Unnecessary Energy Consumption Research Articles

A comprehensive review of sensor node deployment strategies for maximized coverage and energy efficiency in wireless sensor networks.

Wireless Sensor Networks (WSNs) have paved the way for a wide array of applications, forming the backbone of systems like smart cities. These systems support various functions, including healthcare, environmental monitoring, traffic management, and infrastructure monitoring. WSNs consist of multiple interconnected sensor nodes and a base station, creating a network whose performance is heavily influenced by the placement of sensor nodes. Proper deployment is crucial as it maximizes coverage and minimizes unnecessary energy consumption. Ensuring effective sensor node deployment for optimal coverage and energy efficiency remains a significant research gap in WSNs. This review article focuses on optimization strategies for WSN deployment, addressing key research questions related to coverage maximization and energy-efficient algorithms. A common limitation of existing single-objective algorithms is their focus on optimizing either coverage or energy efficiency, but not both. To address this, the article explores a dual-objective optimization approach, formulated as maximizing coverage Max ∑(i = 1) ^ N Ci and minimizing energy consumption Min ∑(i = 1) ^ N Ei for the sensor nodes, to balance both objectives. The review analyses recent algorithms for WSN deployment, evaluates their performance, and provides a comprehensive comparative analysis, offering directions for future research and making a unique contribution to the literature.

A Diagnostic Approach to Improving the Energy Efficiency of Production Processes—2E-DAmIcS Methodology

This article presents the issue of energy waste in manufacturing processes, focusing on reducing unnecessary energy consumption and CO2 emissions. A significant challenge in modern production is identifying and minimizing energy waste, which not only increases operational costs but also contributes to environmental degradation. An improvement methodology referred to as 2E-DAmIcS is proposed. A distinguishing feature of the methodology is a risk map of energy waste in the production process. Application of the methodology is demonstrated using the example of a lead–acid battery production process. It is shown that even small but well-diagnosed changes to the process make it possible to significantly reduce energy consumption. The proposed methodology offers practical tools for managers and decision-makers in various industries to systematically identify and minimize energy waste. It highlights the importance of cross-disciplinary collaboration among specialists in technology, energy consumption, and statistical analysis to optimize energy use. By applying this approach, companies can achieve both financial savings and environmental benefits, contributing to more sustainable production practices.

Chemiluminescence-based evaluation of styrene block copolymers’ recyclability

The study presents the chemiluminescence based (CL) evaluation of the recyclability of linear styrene block copolymers. This analysis can provide insight into the material’s degradation state and predict its suitability for further recycling cycles, helping to avoid unnecessary energy consumption during processing.The thermal stability of four similar copolymer structures − styrene-isoprene-styrene (SIS), styrene-butadiene-styrene (SBS) copolymers with different styrene/butadiene ratios, and styrene-ethylene-butadiene-styrene (SEBS) − was studied using isothermal and non-isothermal chemiluminescence (CL). The activation energies for oxidative degradation were calculated based on oxidation induction times indicated by the CL intensities evolution. The results, which highlight the influence of molecular structure on stability under aging conditions, as presented by the calculated for the activation energy (kJ mol−1), show the following sequence: SBS (styrene 30%) (117 kJ mol−1) ≈ SIS 120 (kJ mol−1) < SBS (styrene 40%) (176 kJ mol−1) < SEBS (180 kJ mol−1). The CL data were correlated with infrared (IR) spectroscopy and differential scanning calorimetry (DSC) data, providing a comprehensive understanding of the thermal stability and degradation mechanisms during recycling proces. The sequence of the composing units determines the degradation process, with weaker points predominantly attacked in the linear moieties of isoprene, butadiene, and vinyl segments. The experimental data indicate that SIS and SBS (30% styrene) copolymers degrade the fastest likely due to the rapid accumulation of hydroperoxide radicals. The SEBS copolymer also experiences significant degradation, but this occurs at higher temperatures (above 220 ⁰C) and progresses more gradually once it begins. In contrast, the SBS copolymer with 40% styrene content degrades more slowly and exhibits minimal mass loss, primarily due to the formation of less reactive keto degradation products.

