Energy Efficiency In Networks Research Articles

A comparative analysis using LEACH protocol to enhance energy efficiency in wireless sensor networks with harmony search algorithm

Wireless sensor networks (WSNs) are critical to many applications, but their use is frequently hampered by energy constraints, particularly in remote locations. WSN nodes are typically installed in remote and frequently inaccessible locations, making battery replacement challenging, thus emphasizing the importance of energy efficiency. This research presents an energy-conserving approach centred on the Harmony Search Algorithm (HSA), optimized by considering key parameters such as the number of sensor nodes, initial energy levels, transmission range, and data rate. The network performance is tested against the LEACH algorithm by evaluating parameters including energy consumption, network lifetime, and throughput. The comparative analysis is performed using graphs depicting the energy consumption of nodes by both approaches. With the Harmony Memory Size (HMS), Harmony Memory Considering Rate (HMCR), and Pitch Adjustment Rate (PAR) adjusted for best outcomes, the suggested method maximizes the performance of WSNs by effectively regulating the energy consumption of sensor nodes. Unlike LEACH, which involves clustering of nodes and periodic rotation of clusters, HSA is based on energy-efficient harmony and does not rely on clustering, thereby finding the most energy-efficient communication path within the network. Simulation results indicate that the HSA algorithm is 9.52% more efficient, with its energy usage being 9.2% more economical compared to the LEACH algorithm. This demonstrates HSA's potential for enhancing WSN performance in energy-constrained environments.

Harnessing spatiotemporal transformation in magnetic domains for nonvolatile physical reservoir computing.

Combining physics with computational models is increasingly recognized for enhancing the performance and energy efficiency in neural networks. Physical reservoir computing uses material dynamics of physical substrates for temporal data processing. Despite the ease of training, building an efficient reservoir remains challenging. Here, we explore beyond the conventional delay-based reservoirs by exploiting the spatiotemporal transformation in all-electric spintronic devices. Our nonvolatile spintronic reservoir effectively transforms the history dependence of reservoir states to the path dependence of domains. We configure devices triggered by different pulse widths as neurons, creating a reservoir featured by strong nonlinearity and rich interconnections. Using a small reservoir of merely 14 physical nodes, we achieved a high recognition rate of 0.903 in written digit recognition and a low error rate of 0.076 in Mackey-Glass time series prediction on a proof-of-concept printed circuit board. This work presents a promising route of nonvolatile physical reservoir computing, which is adaptable to the larger memristor family and broader physical neural networks.

Enhancing Information Freshness and Energy Efficiency in D2D Networks Through DRL-Based Scheduling and Resource Management

Enhancing Information Freshness and Energy Efficiency in D2D Networks Through DRL-Based Scheduling and Resource Management

Enhancing energy efficiency and QoS in 5G networks with dynamic resource optimisation green communication protocol

Enhancing energy efficiency and QoS in 5G networks with dynamic resource optimisation green communication protocol

Enhancing energy efficiency and reliability in wireless sensor networks using BioGAT optimization

The BioGAT model, as proposed, presents a novel methodology for enhancing the efficiency of wireless sensor networks (WSNs), which are essential elements of contemporary communication and sensing systems. For real-time monitoring and data analysis, WSNs are comprised of autonomous sensor nodes that are outfitted with processing, wireless communication, and sensing capabilities. These nodes are deployed in a variety of environments. By means of an advanced optimization model, this work aims to address critical challenges in WSNs, specifically in the areas of node placement, energy efficiency, and network reliability. By utilizing biogeography-based optimization (BBO) and graph attention networks (GAT), the BioGAT model endeavors to dynamically adapt to network changes while achieving a balance between efficient coverage and energy consumption. Cluster heads (CHs), which are essential for the aggregation of data, have a significant impact on improvements in energy efficiency and the longevity of networks. By means of comprehensive simulations and evaluation, this study presents exceptional outcomes. The BioGAT model outperforms prior approaches by attaining a 95% packet delivery ratio and an enhanced throughput. In addition, the model effectively decreases mean energy consumption, underscoring its capacity to improve the sustainability and dependability of networks in a variety of WSN applications.

