs) or drones are flying robots, which can be controlled either remotely or autonomously to accomplish a certain mission. Drones have now become incredibly well-known and are being used in different applications such as defense, telecommunication, agricultural, and disaster management. However, they continue to face several challenges in their design; one challenge of drones is the energy consumption of their batteries during mission execution. Drones need relatively high energy for cancellation of gravity force; as a result, their flight duration is highly constrained. Another challenge that might happen is the failure of any of the drone’s motors. In this case, either hardware or software system should be used to attain safe landing. Therefore, there is a need for a design that can reduce the power consumption in drones and assist in safe landing in case of failure of any drone’s subsystem. This study addresses these issues and proposes an energy-efficient and safe landing design for drones. The proposed design suggests that a drone is constructed from a soft silicon material with Helium injection and uses a Penta-copter that has a fixed motor at the center and four motors around the tube. The advantage of this design is the reduction of drone’s weight which helps to reduce power consumption, increase flight time, and provide safe landing. However, delicate materials can be pierced and environmental factors like wind can quickly alter the motion.

Deep reinforcement learning (DRL) has emerged as a promising technique for optimizing the deployment of unmanned aerial vehicles (UAVs) for data collection in wireless sensor networks (WSNs) and Internet of Things (IoT) applications. With DRL, UAV trajectory can be optimized, optimal data collection points can be determined, sensor node transmissions can be scheduled efficiently, and irregular traffic patterns can be learned effectively. In view of the significance of DRL for UAV-assisted IoT research in general and, more specifically, its use for time-critical applications, this paper presents a review of the existing literature on UAV-aided data collection for WSN and IoT applications related to the application of DRL to minimize the Age of Information (AoI), a recent metric used to measure the degree of freshness of transmitted information collected in data-gathering applications. This review aims to provide insights into the state-of-the-art techniques, challenges, and opportunities in this domain through an extensive analysis of a sizable range of related research papers in this domain. It discusses application areas of UAV-assisted IoT, such as environmental monitoring, infrastructure inspection, and disaster response. Then, the paper focuses on the proposed works, their optimization objectives, architectures, simulation libraries and complexities of the various DRL-based approaches used. Thereafter discussion, challenges, and some opportunities for future work are provided. The findings of this review serve as a valuable resource for researchers and practitioners, guiding further advancements and innovations in the field of DRL for UAV-aided data collection in WSN and IoT applications.

The advancement in IoT expedites the traditional healthcare system from one-to-one interaction to telemedicine. Smart Healthcare System (SHS) offers enormous physician and patient-centric solutions. Alongside, delay and energy degradation-related challenges in healthcare services put the patient’s lives at risk. This is because the healthcare data have not been prioritised and all the IP-based data packets are treated the same during the routing process. Inspired by this, a hybrid and robust priority-based Duty-Cycled with Ant Colony Optimisation Routing (DC-ACOP) mechanism has been proposed in this study. The body sensor nodes use their radios to get activated and deactivated. So, dynamic duty cycling has been introduced to activate the communication unit of sensor nodes on demand. The data packets are defined with precedence to serve the highest priority packets first in the Type of Service (ToS) field of IP packets. The precedence-based data packets facilitate the healthcare services on time based on the criticality of the patient. The priorities on the wireless relay node are defined by comparing the encrypted disease levels and thresholds. For efficient route determination, a metaheuristic-based improved ACO approach has been employed in SHS. The proposed DC-ACOP approach has been evaluated and compared with other state-of-the-art approaches to accomplish tangible results in terms of quality metrics such as residual energy, throughput, network lifetime, delay with prioritisation and without prioritisation, packet delivery rate, and the number of non-alive nodes.

Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications are dynamic and geographically distributed, which may require extensive real-time processing capabilities. Thus, processing UAV data locally can be challenging due to their limited computing capabilities. To overcome such limitations, fog and cloud computing can facilitate UAV application development by providing additional resource capacities when needed. Despite this, designing sophisticated and efficient UAV task offloading strategies that collaborate with fog and cloud technologies considering their service latency and energy consumption, is rarely addressed in the literature. Therefore, a collaborative offloading strategy for UAV applications is presented in this work, leveraging fog and cloud computing advantages and capabilities. This approach aims to minimize UAVs’ service latency and energy consumption, as well as provide the required resources and services in real time. In addition, task offloading decisions are formulated using the Mixed-Integer Linear Programming (MILP) model to reduce the energy consumption of the entire UAV-fog-cloud system by optimizing the allocation of computation resources and communication requested by each UAV. The simulation results demonstrate that the proposed strategy can significantly reduce UAV service latency by 15.38%, 35.29%, and 59.26%, as well as decrease overall energy consumption (including processing and networking) by 3.3%, 7.37%, and 12% when compared to alternative standalone strategies (namely UAV, fog, and cloud).

