Energy Efficiency In Networks Research Articles

A device to device driven approach towards optimizing energy efficiency for 6G networks

Our study aims to develop more energy-efficient mobile communication systems through the exploration of the 6th generation (6G) technology that is expected to be implemented in 2033. We focus on the impact of device-to-device (D2D) communication on power efficiency, which is a crucial need in this domain. To achieve this, we conducted a pioneering experiment using an in-house testbed and K-means clustering to classify locations as D2D enabled or disabled. Our findings show that there is a dynamic clustering mechanism that enables certain nodes to sustain D2D functionality around temporary base stations, resulting in a remarkable 5% improvement in network lifetime per second. This research not only enhances our understanding of 6G networks but also provides a practical methodology for optimizing energy consumption, which holds significant implications for society in advancing sustainable and efficient communication.

Joint Resource Allocation for Maximizing Energy Efficiency in mmWave-Based Wireless-Powered Communication Networks

Joint Resource Allocation for Maximizing Energy Efficiency in mmWave-Based Wireless-Powered Communication Networks

Graphene-Based Artificial Dendrites for Bio-Inspired Learning in Spiking Neuromorphic Systems.

Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to those of digital systems. Though many devices have emerged as candidates for artificial neurons and artificial synapses, there have been few device candidates for artificial dendrites. In this work, we report on biocompatible graphene-based artificial dendrites (GrADs) that can implement dendritic processing. By using a dual side-gate configuration, current applied through a Nafion membrane can be used to control device conductance across a trilayer graphene channel, showing spatiotemporal responses of leaky recurrent, alpha, and Gaussian dendritic potentials. The devices can be variably connected to enable higher-order neuronal responses, and we show through data-driven spiking neural network simulations that spiking activity is reduced by ≤15% without accuracy loss while low-frequency operation is stabilized. This positions the GrADs as strong candidates for energy efficient bio-interfaced spiking neural networks.

Enhanced Whale Optimization Algorithm for Multi-Objective Node Disjoint Routing

Objectives: The research goal is to identify node-disjoint pathways maximizing path lifetime, delivery ratio, etc. while establishing several node-disjoint paths to optimize routing and enhance interference reduction and PDR efficiency. Methods: In the MANET, the Enhanced Whale Optimization Algorithm (EWOA) is utilized to discover the most secure routing path. The dataset used in this research comprises simulated scenarios representing various network conditions and configurations, allowing for a comprehensive evaluation of the proposed work. The software employed for simulation and analysis includes an NS-3 network simulation tool, allowing the model to evaluate the performance of the proposed routing protocol in diverse MANET scenarios. The parameters considered during the evaluation include throughput, end-to-end delay, packet delivery ratio (PDR), and routing overhead. The modifications involve enhancing the routing algorithm to prioritize node-disjoint pathways, thus improving interference reduction, energy efficiency, and overall network performance. Findings: The EWOA for Multi-Objective Node Disjoint Routing demonstrates superior performance compared to existing routing protocols such as SRABC and OLSR across various key metrics. Specifically, in terms of end-to-end delay, EWOA consistently outperforms SRABC and OLSR by reducing delay by 1.3% and 4.7%, respectively. Additionally, EWOA achieves a higher packet delivery ratio, surpassing SRABC and OLSR by 4.3% and 0.4%, respectively. Moreover, EWOA exhibits higher throughput, with a throughput increase of 11.3% compared to SRABC and 6.2% compared to OLSR. Furthermore, EWOA demonstrates lower routing overhead, reducing overhead by 9.6% compared to SRABC and 3.1% compared to OLSR. These findings highlight the efficacy of EWOA in optimizing multi-objective routing in MANETs, offering improved network performance and efficiency compared to existing protocols. Novelty: The Multi-Objective Node Disjoint Routing Protocol with Enhanced Whale Optimization is utilized in MANET to choose the best route, reducing latency resulting from link failure and distributing traffic loads across multiple paths. Keywords: Optimization, Whale Optimization Algorithm (WOA), Optimized Link State Routing (OLSR), Secure Routing Algorithm Blockchain Technology (SRABC)), MANET

Design and performance assessment of improved evolutionary computing based LEACH protocol for energy efficient and lifetime extension of wireless sensor network

