Limited Energy Sources Research Articles

Process parameters optimization of carbon nano tube based catalytic transesterification of algal oil

The requirement for different types of natural fuel and the peak in the price are big issues for mankind. Researchers are more concerned about using environmentally friendly fuel than limited energy sources now that public awareness has been raised. Different recyclable energy sources like nuclear, wind, solar, and biofuels are the best resources, whereas biofuel in different phases has been experimentally performed as one of the best representative candidates in terms of usage and the production process. The energy of the biomass is recyclable and also has properties similar to those fossil fuels. To develop the biodiesel with the help of the transesterification methodology, the catalyst used must be more effective to ensure faster catalytic activity. In various research journals, different varieties of homogenous type catalyst are being experimented with, but the reusability of the catalyst is impossible as compared with heterogeneous catalysts. The biodiesel yield is calculated using response surface methodology and Chlorella vulgaris algae oil as a feedstock and carbon nanotubes as a heterogeneous nano catalyst. The present work is performed under 15 experiments by considering 3 parameters: time of reaction, catalyst concentration, methanol to oil volume, and temperature, which is kept constant at 50 °C.

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in large-scale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network.

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

In recent years, Wireless Sensor Networks (WSNs) are attracting more attention in many fields as they are extensively used in a wide range of applications, such as environment monitoring, the Internet of Things, industrial operation control, electric distribution, and the oil industry. One of the major concerns in these networks is the limited energy sources. Clustering and routing algorithms represent one of the critical issues that directly contribute to power consumption in WSNs. Therefore, optimization techniques and routing protocols for such networks have to be studied and developed. This paper focuses on the most recent studies and algorithms that handle energy-efficiency clustering and routing in WSNs. In addition, the prime issues in these networks are discussed and summarized using comparison tables, including the main features, limitations, and the kind of simulation toolbox. Energy efficiency is compared between some techniques and showed that according to clustering mode “Distributed” and CH distribution “Uniform”, HEED and EECS are best, while in the non-uniform clustering, both DDAR and THC are efficient. According to clustering mode “Centralized” and CH distribution “Uniform”, the LEACH-C protocol is more effective.

Zero Energy IoT Devices in Smart Cities Using RF Energy Harvesting

The invention of batteries made it possible to store electricity for many purposes. One of the purposes is to keep the operations of WSN running without any interruptions. The main drawback of sensor nodes is their limited energy sources. The researcher introduces the energy harvesting (EH) concept for IoT-based WSNs to overcome energy limitations and charge the IoT devices. Many energy harvesting techniques have been introduced, such as solar, thermal, and flow-based ones, but radio frequency (RF)-based EH techniques received great attention from researchers due to their easy availability (from TV, radio, and wireless frequencies). In this paper, we have conducted a real-world experiment on Powercast energy harvesting devices and examined the behavior of sensed data in different scenarios, such as indoor, distance (feet/meters), and directional antennas. In our experiments, we have observed that when a device is removed from the charger, the energy scavenging process degrades and reaches a dead state. To stop a device from entering a dead state, we introduce a mobile charger technique to charge the device and find the optimal place for chargers and sensor devices. During mobile charging, we have also observed that when the directional antennas change their angle, the energy scavenging process degrades. To tackle these problems, we introduced two algorithms for directional and omni-directional antennas that efficiently solve the problem. Furthermore, we have obtained results for these scenarios and show that this technique has a promising output.

Region-of-interest based video coding strategy for rate/energy-constrained smart surveillance systems using WMSNs

Video surveillance systems that involve embedded visual sensor nodes have significant constraints due to their limited energy sources. Reducing the power consumption of the in-node processing and the required bandwidth while maintaining a high QoS is still a challenging task. The difficulty increases when a smart task must be performed on the received video in the destination. In this context, this paper proposes an energy-efficient video coding strategy based on a new and fast region-of-interest (ROI) detection method. The lightweight ROI detection method segments the frame into four regions. And the coding strategy aims to extract two different classes of the ROI for coding and transmission using variable quality levels based on their relevance. Furthermore, the strategy aims to exclude the regions of lower importance and any non-ROI that has insignificant movement. We assess the strategy’s ability to perform object recognition tasks at the destination under quality degradation. The performance results using different datasets demonstrate a better trade-off between awareness of ROI quality, energy consumption and bandwidth savings for the proposed strategy compared to other methods. This results in 96% reduction of bandwidth and 93% reduction in energy for some sequences at the expense of a 1-4 dB decrease in PSNR when compared to MJPEG standard. While the recognition accuracy of the YOLOv3 model at the destination outperforms the other techniques by about 4% to 22%.

