Energy Efficient Hybrid Research Articles

A multi-objective dynamical artificial bee colony for energy-efficient fuzzy hybrid flow shop scheduling with batch processing machines

Energy-efficient hybrid flow shop scheduling problem (EHFSP) with batch processing machines (BPMs) is rarely considered, let alone EHFSP with BPMs and uncertainty. In this study, an energy-efficient fuzzy hybrid flow shop scheduling problem (EFHFSP) with BPMs at a middle stage and no precedence between some stages is solved, and a multi-objective dynamical artificial bee colony algorithm (MODABC) is presented to simultaneously minimize fuzzy makespan and total fuzzy energy consumption. To produce high quality solutions, a swarm decision is used to dynamically determine the employed bee swarm and onlooker bee swarm, a dynamical employed bee phase is implemented, and an onlooker bee phase with adaptive communication is given. The diversity reinforcement strategy and an adaptive scout phase are also adopted. Extensive experiments are conducted, and the optimal combination of key parameters for the proposed algorithm is determined by the Taguchi method. The computational results demonstrate that the new strategies of MODABC are effective, and MODABC is very competitive in solving the considered EFHFSP.

QoS-based energy-efficient hybrid routing protocols in RSU assisted VANET using clustering mechanism

ABSTRACT Vehicular ad hoc networks (VANET) communication framework are employed to improve communication between vehicles on the highway and urban roads. Evaluating the best routing path by satisfying the QoS (Quality of Service) plays a substantial role in networking. Researchers for maximising the routing efficiency have employed numerous methods. But these criteria cannot be attained effectively due to certain limitations, including frequent route failure, high overhead, increased packet loss rate, less throughput and packet delivery rate. Hence, this work presents an effective strategy to offer better communication services with less energy consumption and high flexibility using QoS-based energy efficient routing framework with clustering (QoS_EErcF) network model. Initially, different clusters are formed by considering the constraints like node direction, link reliability, speed and distance. The cluster heads (CH) are chosen from the clusters with the dissemination of gateway nodes and cluster members using an Amended rapid cosine similarity-founded clustering mechanism (Arc_SimC). The best routing strategy for transmitting the data from source to destination is performed by using Integrated Pelican with grasshopper optimisation algorithm (IPel_Ghop). Constraints such as bandwidth, node distance, energy, packet loss rate, network traffic and end-to-end delay are considered to find the optimal path. The results are simulated using MATLAB, whereas the energy efficiency of a proposed model is obtained to be 98.37%, and the packet delivery ratio is attained as 99.60%.

Design and implementation of energy-efficient hybrid data aggregation in heterogeneous wireless sensor network

Heterogeneous wireless sensor network (HWSN) is a trending technology in both the industrial and academic sectors, consisting of a large number of interconnected sensors. However, higher energy consumption and delay are significant drawbacks of this technology in applications such as military, healthcare, and industrial automation. The main objective of this research is to enhance the energy efficiency of HWSN using a clustering technique. In this article, a novel approach, namely power optimization and hybrid data aggregation (POHDA), is proposed to address these challenges in HWSN. POHDA-HWSN focuses on power optimization and congestion avoidance through effective CH selection using hybrid data aggregation based on parameters such as residual energy, distance, mobility, threshold value of the node, and latency. By weight-based effective cluster head (CH) selection, the energy consumption, end-to-end delay, and overhead during communication are reduced in this network. The POHDA-HWSN approach considers specific parameters to compare the results and outcomes with earlier research such as HCCS-WSN, FMCA-WSN, and APCC-WSN. The results prove that the proposed POHDA-HWSN approach achieves higher energy efficiency and delivery ratio.