The uptake of low-carbon domestic heating systems is a significant strategy towards global targets of reducing greenhouse emissions and mitigating climate change. Pre-1900 hard-to-treat houses will still be existing in the next 25 years, and they have the greatest potential for improved energy-efficiency. This study investigates the potential of fabric retrofit to prepare an older, hard-to-treat house type for heat pump use. The house type was modelled in DesignBuilder and validated using the Ulster University test house. The wall, loft and floor insulation, as well as glazing upgrades can yield up to 50% reduction in heating demand for a hard-to-treat house type, thereby preparing it for heat pump installation. Additionally, upgrading insulation and glazing in line with the current building standards was cost-effective, with a net present value of approximately GBP 12,000.

Fog networking has become an established architecture addressing various applications with strict latency, jitter, and bandwidth constraints. Fog Nodes (FNs) allow for flexible and effective computation offloading and content distribution. However, the transmission of computational tasks, the processing of these tasks, and finally sending the results back still incur energy costs. We survey the literature on fog computing, focusing on energy consumption. We take a holistic approach and look at energy consumed by devices located in all network tiers from the things tier through the fog tier to the cloud tier, including communication links between the tiers. Furthermore, fog network modeling is analyzed with particular emphasis on application scenarios and the energy consumed for communication and computation. We perform a detailed analysis of model parameterization, which is crucial for the results presented in the surveyed works. Finally, we survey energy-saving methods, putting them into different classification systems and considering the results presented in the surveyed works. Based on our analysis, we present a classification and comparison of the fog algorithmic models, where energy is spent on communication and computation, and where delay is incurred. We also classify the scenarios examined by the surveyed works with respect to the assumed parameters. Moreover, we systematize methods used to save energy in a fog network. These methods are compared with respect to their scenarios, objectives, constraints, and decision variables. Finally, we discuss future trends in fog networking and how related technologies and economics shall trade their increasing development with energy consumption.

The exponential growth of Internet of Things (IoT) devices has resulted in an unparalleled surge in the need for energy-efficient strategies to guarantee the sustainability and durability of IoT networks. This study article provides a thorough examination of energyefficiency optimization in Internet of Things (IoT) networks, with a specific emphasis on the creation of algorithms, methodologies, and empirical investigations. The article commences by providing an overview of the significant significance of energy efficiency in Internet of Things (IoT) networks and its direct influence on the lifetime of devices, scalability of networks, and environmental sustainability. This emphasizes the urgent need for inventive approaches to tackle the difficulties presented by resource-limited Internet of Things (IoT) devices. Subsequently, the present study undertakes an in-depth examination of existing methodologies and algorithms for optimizing energy efficiency in Internet of Things (IoT) networks. This paper presents a comprehensive examination of each category, including insights into their respective strengths, limits, and suitability for various Internet of Things (IoT) applications. This study presents innovative algorithms and strategies that are especially developed to improve energy efficiency in Internet of Things (IoT) networks. These advancements use cutting-edge technology such as machine learning, edge computing, and low-power device design. The paper provides comprehensive explanations of these methodologies, accompanied by simulations and performance assessments, to showcase their efficacy in attaining energy conservation while maintaining network dependability and service excellence. The case studies presented provide valuable perspectives on the practical use of energy-efficient technologies, demonstrating their effectiveness in reducing operating expenses, carbon emissions, and system downtime. Moreover, this study aims to discuss the many problems and unresolved research inquiries pertaining to energy-efficient Internet of Things (IoT) networks. The aforementioned points underscore the need for further investigation in several aspects, including scalability challenges, security implications, and standardization efforts. These areas of focus are crucial in order to foster the ongoing expansion and long-term viability of Internet of Things (IoT) ecosystems. [ABSTRACT FROM AUTHOR]