The strengths of a Memetic Algorithm and an enhanced Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol are combined in this research study to propose a revolutionary method for improving the energy efficiency and longevity of Wireless Sensor Networks (WSNs). WSNs are widely used for a variety of applications, including industrial automation, healthcare, and environmental monitoring, where extending network lifetime and energy conservation are crucial. Although successful, conventional clustering algorithms like LEACH feature subpar cluster formation and premature cluster head failures. To get over these restrictions, a memetic algorithm is used to maximize cluster formation and reduce energy usage. This algorithm was influenced by natural evolution and cultural learning processes. The Memetic Algorithm iteratively improves the LEACH protocol in the proposed technique by enhancing dynamic clustering, energy-aware routing, and cluster head selection. These methods work together to increase energy efficiency and lengthen the life of the network. In comparison to conventional LEACH-based WSNs and other cutting-edge algorithms, the paper’s study of the simulation results shows how successful the suggested technique is. In light of network longevity, energy use, and data transmission efficiency, the results obtained demonstrate notable improvements.

A Nature-Inspired Approach to Energy-Efficient Relay Selection in Low-Power Wide-Area Networks (LPWAN).

Despite the ability of Low-Power Wide-Area Networks to offer extended range, they encounter challenges with coverage blind spots in the network. This article proposes an innovative energy-efficient and nature-inspired relay selection algorithm for LoRa-based LPWAN networks, serving as a solution for challenges related to poor signal range in areas with limited coverage. A swarm behavior-inspired approach is utilized to select the relays' localization in the network, providing network energy efficiency and radio signal extension. These relays help to bridge communication gaps, significantly reducing the impact of coverage blind spots by forwarding signals from devices with poor direct connectivity with the gateway. The proposed algorithm considers critical factors for the LoRa standard, such as the Spreading Factor and device energy budget analysis. Simulation experiments validate the proposed scheme's effectiveness in terms of energy efficiency under diverse multi-gateway (up to six gateways) network topology scenarios involving thousands of devices (1000-1500). Specifically, it is verified that the proposed approach outperforms a reference method in preventing battery depletion of the relays, which is vital for battery-powered IoT devices. Furthermore, the proposed heuristic method achieves over twice the speed of the exact method for some large-scale problems, with a negligible accuracy loss of less than 2%.

Smart optimization in battery energy storage systems: An overview

The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.

Energy and throughput efficient mobile wireless sensor networks: A deep reinforcement learning approach

AbstractThe efficient development of Mobile Wireless Sensor Networks (MWSNs) relies heavily on optimizing two key parameters: Throughput and Energy Consumption. The proposed work investigates network connectivity issues with MWSN and proposes two routing algorithms, namely Self‐Organising Maps based‐Optimised Link State Routing (SOM‐OLSR) and Deep Reinforcement Learning based‐Optimised Link State Routing (DRL‐OLSR) for MWSNs. The primary objective of the proposed algorithms is to achieve energy‐efficient routing while maximizing throughput. The proposed algorithms are evaluated through simulations by considering various performance metrics, including connection probability (CP), end‐to‐end delay, overhead, network throughput, and energy consumption. The simulation analysis is discussed under three scenarios. The first scenario undertakes ‘no optimisation’, the second considers SOM‐OLSR, and the third undertakes DRL‐OLSR. A comparison between DRL‐OLSR and SOM‐OLSR reveals that the former surpasses the latter in terms of low latency and prolonged network lifetime. Specifically, DRL‐OLSR demonstrates a 47% increase in throughput, a 67% reduction in energy consumption, and a CP three times higher than SOM‐OLSR. Furthermore, when contrasted with the ‘no optimisation’ scenario, DRL‐OLSR achieves a remarkable 69.7% higher throughput and nearly 89% lower energy consumption. These findings highlight the effectiveness of the DRL‐OLSR approach in wireless sensor networks.

A Comprehensive Overview of Network Slicing for Improving the Energy Efficiency of Fifth-Generation Networks.

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks' energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks' energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices' energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future.

Optimizing Energy Efficiency in Opportunistic Networks: A Heuristic Approach to Adaptive Cluster-Based Routing Protocol

Opportunistic Networks (OppNets) are characterized by intermittently connected nodes with fluctuating performance. Their dynamic topology, caused by node movement, activation, and deactivation, often relies on controlled flooding for routing, leading to significant resource consumption and network congestion. To address this challenge, we propose the Adaptive Clustering-based Routing Protocol (ACRP). This ACRP protocol uses the common member-based adaptive dynamic clustering approach to produce optimal clusters, and the OppNet is converted into a TCP/IP network. This protocol adaptively creates dynamic clusters in order to facilitate the routing by converting the network from a disjointed to a connected network. This strategy creates a persistent connection between nodes, resulting in more effective routing and enhanced network performance. It should be noted that ACRP is scalable and applicable to a variety of applications and scenarios, including smart cities, disaster management, military networks, and distant places with inadequate infrastructure. Simulation findings demonstrate that the ACRP protocol outperforms alternative clustering approaches such as kRop, QoS-OLSR, LBC, and CBVRP. The analysis of the ACRP approach reveals that it can boost packet delivery by 28% and improve average end-to-end, throughput, hop count, and reachability metrics by 42%, 45%, 44%, and 80%, respectively.