Energy Efficient Graph-Based Hybrid Learning for Speech Emotion Recognition on Humanoid Robot

This paper presents a novel deep graph-based learning technique for speech emotion recognition which has been specifically tailored for energy efficient deployment within humanoid robots. Our methodology represents a fusion of scalable graph representations, rooted in the foundational principles of graph signal processing theories. By delving into the utilization of cycle or line graphs as fundamental constituents shaping a robust Graph Convolution Network (GCN)-based architecture, we propose an approach which allows the capture of relationships between speech signals to decode intricate emotional patterns and responses. Our methodology is validated and benchmarked against established databases such as IEMOCAP and MSP-IMPROV. Our model outperforms standard GCNs and prevalent deep graph architectures, demonstrating performance levels that align with state-of-the-art methodologies. Notably, our model achieves this feat while significantly reducing the number of learnable parameters, thereby increasing computational efficiency and bolstering its suitability for resource-constrained environments. This proposed energy-efficient graph-based hybrid learning methodology is applied towards multimodal emotion recognition within humanoid robots. Its capacity to deliver competitive performance while streamlining computational complexity and energy efficiency represents a novel approach in evolving emotion recognition systems, catering to diverse real-world applications where precision in emotion recognition within humanoid robots stands as a pivotal requisite.

Multiobjective-energy centric honey badger optimization based routing for wireless body area network

Wireless Body Area Network (WBAN) is an interconnection of tiny biosensors that are organized in/on several parts of the body. The developed WBAN is used to sense and transmit health-related data over the wireless medium. Energy efficiency is the primary challenges for increasing the life expectancy of the network. To address the issue of energy efficiency, one of the essential approaches i.e., the selection of an appropriate relay node is modelled as an optimization problem. In this paper, energy efficient routing optimization using Multiobjective-Energy Centric Honey Badger Optimization (M-ECHBA) is proposed to improve life expectancy. The proposed M-ECHBA is optimized by using the energy, distance, delay and node degree. Moreover, the Time Division Multiple Access (TDMA) is used to perform the node scheduling at transmission. Therefore, the M-ECHBA method is used to discover the optimal routing path for enhancing energy efficiency while minimizing the transmission delay of WBAN. The performances of the M-ECHBA are analyzed using life expectancy, dead nodes, residual energy, delay, packets received by the Base Station (BS), Packet Loss Ratio (PLR) and routing overhead. The M-ECHBA is evaluated with some classical approaches namely SIMPLE, ATTEMPT and RE-ATTEMPT. Further, this M-ECHBA is compared with the existing routing approach Novel Energy Efficient hybrid Meta-heuristic Approach (NEEMA), hybrid Particle Swarm Optimization-Simulated Annealing (hPSO-SA), Energy Balanced Routing (EBR), Threshold-based Energy-Efficient Routing Protocol for physiological Critical Data Transmission (T-EERPDCT), Clustering and Cooperative Routing Protocol (CCRP), Intelligent-Routing Algorithm for WBANs namely I-RAW, distributed energy-efficient two-hop-based clustering and routing namely DECR and Modified Power Line System (M-POLC). The dead nodes of M-ECHBA for scenario 3 at 8000 rounds are 4 which is less when compared to the dead nodes of EBR.

An Adaptive Two-Class Teaching-Learning-Based Optimization for Energy-Efficient Hybrid Flow Shop Scheduling Problems with Additional Resources

Energy-efficient scheduling problems with additional resources are seldom studied in hybrid flow shops. In this study, an energy-efficient hybrid flow shop scheduling problem (EHFSP) with additional resources is studied in which there is asymmetry in the machine. An adaptive two-class teaching-learning-based optimization (ATLBO) which has multiple teachers is proposed to simultaneously minimize the makespan and the total energy consumption. After two classes are formed, a teacher phase is first executed, which consists of teacher self-learning and teacher training. Then, an adaptive learner phase is presented, in which the quality of two classes is used to adaptively decide the learner phase or the reinforcement search of the temporary solution set. An adaptive formation of classes is also given. Extensive experiments were conducted and the computational results show that the new strategies are effective and that ATLBO was able to provide better results than comparative algorithms reported in the literature in at least 54 of 68 instances.