We aim to preserve a large amount of data generated inside base station-less sensor networks (BSNs) while considering that sensor nodes are selfish. BSNs refer to emerging sensing applications deployed in challenging and inhospitable environments (e.g., underwater exploration); as such, there do not exist data-collecting base stations in the BSN to collect the data. Consequently, the generated data has to be stored inside the BSN before uploading opportunities become available. Our goal is to preserve the data inside the BSN with minimum energy cost by incentivizing the storage- and energy-constrained sensor nodes to participate in the data preservation process. We refer to the problem as DPP: data preservation problem in the BSN. Previous research assumes that all the sensor nodes are cooperative and that sensors have infinite battery power and design aminimum-cost flow-based data preservation solution. However, in a distributed setting and under different control, the resource-constrained sensor nodes could behave selfishly only to conserve their resources and maximize their benefit. In this article, we first solve DPP by designing an integer linear programming (ILP)-based optimal solution without considering selfishness.We then establish a game-theoretical framework that achieves provably truthful and optimal data preservation in BSNs. For a special case of DPP wherein nodes are not energyconstrained, referred to as DPP-W, we design a data preservation game DPG-1 that integrates algorithmic mechanism design (AMD) and a more efficient minimum cost flow-based data preservation solution.We show that DPG-1 yields dominant strategies for sensor nodes and delivers truthful and optimal data preservation. For the general case of DPP (wherein nodes are energy-constrained), however, DPG-1 fails to achieve truthful and optimal data preservation. Utilizing packet-level flow observation of sensor node behaviors computed by minimum cost flow and ILP, we uncover the cause of the failure of the DPG-1. It is due to the packet dropping by the selfish nodes that manipulate the AMD technique. We then design a data preservation game DPG-2 for DPP that traces and punishes manipulative nodes in the BSN. We show that DPG-2 delivers dominant strategies for truth-telling nodes and achieves provably optimal data preservation with cheat-proof guarantees. Via extensive simulations under different network parameters and dynamics, we show that our games achieve system-wide data preservation solutions with optimal energy cost while enforcing truth-telling of sensor nodes about their private cost types. One salient feature of our work is its integrated game theory and network flows approach. With the observation of flow level sensor node behaviors provided by the network flows, our proposed games can synthesize "microscopic" (i.e., selfish and local) behaviors of sensor nodes and yield targeted "macroscopic" (i.e., optimal and global) network performance of data preservation in the BSN. [ABSTRACT FROM AUTHOR]

Computational efficiency, CE, of a baseband unit, BBU, has become a key issue for a cloud radio access network, CRAN. The CE is defined as the sum of computational power, CP and computing resources, CR. Where CP and CR can be defined as the amount of power and processing resources of the core computing unit, CCU, of the BBU consumed during execution of traffic requests from remote radio heads, RRHs, respectively. A CCU is nothing but a general-purpose processor. To optimize CR utilization, less number of BBUs has to execute the same amount of RRH-requests. This causes dramatic rise in CCU's operating temperature and imposes CP efficiency problem, because leakage power of a CCU, which is a wasted power, increases with its operating temperature. On the other hand, a CCU can reduce its CP remarkably by executing the RRH-requests slowly at lower frequency. However, the execution has to meet deadline of the RRH-request that is equivalent to a circuit delay in CCU, which depends on supply voltage, CCU-temperature, and frequency. Therefore, it is very challenging to minimize CP of GPP while optimizing CR subject to RRH-request's deadline. Previous approaches, including our previously proposed TADCRA scheme, tries to turn off as many GPPs as possible by integrating traffic load on fewer GPPs to maximize the CR utilization while working at allowable temperature. Compared to TADCRA, it is observed that significant CP can be saved from the active GPP by dynamically adjusting its voltage and frequency (DVFS) while executing RRH-requests. Thus, given optimal number of GPPs obtained from TADCRA that maximizes the CR efficiency, a computational efficiency problem is formulated subject to minimum operating frequency, voltage and RRH-request's deadline constraints. To address this problem, we propose computational efficient allocation (CEA) algorithm, where Lagrange multiplier tool is used for CP efficiency problem, while our proposed heuristic, Win–Win, solves the CR optimization problem subject to RRH-request's deadline. Simulation results substantiate the proposed algorithm can save 16%, 29% and 46% more CP than TADCRA, LDA and conventional methods, respectively. The CEA increases CR utilization rate by 15% and 45% from LDA and conventional schemes, respectively, with significantly less number of CCUs requirements. Moreover, Win–Win heuristic satisfies the deadline constraint compared to first fit decreasing, FFD, approach, which misses deadline by 1 ms. [ABSTRACT FROM AUTHOR]