Oven-Dried Cupuaçu and Bacuri Fruit Pulps as Amazonian Food Resources

The Amazon is one of the largest and most diverse biomes on the planet. Cupuaçu (Theobroma grandiflorum (Willd. ex Spreng.) Schum) and bacuri (Platonia insignis Mart.) are Amazonian fruit species appreciated for their sensory characteristics and promising availability of bioactive compounds. However, high moisture levels (&gt;80%) make these pulps susceptible to deterioration during storage. In this study, the oven-drying process was monitored to produce dry and more stable pulps. The process was monitored at 40 °C, 55 °C and 70 °C, and the bioactive compounds and antioxidant capacity were determined as quality indicators. In general, drying at 70 °C for 340 min produced dried cupuaçu and bacuri pulps with high levels of total phenolic compounds: 288 and 652 mg gallic acid equivalents/100 g, respectively. The hygroscopic evaluation suggested that both of the dried pulps should be stored at a relative humidity of &lt;40% to avoid rapid water adsorption and it is advised to carry out the oven-drying process until up to 12% moisture is reached for cupuaçu and 9% for bacuri to avoid unnecessary energy consumption. Thus, this study expands the potential of bacuri and cupuaçu pulps for application in food industries, contributing to the economic and social development of the Amazon region.

Development and performance evaluation of an indoor thermal environment control algorithm incorporating MET estimation model with object detection

As people increasingly spend time indoors, the significance of the indoor environment in influencing occupant quality of life is becoming more pronounced. Traditionally, indoor environment control primarily relied on fixed temperature settings, which failed to accommodate the diverse circumstances of occupants. This approach limited the creation of a comfortable indoor environment and the enhancement of energy efficiency. Consequently, there is growing interest in occupant-centric control (OCC), which integrates metabolic rate (MET) information, which is a critical factor in determining the thermal sensation of occupants. Previously, a method was developed to estimate MET by classifying occupant poses and detecting the objects they interact with from indoor images. This study aims to develop and experimentally validate an indoor thermal environment control algorithm (ITEC-algorithm) using the MET estimation model and assess its effectiveness and applicability in real building environments.The performance evaluation revealed that the ITEC-algorithm significantly enhanced the comfort ratios, achieving improvements of up to 59% compared to the fixed temperature control and 28% compared to the control methods that only used the pose classification model for MET estimation. The energy consumption varied depending on the activity and control method, with a reduction of up to 88% compared to fixed temperature control. These results indicate that thermal comfort can be enhanced while minimizing unnecessary energy consumption by incorporating the MET of the occupants. Consequently, it has been confirmed that the ITEC-algorithm effectively improves thermal comfort by managing the MET of various occupants.

Electric delivery vehicle routing problem optimization model with time-varying traffic congestion

In order to plan a reasonable scheme for the fleet travel and minimize the energy consumption of electric vehicles during traffic congestion periods, while relieving the mileage anxiety of delivery personnel, the pure electric delivery vehicle routing problem considering time-varying traffic congestion was studied. Firstly , a novel mixed integer programming model (TD-EVRP) is established to step for its extended model of EVRP, which especially characterizes the time-varying traffic congestion state and measures the energy consumption of electric delivery vehicles. This model also refines its constraints including customer time window, vehicle load, and vehicle charging requirements. Secondly, a traffic congestion avoidance strategy is introduced into the model, which allows vehicles to park and charge at customer points during peak traffic periods in order to actively avoid unnecessary energy consumption caused by traffic congestion. Finally, the improved ant colony algorithm is designed to acquire the optimal solution.

Neuro‐fuzzy‐based cluster formation scheme for energy‐efficient data routing in IOT‐enabled WSN

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

Occupant-Detection-Based Individual Control of Four-Way Air Conditioner for Sustainable Building Energy Management

In this study, individual control of a four-way air conditioner was developed based on the distribution of occupants to prevent unnecessary energy consumption during room-wide control. An occupancy detection algorithm was created in Python using YOLOv5 object recognition technology to identify the occupants’ distribution in space. Recorded video data were used to test the algorithm. A simulation case study for a building energy model was conducted, assuming that this algorithm was applied using surveillance cameras in commercial buildings, such as cafés and restaurants. A grey-box model was established based on measurements in a thermal zone, dividing one space into two zones. The temperature data for the two zones were collected by individually turning on the air conditioner for each zone in turns for a specific period. Manual closure was applied to each supply blade using a tape to provide cooling to the target zone. Finally, through energy simulations, the decreased rates in energy consumption between the proposed individual control and existing room-wide controls were compared. Different scenarios for the occupants’ schedules were considered, and average rates in energy savings of 21–22% were observed, demonstrating the significance of individual control in terms of energy consumption. However, marginal comfort violations were observed, which is inevitable. The developed control method is expected to contribute to sustainable energy management in buildings.