A fast and energy-efficient hybrid 4–2 compressor for multiplication in nanotechnology

A fast and energy-efficient hybrid 4–2 compressor for multiplication in nanotechnology

A Collision Proof Energy Efficient Lightweight Hybrid Cryptosystem for Fog

A Collision Proof Energy Efficient Lightweight Hybrid Cryptosystem for Fog

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

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

Energy Efficient Hybrid Evolutionary Algorithm for Internet of Everything (IoE)-Enabled 6G

Energy Efficient Hybrid Evolutionary Algorithm for Internet of Everything (IoE)-Enabled 6G

Energy-Efficient Hybrid Federated and Centralized Learning for Edge-Based Wireless Traffic Prediction in Aerial Networks

Energy-Efficient Hybrid Federated and Centralized Learning for Edge-Based Wireless Traffic Prediction in Aerial Networks

Energy Efficient Hybrid Precoding for Adaptive Partially-Connected mmWave Massive MIMO: A Decomposition-Based Approach

Energy Efficient Hybrid Precoding for Adaptive Partially-Connected mmWave Massive MIMO: A Decomposition-Based Approach

Design and analysis of hybrid 10T adder for low power applications

Recently, a growing focus has been on the need for ultra-low-voltage (ULV) functioning to reduce energy usage. The full adder is a fundamental computational arithmetic unit in various image and signal processing applications. This study presents a novel architectural design for a 1-bit adder that utilizes 10 transistors. The concept relies on an energy-efficient hybrid logic multiplexer approach incorporating Gate Diffusion Input (GDI) logic. The primary goal of the suggested design for a hybrid full adder is to decrease power consumption while concurrently decreasing the required area. The suggested innovative architectural design for a full adder incorporates level restoration carry logic and ensures a complete swing output voltage. The designs have been analyzed in a standard environment using 45-nm CMOS process technology, with various supply voltages utilized. The evaluation of the adder cells focuses on speed, power consumption, power-delay-product (PDP), and space efficiency compared to the existing full adders. The recommended full adder cells were subjected to comprehensive simulation tests utilizing the Mentor Graphics environment. The results suggest that these cells surpass their counterparts, exhibiting a minimum of 80 % enhancements in their power-delay product (PDP) parameters. Also the comparisons have been made between the proposed full adders with and without level restoration techniques. The proposed level restoration technique based full adder yields better performance in terms of power and delay when compared to proposed full adder without level restoration.

Simulation of Energy Dissipation in BLDC Motor and Analysis of Speed-Acoustic Characteristics

The hybrid electric vehicle combines dual propulsion systems: one employs the traditional internal combustion engine, while the other utilizes a PMDC or BLDC motor. Generally, BLDC motors exhibit an efficiency of 85% to 90%. Conversely, the PMDC motor inherently achieves an efficiency of 88% under optimal running conditions. Moreover, improving the efficiency of both motors is impeded by their design constraints. Power losses in these motors primarily stem from factors such as heat generation in the rotor (I²R losses), bearing friction, vibrations, and noise. This paper aims to investigate energy loss in a 4 kW BLDC motor manufactured by Bosch, attributable to speed-induced acoustic noise effects. Harmonic analysis of the phase current reveals the presence of odd-order harmonics, specifically those below the 11th order, resulting in current ripples. These dominant harmonic frequencies interact, ultimately generating noise and consuming a certain amount of power. These predominant frequencies correlate with fundamental electrical parameters: current frequency (Hz), RMS current (A), and threshold (%r), all associated with the motor's input power source. The 'K' factor, a parameter dependent on these three electrical factors, is calculated to determine harmonic stability. Variations in stable speeds (ranging from 1280 to 7500 RPM) are used to record 'K' factor readings. Based on IEEE Standard 85-1980 guidelines, motors are classified as either acceptable (OK) or not acceptable (not OK) according to their harmonic stability 'K' factor. Readings for each speed step are captured at a specific predominant frequency, facilitating subsequent comparisons. To validate the experimental findings, MATLAB/Simulink and a Fluke scope meter are employed to conduct power analyses for each motor, thus assessing energy loss attributed to noise. The waveform of the phase current proves instrumental in quantifying energy consumption in the BLDC motor. Notably, higher-frequency noise waves with shorter wavelengths entail greater energy consumption. Evaluating energy loss significantly contributes to the analysis of BLDC motor speed acoustics, serving to inform the design of energy-efficient hybrid electric vehicle models powered by BLDC motors, which are intended for customer service.

