Limited Energy Sources Research Articles

Towards Ensemble Feature Selection for Lightweight Intrusion Detection in Resource-Constrained IoT Devices

The emergence of smart technologies and the wide adoption of the Internet of Things (IoT) have revolutionized various sectors, yet they have also introduced significant security challenges due to the extensive attack surface they present. In recent years, many efforts have been made to minimize the attack surface. However, most IoT devices are resource-constrained with limited processing power, memory storage, and energy sources. Such devices lack the sufficient means for running existing resource-hungry security solutions, which in turn makes it challenging to secure IoT networks from sophisticated attacks. Feature Selection (FS) approaches in Machine Learning enabled Intrusion Detection Systems (IDS) have gained considerable attention in recent years for having the potential to detect sophisticated cyber-attacks while adhering to the resource limitations issues in IoT networks. Apropos of that, several researchers proposed FS-enabled IDS for IoT networks with a focus on lightweight security solutions. This work presents a comprehensive study discussing FS-enabled lightweight IDS tailored for resource-constrained IoT devices, with a special focus on the emerging Ensemble Feature Selection (EFS) techniques, portraying a new direction for the research community to inspect. The research aims to pave the way for the effective design of futuristic FS/EFS-enabled lightweight IDS for IoT networks, addressing the critical need for robust security measures in the face of resource limitations.

Resilience-centered optimal sizing and scheduling of a building-integrated PV-based energy system with hybrid adiabatic-compressed air energy storage and battery systems

This study investigates the economic and resilience co-optimization of a decentralized hybrid energy system (HES) within scenarios involving limited energy sources and a hybrid energy storage solution. The HES is comprised of a building-integrated Photovoltaic (PV) system incorporating an adiabatic compressed air energy storage (A-CAES) and batteries, with the main grid, serving as a backup. A two-stage sizing-scheduling model is proposed to optimize the configuration, minimize lifetime costs, and enhance both long and short-term resiliency while achieving an optimal schedule. The charging/discharging transition time of A-CAES is also adopted in the operational model. The results indicate a substantial annual resiliency improvement of approximately 41.1 %, with optimal sizing and energy storage integration. Additionally, they highlight the cost-effectiveness of the PV/A-CAES system while emphasizing the higher self-sufficiency achieved by the PV/A-CAES/battery system, attaining an electrical load management ratio of 47.3 % and a PV self-consumption rate of 96 %, a 6 % improvement over HESs with individual A-CAES. Furthermore, it is presented that under an optimal operational condition, even with the highest PV power availability during grid interruptions, HES relying solely on individual A-CAES could meet 94 % of the load demand. Integrating a fast-response battery enhances this resiliency to 100 %, effectively reducing PV-power curtailment, particularly during grid interruptions and A-CAES transitions.

Piezoelectric energy harvesting from the atomic oxygen hypervelocity impact in low Earth orbit

Atomic oxygen (AO) in low Earth orbit (LEO) can cause severe progressive damage to spacecraft orbiting at hypervelocities over 7 km/s; therefore, all space-graded materials need to be qualified for protection against these high energy impacts. Instead of treating AO as a threat, can we use these atomic impacts, which have enough energy to cause material failure, to generate power? Despite its potential, no attempt has yet been made to utilize the hypervelocity impact energy of AO. Thus, we propose a piezoelectric energy harvesting method through the hypervelocity AO impact in LEO. In this study, energy harvesting with various configurations was experimentally demonstrated using a conventional lead zirconate titanate (PZT) ceramic plate under oxygen plasma exposure to simulate AO impact. The average output power density was 4 W/m2 under 1.43 × 1016 atoms/cm2s effective AO flux conditions. Our study provides a new method for generating energy in a space environment with limited energy sources.