An Adaptive Cell Switch Off framework to Increase Energy Efficiency in Cellular Networks

An Adaptive Cell Switch Off framework to Increase Energy Efficiency in Cellular Networks

Design and implementation of duty cycle-based futuristic clustering technique in WSN

In recent times, wireless sensor networks (WSNs) and their applications have exhibited a remarkable surge. These networks strive to devise and implement strategies that optimize network energy utilization, thereby extending their operational lifespan. An energy efficient network can be achieved using renewable source of energy and by controlling the duty cycle of nodes. The pivotal role of duty cycle in curtailing energy consumption in WSNs cannot be overstated. In this work, we introduce a novel duty cycle based futuristic clustering technique (DCBFCT) employing a nearest neighbor approach. This technique selectively induces sleep and awake modes in nodes, effectively minimizing the network’s overall energy consumption and, consequently, prolonging its lifespan. It calculates optimal node duty cycle values based on distance. Results demonstrate a substantial reduction in energy consumption, exhibiting an improved network lifetime. Empirical results presented in this study not only affirm the effectiveness of DCBFCT but also contribute valuable insights toward the development of sustainable and resilient WSNs in the era of burgeoning sensor network applications. The experimentation is conducted using the MATLAB/Simulink tool, considering diverse cases. The scalability and versatility of DCBFCT make it suitable for deployment in real-world applications, ranging from environmental monitoring to industrial automation.

Energetic and financial evaluation of a district heating network under varying supply temperatures and booster technologies

Low-temperature district heating (LTDH) enables the use of various renewable energy sources, reduces heat losses and increases the energy efficiency of the distribution network. LTDH is especially applicable in energy-efficient buildings as the supply temperature for space heating can be reduced. However, urban areas consist of energetically refurbished and non-refurbished buildings. In these scenarios a LTDH network with a central heat pump (HP) and decentral booster units, such as a booster HP or electrical heater, can be a solution. This study investigates and compares the energetic performance and levelized cost of heat (LCOH) of eight concepts for refurbished and non-refurbished buildings for a district heating network in the city of Ghent, Belgium. The simulations consider supply temperatures ranging from 10 °C to 75 °C. Results show that the primary energy use is lowest when using booster HPs, for both refurbished buildings (402 MWh/year) and non-refurbished buildings (139.6 MWh/year). The LCOH, however, is lowest when booster units are not necessary as the LCOH is mainly driven by the high investment cost of the network and the booster units. This results in a LCOH of 213 €/MWhth for non-refurbished buildings at a network temperature of 75 °C and 297 €/MWhth for refurbished buildings at 55 °C.

DRL-Driven Optimization for Energy Efficiency and Fairness in NOMA-UAV Networks

DRL-Driven Optimization for Energy Efficiency and Fairness in NOMA-UAV Networks

Accurate and energy efficient ad-hoc neural network for wafer map classification

AbstractYield is key to profitability in semiconductor manufacturing and controlling the fabrication process is therefore a key duty for engineers in silicon foundries. Analyzing the distribution of the defective dies on a wafer is a necessary step to identify process shifts, and a major step in this analysis takes the form of a classification of these distributions on wafer bitmaps called wafer maps. Current approaches use large to huge state-of-the-art neural networks to perform this classification. We claim that given the task at hand, the use of much smaller, purpose defined neural networks is possible without much accuracy loss, while requiring two orders of magnitude less power than the current solutions. Our work uses actual foundry data from STMicroelectronics 28 nm fabrication facilities that it aims at classifying in 58 categories. We performed experiments using different low power boards for which we report accuracy, power consumption and power efficiency. As a result, we show that to classify 224$$\times $$ × 224 wafer maps at foundry-throughput with an accuracy above 97% using a bit more than 1 W, is feasible.