Banking Support for Energy Security: The Customer Aspect

Within the increased concern for environmental protection, an important aspect is related to renewable energy not only because of the potential for carbon emission reductions but also because of energy security. Relying on renewable energy sources can be a basis for less dependence of the economy on limited energy sources, especially when considering their potential price fluctuations. The role of banks has already been recognized in the context of supporting the greater use of renewable energy solutions. In this paper, the focus is on green banking (GB) services and customers’ intention to adopt them. For its analysis, the theory of planned behavior (TPB) was applied. It was extended with environmental consciousness, as a predictor of attitude and behavioral intention. Moreover, relations between customer intention and its predictors were examined separately for the male and female gender. To the authors’ knowledge, this is the first research in which behavioral intention toward green banking services was investigated by including mentioned variables from the gender aspect. According to the obtained results, environmental consciousness, attitude and perceived behavior control have the largest influence on customers’ intention. In addition, from the gender aspect, unlike male, the intention of female customers is significantly affected by perceived behavior control.

Novel reliable and dynamic energy-aware routing protocol for large scale wireless sensor networks

<p>Wireless sensor networks (WSN) are made up of an important number of sensors, called nodes, distributed in random way in a concerned monitoring area. All sensor nodes in the network are mounted with limited energy sources, which makes energy harvesting on top of the list of issues in WSN. A poor communication architecture can result in excessive consumption, reducing the network lifetime and throughput. Centralizing data collection and the introduction of gateways (GTs), to help cluster heads (CHs), improved WSN life time significantly. However, in vast regions, misplacement and poor distribution of GTs wastes a huge amount of energy and decreases network’s performances. In this work, we describe a reliable and dynamic with energy-awareness routing (RDEAR) protocol that provides a new GT’s election approach taking into consideration CHs density, transmission distance and energy. Applied on 20 different networks, RDEAR reduced the overall energy consumption, increased stability zone and network life time as well as other compared metrics. Our proposed approach increased network’s throughput up to 75.92% , 67.7% and 9.78% compared to the low energy adaptive clustering hierarchy (LEACH), distributed energy efficient clustering (DEEC) and static multihop routing (SMR), protocols, respectively.</p>

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.

PREDICTION SYSTEM DATA MODEL IN OBTAINING ENERGY POTENTIAL OF BIOMASS BRIQUETTES COMPARED TO OTHER ENERGY SOURCES

Indonesia has rich production potential by treating and disposing of a large amount of waste. The need for new and renewable energy is increasing daily along with increasingly limited energy sources, such as fuel oil worldwide, including Indonesia. Organic waste has potency as the energy that needs to be appropriately managed by the city government, environmental services, entrepreneurs, and the community. However, the energy needs of the community and companies in development are increasing. The research aims to determine the consumption and trends of fuel used by the community and the potential of coconut shell waste as a raw material for making biomass briquette fuel. There are no research results for calculating community energy needs based on organic raw materials for coconut shell organic waste. The prediction and calculation methods used a linear regression model. The regression model can predict the community's energy needs based on the amount of waste generated and the energy potential from the briquettes, which has not become a top priority for energy. Other fuels such as electricity, LPG, kerosene, wood, and others influence the need for briquette fuel. The results showed that the need for briquettes is inversely proportional to the demand for other fuels following multiple linear regression with a high coefficient of determination (0.989). Briquette is an alternative fuel from organic waste, such as coconut shell waste, which is environmentally friendly for future life. Coconut shell waste affects charcoal weight, which follows from the linear regression and gives a good correlation test.

