Energy Efficient Techniques Research Articles

Determining the kinetic and energy parameters for a combined technique of drying apple raw materials using direct electric heating

The development of technology and drying equipment tackles a triune task: to intensify drying processes, to save energy, to ensure that proper product quality is achieved. This issue is resolved by modern advancements by supplying thermal energy throughout the entire sample volume. The simplest option among the known techniques is to heat wet raw materials by passing an electric current directly, with an external blowing by a hot heat carrier.
 This paper reports an experimental study of the combined process of drying apple raw materials using direct electric heating.
 The influence of control factors such as the field intensity and a heat carrier temperature on the kinetic parameters of the process has been determined, namely: the duration of the combined drying of apples, the rate of moisture removal, and a change in the temperature of the sample.
 It was established that the application of additional electric heating with an electric field intensity of 20–40 V/cm during convective drying with a heat carrier temperature of 25–55 °C reduces the duration of apple dehydration by 3‒5 times.
 Permissible limits for changing the combinations of basic technological parameters have been determined, as well as the rational modes for treating raw materials in order to ensure the predefined quality of finished products. Such combinations of technological parameters of heating, in particular the intensity of the electric field and air in the dryer, are 30 V/cm+40 °C, and 25 V/cm+55 °C.
 The energy parameters of the proposed combined technique of drying apple raw materials have been determined. It was established that the specific energy consumption for the removal of 1 kg of moisture at direct electric heating is 2,350–2,400 kJ/kg (0.66 kWh/kg).
 The study performed could provide a prerequisite for devising an energy-efficient technique for the combined drying of fruit and vegetable raw materials using direct electric heating

Energy Efficient Clustering Algorithm Based on Particle Swarm Optimization Technique for Wireless Sensor Networks

Maximizing network lifetime in wireless sensor networks is one of the critical issues, particularly for transmitting multimedia data. The wireless sensor network's lifetime is directly linked to energy conservation at each sensor node in the network. Clustering is the most energy-efficient technique for saving energy in sensor networks. The appropriate method for selecting the cluster head is still lagging. The sink node divides the deployment region into the optimal number of sub-regions depending upon its placement in the sensing region. The initial cluster heads are chosen randomly in each region, and this is not an energy-efficient method. The sink node adopts particle swarm optimization technique to select the cluster head in each region efficiently. The chosen cluster head in each region advertises its role to member nodes. Then, the cluster head node is chosen forms the new cluster. PSO optimization technique with the optimization parameters of clustering coefficient, the sensor node's remaining energy, and the distance from the sink and the head of the cluster to the members is adopted to select the cluster head sensor node. The cluster head spends most of its energy aggregating and transferring the data to the sink node. For unloading the cluster head responsibilities, the assistant cluster head and super cluster head are selected for the aggregation and transfer of data, respectively. The proposed energy efficient cluster head selection algorithm has improved the network lifetime by an average of 65 percent better than the existing clustering algorithms.

A Novel QoS Based Secure Unequal Clustering Protocol with Intrusion Detection System in Wireless Sensor Networks

Wireless sensor network (WSN) becomes a hot research topic owing to its application in different fields. Minimizing the energy dissipation, maximizing the network lifetime, and security are considered as the major quality of service (QoS) factors in the design of WSN. Clustering is a commonly employed energy-efficient technique; however, it results in a hot spot issue. This paper develops a novel secure unequal clustering protocol with intrusion detection technique to achieve QoS parameters like energy, lifetime, and security. Initially, the proposed model uses adaptive neuro fuzzy based clustering technique to select the tentative cluster heads (TCHs) using three input parameters such as residual energy, distance to base station (BS), and distance to neighbors. Then, the TCHs compete for final CHs and the optimal CHs are selected using the deer hunting optimization (DHO) algorithm. The DHO based clustering technique derives a fitness function using residual energy, distance to BS, node degree, node centrality, and link quality. To further improve the performance of the proposed method, the cluster maintenance phase is utilized for load balancing. Finally, to achieve security in cluster based WSN, an effective intrusion detection system using a deep belief network is executed on the CHs to identify the presence of intruders in the network. An extensive set of experiments were performed to ensure the superior performance of the proposed method interms of energy efficiency, network lifetime, packet delivery ratio, average delay, and intrusion detection rate.

