Energy Efficient Techniques Research Articles

Identifying driving factors in cascaded packed bed latent thermal energy storage: An experimental validation

The cascaded packed bed latent thermal energy storage (PBLTES) system, an innovative and efficient technique, remains unexplored experimentally in terms of driving factors and cyclic stability. To address this gap, this study designed a cascaded PBLTES system, employing three phase-change-materials with varied phase transition temperatures. Parametric experiments were conducted to measure phase transition in capsules and temperature changes in heat transfer fluid. Pearson's correlation coefficients were used to establish relationships between driving factors and thermal performance metrics. This study developed multiple linear regression models based on experimental correlations to evaluate and predict thermal performance under various conditions. These results indicated that the employed multiple regression models are capable of making reliable quantitative predictions regarding the thermal behavior of cascaded PBLTES systems. The models showed a good fit to the experiment data (lowest R2 value at 0.776). The results also showed that the flow rate significantly affected total and phase transition times of the cascaded PBLTES for charging/discharging, with substantial Standardized Linear Regression Coefficients of −0.79/-0.8 and −0.74/-0.72, respectively. In contrast, inlet temperature, with coefficients of −0.18/0.15 and −0.34/0.21, has about a quarter of the flow rate's impact. These findings provide compelling experimental substantiation for the design of cascaded PBLTES.

An energy efficient encryption technique for the Internet of Things sensor nodes

An energy efficient encryption technique for the Internet of Things sensor nodes

Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks

Anomaly detection in Wireless Sensor Networks (WSNs) is critical for their reliable and secure operation. Optimizing resource efficiency is crucial for reducing energy consumption. Two new algorithms developed for anomaly detection in WSNs—Ensemble Federated Learning (EFL) with Cloud Integration and Online Anomaly Detection with Energy-Efficient Techniques (OAD-EE) with Cloud-based Model Aggregation. EFL with Cloud Integration uses ensemble methods and federated learning to enhance detection accuracy and data privacy. OAD-EE with Cloud-based Model Aggregation uses online learning and energy-efficient techniques to conserve energy on resource-constrained sensor nodes. By combining EFL and OAD-EE, a comprehensive and efficient framework for anomaly detection in WSNs can be created. Experimental results show that EFL with Cloud Integration achieves the highest detection accuracy, while OAD-EE with Cloud-based Model Aggregation has the lowest energy consumption and fastest detection time among all algorithms, making it suitable for real-time applications. The unified algorithm contributes to the system's overall efficiency, scalability, and real-time response. By integrating cloud computing, this algorithm opens new avenues for advanced WSN applications. These promising approaches for anomaly detection in resource constrained and large-scale WSNs are beneficial for industrial applications.

Energy Efficient Localization Technique Using Multilateration for Reduction of Spatially and Temporally Correlated Data in RFID System

RFID plays a vital role in data communication in multidimensional WSNs as it collects vast amounts of redundant data. The physical phenomena constitute the correlated observations in the space domain and generate spatial correlation. Periodic observations of sensor nodes result in a temporal correlation in the data. Reducing these spatio-temporal correlations in RFID surveillance data is necessary for the smooth functioning of the network. This paper proposes a Voronoi diagram-based spatio-temporal data redundancy elimination approach for RFID systems having multiple readers so only one reader will read every RFID tag depending on the distance between the tag and the center of the Minimum Enclosing Circle of the Voronoi cell to which the reader belongs. This approach eliminates spatial redundancy in the gathered data. Reading the RFID tags at regular time intervals larger than a chosen threshold value minimized temporal redundancy. In contrast to existing methods, the proposed technique is free from any false positive and false negative errors, with no loss of data and every tag being read by only one reader. Simulation of the proposed approach also established its superiority to the existing techniques in terms of these performance parameters.

A new energetic scenario with renewable energy

The article presents a summary of the new energy scenario, based on distributed generation and on the integration into the electricity networks of the generators of small and medium power. They are based on clean technologies and renewable energy, including cogeneration, micro-generation with renewables (non-fossil energy sources, such as wind, solar, geothermal, tidal, hydroelectric, biomass), energy storage systems, energy efficiency techniques, and flexibility and demand responsibility techniques. The perspective shows a new system, where thousands or millions of users are their own generators, becoming producers and consumers of electricity. All these generators will be interconnected through an electricity network totally interactive and intelligent. For this new environment a sophisticated control and communications technologies are required to ensure the correct operation of the electricity networks, the creation of new models of power distribution, and advanced technology development of energy storage, power electronics and superconductor devices. The main technology that can lead to this new revolution, equivalent to the technology of silicon, which is revolutionizing the conventional limits of electricity and electronics, is nanotechnology, as well as its applications in the development of new nanostructured materials. The new energy systems will present smaller scale, will be easier to build and dismantle, decentralized, renewable, with localized and decentralized energy production.