ADOPTING SMART BUILDING CONCEPT IN HISTORICAL BUILDING: CASE OF ABU JABER MUSEUM, JORDAN

Introduction: Heritage conservation attracts wide attention worldwide. Jordan has many heritage buildings that were preserved, rehabilitated, and adapted to new functions. However, these conservation efforts could not solve the energy consumption problem or maintain the economic and financial balance, thus reducing building efficiency, slowing down the conservation activity, and forcing us to consider new solutions, especially since Jordan has limited energy sources. Integrating smart technologies in historical buildings is widely effective in achieving the sustainability of their historical, symbolic architectural values. Purpose of the study: We aimed to explore the potential integration of smart technologies in cultural and heritage buildings. Methods: We suggested an alternative solution — adopting the smart building concept in the Abu Jaber Museum in Al-Salt (which recently got on the UNESCO World Heritage List since it has more than 1000 heritage buildings), using an automated lighting control system. Approach: We deployed the qualitative method and case study approach to investigate the potential of adopting the smart building concept in the historical building in Al-Salt, Jordan. The study sheds light on the possibilities of utilizing smart technologies in historical buildings in Al-Salt. The findings indicate that smart technologies can offer great opportunity in preserving the architectural heritage and raising the efficiency of heritage buildings. Novelty: The study provides a framework based mainly on automated lighting systems in historical buildings. For the first time, the focus was on historical buildings in Jordan and their performance.

Smart Estimation Model for Energy Performance Certificates of Residential Buildings

The wasted amount of energy has become a critical problem for many countries due to limited energy sources and energy production costs. These forced countries to increase energy usage awareness by making regulations in the construction sector, which might dramatically decrease energy consumption cause of the size of the domain. One of them is standardizing heating and cooling loads (HL/CL) to avoid energy waste. HL and CL need an advanced engineering process because of different parameters such as the thermal characteristics of the building, hot water supply, passive solar systems, etc. Hence, it can only be carried out by expert engineers in calculations. In this paper, a classification model as the decision support system is proposed for predicting the energy consumption of residents which is an efficient indicator of architectural features of the construction about energy consumption concept. The data is collected from architectural projects and energy performance certificates. Multilayer Perceptrons, Bagging, and Random subspace are used to predict the energy class of buildings. Based on findings, the most accurate results were achieved by Bagging. Moreover, main input features affecting the prediction performance of HL were revealed and classification success was observed.

Experimental Design and Analysis of Adaptive LED Illumination System

Light emitting diode (LED) luminaires are widely used in street, road, and tunnel illumination. It is necessary to detect their negative effects on power grid, especially in terms of power quality distortions. In addition, because of the limited energy sources, they have to be operated in most efficient way. This paper proposed an adaptive illumination system that can be used in highway tunnels. In this context, an experimental system that consist of LED luminaires with various power values that are possible to be used in tunnel illumination was implemented. Scenarios have been created for the designed system in accordance with possible tunnel illumination scheme. Siemens S7–1200 PLC was used to control these scenarios. FFT analysis of the measurements obtained with adaptive illumination scenarios was performed and power quality distortions were determined for different harmonic levels. Similar analysis was repeated for the non-adaptive system, and a detailed comparison was proposed. In addition, both systems were compared in terms of energy consumption. Operational characteristics of adaptive illumination systems were discussed by using the obtained data. Results show that total harmonic distortion (THD) for all luminaires are active situation is quite lower than adaptive illumination scenarios, where the values of current THD are 149.7 %, 119.9 %, and 85.8 % that scenario 1, scenario 2, and all luminaires are in active situation, respectively. On the other hand, as advantage, a power saving of 73.4 % and 60.3 % can be achieved compared to different adaptive illumination scenarios according to all luminaires are active.

Hierarchical stable election protocol for WSN‐based IoT inhabitant and environmental monitoring applications

SummaryWireless sensor networks (WSNs) play an essential role in IoT applications, especially for long‐term data gathering and monitoring applications. The deployment field and the limited energy sources are two critical constraints directly influencing WSN lifespan, and both must be considered while designing a routing protocol for WSN‐based IoT applications. Routing the data consumes a significant amount of energy in such an extensive network. Several hierarchical clustering protocols have been developed in recent years to address this purpose. We propose a new hierarchical stable election protocol (HISEP), an improved version of the Stable Election Protocol (SEP) equipped with the Dual CH election algorithm using a new enhanced threshold formula. Accepting nodes' heterogeneity and incorporating Voronoi‐based cluster formation ensures that the 2D/3D WSN network stays alive for an extended period and the HISEP algorithm will be suitable for habitat tracking and environmental monitoring applications. The simulation results show that the proposed algorithm outperforms other protocols in terms of routing efficiency and network lifespan.