Secured node detection technique based on artificial neural network for wireless sensor network

The wireless sensor network is becoming the most popular network in the last recent years as it can measure the environmental conditions and send them to process purposes. Many vital challenges face the deployment of WSNs such as energy consumption and security issues. Various attacks could be subjects against WSNs and cause damage either in the stability of communication or in the destruction of the sensitive data. Thus, the demands of intrusion detection-based energy-efficient techniques rise dramatically as the network deployment becomes vast and complicated. Qualnet simulation is used to measure the performance of the networks. This paper aims to optimize the energy-based intrusion detection technique using the artificial neural network by using MATLAB Simulink. The results show how the optimized method based on the biological nervous systems improves intrusion detection in WSN. In addition to that, the unsecured nodes are affected the network performance negatively and trouble its behavior. The regress analysis for both methods detects the variations when all nodes are secured and when some are unsecured. Thus, Node detection based on packet delivery ratio and energy consumption could efficiently be implemented in an artificial neural network.

INERTIA PARTICLE SWARM OPTIMIZATION WITH DYNAMIC VELOCITY BASED CLUSTERING TECHNIQUE IN WIRELESS SENSOR NETWORKS

Wireless Sensor Networks (WSN) comprises a collection of nodes commonly employed to observe the physical environment. Different sensor nodes are linked to an inbuilt power unit to carry out necessary operations and data transmission between nearby nodes. The maximization of network lifetime and minimization of energy dissipation are considered as the major design issue of WSN. Clustering is a familiar energy efficient technique and the choice of optimal cluster heads (CHs) is considered as an NP hard problem. This paper presents an Inertia Particle Swarm Optimization algorithm with dynamic velocities (IPSO-DV) algorithm based clustering technique in WSN. The aim of the IPSO-DV technique is to select the CHs in such a way to maximize network lifetime. The IPSO-DV algorithm derives a fitness function (FF) to select CHs using distance to BS and remaining energy level. The application of dynamic velocities helps to improvise the effectiveness of the conventional PSO algorithm. To assure the performance of the presented IPSO-DV algorithm, a series of experiments were carried out and the results are investigated under several aspects. The experimentation outcome ensured the effective performance of the IPSO-DV algorithm over the compared clustering techniques.

Energy efficiency and economic profitability between standard and high energy performance dwellings in a semi-arid climate

This work focuses on the experimental study of a standard dwelling and a high energy performance dwelling, located in the city of Djelfa in Algeria whose climate is semi-arid, in order to study their thermal performance for 12 months. The high energy performance dwelling was built as part of the pilot project of 600 dwellings in 11 locations spread all over the different climatic zones in Algeria and the first to be finalised compared to the other locations. According to measurements, the temperature and the relative humidity were maintained in the thermal comfort range with the use of electricity for air-conditioning and the natural gas for heating. The results indicate that the reduction of heating demand is 57% and of air-conditioning is 51% by using the passives energy efficiency techniques in the buildings. In order to improve the economic profitability of the thermal insulation, new correlations were used for the payback period and financial profits realised that can be achieved in countries that subsidise energy. It was found that depending on the assumed correlation on the energy used, the investment in thermal insulation and double glazed window can be profitable from a payback period of 4 years or less. Highlights Monitoring done on two dwellings in semi-arid climate region. Comparison between a standard dwelling and high energy performance dwelling. Experimental determination of hygrothermal and energy consumption during twelve months. Use of new correlations to estimate the payback period for different scenarios and different energies. Estimation of realised profits by the reduction of energy consumption during building life cycle.

Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks

This paper surveys the energy-efficient routing protocols in wireless sensor networks (WSNs). It provides a classification and comparison following a new proposed taxonomy distinguishing nine categories of protocols, namely: Latency-aware and energy-efficient routing, next-hop selection, network architecture, initiator of communication, network topology, protocol operation, delivery mode, path establishment and application type. We analyze each class, discuss its representative routing protocols (mechanisms, advantages, disadvantages…) and compare them based on different parameters under the appropriate class. Simulation results of LEACH, Mod-LEACH, iLEACH, E-DEEC, multichain-PEGASIS and M-GEAR protocols, conducted under the NS3 simulator, show that the routing task must be based on various intelligent techniques to enhance the network lifespan and guarantee better coverage of the sensing area.