Asymmetric wake-up scheduling based on block designs for Internet of Things

In general, wireless sensors operate with a limited energy source, and energy efficiency is a critical design issue. In order to extend the operation time of wireless sensors, there have been many energy efficiency neighbor discovery protocols designed for wireless sensor networks (WSNs) such as Quorum-based, prime-number-based, and block-design-based protocols. Among them, the block-design-based approach was known to be more effective solutions for neighbor discovery in terms of the worst-case discovery latency for a given duty cycle. However, the original block- design-based approach is only applicable to a sensor network where all sensors have the same duty cycle. In order to expand a block-design-based neighbor discovery solution to asymmetric WSNs, we introduce a new neighbor discovery protocol (NDP) that combines two block-design-based schedules to produce a new set of discovery schedules for asymmetric WSNs. Furthermore, by using the Kronecker product method, we prove that any pair of neighboring sensors in the proposed protocol has at least one common active slot within a length of their discovery cycle. Furthermore, the results of the simulation study show that the proposed method is better than representative NDPs (such as Quorum, U-Connect, Disco, SearchLight, Hedis, and Todis) in terms of discovery latency and energy efficiency.

EVRP: A novel geometrical based energy efficient eye vision routing protocol for wireless sensor networks based on the k-means algorithm

The limited energy source used in wireless sensor networks (WSNs) is a crucial aspect when designing a routing protocol. Developing a routing protocol that tries to maximize energy utilization can significantly improve the system's lifetime and balance energy consumption. A novel geometrical-based energy-efficient routing protocol called "EVRP" is presented in this paper. The Cluster generation process is based on the k-means algorithm for nodes grouping. To choose a cluster head (CH) for each cluster, a cost function is calculated for each node considering factors such as remaining energy level for the node, and the mean distance between the node and its neighbors. In the inter-cluster routing phase, a novel geometrical-based routing protocol is introduced considering factors such as the distance to a base station (BS), remaining energy level, and distance between CHs to find the optimal path between BS and CH. The cluster structure is integrated with direct communication in addition to a dynamic clustering mechanism based on the optimum number of CHs and a load balancing mechanism to reduce the load on CHs near BS. The simulation results demonstrate that in different scenarios, the proposed protocol can significantly increase the network lifetime, network throughput, and network stability by 52 %, 45 %, and 30 % respectively compared with the EECRAIFA protocol and by 100 %, 97.6 %, 64.3 % respectively compared with D2CRP protocol.

An efficient intrusion detection model based on convolutional spiking neural network

Many intrusion detection techniques have been developed to ensure that the target system can function properly under the established rules. With the booming Internet of Things (IoT) applications, the resource-constrained nature of its devices makes it urgent to explore lightweight and high-performance intrusion detection models. Recent years have seen a particularly active application of deep learning (DL) techniques. The spiking neural network (SNN), a type of artificial intelligence that is associated with sparse computations and inherent temporal dynamics, has been viewed as a potential candidate for the next generation of DL. It should be noted, however, that current research into SNNs has largely focused on scenarios where limited computational resources and insufficient power sources are not considered. Consequently, even state-of-the-art SNN solutions tend to be inefficient. In this paper, a lightweight and effective detection model is proposed. With the help of rational algorithm design, the model integrates the advantages of SNNs as well as convolutional neural networks (CNNs). In addition to reducing resource usage, it maintains a high level of classification accuracy. The proposed model was evaluated against some current state-of-the-art models using a comprehensive set of metrics. Based on the experimental results, the model demonstrated improved adaptability to environments with limited computational resources and energy sources.

