Network Energy Consumption Research Articles

Adaptive sparse structure development with pruning and regeneration for spiking neural networks

Spiking Neural Networks (SNNs) are more biologically plausible and computationally efficient. Therefore, SNNs have the natural advantage of drawing the sparse structural plasticity of brain development to alleviate the energy problems of deep neural networks caused by their complex and fixed structures. However, previous SNNs compression works are lack of in-depth inspiration from the brain development plasticity mechanism. This paper proposed a novel method for the adaptive structural development of SNN (SD-SNN), introducing dendritic spine plasticity-based synaptic constraint, neuronal pruning and synaptic regeneration. We found that synaptic constraint and neuronal pruning can detect and remove a large amount of redundancy in SNNs, coupled with synaptic regeneration can effectively prevent and repair over-pruning. Moreover, inspired by the neurotrophic hypothesis, neuronal pruning rate and synaptic regeneration rate were adaptively adjusted during the learning-while-pruning process, which eventually led to the structural stability. Experimental results on spatial and temporal datasets demonstrate that our method can flexibly learn appropriate compression rate for various tasks and effectively achieve superior performance while massively reducing the network energy consumption. Specifically, for the neuromorphic DVS-Gesture dataset, 98.56% accuracy with 1.45% improvement is achieved by our method when the compression rate reaches 61.10%. Our code is available at: https://github.com/BrainCog-X/Brain-Cog/tree/main/examples/Structural_Development/SD-SNN.

Impact of early-stage cooperative vehicle infrastructure systems on traffic and energy consumption under various traffic conditions: From application to policy

Cooperative Vehicle Infrastructure Systems (CVIS) play a crucial role in promoting sustainable transportation. Existing studies have primarily focused on the theoretical algorithms of CVIS, with insufficient research on energy consumption evaluation using practical data, particularly under diverse traffic conditions. This study addresses this gap by collecting long-term sequential application data (2.19 million) of CVIS across a wide region, analyzing the performance trends of CVIS under various traffic conditions, and offering practical policy recommendations for optimizing and deploying CVIS effectively. The data from drivers using CVIS are categorized into passive CVIS (P-CVIS) or active CVIS (A-CVIS) based on the utilization of the speed guidance function. The study reveals that drivers with insufficient proficiency in A-CVIS tend to engage in more aggressive driving behaviors, characterized by higher acceleration rates and greater variability in acceleration distribution in response to recommended speeds. These behaviors lead to increased acceleration energy consumption for both vehicles (2.7 % and 9.0 %) and road networks (65.2 % and 24.7 %) during peak and off-peak hours, respectively. While A-CVIS can still play an optimizing role during peak hours, the majority of optimization metrics are slightly lower compared to off-peak periods. The study concludes by offering recommendations and regulatory measures to enhance A-CVIS.

Energy-Efficient Algorithm for Data Path Selection in High-Density Wireless Sensor Networks

Relevance. Increasing the number of sensor devices per unit area consequently reduces the physical distance between the devices in the sensor network. Such networks are usually deployed over a large area and the sensor device that wants to transmit a data packet is located far away from the base station. In such a case, the source device is challenged to choose a transmission path that consumes the least amount of energy resources and satisfies the delivery time requirements. The objective of this study is to develop and validate the effectiveness of an empirical algorithm for selecting a transmission path that reduces the energy consumption of high-density wireless sensor networks. Methods of system analysis, analytical modeling, geometry and probability theories are used. Solution. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. When building data transmission routes, any sensor devices can be used as repeaters. At the same time, the increase in the number of repeaters leads to an increase in the time of data delivery. Novelty. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. Any sensor devices may be used as repeaters when constructing data transmission routes.Significance (theoretical). Dependences of power consumption level on various system parameters affecting the processes of functioning of high-density wireless sensor networks have been obtained. Significance (practical). The proposed empirical algorithm for selecting a rational data transmission route in a wireless sensor network allows us to determine, among all alternatives, the route to the coordinator that requires the least power. The effectiveness of the proposed empirical power-saving algorithm is confirmed by simulation modeling.