Research on control strategy of electric vehicle battery thermal management system based on MPC

To reduce the battery performance attenuation of electric vehicles under high and low temperature conditions, it is necessary to design a battery thermal management system to avoid unnecessary energy consumption, while improving safety and endurance. In this paper, a battery thermal management system is established. Considering the time-delay and nonlinear characteristics of the battery system itself, the control strategy is designed based on multivariate model predictive control (MPC), and the simulation is carried out based on MATLAB. Simulation results show that MPC controller can save energy and improve efficiency, and have a better control effect than PID control.

Exploration of Energy-Saving Technologies in Building Electrical System Design

Green energy conservation is the mainstream trend in the current development of the construction industry. The application of energy-saving technology in building electrical system design can effectively reduce energy consumption, avoid unnecessary energy consumption, and truly achieve energy conservation and environmental protection. Based on this, the article elaborates on the principles of energy-saving design in building electrical systems, and actively explores the application of energy-saving technologies from different perspectives such as optimizing power supply and distribution system design, adopting high-efficiency energy-saving lighting equipment, applying renewable energy, promoting smart home technology, and improving the efficiency of building electrical equipment.

An aeration requirements calculating method based on BOD5 soft measurement model using deep learning and improved coati optimization algorithm

Conventionally, the supply-side is over-aerated to meet the oxygen demand of biological wastewater treatment in wastewater treatment plants (WWTPs). Such systems do not consider the various influent loadings and actual oxygen consumption. It cannot respond effectively to fluctuating influent, resulting in unnecessary aeration and energy wastage. In this study, we developed a novel approach to calculate aeration requirements in real time. Firstly, the oxygen demand formula for biological nitrogen and phosphorus removal is modified by considering realistic factors (temperature, oxygen partial pressure and so on) to construct the aeration demand formula. Then, to address the problem that BOD5 in aeration formula is difficult to measure in real time, a novel hybrid model integrating Fuch mapping, reverse learning strategy, Coati Optimization Algorithm (COA), and Backpropagation Artificial Neural Network (BP-ANN) is proposed in this study. Finally, aeration requirements are calculated based on real-time water quality data. Experimental results show that the ICOA-BP model with 9 neurons in hidden layer predicted the optimum BOD5 concentration by achieving a 25.146 mean absolute error and a 0.735 coefficient of determination (all-R2) in all datasets. The optimization led to a 15.164 % aeration savings and 38.942 % energy savings. The proposed method of aeration calculation is automated and intelligent, responding to fluctuating changes in influent quality and reducing unnecessary aeration and energy consumption while meeting the requirements of effluent quality.

Time‐energy consumption optimal path‐constrained trajectory planning of excavator robotics

AbstractReasonable operation of each excavator joint under path constraints directly affects operation efficiency and energy consumption. Efficiency and energy consumption are considerably important performance indicators, particularly for large equipment, such as excavators. Therefore, we propose a quadratic trajectory optimization method based on time–energy consumption. With the pseudo‐path velocity, acceleration, and equivalent convex jerk as constraints, the interior‐point method was used to obtain the time–energy consumption optimal trajectory of an excavator. The time and energy consumption values for different weight coefficients were obtained, and the optimal trajectory planning of the excavator was achieved. Furthermore, the simulation results of the proposed method for lifting 70 kg of materials for a weight coefficient of 0.5 were compared with the result of second‐order cone programming. Experimental results showed that using proposed method to plan the motion of each joint reasonably and effectively reduced the pseudo‐path jerk, acceleration, and velocity peaks by 40.67%, 25.03%, and 12.54%, respectively, ensuring smooth excavator movement. It also improved the working efficiency of the excavator; reduced unnecessary energy consumption; and improved the stability, efficiency, and energy saving of the autonomous operation of the excavator.

Effectiveness of natural ventilation through single-sided window opening in air-conditioning rooms

Since many buildings are not equipped with fresh air systems, many people open windows to allow for natural ventilation while the air conditioner is running. However, prolonged window opening may cause unnecessary energy consumption. Additionally, the diverse ways of window opening lead to significant variations in indoor airflow distribution, consequently impacting ventilation efficiency. Therefore, this study used field experiments and computational fluid dynamics (CFD) to determine the air exchange efficiency and air change rate of single-sided natural ventilation in air-conditioning rooms by fitting the decay curves of tracer gas. CFD simulation was performed using the unsteady Reynolds-averaged Navier-Stokes (RANS) model to examine how window openings affect the room’s air distribution, and the computed indoor CO2 concentration was compared to the corresponding field experiment data. The results show that natural ventilation through the window opening induced constant heat and mass exchange between indoor and outdoor air, which caused extra cooling losses but was globally effective in eliminating indoor contaminants. The air exchange efficiency is influenced to varying degrees by factors such as the air supply register’s position, window opening percentage, and window opening position. The air supply register’s position exerted the most significant impact at 46.45%. Efficient indoor-outdoor air exchange can be achieved by opening windows intermittently. When the window is opened from one side and the opening percentage is 100%, the indoor environment can be improved by opening the window for about 10 min every 39 min for X-direction air supply and about 7 min every 39 min for Y-direction.