Reliable Data Delivery and Load Balancing in Improved Energy Efficient RPL Routing Protocol for Low Power and Lossy Networks

ABSTRACTThe energy‐constrained nodes and tree‐like topology of Low‐Power and Lossy Networks (LLNs) frequently result in serious problems when energy bottlenecks occur. This occurrence affects the network performance in terms of node failure, lower connectivity, and higher energy consumption. This paper proposes a novel solution to these problems by integrating an Energy Efficient Multipath Routing Protocol (EM‐RPL) with a Load‐Based Energy Efficient RPL (LBEE‐RPL) protocol. To address energy bottlenecks, the proposed EM‐RPL protocol initiates the crucial mechanisms as subnode switching mechanism and an improved Trickle timer. The network's energy load is balanced by the subnode switching mechanism, which redistributes traffic away from overloaded nodes. The enhanced Trickle timer guarantees prompt identification and response to energy shortage conditions. This method enhances overall network performance while addressing the issue of data imbalance in energy consumption. An extensive simulation shows the proposed EM‐RPL significantly increases network lifetime and energy efficiency. Specifically, integrating these protocols leads to a notable reduction in power consumption and a rise in the network's operational lifespan. The important problem of energy management in LLNs is effectively resolved by this work, which offers improved performance and sustainability for a range of Internet of Things (IoT) and related fields applications.

Resource Allocation of Device‐To‐Device–Enabled Millimeter‐Wave Communication: A Deep Reinforcement Learning Approach

ABSTRACTDevice‐to‐device (D2D) communication is a promising development in 5G networks, offering potential benefits such as increased data rates, reduced costs and latency, and improved energy efficiency (EE). This study analyzes the operation of millimeter‐wave (mmWave) in cellular networks. A client's device can establish a connection to either a base station or another client, facilitating D2D communication based on a distance threshold and accounting for interference. The research employs a deep reinforcement learning (DRL)–based resource allocation (RA) scheme for D2D‐enabled mmWave communications underlaying cellular networks. It evaluates the effectiveness of several metrics: coverage probability, area spectral efficiency, and network EE. Among networks limited by noise, the proposed strategy demonstrates the highest coverage probability performance. The paper also suggests an optimization approach based on the firefly algorithm for RA, taking into account the stochastic nature of wireless channels. An asynchronous advantage actor–critic (A3C) DRL algorithm is modeled for this purpose. The performance of the proposed scheme is compared with two existing algorithms: soft actor–critic and proximal policy optimization. Overall, the numerical results indicate that our proposed firefly algorithm–optimized A3C method outperforms the other analytical methods.

Hybrid Kalman Filter and Optimization-Based Routing for Energy Efficiency in Heterogeneous Wireless Sensor Networks

Hybrid Kalman Filter and Optimization-Based Routing for Energy Efficiency in Heterogeneous Wireless Sensor Networks

Acoustic Sensors data transmission integrity and endurance with IoT-enabled location-aware framework

Environmental monitoring and disaster mitigation are critical applications of underwater acoustic sensor networks (UASNs). However, UASNs face significant challenges, including high latency, limited bandwidth, and energy constraints. This study introduces an Internet of Things (IoT)-driven location-aware framework (ILAF) designed to enhance UASN performance by utilizing non-GPS geographic coordinates for determining the location of sensor and sink nodes, identifying their neighbors based on coordinates and transmission range, and optimizing node placement and routing without the need for GPS modems. The framework is compared with several state-of-the-art protocols, including Bald Eagle Search inspired optimized energy efficient routing protocol (BES-OEERP) and IoT-enabled depth-based routing technique (IDBR), demonstrating superior performance. Specifically, ILAF achieved a packet delivery ratio (PDR) of 99%, which outperforms energy-efficient region-based source distributed routing algorithm (EERSDRA) (98%) and energy-efficient geo-opportunistic routing protocols (EEGORP) (96%). Additionally, ILAF reduced energy consumption by 20% compared to these existing protocols. These improvements result in a more energy-efficient network with fewer dead nodes (12 after 1,000 rounds) and higher throughput (5.7 kbps at 1,000 rounds), making ILAF suitable for real-time underwater applications. Future research will explore integrating lightweight IoT protocols like Message Queue Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP) to enhance the framework’s performance and reliability further.