Passivated Emitter and Rear Cell (PERC) Assessment and Potential in Palestine

Palestine has limited energy sources and must import different fuels from neighboring countries, hence renewable energy sources like solar energy are necessary. One of the most important and promising clean renewable energy sources available today that also helps cut expenses and effort is solar energy. Solar energy is captured and stored by solar panels. Solar panels come in a wide variety now, and new kinds are always being developed to maximize solar energy absorption, require the least amount of maintenance, and endure a longer time. This study will compare the performance of the polycrystalline solar cell unit and the PERC unit, both of which will be impacted by Palestine's environment, by conducting an experimental investigation into the behavior of the unit. The PERC panel's temperature was lower than the polycrystalline panel's because of the addition of a heat-reflecting layer, which increased the PERC panel's efficiency. The efficiencies of the two panels were compared at the same level of solar radiation. It has been demonstrated that PERC efficiency is higher when solar radiation is the same. The solar energy absorbed in the PERC panel actually increases because of the reflective coating that helps the PERC cell to optimize its benefit from the sun's radiation. When polycrystalline and PERC panels were compared, the former produced more electricity per unit of area

Analysis of the Science Literacy Competency Profile of High School Students on Limited Energy Sources

Learning in the 21st century requires students to have the ability to face the times. One of the essential skills that must be possessed is scientific literacy. Scientific literacy is using scientific knowledge to identify questions, acquire new knowledge, explain scientific phenomena, and draw conclusions based on scientific evidence. This research aims to describe the scientific literacy profile of high school students in the subject matter of limited energy sources. Three competency indicators refer to PISA 2018: a) Explaining phenomena scientifically, b) Evaluating and designing scientific investigations, and c) Interpreting data and evidence scientifically. This research uses a quantitative approach with a descriptive analysis method. The study was conducted in June 2023 in the Even Semester of the 2022/2023 Academic Year at a public high school in Surabaya. The population in this study was 60 students. The instrument used is a scientific literacy test developed with a valid category and a reliability value of 0.811. The results of this study were that students' scientific literacy competency profiles were obtained at 37% low, 33% sufficient, 18% high, and 12% very high. As for the results of the percentage of students' scientific literacy abilities based on scientific literacy competencies, the highest category in the first competency of 63%, namely explaining phenomena scientifically, while the second competency of 62%, namely evaluating and designing scientific investigations obtains a lower percentage than the first competence, and 56%, namely interpreting data and evidence scientifically, has the lowest result among the three scientific literacy competencies.

Analysis of Efficiency Characteristics of a Deep-Sea Hydraulic Power Source

Deep-sea submersibles carry limited energy sources, so a high efficiency of the equipment is required to improve endurance. In the deep-sea environment, the hydraulic power source is filled with oil, which causes structural deformation of the power source and changes in the physical properties of the medium, leading to unknown changes in the efficiency characteristics of the power source. In order to explore the efficiency characteristics of the deep-sea hydraulic power source composed of a gear pump and a DC (direct current) brushless motor in a variable sea depth environment, we undertook the following. First, considering the effects of seawater pressure and temperature on the physical properties of the medium and the radial clearance deformation of the gear pump, a mathematical model for the total efficiency of the hydraulic power source was established. The results indicate that the deformation of the pump body is mainly determined by the seawater pressure and working pressure. Subsequently, by analyzing the effects of the two factors on the efficiency of the power source, respectively, when the oil temperature range is large enough, the total efficiency of the power source will increase and then decrease under six sea depths; the total efficiency of the power source decreases with the increase in the rotational speed. However, in a land environment, the trend of the efficiency characteristics of the power source is opposite to that of the remaining six deep-sea environments, both in terms of oil temperature and rotational speed. Finally, the efficiency trend of the power source with changes in sea depth under rated conditions was obtained. Under different sea depth ranges, the optimal operating oil temperatures and suitable rotational speed ranges of the power source could be obtained. This paper could provide a certain theoretical basis for the research and development of deep-sea equipment.