Design and investigation of stability‐ and power‐improved 11T SRAM cell for low‐power devices

AbstractThe modern system‐on‐chips require stable and low‐power SRAM cells due to technology scaling and limited sources of energy. Therefore, a stability‐ and power‐improved 11T SRAM cell is proposed in this study. The proposed cell core is composed of a strong cross‐coupled structure of a conventional inverter with a N‐type stacked transistor and a Schmitt‐trigger (ST) with a double channel‐length pull‐up network. This coupled with a separate read path enhances the read static noise margin (RSNM). Moreover, a feedback‐cutting N‐type transistor is placed inside the cell core to improve the write static noise margin (WSNM)/write margin (WM). Though the presence of stacked transistors in access paths increases read delay (TRA)/write delay (TWA), it reduces leakage. This metric further minimizes with the aid of a stacked structure of latch core and double channel‐length of pull‐up transistor in the ST inverter. The moderate read/write frequency and single‐ended structure of the suggested cell result in low dynamic power. Simulated results by using 16‐nm CMOS at VDD = 0.7 V show that the proposed cell has the second‐best RSNM/WSNM, which is 4.65/1.44 times higher than that of the fully differential 8T (FD8T) cell, as the basic cell. Moreover, it reduces leakage power by at least 2.01 times and consumes moderate read/write power, nearly 1.44/1.81 times lower than that of FD8T cell, at the cost of 1.61 times area overhead. The proposed cell can eliminate read/write‐half‐select‐disturbance issues to support bit‐interleaving architecture to increase soft error immunity.

Sulfur Enhancement for the Improvement of Castor Bean Growth and Yield, and Sustainable Biodiesel Production.

Due to limited conventional energy sources, there is a need to find substitute non-conventional sources of energy to meet the societal demands on a sustainable basis. Crude oil and edible oil remain major import items in Pakistan, the deficit of which can be compensated by using biomass, preferably inedible oilseeds. Therefore, the current study evaluated the role of sulfur (S) fertilization for improving yield (seed and oil) and biodiesel value of castor bean, a potential inedible crop with minimum input requirements. For this purpose, a combined approach of field experimentation and laboratory analysis was conducted to explore the potential of two castor bean cultivars (DS-30 and NIAB Gold) against four S supply rates, namely, 0, 20, 40, and 60 kg S ha–1, in terms of growth, phenology, and yield parameters. Subsequently, the obtained seed samples were analyzed for biodiesel-related parameters in the Bio-analytical Chemistry lab, Punjab Bio-energy Institute, Faisalabad. The incremental S rates increased the seed yield for both cultivars, and the highest yield was recorded at 60 kg S ha–1 for NIAB Gold. For NIAB Gold, the oil content increased by 7% with S fertilization at 60 kg ha–1, and for DS-30, the oil content increased by 6% at 60 kg ha–1. As with incremental S fertilization, the oil yield increased on a hectare basis, and the quantity of biodiesel produced also increased. Importantly, the tested quality parameters of biodiesel, except biodiesel viscosity, were in the ASTM standard range. Overall, it has been concluded that castor bean is a promising and sustainable option for producing biodiesel as it is non-competitive to food crops and requires little input.

Renewable Energy Sources in the Era of the Fourth Industrial Revolution: a Perspective of Civilization Development

Every emergence and development of industrial revolution in the history of human civilization is based on the extensive utilization of different energy sources. More than fifty years, the discussion of limited energy sources and sustainable development has acknowledged huge importance from the environmentalists. This study expanded the horizon of the debate about primary and renewable energy, analyzed the trend of renewable energy sources driven by the fourth industrial revolution. In this context, the study discussed the aspects of The Paris Agreement 2015, the responses to climate change and sustainable growth. Results indicated that primary energy sources, such as coal, oil, natural gas, hydroelectricity and nuclear energy, are the major energy sources which boosted up past industrial revolutions and hold the largest share of energy at present; whereas the share of renewable energy sources, especially the wind and solar sources which is going to be accelerated by the fourth industrial revolution, will be risen to high record in the future. The fourth industrial revolution, which has surged directly driven by artificial intelligent technologies, is going to speed up the utilization of renewable energy sources, and the clean, non-polluting, renewable energy is going to take the place of primary one sooner or later.