As energy demand escalates, sustainable development requires energy conservation and emission reduction. Technological advancements have caused energy shortages and global warming. This research article investigates the influence of Aluminium oxide (Al2O3) nano-additives in Polyol-Ester (POE) compressor oil and R290/R600a mixture refrigerants with varying volume proportions of 0.05% to 0.2% on the performance of household vapour compression refrigeration framework. The thermophysical properties of R290/R600a (composed in the ratio of 80:20 by a mass fraction) refrigerant mixtures have been extracted from REFPROP (NIST properties of fluid reference), utilized for experimental study, and imported into Matlab-Simulink. This study explores the effect of dispersing aluminium oxide (Al2O3) nanoparticles in the compressor oil with varying concentrations on its thermal, tribological, and rheological properties on the performance of a refrigeration framework using environmentally friendly mixture refrigerants as an alternative to R134a and providing better performance compared to R600a. The experimental outcomes showed that the actual coefficient of performance (COP) and cooling capacity increased by 30% and 15.3%, respectively. The maximum power saving is up to 11.1%, and the coefficient of friction is reduced by 25%. With an optimal concentration of 0.15 vol%, the performance of the refrigeration system has improved. The Matlab Simulink results agree well with experimental data in terms of COP and power consumption. This study offers insight into providing appropriate energy-efficient and low-GWP refrigerant alternatives for domestic refrigerators. [ABSTRACT FROM AUTHOR]

Energy-awareness in the industrial sectors has become a global consensus in recent decades. Green scheduling is acknowledged as an effective weapon to reduce energy consumption in the industrial sectors. Therefore, this paper is devoted to the green scheduling of flexible manufacturing cells (FMC) with auto-guided vehicle transportation, where conflict-free routing of the vehicles is considered. To deal with this problem, a bi-objective optimization model is proposed to achieve the minimization of the maximum completion time and the total energy consumption in an FMC. The studied problem is an extension of flexible job shop problem which is NP-hard. Thus, an improved bi-objective salp swarm algorithm based on decomposition (IMOSSA/D) is proposed and applied to the problem. The approach is based on the decomposition of the bi-objective problem. Salp swarm intelligence along with three stochastic-distribution-based operators are incorporated into the approach, to enhance and balance its exploring and exploiting ability. Computational experiments are performed to compare the proposed approach with two state-of-the-art algorithms. This study allows the decision makers to better trade-off between energy savings and production efficiency in flexible manufacturing cellular environment. [ABSTRACT FROM AUTHOR]

The current global warming challenges have drawn refrigerants with low Global Warming Potential (GWP). R32 (difluoromethane) is the most promising low-GWP refrigerant and is slightly flammable. The addition of a flame retardant to HFC-32 can improve its flammability. Trifluoroiodomethane (R13I1) is a flame retardant with an extremely low GWP that can be employed as a refrigerant component. This experimental study investigates the effect of dispersing aluminium oxide (Al2O3) nanoparticles in the compressor oil with varying concentrations of 0.045, 0.09, 0.135, and 0.18 vol% in the Polyol Ester (POE) oil on its thermal, tribological, and rheological properties as well as on the overall performance of a vapour compression air conditioning system using ultra-low GWP mixture refrigerants consisting of R32, R161, and R13I1 as the working fluid. The thermophysical properties of the mixture refrigerant have been calculated using REFPROP (NIST properties of fluid reference). The mixture refrigerant (R32/R161/R13I1) performed better in alumina nanolubricant and the comparison has been made with reference to the base lubricant (POE) in terms of power consumption, COP, coefficient of friction, and cooling capacity. The results showed that the coefficient of performance (COP) and cooling capacity increased by 32.6% and 11.7%, respectively. The maximum power saving is up to 16.1%, and the coefficient of friction is reduced by 32.4%. The highest recorded thermal conductivity reached 22.5% at 343.15 K and a volume concentration of 0.18%, compared to base oil at 303.15 K. The air conditioning system's performance showed improvement at an optimal concentration of 0.135 vol%. This study provides valuable insights into identifying energy-efficient and low-GWP refrigerant alternatives suitable for air conditioning systems. [ABSTRACT FROM AUTHOR]