Reducing the Highway Networks Energy Bills using Renewable Energy System

Jordan has significant renewable energy potential due to its remarkable geographical location and climate conditions. This potential elevates engaging several innovative renewable alternatives in energy development, which may efficiently minimize the excessive import of traditional energy sources. The objective of this research is to study the potential of utilizing clean and affordable solar energy along roadways such as Jordan’s Desert Highway-15 to be in line with the United Nations Sustainable Development Goals (UN-SDG’s) by installing selected solar panels that possess adequate friction and the ability to allow solar radiation to reach the solar cells, in addition to allowing the load to be bypassed around the cells. The shoulder of the highway, with a length of 315 km and a width of 3.0 meters, has been exploited in order to supply the neighboring areas with energy for those roads, particularly those paved roads, which are poorly lit at night. Furthermore, this study provides direction and guidance concerning the structural performance of non-traditional pavement materials, which are a form of subgrade or pavement reinforcement. The performance of a prototype board on a variety of structural bases has also been evaluated. Overall, this paper found that it is possible to design a solar road panel to withstand traffic loading and that the concrete structural base allows for a significant improvement of the analyzed prototype design, especially in countries with limited energy sources and dependent on imports such as Jordan. Doi: 10.28991/CEJ-2023-09-11-019 Full Text: PDF

Motion control of mechanical systems under conditions of force-moment uncertainty of the external environment based on the principle of the Archimedes’ lever

If it is impossible to directly control the movement of a heavily loaded mechanical system with a limited power source of energy (HM-motor) under the condition of a force-torque uncertainty of the medium, it is shown that an effective solution to this problem is the transition to robust control, the technical implementation of which is achieved by embedding a gearbox-variator in the control system, which is one of the modern modifications of the classic Archimedes’ lever. Keywords: source of limited mechanical power, force-torque uncertainty, force-torque robustness, inherent robustness, mechanical characteristic, gearbox-variator, power sources, robustness index, dynamic self-levelling, d'Alembert's principle, Archimedes' lever equation. lutikovaolga@mail.ru

Development and thermophysical analysis of binary eutectics phase change materials for solar drying application

Background: In the past 30–40 years, conflicts over limited conventional energy sources and the negative climate change caused by them have attracted researchers and analysts to new, clean, and green energy technologies. Thereby reducing the consumption of conventional fuel and the negative impact on the climate. The production of alternative energy in the form of thermal energy storage using phase change materials (PCMs) is one of the techniques that not only reduces the gap between the supply and demand of energy but also increases the stability of the energy supply. The tendency of PCMs to melt and solidify over a wide temperature range makes them more attractive for use in many applications. The effective and efficient storage of solar energy by PCM has the potential to significantly advance the use of renewable energy. Methods: Organic non-paraffin compound beeswax (BW) mixed with other non-paraffin compounds stearic acid (SA), Palmitic acid (PA), Myristic acid (MA), and Lauric acid (LA) in different compositions with the help of magnetic stirrer at 50–60°C for 3–4 hours to prepare BWSA, BWPA, BWMA, and BWLA eutectic PCM. Results: Prepared eutectics melt and solidify in the temperature range 36–56°C and with latent heat in the range of 155–211 kJ/Kg. Conclusions: Due to suitable temperature and good latent heat storage range, it is a good choice as thermal energy storage, for solar drying applications.

Study on adaptive thermal comfort model and behavioral adaptation in naturally ventilated residential buildings, Jimma Town, Ethiopia

Ethiopia boasts abundant untapped energy resources, yet access to electricity remains limited (30%), with frequent and prolonged outages. Research on occupant thermal comfort in Ethiopia remains limited as well. This study aimed to investigate comfort temperature, adaptive models, and behavioral adaptations in naturally ventilated residential buildings in Jimma town, Ethiopia. Our thermal comfort field survey involved 870 occupants from 104 houses, generating 5220 datasets between February and September 2020. Indoor environments were simultaneously measured using handheld digital instruments. Notably, the subjects lacked electrical fans, space heaters, or air-conditioners, relying instead on charcoal and plant residue for cooking and heating.Overall, only 56.2% of subjects felt comfortable within the thermal sensation scale's defined band, while 70.9% preferred either warmer or cooler environments, and 63.4% accepted their environments. The mean comfort temperature was 23.3˚C ± 3.44 based on all data. Our adaptive model predicted a 1.82 K perturbation in outdoor running mean temperature, resulting in a unit change in indoor comfort temperature. Significant occupant behavioral adaptation through operable environmental controls, clothing choices, and activity levels was observed. Our model is essential for building simulation in Ethiopia, emphasizing the need for an Ethiopian adaptive standard. Given the limited modern energy sources, understanding low-energy thermal comfort and human adaptation becomes vital for sustainable living conditions.