Spectral and Energy Efficient Pilot-Assisted PAPR Reduction Technique for Underwater Wireless Optical Communication Systems

Spectral and Energy Efficient Pilot-Assisted PAPR Reduction Technique for Underwater Wireless Optical Communication Systems

Energy Efficient Routing Technique Using Enthalpy Ant Net Routing for Zone-Based MANETS

Routes discovery that can provide reliable data transmission in Mobile Ad-hoc Networks is challenging due to its wireless channel characteristics and dynamic transmission environment. Ad-hoc networks frequently experience link failure because nodes are mobile and their positions are not fixed. The cellular ad hoc community's dynamic nature makes it possible to analyze multi-route routing protocols specifically. One of the most trustworthy and environment-friendly routing methods is the Zone Routing Protocol used in Mobile Ad Hoc Networks. Offering timely and trustworthy communication services, however, depends critically on maintaining the Quality of Service, electricity performance, and outstanding resource management. In this research, we suggested an energy-efficient routing method for zone-based MANETs called enthalpy and net routing. First, a completely fuzzy quarter clustering based on an Energy guide is used to perform the clustering. With the help of fuzzy memberships, the AFCM set of rules permits the input of statistics for each elegance. In the second, the best route is selected using Enthalpy Ant Net Routing (EANR) while taking the following factors into account: community disconnection, channel error, buffer overflow, and contention at the link layer. The results of the experiment show that the collection of rules performs better than many existing algorithms in terms of community longevity, electricity consumption, and other metrics.

Image Signal Processing in the Context of Deep Learning Applications

Deep learning accelerators are a specialized sort of hardware architecture designed to enhance the computational efficiency of computers engaged in deep neural networks (DNNs) training. The implementation of DNNs in embedded vision applications might potentially be facilitated by the integration of energy-effective accelerators of deep learning into sensors. The lack of recognition for their significant impact on accuracy is a notable oversight. In previous iterations of deep learning accelerators integrated inside sensors, a common approach was bypassing the image signal processor (ISP). This deviation from the traditional vision pipelines had a detrimental impact on the performance of machine learning models trained on data that had undergone post-ISP processing. In this study, we establish a set of energy-efficient techniques that allow ISP to maximize their advantages while also limiting the covariate shift between the target dataset (RAW images) and the training dataset (ISP-analyzed images). This approach enables the practical use of in-sensor accelerators. To clarify, our results do not minimize the relevance of in-sensor accelerators. Instead, we highlight deficiencies in the methodology used in prior research and propose methodologies that empower in-sensor accelerators to fully exploit their capabilities.

Energy Efficient Techniques in 5G Communication: A Survey

Energy Efficient Techniques in 5G Communication: A Survey

Energy-efficient techniques in 5G communication

Energy-efficient techniques in 5G communication

An energy efficient hybrid membrane technique for the removal of toxic metals from aquatic environment

Electrodeionization (EDI), integrates electrodialysis (ED) and ion exchange (IE) technology. The purpose of this study is to utilize an EDI unit for eliminating copper from wastewater. This process was demonstrated using a three-compartment system, comprising a central compartment filled with a cation exchange resin (CER), separated from the anodic and cathodic chambers by two cationic exchange membranes (CEM), with the anode and cathode placed in the anodic and cathodic chambers, respectively. Purolite C 160 was used to showcase how operational variables, such as initial concentration, contact duration, resin dosage, and temperature, were optimized for the most effective copper removal during batch mode operation. The optimized conditions were an initial concentration of 100 ppm, a Purolite C dose of 0.5 g/L, a contact time of 1 h, and a pH of 8. At an initial copper concentration of 100 ppm and a supply voltage in the range of 10 to 25 V, the highest copper removal achieved was close to 99.98 per cent. For this study, copper was eliminated in the described experiments up to 99.98%, with energy consumption in the EDI unit varying between 3.87 and 25.29 kWh per kg of removed copper.