Water ring vacuum (WRV) pump is the main power equipment for the safe transportation of flammable and explosive gases such as methane, and its performance has a significant impact on the effectiveness of coal mine gas drainage. In the view of the problems that the WRV pump is a high-energy consumption and low efficiency rotodynamic machine, based on the working principle and the energy loss process of the WRV pump, a new energy saving method for WRV pump is proposed. The influence mechanism of the viscoelastic property of the polymer drag-reducing fluid on the gas-liquid two-phase flow field in the pump is presented. The WRV pump performance tests are conducted to investigate the influence of drag reducer type, concentration and pump speed on the energy-saving performance, and a polymeric drag reducer suitable for high turbulence and strong shear environment in WRV pump is developed. A set of energy-efficiency system of WRV pump is proposed to adapt to the working conditions for the coal mine gas drainage station, and the functions and technical processes of key units such as the preparation and replenishment device of drag-reducing fluid, quantitative water supply device and energy-saving operation and monitoring system for WRV pump are introduced. The results show that with the increase of the concentration of drag reducing fluid, the energy-saving rate and gas flow rate increase, and the optimal energy saving concentration is found to be 4.5 g/L. With the increase of inlet pressure, the energy-saving rate increases rapidly as a quadratic function, but the increase rate of gas flow rate decreases. When the pump speed increases, the energy saving effect become more obvious. The results of the field application in the Zhangji coal mine show that the technology can significantly reduce the energy consumption of the WRV pump operation. Under the condition of the constant drainage negative pressure, the energy-saving rate of the pump is 21.1%, and the net energy-saving rate from the cost-benefit analysis is 18.2%, which has a significant energy-saving benefit and effectively alleviates the problem of high-energy consumption of gas drainage pumping station.

Autonomous vehicles (AV) are expected to improve road safety and reduce traffic congestion by optimizing routes and reducing human errors. AVs have the potential to increase accessibility for people with disabilities and reduce the environmental impact of transportation. AVs require radio transmitters to communicate with other vehicles and infrastructure, external charging to power their electric motors, and communication equipment to receive real-time data about traffic and road conditions. Additionally, these requirements must be met for AVs to operate efficiently and conserve energy. Therefore, this work introduces a novel technique called energy-conservation guided route adaptation (EC-GRA) that aims to enhance the energy efficiency of connected vehicles. With the balance in energy adaptation for distinct purposes, the utilization rate is adjusted for communication and navigation. The complex decisions are confined to the energy availability and conservation factors required in an adaptive driving condition. This technique employs transfer learning to update the available and adaptable energy ratios under displacement-based route adaptations. In the learning process, the transfer and update states for displacement-aware decisions under varying scenarios are modeled. This study validates the state transitions involved in recommending energy utilization during both autonomous and guided driving scenarios. The results show that the proposed methodology exhibits superior performance compared to the currently available techniques. The EC-GRA under consideration has demonstrated an average energy conservation ratio of 45.58. The decision rate for this method is 0.63/navigation, while its energy utilization is 126.37 Joules. The number of failures observed in the proposed EC-GRA is 6/navigation, which represents an improvement over the existing approach.

Effective emission control technologies and eco-friendly propulsion systems have been developed to decrease exhaust particle emissions. However, more work must be conducted on non-exhaust traffic-related sources such as tyre wear. The advent of automated vehicles (AVs) enables researchers and automotive manufacturers to consider ways to further decrease tyre wear, as vehicles will be controlled by the system rather than by the driver. In this direction, this work presents the formulation of an optimal control problem for the trajectory optimisation of automated articulated vehicles for tyre wear minimisation. The optimum velocity profile is sought for a predefined road path from a specific starting point to a final one to minimise tyre wear in fixed time cases. Specific boundaries and constraints are applied to the problem to ensure the vehicle’s stability and the feasibility of the solution. According to the results, a small increase in the journey time leads to a significant decrease in the mass loss due to tyre wear. The employment of articulated vehicles with low powertrain capabilities leads to greater tyre wear, while excessive increases in powertrain capabilities are not required. The conclusions pave the way for AV researchers and manufacturers to consider tyre wear in their control modules and come closer to the zero-emission goal.