The Importance Of Vo2 Max In Young Soccer Players

"Soccer is a high-level skill sport that requires aerobic and anaerobic strength, as well as physical skills related to speed, agility, strength, and power. To meet these physical needs of soccer players, coaches now offer a variety of aerobic exercises that essentially use the lower and upper extremities, as well as large muscle groups. Correct technical performance of exercises protects not only from various injuries but also saves unnecessary energy consumption. For this reason, the purpose of this research is to critically analyze research that has examined the importance of aerobic endurance in young soccer players. The following databases were searched: Pub Med, Scopus, Google Scholar, CrossRef, etc. to find all relevant publications. The following keywords were used in this search: ""soccer"", ""young soccer players"", ""VO2 max"", and ""soccer performance"", along with adjectives such as ""effect"", ""change"" and ""impact"". It was found that the aerobic demands of soccer players are crucial and strategic for the game. Keywords: ""soccer"", ""young soccer players"", ""VO2 max"", ""soccer performance"", ""effect"", ""change"" and ""impact""."

Model predictive control for thermal comfort and energy optimization of an air handling unit system in airport terminals using occupant feedback

The dynamic fluctuations in occupant flow within airport terminals contribute to delays in the system's response under traditional feedback control. This imbalance between supply and demand can result in discomfort and unnecessary energy consumption. While Occupant-Based Model Predictive Control (OBMPC) has gained research interest as it leverages occupant forecasts to enhance control performance, there is limited knowledge regarding its application in the air-conditioning systems of airport terminals, mainly due to the intricate dynamics of occupant flow in terminal buildings. In this study, we proposed a model predictive control strategy based on dynamic occupant flow with the goal of fast responding to occupant flow and reducing energy consumption. We integrated mathematical formulas and the Anylogic simulation software to predict occupant variations in the baggage claim hall. The model predictive control strategy of the air conditioning system was proposed with the predicted occupant flow. The proposed strategy was evaluated throughout the whole cooling season in the validated simulation model of the air handling unit system of the baggage claim hall at an airport terminal. The results demonstrated that the proposed strategy could effectively respond to variations in occupancy and achieve 10% energy savings throughout the entire cooling season, compared to conventional feedback control. Our work presents a viable solution to address system regulation lag and reduce energy consumption without jeopardizing thermal comfort.

A Scenario-Based DVFS-Aware Hybrid Application Mapping Methodology for MPSoCs

Sound techniques for mapping soft real-time applications to resources are indispensable for meeting the application deadlines and minimizing objectives such as energy consumption, particularly on heterogeneous MPSoC architectures. For applications with input-dependent workload variations, static mappings are not able to sufficiently cope with the run-time variation, which can lead to deadline misses or unnecessary energy consumption. As a remedy, hybrid application mapping (HAM) techniques combine a design-time optimization with run-time management that adapts the mappings dynamically to the changes of the arriving input. This paper focuses on scenario-based HAM techniques. Here, the application input space is systematically clustered such that data inside the same scenario exhibit similar characteristics concerning workload when being processed under the same operating points. This static clustering of the input space into data scenarios has proven to be a good abstraction layer for simplifying the design and employment of high-quality run-time managers. However, existing state-of-the-art scenario-based HAM approaches neglect or underutilize the synergistic interplay between mapping selection and the usage of dynamic voltage/frequency scaling (DVFS) when adapting to workload variation. By combining mapping and DVFS selection, variations in the input can be either compensated by a complete re-mapping of the application, evoking a potential high reconfiguration overhead or by just changing the DVFS settings of the resources, offering a low-overhead adaptation alternative and thus significantly reducing the necessary overhead compared to DVFS-agnostic HAM. Furthermore, DVFS enables a fine-grained adaptation of a mapped application to the input data variation, e.g., by slowing down tasks with no impact on the end-to-end latency for the current input using low-frequency DVFS settings. It is shown that this combined approach can save even more energy than a pure mapping adaptation scheme, especially in the presence of data scenarios. In particular, scenario-based design operates as a catalyst for eliciting the synergies between a combined DVFS and mapping optimization and the peculiarities inside a data scenario, i.e., exploiting the commonalities inside a data scenario by perfectly tailored DVFS settings and task mapping. In this scope, this paper proposes two supplementary scenario-based DVFS-aware HAM approaches that consistently outperform existing state-of-the-art mapping approaches in terms of the number of deadline misses and energy consumption as we demonstrate in an empirical study on the basis of four different applications and three different architectures. It is also shown that these benefits still apply to target architectures with increasing mapping migration overheads, thwarting frequent mapping reconfigurations.