A Privacy-Aware Approach for Managing the Energy of Cloud-Based IoT Resources Using an Improved Optimization Algorithm

Cloud-based Internet of Things (IoT) is a network of physical things controlled and monitored via the Internet to develop numerous smart systems. In this network, numerous physical things with different types of sensors are created that can be linked to the Internet. IoT devices need to work with limited energy sources for a long time, and wireless communications are the primary source of energy usage. As a result, the lifespan of IoT devices can be extended by minimizing radio communications. Because of the great capability of the IoT, the issue of resource energy consumption is raised. On the other hand, privacy concerns must be considered when designing cloud-based IoT applications. The current article introduces programming formulations of the mentioned issues using an improved ant colony optimization (IACO) algorithm. The method is combined with a new kind of local search algorithm to obtain higher-quality responses. Also, for responding to privacy concerns, a privacy-preserving method has been suggested. The suggested approach has been tested widely, and the outcomes have been compared to the genetic and particle swarm optimization (PSO) algorithms. The results indicated the good ability of the proposed method in terms of network lifespan and energy consumption.

An enhanced adaptive duty cycle scheme for energy efficiency and QoS awareness in wireless sensor networks

A Wireless Sensor Network (WSN) is a small device that has batteries and radios to connect to the internet. The main problem with WSNs is a limited energy source, energy studies needed to ensure that these sensor nodes can last longer, especially since a lot of energy is wasted during idle listening, overhearing, and data collision that occurs at the medium access control (MAC) layer. The common mechanism used for saving energy in WSN, specifically at the MAC layer is the duty cycle schedule. Duty cycling coordinates sleep-wake time sensor nodes to maximize network lifetime while achieving specific application goals such as high throughput or low latency. Duty cycling of every node should be adjusted separately at any runtime depending on the network conditions to achieve desired delay guarantees and energy efficiency. Recently, a few adaptive duty cycle schemes were introduced, these schemes have reduced energy consumption by some degree, this leaves an open end to the degradation of the quality of service. In this study, adaptive duty cycles enhanced with a priority queue where packet size is the parameter to adjust the duty cycle in order to get efficient energy consumption. A variant of packet size tested to ensure optimum quality of services (QoS). These factors determine the duration of a node’s listen period for various packet transmission scenarios and requirements. As the result, the proposed Enhanced S-MAC (ESMAC) shows an improvement in the energy consumption and QoS compared to the default MAC protocol and S-MAC protocol. The success of this project will contribute to the performance improvement of sensing devices.

ENERGY AWARE ROUTING FOR WIRELESS SENSOR NETWORKS

Wireless sensor networks are used in improving conditions in the practical field and real life which lead researchers and developers to further research it and work into improving this field. These networks consist of sensor nodes that can help acquire data and information about temperature and pressure dependent on the environment of the location which are sent from. After all that, we are bounded by a really important factor which can determine everything which is Energy. Since sensor nodes send data and information to web applications, they need an energy source to operate. Their main energy source is their batteries which offer limited source of energy. Hence, various protocols are introduced to help in many parameters of a wireless sensor network such as increasing lifetime and decreasing consumption of energy, in other words, increasing the Energy Efficiency (EF). In this paper, we evaluate consumption of average energy for various protocols used in this context after each complete logical round for these protocols, such as Energy Efficient Clustering Scheme and Stable Election Protocol. Finally, we used Matlab tool to generate results which indicate that the protocol used in this paper is efficient and reliable.

Literature Review: Energy Efficiency Mechanisms using Data Reduction in Wireless Sensor Networks (WSNs) Applications

It has been stated that the implementation of Wireless Sensor Networks (WSN) has majorproblems that can affect its performance. One of the problems he faced was the limited energy source (battery-powered). Therefore, in an attempt to use energy as best as possible, several mechanisms have been proposed. Energy efficiency in WSN is a very interesting issue to discuss. This problem is a challenge for researchers. This paper focuses on the discussion of how research has developed in energy efficiency efforts in the WSNs over the past 10 years. One of the proposed mechanisms is data reduction. This paper discusses data reduction divided into 4 Parts; 1) aggregation, 2) adaptive sampling, 3) compression, and 4) network coding. Data reduction is intended to reduce the amount of data sent to the sink. Data reduction approachescan affect the accuracy of the information collected. Data reduction is used to improve latency, QoS (Quality of Service), good scalability, and reduced waiting times. This paper discusses more adaptive sampling techniques and network coding. It was concluded that using data reduction mechanisms in target detection applications proved efficient compared to without using data reduction mechanisms. To save energy, data reduction (especially with adaptive sampling algorithms) can save up to 79.33% energy.