Enhancing Energy Efficiency & Productivity With Solar Cells For Wifi Operations In Mining Are-as Pt Antareja Mahada Makmur Jobsite Mifa Ber-saudara (2024)

This improvement aims to evaluate the effectiveness of the use of solar cell systems in improving energy efficiency and WiFi network productivity in coal mining environments. The research method involves the installation of solar cell systems and the measurement of energy performance and productivity before and after implementation. The implementation of solar cell systems on WiFi networks in mining environments can improve energy efficiency and productivity. This study aims to analyze the influence of the implementation of solar cell systems on fossil energy use and environmental impact. The results show that the implementation of the solar cell system can reduce the use of fossil energy by 75% and reduce greenhouse gas emissions by 60%. The implementation of the solar cell system can also increase productivity by 25%. Therefore, the implementation of a solar cell system on a WiFi network in a mining operational environment is highly recommended. The results showed a significant improvement in the energy efficiency and stability of the WiFi network, which had a positive impact on operational productivity. The results of the study show that the use of solar cells can significantly reduce the consumption of electricity/fossil energy, as well as increase the availability of WiFi networks to support PT AMM's operational support activities. The implication of this study is the great potential of the use of solar energy to improve operational efficiency and reduce environmental impact.

Energy-Efficient Cooperative Transmission in Ultra-Dense Millimeter-Wave Network: Multi-Agent Q-Learning Approach.

In beyond fifth-generation networks, millimeter wave (mmWave) is considered a promising technology that can offer high data rates. However, due to inter-cell interference at cell boundaries, it is difficult to achieve a high signal-to-interference-plus-noise ratio (SINR) among users in an ultra-dense mmWave network environment (UDmN). In this paper, we solve this problem with the cooperative transmission technique to provide high SINR to users. Using coordinated multi-point transmission (CoMP) with the joint transmission (JT) strategy as a cooperation diversity technique can provide users with higher data rates through multiple desired signals. Nonetheless, cooperative transmissions between multiple base stations (BSs) lead to increased energy consumption. Therefore, we propose a multi-agent Q-learning-based power control scheme in UDmN. To satisfy the quality of service (QoS) requirements of users and decrease the energy consumption of networks, we define a reward function while considering the outage and energy efficiency of each BS. The results show that our scheme can achieve optimal transmission power and significantly improved network energy efficiency compared with conventional algorithms such as no transmit power control and random control. Additionally, we validate that leveraging channel state information to determine the participation of each BS in power control contributes to enhanced overall performance.

Joint Adaptive Modulation and Power Control Scheme for Energy Efficient FSO-based Non-Terrestrial Networks

Free-space optics (FSO)-based non-terrestrial networks (NTN) have garnered significant attention as a potential technology for forthcoming 6G wireless communications due to their exceptional data rate and extensive global coverage capability. Nevertheless, atmospheric attenuation, cloud attenuation, geometric loss, and atmospheric turbulence present numerous difficulties in developing these networks. To cope with these difficulties, we propose to apply a joint adaptive modulation and power control (JAMPC) scheme to FSO-based NTN. Our proposed JAMPC algorithm aims to enhance energy efficiency while guaranteeing the targeted outage probability, bit-error rate, and the required data rate. We develop mathematical models and derive closed-form expressions to implement the proposed algorithm and solve the optimization problem. The numerical results confirm that the JAMPC scheme helps NTN provide better energy efficiency and the ability to adapt to various channel conditions.