Electrochemical degradation of antivirus drug lamivudine formulation: photoelectrocoagulation, peroxi-electrocoagulation, and peroxi-photoelectrocoagulation processes

This study evaluates the performance of photoelectrocoagulation, peroxi-electrocoagulation, and peroxi-photoelectrocoagulation for the removal of the antiviral drug lamivudine formulation from wastewater by a stainless-steel electrode. To investigate matrix effects for this oxidation process, the influence of substrates such as urea and simulated wastewater (SWW) was studied. Moreover, degradation kinetics and energy efficiency are also discussed. Results indicate that the removal efficiency was in the order of peroxi-photoelectrocoagulation > peroxi-photoelectrocoagulation (in the presence of urea) > peroxi-photoelectrocoagulation (in the presence of SWW) > peroxi-electrocoagulation > photoelectrocoagulation. In peroxi-photoelectrocoagulation, the 96% degradation of lamivudine formulation indicates a nearly complete degradation of lamivudine. In this process, the presence of urea and SWW resulted in a substantial reduction of chemical oxygen demand (COD) decay. Kinetic studies using linear pseudo-first and pseudo-second-order reaction kinetics showed that the pseudo-first-order equation effectively described the removal of lamivudine formulation. The highest energy consumption per kgCOD decay (i.e., kWh kgCOD− 1) was obtained for the photoelectrocoagulation process, while the lowest energy consumption was obtained for peroxi-electrocoagulation, for all electrolysis times. The peroxi-photoelectrocoagulation process was shown to be an effective and energy-efficient technique for removing the antiviral drug lamivudine formulation from wastewater.

FAJIT: a fuzzy-based data aggregation technique for energy efficiency in wireless sensor network

Wireless sensor network (WSN) is used to sense the environment, collect the data, and further transmit it to the base station (BS) for analysis. A synchronized tree-based approach is an efficient approach to aggregate data from various sensor nodes in a WSN environment. However, achieving energy efficiency in such a tree formation is challenging. In this research work, an algorithm named fuzzy attribute-based joint integrated scheduling and tree formation (FAJIT) technique for tree formation and parent node selection using fuzzy logic in a heterogeneous network is proposed. FAJIT mainly focuses on addressing the parent node selection problem in the heterogeneous network for aggregating different types of data packets to improve energy efficiency. The selection of parent nodes is performed based on the candidate nodes with the minimum number of dynamic neighbors. Fuzzy logic is applied in the case of an equal number of dynamic neighbors. In the proposed technique, fuzzy logic is first applied to WSN, and then min–max normalization is used to retrieve normalized weights (membership values) for the given edges of the graph. This membership value is used to denote the degree to which an element belongs to a set. Therefore, the node with the minimum sum of all weights is considered as the parent node. The result of FAJIT is compared with the distributed algorithm for Integrated tree Construction and data Aggregation (DICA) on various parameters: average schedule length, energy consumption data interval, the total number of transmission slots, control overhead, and energy consumption in the control phase. The results demonstrate that the proposed algorithm is better in terms of energy efficiency.

Efficacy of incorporating PCM into the building envelope on the energy saving and AHU power usage in winter

Commercial and residential buildings consume 20% of the world's total energy usage and forecasts indicate a growing trend of 1.5% per year by 2040. Share of energy consumption in the heating ventilation and air conditioning (HVAC) reached to 40–70% of the building energy usage. Reduction in the heat exchange between the interior and exterior spaces is an energy-efficient technique for lowering HVAC energy demand. In this study, the thermal performance of a PCM-based wall was numerically examined in the winter. The main purpose of this research was to investigate the efficacy of installing PCM inside the envelope on reducing heat loss from the conditioned space and at the same time its effect on air handling unit (AHU) energy usage. For this, seven different PCM layers at melting temperatures of 276.15–293.15 K with a thickness of 1 cm were loaded into the envelope. Based on the results, among the PCMs thermophysical properties, thermal conductivity has more manifest efficacy. Under the best conditions, owing to installing PCM of A3 inside the envelope, the energy-saving in December, January and February diminished by 11.25%, 11.23% and 10.35%, respectively. Also, thermodynamic calculations affirmed that the maximum reduction in AHU power usage owing to PCM loading was 11.73%. Using payback time technique, the economic analysis was performed and it was found that under the best conditions, the payback time is 18.6 years.

Energy security, economic growth and environmental sustainability in India: Does FDI and trade openness play a role?

India is one of the world's largest emerging economies and, therefore, has a critical role to play in ensuring global sustainability, while the Indian economy is also prioritizing energy security. The paper explores the dynamic linkages between energy security captured through national-level energy use, foreign direct investment (FDI), economic output, carbon emissions, and trade openness in India spanning the period from 1978 to 2016 in a multivariate framework based on the theoretical premise of an Environmental Kuznets Curve. Time series econometric modelling based on the ARDL model and VECM Granger causality tests are employed for this purpose. The results confirm the presence of a co-integrating relationship and finds a strong energy-output–CO2–FDI long-run nexus. A 1% increase in FDI results in a 0.013% reduction in energy use. Energy use is found to be Granger caused by output, carbon emissions, FDI and trade openness in the long-run. The adoption of energy-efficient techniques through FDI is essential for reducing carbon emissions in India based on our findings. The Indian government should also galvanize FDI inflow in the renewable energy sectors by assuring incentives to investors to concurrently achieve favorable macroeconomic outcomes and ensure sustainable economic development. These are globally important policy lessons for other developing and emerging economies.