Study on the rigid-discrete coupling effect of scraper conveyor under different chain speed-load conditions

The complexity of the underground environment in coal mines often leads to varying load conditions during the operation of the scraper conveyor, which can affect the lifespan of its components and result in unnecessary energy consumption. A test platform for the scraper conveyor was constructed based on the similarity theory to measure torque, speed, chain tension, and scraper acceleration during transportation. A DEM-MBD model of the scraper conveyor was developed and validated through transport tests and similarity theories to analyze the rigid-discrete coupling effect under different chain speed-load conditions. The results revealed a stratification phenomenon and a Brazilian fruit effect in the movement of coal. The average velocity of the upper and lower coal layers gradually increased during the transportation, while the difference between them gradually decreased. As the load increased, the stacking density and height of coal between scrapers also increased, leading to a higher force exerted on the scraper and chain. As the chain speed increased, the stacking density and height of coal between scrapers decreased, along with a decrease in the force applied to the scraper and chain. The formation of three-body wear necessitates a specific positional condition. When the scraper (chain)- coal-deck plate (chute liner) forms a particle stagnation state, severe wear occurs on the parts. This study provides a foundation for analyzing the transport mechanism of scraper conveyor from the particle perspective, offers a simulation reference for analyzing the mechanical and tribological characteristics of the line pan and scraper chain, and serves as a guideline for the future development of transportation state monitoring and the optimization and enhancement of components under different working conditions.

UGV에서 효율적인 노면 모니터링을 위한 퓨전 센서 시스템

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of =10, =10.

Secure multi-cloud resource allocation with SDN and self-adaptive authentication

The multi-cloud-based resource allocation is based on multiple constraints and multiple configurations that may vary based on customer requirements and cloud service providers' schemes. Here, security is a serious concern that can be ensured by an authentication scheme based on the Self-Adaptive Flower Pollination based RSA (SA-FPRSA) scheme to ensure data integrity. The SA-FPRSA plays an initial role in ensuring security by enabling authenticated data availability. On the other hand, the resource allocation to the Virtual Machine (VM) is a challenging task due to overwhelmed data in a VM that may face unnecessary energy consumption and time complexity issues. Therefore, the proposed model has introduced a Self-adaptive Heap-based optimizer (HBO) for Multi-Cloud based resource allocation. The performance of the models is evaluated. The proposed model attained an Efficiency of 99.09% and Security of 98.72%. Thus, from the results, the recommended model is proven to be better than the existing models.

ENVIRONMENTAL IMPACT OF CRYPTOCURRENCY MINING: SUSTAINABILITY CHALLENGES AND SOLUTIONS

The rapid growth of cryptocurrencies over the past 14 years has led to increased deep-level mining activities. This research aims to explore the environmental impacts resulting from the surge in crypto mining and proposed solutions to mitigate these impacts. Cryptocurrencies, gaining popularity as alternative investments and global payment tools, have significantly boosted crypto mining activities. However, the increasing number of transactions requiring computer validation has resulted in adverse consequences for the environment, particularly in terms of substantial energy consumption. Literature review and systematic analysis were conducted to comprehend the environmental impact of crypto mining, focusing on major cryptocurrencies such as Bitcoin, Ethereum, and others. The analysis highlights that crypto mining, especially Bitcoin, requires a significant amount of electricity, leading to a substantial carbon footprint and broad environmental repercussions. Proposed solutions to address the environmental impact of crypto mining include the use of renewable energy sources such as solar and wind power, enhancing the efficiency of specialized mining devices (ASICs), and exploring more energy-efficient consensus mechanisms like Proof of Stake (PoS) compared to the currently utilized Proof of Work (PoW). Reducing redundancy in blockchain technology has also been identified as a crucial step in minimizing unnecessary energy consumption. However, this research has limitations concerning data consistency, a comprehensive understanding of overall environmental impacts, and continuous technological changes in the crypto world. Therefore, future research should focus on developing more efficient consensus mechanisms, effective policy frameworks and governance, as well as real-world implementation studies to evaluate the sustainability solutions proposed.