IMPORTANCE OF INDUSTRIAL SYMBIOSIS STRATEGIES ON ENERGY EFFICIENCY IMPROVEMENT IN ORGANIZED INDUSTRIAL ZONES: KONYA OIZ CASE

Industrial symbiosis (IS) refers to the integration and optimization of numerous industrial systems and processes and is inspired by the nature. By allowing the exchange of materials, energy, other resources, byproducts or valuable waste among companies, the goal is to increase resource efficiency and decrease potential waste. In this study, the IS relevant concepts and examples from the literature are briefly discussed, and a survey is conducted in Konya organized industrial zone. The primary obstacle faced during the survey study is the companies' reluctance to communicate and share information. The survey explores the current level of knowledge, existing energy efficiency efforts, and organizations' willingness to address the topic if grants and subsidies are available. Following the survey, exchange opportunities from the literature are compiled, considering the sectoral structure of the selected zone. Sectoral pairings are formed to encourage businesses and to demonstrate applicability and symbiosis opportunities. Hence, outcomes show how industrial symbiosis has an impact on energy and resource efficiency and waste reduction within selected industrial zone. In addition, its connection and relationship with government incentives, investments and energy efficiency networks are also discussed. As a result, it has been found that there is significant symbiosis potential in a growing and developing region like the Konya industrial zone, through sectoral matching of companies. By increasing awareness among companies, providing financial and legal support, and assisting them in setting targets, inter-company symbiosis efforts can be facilitated and expanded.

Novel Energy-efficient Modified LEACH Routing Protocol for Wireless Sensor Networks

Introduction: In wireless sensor networks (WSNs), hierarchical clustered routing protocols play a crucial role in minimizing energy consumption. The Low Energy Adaptive Clustering Hierarchy (LEACH) architecture is commonly employed for application-specific protocols in WSNs. However, the LEACH protocol may lead to increased energy consumption within the network if the rotational distribution of cluster heads (CHs) is not considered. Methods: A novel average energy, residual energy-based modified LEACH (aerem-LEACH) routing protocol for improving the WSN’s energy efficiency is proposed. This approach simultaneously considers the average energy of the networks and the residual node energy for routing, thereby reducing overall power consumption. Results: The suggested approach in aerem-LEACH accounts for optimal CHs numbers, and nodes in close proximity to the sink are forbidden from participating in cluster formation in order to achieve sufficient performance in the form of reduced sensor node energy consumption. Furthermore, a new threshold is employed in the proposed approach for selecting CHs for the network, and the aerem-LEACH uses free space, multiple hopping, and a hybrid communicating model for an energy-efficient network. Conclusion: The simulation result demonstrates that there is a substantial reduction in the consumption of energy in WSNs with the proposed aerem-LEACH routing protocol compared with existing routing protocols, namely Stable Energy Efficient Network (SEEN), Energy Efficient LEACH (EE LEACH), Optical LEACH (O-LEACH), LEACH-Mobile (LEACH-M), LEACHCentralized (LEACH-C), and LEACH for small-scale as well as large-scale sensor field.

Performance Analysis of Reconfigurable Intelligent Surface (RIS)-Assisted Satellite Communications: Passive Beamforming and Outage Probability

Reconfigurable intelligent surfaces (RISs), which consist of numerous passive reflecting elements, have emerged as a prominent technology to enhance energy and spectral efficiency for future wireless networks. RISs have the capability to intelligently reconfigure the incident wave, reflecting it towards the intended target without requiring energy for signal processing. Consequently, they have become a promising solution to support the demand for high-throughput satellite communication (SatCom) and enhanced coverage for areas inaccessible to terrestrial networks. This paper presents an asymptotic analysis of an RIS-assisted SatCom system. In this system, an unmanned aerial vehicle equipped with an RIS operates as a mobile reflector between a satellite and users. In particular, a passive beamformer is designed with the aim of asymptotically attaining optimal performance, considering the limitations imposed by practical SatCom systems. Moreover, the closed-form expressions for the ergodic achievable rate and outage probability are derived considering the proposed passive beamforming technique. Furthermore, we extend the system model to a multicast system and asymptotically analyze the optimality of the proposed scheme, leveraging the derived asymptotic results in the unicast system. The results of the simulations confirm that our analyses can precisely and analytically assess the performance of the RIS-assisted SatCom system, confirming the asymptotic optimality of the proposed scheme.