LOW POWER CODER FOR WIRELESS SENSOR NETWORKS

An Energy efficiency technique is a important subject for Wireless Sensor Networks (WSNs), that have received extensive attention to reduce energy consumption and improve network lifetime. Most of the power in sensor networks is consumed by data transmission and reception. This power can be reduced by using suitable data compression technique which reduces transmitted data over wireless channels. In this article, the design of an energy efficient coder is proposed. When this algorithm is applied to recursive systematic convolutional (RSC) code, it reduces both computation complexity and decode error probability. Applying this RSC code to wireless sensor network ensures correct data reception and reduced energy consumption. To validate and evaluate our work, simulation is done in Xilinx software and Power is calculated using Xilinx xpower tool. The simulation result shows that the proposed algorithm performs better than existing one

An energy efficient optimal path selection technique for IoT using genetic algorithm

An energy efficient optimal path selection technique for IoT using genetic algorithm

An Energy Efficient Optimal Path Selection Technique for IoT using Genetic Algorithm

In the real world, it is essential to establish efficient routes in the internet of things (IoT) since sensor nodes operate mainly on battery and have limited energy. An energy efficient optimal routing technique is proposed to find an optimal path from source to destination. The proposed work obtains the optimal path by selecting an efficient cluster head in the homogeneous IoT network. GA is an optimisation technique that is integrated into the proposed work to achieve the optimal routing benefit. The proposed fitness function enables determining the optimal directions in selecting the multipath routing while exchanging messages from all positions in the network. Extensive mapping of genetic algorithm to proposed IoT routing is presented. Since the proposed method selects an optimal path from source cluster head to the sink, this reduces an energy consumption and improvises the overall network lifetime. The validity of the proposed algorithm is evaluated in the MATLAB and results generate superiority while considering parameters such as energy consumption, end to end delay and number of failed nodes.

A Survey on Non-Orthogonal Multiple Access with Energy Harvesting for Underwater Acoustic Sensor Networks

Underwater sensor networks play a key role for unique applications such as naval surveillance, underwater multimedia, internet of underwater things, aided navigation and so on. Mainly, acoustic waves play an important role in underwater communications between sensor nodes, buoys and autonomous underwater vehicles (AUVs). As a result, the available bandwidth of underwater acoustic sensor networks (UASNs) is limited to a few kHz. On the other hand, the sensor node batteries can be neither rechargeable nor replaceable when the node battery is running out. Hence, there is a great need for UASNs to simultaneously enhance the channel capacity and energy efficiency. In this paper, we study the various energy harvesting techniques proposed for UASNs to improve the energy efficiency and non-orthogonal multiple access (NOMA) techniques to improve the channel capacity. Firstly, we will discuss various NOMA techniques used in the papers to improve data rate and channel capacity, we can use any one or combination of techniques discussed in the papers to improve data rate, channel capacity and connectivity. Secondly, we will discuss various energy harvesting techniques used in the papers, we can use any one or hybrid combination of two or more techniques discussed in the papers to improve energy efficiency.

Hierarchical Routing Protocols for Wireless Sensor Networks: Functional and Performance Analysis

Wireless sensor network (WSN) comprises of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, and pressure, and to cooperatively forward the collected information to the destination through the network infrastructure. As sensor nodes are energy constraint devices, therefore, the importance of energy efficient routing protocols has been increased. In order to minimize energy consumption, recently, a number of hierarchical routing protocols are proposed. For instance, LEACH is an elementary hierarchical routing protocol that employs clustering technique to achieve energy efficiency. A lot of research work has been performed to remove shortcomings and to improve the performance of hierarchical routing protocols. Therefore, a comprehensive review is required which can review state‐of‐the‐art technologies, analyze functional and performance aspects, and highlight hierarchical routing protocol issues and challenges in WSNs. This paper proposes a taxonomy for the classification of existing hierarchical routing protocols for WSNs and analyzes the functionality and performance of existing hierarchical routing protocols. Moreover, it compares existing routing protocols to highlight key technological differences and provides performance comparison for the selected LEACH based routing protocols. Finally, the paper spotlights issues and challenges in existing routing protocols of WSNs, which can assist in future research for the selection of appropriate research domain and provide guidance in selection of energy efficient techniques in the design of energy efficient of routing protocols for WSNs.