An energy-efficient hybrid CMOS spiking neuron circuit design with a memristive based novel T-type artificial synapse

Applications exploiting Artificial Intelligence (AI) have grown rapidly and are encountered in significant energy consumption. The organic neurons used in digital computers are mimicked by artificial neural networks (based on AI). In this perspective, AI’s Conventional computing technology, relying on the Von Neumann structure, is approaching the end of Moore’s law. To solve this problem, the memristor device concept is identified as a futuristic option for the design of neurons and synapses. The primary component of the central nervous system is the neuron, that serves as one of the crucial building blocks in the hardware implementation of spiking neural networks. Energy efficiency, power, and firing frequency are major constraints in the modelling of neurons. This work delivers the development of neurons using a Hybrid CMOS Memristor (HCM) Morris–Lecar (ML) model with a novel T-type synapse. Low power consumption in nano-watts, reduction in circuit complexity, and energy efficiency in femtojoules are the key benefits achieved through the proposed neuron model. Here, the achieved power consumption, energy efficiency, and spike frequency are 17.42 pW, 1.05 fJ, and 28.52 kHz, respectively. In addition, Moreover the achieved spike frequency in the order of kHz is helpful to advance biological plausibility.

ESEIABS: Energy and spectrum efficient hybrid technique for 5G and beyond networks using interference aware beam selection

ESEIABS: Energy and spectrum efficient hybrid technique for 5G and beyond networks using interference aware beam selection

PLANT MICROBIAL FUEL CELLS: THE CURRENT STATE AND POTENTIAL APPLICATIONS

Phyto-treatment engineering structures have recently found wide application in the biotechnology of wastewater treatment. Currently, it is important to study and use the possibility of bioelectric power generation, as well as develop energy-efficient hybrid technologies based on plant-microbial fuel cells. The purpose of the study is to summarize information on the ability of plant organisms to generate bioelectric potentials and to create plant-fuel cells as a component of an integrated phytotechnology for wastewater treatment in Eastern Siberia. The article shows applications of phytotechnologies for wastewater treatment using a system of algae, higher aquatic vegetation, microorganisms in different climatic zones, including the Arctic. These technologies have been implemented in China, where dozens of such facilities are annually built. The article describes the designs of plant-microbial fuel cells (PMFC), the electrogenic activity of s plants and microorganisms, promising areas for the use of microbial fuel cells, PMFC as power sources for small devices, sensors for early detection of water pollution. Design solutions for creating RMFC batteries for the neutralization of soils contaminated with antibiotics are presented.

Study on the arc behavior and mechanical properties of energy-efficient hybrid CMT-pulsed gas metal arc narrow gap mild steel welds

Study on the arc behavior and mechanical properties of energy-efficient hybrid CMT-pulsed gas metal arc narrow gap mild steel welds

Smart dynamic hybrid membranes with self-cleaning capability

The growing freshwater scarcity has caused increased use of membrane desalination of seawater as a relatively sustainable technology that promises to provide long-term solution for the increasingly water-stressed world. However, the currently used membranes for desalination on an industrial scale are inevitably prone to fouling that results in decreased flux and necessity for periodic chemical cleaning, and incur unacceptably high energy cost while also leaving an environmental footprint with unforeseeable long-term consequences. This extant problem requires an immediate shift to smart separation approaches with self-cleaning capability for enhanced efficiency and prolonged operational lifetime. Here, we describe a conceptually innovative approach to the design of smart membranes where a dynamic functionality is added to the surface layer of otherwise static membranes by incorporating stimuli-responsive organic crystals. We demonstrate a gating effect in the resulting smart dynamic membranes, whereby mechanical instability caused by rapid mechanical response of the crystals to heating slightly above room temperature activates the membrane and effectively removes the foulants, thereby increasing the mass transfer and extending its operational lifetime. The approach proposed here sets a platform for the development of a variety of energy-efficient hybrid membranes for water desalination and other separation processes that are devoid of fouling issues and circumvents the necessity of chemical cleaning operations.

RME–SEP: An IoT Favorable Approach of Minimum Energy-Efficient Hybrid SEP for Heterogeneous WSN Data Routing

RME–SEP: An IoT Favorable Approach of Minimum Energy-Efficient Hybrid SEP for Heterogeneous WSN Data Routing