A Comprehensive Analysis of the Impact of an Increase in User Devices on the Long-Term Energy Efficiency of 5G Networks

The global deployment of fifth-generation (5G) mobile networks, especially in urban cities, is dedicated to accommodating the demand for high data rates and reliable wireless communications. While the latest 5G networks improve service quality, the support for a simultaneous serving of more user devices (UDs) with higher data rates than previous mobile network generations will require a massive installation of different 5G base station (BS) types dominantly in urban cities. Besides contributing to the smart city service improvements, this massive installation of heterogeneous 5G BSs will also contribute to the increase in 5G network energy consumption (EC) and carbon dioxide emissions. Since this increase in installed 5G BSs imposes environmental and economic challenges, this paper analyzes the impact of the continuously rising number of 5G UDs on the energy efficiency (EE) of the radio part of Croatian and Dutch 5G networks as example cases in the period of 2020s. Analyses consider the countries’ rural, suburban, urban, and dense urban UD density areas by utilizing the proposed simulation framework for the EE evaluation of 5G heterogeneous networks (HetNet) valued through standardized mobile networks EE metrics. The study examines four proposed BS installation and operation scenarios for reducing energy costs of 5G networks that differ in optimizing energy consumption via different BS installations, sleep modes, and transmission power scaling techniques. The obtained results indicate that dynamic adaptation of BS deployments and radio resource management during operation according to the increase in the number of UDs and corresponding DVs can enhance 5G HetNet EE. The findings provide valuable insights for mobile network operators looking to optimize 5G network EE in the upcoming decade.

Advancing Neural Networks: Innovations and Impacts on Energy Consumption

AbstractThe energy efficiency of Artificial Intelligence (AI) systems is a crucial and actual issue that may have an important impact on an ecological, economic and technological level. Spiking Neural Networks (SNNs) are strongly suggested as valid candidates able to overcome Artificial Neural Networks (ANNs) in this specific contest. In this study, the proposal involves the review and comparison of energy consumption of the popular Artificial Neural Network architectures implemented on the CPU and GPU hardware compared with Spiking Neural Networks implemented in specialized memristive hardware and biological neural network human brain. As a result, the energy efficiency of Spiking Neural Networks can be indicated from 5 to 8 orders of magnitude. Some Spiking Neural Networks solutions are proposed including continuous feedback‐driven self‐learning approaches inspired by biological Spiking Neural Networks as well as pure memristive solutions for Spiking Neural Networks.

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

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

Taming Prolonged Ionic Drift-Diffusion Dynamics for Brain-Inspired Computation.

Recent advances in neural network-based computing have enabled human-like information processing in areas such as image classification and voice recognition. However, many neural networks run on conventional computers that operate at GHz clock frequency and consume considerable power compared to biological neural networks, such as human brains, which work with a much slower spiking rate. Although many electronic devices aiming to emulate the energy efficiency of biological neural networks have been explored, achieving long timescales while maintaining scalability remains an important challenge. In this study, a field-effect transistor based on the oxide semiconductor strontium titanate (SrTiO3) achieves leaky integration on a long timescale by leveraging the drift-diffusion of oxygen vacancies in this material. Experimental analysis and finite-element model simulations reveal the mechanism behind the leaky integration of the SrTiO3 transistor. With a timescale in the order of one second, which is close to that of biological neuron activity, this transistor is a promising component for biomimicking neuromorphiccomputing.

ML Based Hybrid Computational Intelligence Protocol to Improve Energy Efficiency and Security in Opportunistic Networks (Oppnets)

ML Based Hybrid Computational Intelligence Protocol to Improve Energy Efficiency and Security in Opportunistic Networks (Oppnets)