Fuzzy Extended Krill Herd Optimization with Quantum Bat Algorithm for Cluster Based Routing in Mobile Adhoc Networks

ABSTRACT MANET integrates a set of autonomous mobile nodes which move independently and send data through wireless links. Clustering and routing are the commonly employed energy efficient techniques, which can be treated as an NP hard problem and is resolved by computational intelligence algorithms. The mobility of the nodes leads to repeated link failures and low energy efficiency. In order to achieve high energy efficiency and network connectivity, this paper presents a new Fuzzy Extended Krill Herd Optimization with Quantum Bat algorithm (FEKHO-QBA) for Cluster Based Routing in MANET.The presented model uses FEKHO algorithm by integrating the concepts of fuzzy logic and KHO algorithm for clustering process and effective selection of cluster heads (CHs). Besides, the QBA is applied as a routing technique to determine the optimal paths to the destination nodes. The QBA involves the features of faster convergence rate, easier to implement, and improved accurateness. The application of FEKHO-QBA algorithm offers maximum energy efficiency and network longevity. For determining the effectual performance of the EKHO-QBA algorithm, a set of different experiments were carried out and highlighted the supremacy over the compared methods interms of different performance measures.

Modified Grey Wolf Optimization with Levy Flights Based Energy Efficient Clustering Technique in Wireless Sensor Networks

Wireless Sensor Network (WSN) comprises a set of inexpensive, compact and battery powered sensor nodes, deployed in the sensing region. WSN is highly useful for data gathering and tracking applications. Owing to the battery powered nature of sensor nodes, energy efficiency remains as a crucial design issue. Earlier works reported that clustering is considered as an energy efficient technique and effective selection of cluster heads (CHs) remains a major issue in WSN. Since clustering process is considered as an NP hard problem, optimization algorithms are employed to resolve it. This paper develops a new energy efficient clustering technique using Modified Grey Wolf Optimization with Levy Flights (MGWO-LF) for WSN. The proposed MGWO-LF algorithm incorporates the levy flight (LF) mechanism into the hunting phase of traditional GWO algorithm to avoid local optima problem. The proposed model has the ability of proficiently selecting the cluster heads (CHs), achieves energy efficiency and maximum network lifetime. The detailed simulation analysis ensured that the MGWO-LF algorithm has prolonged the network lifetime in a considerable way.

Energy Efficient and Interference-aware Spectrum Sensing Technique for Improving the Throughput in Cognitive Radio Networks

Cognitive Radio network (CRN) depends on opportunistic spectrum access and spectrum sensing for improving the wireless networks’ spectrum efficiency. Since throughput maximization can result in high energy consumption, the spectrum sensing technique should address the energy-throughput trade off. The spectrum sensing time has to be determined by the considering the residual battery energies of each secondary user (SU). The Primary User (PU) interference degrades the throughput of the entire network. Hence the transmit power level should be determined considering the PU interference and SU battery energy. This paper proposes Energy Efficient and Interference-aware Spectrum Sensing (EEISS) technique for improving the throughput in CRN. In this work, the sensing time is dynamically estimated based on the battery energy levels of SUs and the transmit power is determined depending on PU signal and battery energy levels of SUs. A Game theory model is formulated to maximize the throughput based on these constraints. Experimental results show that EEISS attains improved throughput, higher probability of detection and higher residual energy.

Energy efficient compressive sensing with predictive model for IoT based medical data transmission

Internet of Things (IoT) systems tends to produce massive and diverse kinds of data which needs to be processes and responds in a smallperiod. A most important challenge exists in IoT devices is the amount of energy utilization while transmitting data into cloud. This paper presents a new energy efficient compressive technique with predictive model for IoT based medical data collection and transmission. The proposed model make use of Sensor-Lempel Ziv Welch (SLZW) technique is utilized to perform compressive sensing earlier to data transmission followed by particle swarm optimization (PSO) based deep neural network (DNN) based prediction. The PSO algorithm is applied for optimizing the node count of hidden layer in DNN due to the issue that the classical DNN got trapped into local minima and the node count in hidden layer have to select manually. The performance of the presented SLZW-PSO-DNN algorithm has been validated and the results are investigated under distinct scenarios. The obtained experimental outcome indicated that the SLZW-PSO-DNN algorithm is found to be effective under several aspects over the existing method. The experimental results stated that the PSO-DNN model has resulted in a maximum predictive average accuracy of 98.5% and 98.4% under original and compressed data respectively.