New anergy tide control strategy based on Eviews econometric model

Abstract Along with the limited conventional energy sources and the increasingly prominent environmental problems, new energy sources characterized by “environmental protection” and “renewable” are gradually gaining the attention of many countries in the world. In order to accelerate the development of new energy, countries have introduced control strategies for the new energy industry, but the effect is that the seeds are widely planted and thinly sown. This paper constructs a control strategy for new energy trend based on Eviews econometric model through sliding average model calculation, multiple linear regression prediction, statistics of total new energy, new energy usage and new energy market share calculation. The strategy was also experimented, and the experiments analyzed and predicted the development of new energy in the world and China under the new energy trend. The results show that under the Eviews econometric model, the share of world new energy consumption in the total energy consumption market increases from 25% to 80%, while in China the new energy represented by natural gas grows from 698 million cubic meters to 9,671 million cubic meters, a full 70.76%, and with the development of the economy new energy consumption will rise from 5 billion to 11 billion.

Genelleştirilmiş regresyon modelleri kullanılarak enerji talebi ve tüketimi için bir modelleme yaklaşımı

Energy management is an important process for maintaining available energy resources and meeting basic household energy needs. Many studies seek to optimize the household energy consumption patterns to manage the load demand and minimize energy costs. Adopting such optimizations in conflict-affected countries is more beneficial due to limited energy sources. This study identifies the optimal energy consumption model for households in northern Syria. The objective is to identify the most cost-efficient energy sources while considering the prices, average monthly household income, the main source of electricity, battery storage capacity, and energy demands for space heating, water heating and cooking. One hundred and thirty-six (136) standardized surveys of residential households are collected and used as a test case. Statistical analysis of the data was carried out using the R-Studio software, where Poisson regression and negative binomial regression were employed. Findings revealed that the Negative Binomial (NB) model used has high explanatory power. In addition, the energy sources used for space heating and water heating have a direct impact on monthly expenditures. The produced model showed that the most cost-effective energy sources are coal for space heating and natural gas and kerosene for water heating.

Dealing with Aspects of Performance and Environmental Impact of Aircraft Engine with Thermodynamic Metrics

The limited energy source indicates the necessity of efficient energy consumption in every field of life. Especially, the prompt growth in aviation sector makes this issue more important. In this study, effects of power settings on several thermodynamic indicators regarding low by-pass turbofan engine (LBP-TFE) are investigated. For this aim, the energy and exergy analyses are implemented to the system of turbofan engine for eighteen operating points. According to performance analysis, thrust value of the LBP-TFE changes from 10.77 kN to 71.8 kN throughout RPM values. According to exergetic findings, relative exergy losses from Fan outlet decreases from 52.34 % to 30.58 % whereas exergy efficiency of the LBP-TFE increases from 10.9 % to 30.1 %. Considering improved exergy efficiency, it changes 25.03 % and 41.03 % at the same RPM intervals. As for environmental assessments, environmental effect factor (EEF) of LBP-TFE diminishes from 5.8 to 1.32 while ecological effect factor decreases from 9.16 to 3.31. Finally, specific irreversibility production of LBP-TFE decreases from 0.4811 MW/kN and 0.2716 MW/kN. Considering these outcomes, behaviour of the investigated metrics regarding main components is different from each other. Therefore, the results of these parameters calculated for the whole engine could help understanding optimum running point in terms of exergetic and environmental sustainability.