Energy Efficiency Assessment in Smart Homes: A Comparative Study of Energy Efficiency Tests

The energy efficiency of smart home technology, such as solar panels, lighting controls, thermostats, and smart appliances, was thoroughly assessed by the study. Notable energy savings were achieved by energy-efficient settings; smart ovens, washing machines, and refrigerators had average consumption reductions of 10% to 15%. When smart lights and dishwashers were configured in energy-efficient settings, their Energy Star ratings increased dramatically. During times when the thermostat is not in use, smart thermostats preserve comfort while cutting energy consumption by an average of 1°C. Consistent power generation from solar panels lessens reliance on the grid. The research promotes holistic energy efficiency techniques by highlighting cost savings, environmental advantages, and possible synergies when integrating several energy-efficient devices in smart homes. In order to improve domestic energy efficiency, future study fields include long-term evaluations, user behavior analysis, and smart grid integration.

Research Results of Municipal Sewage Sludge Applied for Building Material Production

The study presents the experimental results on the use of ash obtained as a result of incineration of municipal sewage sludge from the water utility of Izhevsk, Udmurt Republic, for construction material production. Research methods are given and the results of strength tests of building material samples are given. The impact of the manufactured samples of construction materials on the ecology of the surrounding space and human health is assessed. To achieve the set objectives of the research laboratory experiment and determination of indicators of investigated values of sludge, ash and water extracts with the use of devices and measuring instruments are used. On the basis of the research laboratories of Kalashnikov Izhevsk State Technical University and MUE “IzhVodokanal” of the Udmurt Republic experimental studies on incineration of sewage sludge and the use of the resulting ash as a substituting or modifying component of the construction mixture in the manufacture of experimental samples of ceramics, cement-sand mortar and concrete were carried out. Methodology of manufacturing and algorithm of ceramic (fired) and cement-based (nonfired) testing specimens are presented. Measuring means for determination of gamma-radiation intensity indices of ash, cement and their influence on human health are used. The algorithm of experimental research, applied energy-efficient techniques used in the experiment and analysis of the obtained experimental results are presented. The analysis of experimental research results of ceramic and cement-based products, as well as the analysis of radiation safety, migration of heavy metals is presented. The energy-efficient methodology of sewage sludge utilization and further ways of applying ash as an environmental and resource-saving component used in manufacturing of experimental building materials are considered.

Improvement of Energy Consumption in Fog Computing Via Task Offloading

The role of fog computing is to lessen the burden of Cloud and device computing. The purpose of this project is to solve the energy consumption and latency issues in the IoT 6G device as well as propose an approach to match the fog nodes with the IoT devices in task-offloading. This project implemented the Gale Shapley Stable Marriage Matching Game with a task-offloading energy efficiency technique. One-to-one matching is applied, in four fog nodes and four IoT devices according to the size of the task in IoT devices and energy in fog nodes. Simulation results prove that the proposed approach improves energy consumption and latency by 36.15% and 37.5% respectively.

Studies on energy efficient techniques for agricultural monitoring by wireless sensor networks

Wireless Sensor Networks (WSN) are pivotal for real-time monitoring in diverse sectors through the Internet of Things (IoT). However, current wireless communication protocols face challenges in integrating Precision Agriculture (PA) management systems with Artificial Intelligence (AI). PA optimizes nutrient supply and pesticide use, enhancing crop yield and product quality. Despite these advantages, structural changes in WSNs, including deployment, coverage, scalability, and energy consumption, hinder their potential. This paper conducts a comparative study of WSNs in agriculture, focusing on wireless protocols, energy harvesting, and efficiency procedures. The research delves into challenges faced by WSNs in agriculture, emphasizing the need for power-saving agricultural management strategies to ensure sustained, effective long-term monitoring. The study addresses integration issues, explores agricultural benefits, and proposes solutions to enhance WSN performance in dynamic field environments.

Smart Demand Side Management for Techno-Economic Analysis of Microgrid System

The world uses about 20 trillion kilowatt hours of electricity annually. Because of this great level of necessity, the use of green energy is growing and is becoming more and more significant in today's society. Demand-side management (DSM) programs are described as the procedures for planning, carrying out, and overseeing the tasks assigned by electric utilities to encourage customers to adjust their loads and energy usage in order to reduce power consumption and lower their electricity costs. It is utilized in smart grid systems to balance energy generation and consumption in a way that benefits both customers and utilities, to achieve the goals of utility energy policies, and to modify the quantity and quality of power and electricity used. Customers should have the capacity to control their energy consumption, especially during peak usage, to enable them to make better informed decisions. Demand response (DR) and energy efficiency techniques were employed by DSM to assist energy users in lowering their consumption. to increase resilience, economy, sustainability, and efficiency. The main objective of this study is to apply two demand side management strategies, examine power flow analysis on distribution networks utilizing IEEE rules both before and after PV cells are integrated, and assess the impact of doing so. The approaches' practicality is confirmed by comparing the simulation results from one technique with the other. For the end user, lowering their electricity cost is their primary goal.