Optimizing Energy Efficiency in Wireless Sensor Networks via Cluster-Based Routing and a Hybrid Optimization Approach

Optimizing Energy Efficiency in Wireless Sensor Networks via Cluster-Based Routing and a Hybrid Optimization Approach

LEACH Protocol to Improve Energy Efficiency of Wireless Sensor Networks in Smart Agriculture

Smart agriculture is the application of technology to improve efficiency, productivity, and sustainability in agricultural practices. However, smart agriculture systems face major challenges related to connectivity and energy management. To address connectivity issues, the Wireless Sensor Network (WSN) architecture is utilized, consisting of sensor nodes to collect and transmit sensor data wirelessly. Despite the implementation of WSN, there are still issues related to high power consumption in smart agriculture systems. This can lead to reduced battery life for each sensor node in the WSN architecture. Therefore, increasing energy efficiency is crucial to optimizing the performance of smart agriculture systems. This study proposes the use of the LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol in smart agriculture to manage clusters within the WSN and reduce energy consumption in each sensor node. Experimental methods were conducted by building the WSN using the nRF24L01 as the sensor data transmitter and Arduino / Node MCU as the microcontroller. The use of the LEACH protocol aims to address energy issues. Additionally, data from each sensor is collected using the Message Queuing Telemetry Transport (MQTT) protocol to facilitate monitoring of sensor data transmission and battery power information. Test results show that the integration of the LEACH protocol into the WSN can be carried out at each stage, from Discovery-State to Steady-State, to Setup-State. These steps are aimed at significantly reducing energy consumption in sensor nodes by 13% over a 12-hour testing period. Furthermore, it can extend battery life and improve the overall system efficiency.

Evolution of Energy Efficient Wireless Sensor Network: A Survey on Energy and Clustering Issues

In different fields of human endeavor, wireless sensor networks (WSNs) are being developed to collect, aggregate, fuse, and send data collected by a wide range of sensors in real-time. When developing a wireless network's data transmission routing system, energy consumption is among the most essential components. Designing an energy-efficient (WSN) routing framework is challenging. Several energyefficient routing algorithms exist for effectively transferring data packets between cluster members (CM) and cluster heads (CH). Some suggest the change of state of the nodes that made up the network from active to sleep in the event where the nodes are not sensing of sending data as a method of energy optimisation while others reduce the distance between the nodes because the distance between nodes is directly proportional to the amount of energy use during data transmission. Whatever the method may be, achieving reduced energy consumption in a WSN environment is an enormous task due to the need for both long network life and network efficiency. This paper reviews some research and energy saving techniques that had been carried out on WSN over a period of time.

Optimizing the Energy Efficiency of a Lighting Network using Graph Theory

In this paper, we discuss how to make electric street lighting systems more energy efficient by creating an algorithm and mathematical model for optimizing parameters, minimizing active power losses, and finding the best topology for the lighting network when it is being designed or updated. Scientific and technological progress has led to an increase in the complexity of every human being's daily life. Companies, institutions, and countries constantly need to find modern tools to help them make the best decisions. Graph theory has numerous applications to many everyday problems. It can resolve and simplify them. An algorithm was developed to determine the shortest length in the form of a modified Dijkstra graph, with nodes supporting the street lights and ribs being the wires connecting them.

Kalman filtering and sine arithmetic optimization algorithm (KSAOA) for wireless sensor network clustering

Hierarchical routing is a crucial approach for enhancing the energy efficiency of Wireless Sensor Networks (WSN). The selection of sensor clusters poses a Non-deterministic Polynomial (NP) problem, typically addressed using meta-heuristic algorithms. However, existing research has not adequately considered the observation error of node energy and the limited convergence performance of their meta-heuristic algorithms. To address these challenges, we proposes a novel approach called KSAOA (Kalman filtering and Sine Arithmetic Optimization Algorithm) for WSN clustering, aimed at prolonging the service lifetime and enhancing the network balance of WSN. The proposed model combines Kalman filtering for estimating node energy and the Sine Arithmetic Optimization Algorithm (SAOA) for clustering nodes. SAOA builds upon the Arithmetic Optimization algorithm (AOA) by introducing a sine factor to extend the exploration boundary and a levy flight strategy to improve local exploration. Additionally, greedy algorithms are employed in the data transfer phase to enhance operational efficiency. The experimental results demonstrate the effectiveness of the proposed method in tabular and graphical form. The results are analyzed through comparative experiments to show that the proposed strategy is effective in improving the balance of network lifetime and network energy consumption.