Forest biomass and bioenergy supply chain resilience: A conceptual decision-making model

In recent years, with the increasing concerns about the global warming, limited non-renewable energy sources, and uncertainty in renewable energy sources accessibility, how to continuously supply clean sources of energy in form of a resilient system structure has become a topic of intense scholarly interest. Based on the milestone literature on the research field of renewable and non-renewable energies, forest biomass and bioenergy have significant compatibility with both industry and nature. Through this, our proposal contributes to supply one of the most available clean energy sources and direct this type of energy needed in an optimal way. The essential resilience components are determined and classified according to the theoretical and practical concepts. Then, this research is made use of a conceptual decision-making model based on third order confirmatory factor analysis to explore the interactions among the components in form of a network. The results reveal that the proposed model has sufficient validity and reliability levels. Finally, key interactions among the elements of model that contributed to most of the impacts on the forest biomass and bioenergy supply chain resilience were found to be optimized constantly within the network.

Research Progress on the Application of Biomass-Based Porous Carbon Materials in Lithium Battery Electrode

With the growth of the world population, limited non-renewable energy sources, oil and coal mine are becoming increasingly exhausted. The long-term consumption of fossil energy not only causes an energy crisis, but also makes ecological and environmental problems more significant. Therefore, lithium batteries, as an energy storage tool, have been studied by a number of researchers due to their advantages such as high specific energy, little environmental pollution and long cycle life. For secondary batteries, the performance of electrode materials affects the development of batteries. Biomass-derived porous carbon materials have the advantages of cheap, easy-to-obtain from raw materials, large specific surface area, many pore structures, strong electrical conductivity, adjustable pore structure, and have been widely concerned in recent years and used as electrode materials for energy storage lithium batteries application. In the process of charging and discharging lithium batteries, porous carbon materials can provide more space for lithium ions to be embedded and removed from the electrode, improving the capacity and thus the efficiency of the battery. In this paper, the main preparation technology of biomass-derived carbon materials and the improvement technology of heteroatom doping are reviewed. The application effects of different activation methods on the porous carbon materials prepared by plant materials in the negative electrode of lithium battery are compared, and the performance of the materials in practical application is evaluated in the paper, which provides reference materials for the beginners of using biomass structure to prepare porous carbon materials.

A Lightweight Self-Organized Friendly Jamming

Wireless networks are inherently more susceptible to attacks than their wired counterparts. The easiness of transmitting and receiving messages for legitimate users is also valid for attackers, and traditionally cryptographic methods have been exploited to provide security and privacy. However, these are far from fulfilling the needs of current technologies, such as IoT devices with limited computation and energy sources. Therefore, friendly jamming (FJ) emerges as a promising solution to this problem due to being a computationally cheap and energy-efficient operation. However, one of the tradeoffs of FJ is its viability. Although some studies try to cope with this challenge, they are complicated and focus on old technologies. This study proposes a lightweight and flexible FJ scheme to address these challenges. We also demonstrate that our model has the same performance as one of the mentioned studies above in a more straightforward way.

A novel method of improving energy efficient QoS in FANET using Correlation And Confidence Based Gradient Clustering Metric approach

SummaryFlying Ad‐hoc Networks (FANETs) being the most popular wing of the research in recent time had many evolutions to cope up with the growing demands in defense and other civil applications. Limited number of nodes, limited source of energy and increased mobility of the drones were the major problems to be addressed while building an effective FANET. Drones were aerial‐nodes that are driver‐less; their actions were prescribed by means of algorithms devoid of some human relations and could be easily deployed in the system. Hence this article aims to propose a novel framework to improvise the energy efficiency of FANET through Correlation and Confidence based Gradient Clustering Metric (CC‐GCM) approach in the clustering algorithm. The results obtained through the proposed model are proven to be outstanding by producing an improvised performance efficiency and augmented energy efficiency. This article also involves a comparative study of the performance attributes with the existing models with tabulated values and graphs in terms of energy consumption, average end‐to‐end delay, cluster building time, average cluster life time, and Packet Delivery Ratio (PDR).