Mevcut Konut Yapılarında Enerji Etkin ve Depreme Dayanıklı İyileştirme Yöntemlerinin İncelenmesi

Many studies are carried out in the rehabilitation of existing residential buildings, addressing the energy released during the pre-construction, construction, use and demolition stages of the building and circularity. Furthermore, in Türkiye, earthquakes are one of the primary influencers impacting the lifespan of buildings. However, a limited number of studies take a holistic approach, integrating both energy efficiency and earthquake resistance in existing residential buildings. To promote awareness on the subject, it is aimed to systematically examine the rehabilitation methods in reinforced concrete residential buildings. Seven studies were reviewed through literature analysis to investigate structural strengthening methods, energy efficient improvement techniques and their costs. The studies concluded that rehabilitation and strengthening were carried out in the structure and different building elements, and energy costs were reduced. Based on the data obtained, the steps to be followed in rehabilitating the residential buildings in this context were determined.

A Study on Energy-Efficient Wireless Sensor Network based on Machine Learning Techniques

Wireless sensor networks (WSNs) are advantageous when there is no existing infrastructure (such as in military applications, emergency relief efforts, etc.) and it is necessary to develop a network at a low cost. A predetermined routing protocol or intrusion detection system is not available to Wireless Sensor Networks because they are dynamic by nature and need separating the network's nodes to do this. Because nodes in the majority of WSN applications are mobile and rely on battery capacity and the availability of restricted resources, energy consumption is an important research area for carrying out a variety of activities in WSNs. Self-learning algorithms that function without scripting or human involvement can be effectively used to report this problem depending on the applications need. This study investigated different ML-based WSN systems and exploring the ML techniques for energy efficiency along with some open issues.

Adaptive squirrel coyote optimization-based secured energy efficient routing technique for large scale WSN with multiple sink nodes

In general, Wireless Sensor Networks (WSNs) require secure routing approaches for delivering the data packets to their sinks or destinations. Most of the WSNs identify particular events in their explicit platforms. However, several WSNs may examine multiple events using numerous sensors in a similar place. Multi-sink and multi-hop WSNs include the ability to offer network efficiency by securing effective data exchanges. The group of nodes in the multi-sink scenario is described through a distance vector. Though, the efficiency of multi-sink WSNs is considerably impacted by the routing of data packets and sink node placement in the cluster. In addition, many WSNs for diverse reasons existed in the similar geographical region. Hence, in this task, a secured energy-efficient routing technique is designed for a Wireless sensor network with Large-scale and multiple sink nodes. Here, the concept of an improved meta-heuristic algorithm termed Adaptive Squirrel Coyote Search Optimization (ASCSO) is implemented for selecting the accurate selection of cluster head. The fitness function regarding residual distance, security risk, energy, delay, trust, and Quality of Service (QoS) is used for rating the optimal solutions. The consumption of energy can be reduced by measuring the mean length along with the cluster head and multiple sink nodes. The latest two heuristic algorithms such as Coyote Optimization Algorithm (COA) and Squirrel Search Algorithm (SSA) are integrated for suggesting a new hybrid heuristic technique. Finally, the offered work is validated and evaluated by comparing it with several optimization algorithms regarding different evaluation metrics between the sensor and sink node.

Investigation of the Formability of AA6010 in an Integrated Forming and Hardening Process Aiming to Reduce the Energy Consumption in High Volume Production of Automotive Components

Hot deformation and in-die quenching of aluminum components for the automotive industry is a cost and energy efficient technique that has been developed and thoroughly evaluated in recent years. The performance of this process is considered higher when compared to traditional cold metal forming due to shorter process times, low-cost machinery, and a high level of structural integrity in fabricated parts. The work presented in this paper provides several approaches for the formability of age hardenable 6xxx alloy sheets when forming at different temperatures. Warm tensile testing and formability cup testing were carried out to investigate the alloy formability at different temperatures. The results indicate that the formability of candidate alloys is not significantly affected by deformation temperatures or conditions, which provides great freedom when designing an automated production process with high productivity and minimal environmental impact. The candidate alloy can be deep drawn without severe thinning at the whole temperature range, from room temperature (RT) to solutionizing temperature.