An Intelligent Cryptographic Approach for Preserving the Privacy and Security of Smart Home IoT Applications

Background: Today, computer networks are everywhere, and we utilize the Internet to access our home network. IoT networks connect home appliances and provide remote instructions. Access to any tool over an uncertain network attracts assaults. User authentication might be password- or biometric-based. Data security across a secure network like the Internet is difficult when authenticating a device. Hashing is used for validation and confidentiality in several encryption and decryption schemes. Classic cryptographic security methods require a lot of memory, processing power, and power. They cannot work with low-resource IoT devices. Methods: Automatic Device-to-Device communiqué opens up new applications, yet network machines and devices have limited resources. A remote-access home device authentication mechanism is proposed in this research. A new, lightweight encryption approach based on Deoxyribonucleic- Acid (DNA) sequences is developed to make IoT device connections easy and secure. Home network and appliance controller devices use authentication tools. DNA sequences are random therefore we utilized them to create a secure secret key. Results: Efficiency and strength are advantages of the proposed method. Our method prevents replay, server spoofing, and man-in-the-middle attacks. The suggested method protects network users and devices. Conclusion: Meanwhile, we model the system and find that the network's delay, throughput, and energy consumption don't degrade considerably.

Minimizing Energy Consumption in Cell-Free Massive MIMO Networks

Minimizing Energy Consumption in Cell-Free Massive MIMO Networks

Optimise Energy Consumption of Wireless Sensor Networks by using modified Ant Colony Optimization

Routing represents a pivotal concern in the context of Wireless Sensor Networks (WSN) owing to its divergence from traditional network routing paradigms. The inherent dynamism of the WSN environment, coupled with the scarcity of available resources, engenders considerable challenges for industry and academia alike in devising efficient routing strategies. Addressing these challenges, a viable recourse is applying heuristic search methodologies to ascertain the best path in WSNs. Ant Colony Optimization (ACO) is a well-established heuristic algorithm that has demonstrated notable advancements in routing contexts. This paper presents an altered routing protocol that is based on ant colony optimization. In this protocol, we incorporate the inverse of the distance between nodes and their neighbours in the probability equations of ACO, along with considering pheromone levels and residual energy. These formulation modifications facilitate the selection of the most suitable candidate for the subsequent hop, effectively minimising the average energy consumption across all nodes in each iteration. Furthermore, in this protocol, we iteratively fine-tune ACO's parameter values based on the outcomes of several experimental trials. The experimental analysis is conducted through a diverse set of network topologies, and the results are compared against well-established ACO algorithms and routing protocols. The efficacy of the proposed protocol is assessed based on various performance metrics, encompassing throughput, energy consumption, network lifetime, energy consumption, the extent of data transferred over the network, and the length of paths traversed by packets. These metrics collectively provide a comprehensive evaluation of the performance attainments of the routing protocols.

Privacy protection of communication networks using fully homomorphic encryption based on network slicing and attributes

At present, social networks have become an indispensable medium in people's daily life and work. However, concerns about personal privacy leakage and identity information theft have also emerged. Therefore, a communication network system based on network slicing is constructed to strengthen the protection of communication network privacy. The chameleon hash algorithm is used to optimize attribute-based encryption and enhance the privacy protection of communication networks. On the basis of optimizing the combination of attribute encryption and homomorphic encryption,, a communication network privacy protection method using homomorphic encryption for network slicing and attribute is designed. The results show that the designed network energy consumption is low, the average energy consumption calculation is reduced by 8.69%, and the average energy consumption calculation is reduced by 14.3%. During data transmission, the throughput of the designed network can reach about 700 Mbps at each stage, which has a high efficiency.. The above results demonstrate that the designed communication network provides effective privacy protection. Encrypted data can be decrypted and tracked in the event of any security incident. This is to protect user privacy and provide strong technical support for communication network security.

Energy-Efficient Clustering in Wireless Sensor Networks Using Grey Wolf Optimization and Enhanced CSMA/CA.

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

Energy efficient clustering and routing protocol based on quantum particle swarm optimization and fuzzy logic for wireless sensor networks

Clustering and routing protocols play a pivotal role in reducing energy consumption and extending the lifespan of wireless sensor networks. However, optimizing energy efficiency to maximize network longevity remains a primary challenge for these protocols. This paper introduces QPSOFL, a clustering and routing protocol that integrates quantum particle swarm optimization and a fuzzy logic system to enhance energy efficiency and prolong network lifespan. QPSOFL employs an enhanced quantum particle swarm optimization algorithm to select optimal cluster heads, utilizing Sobol sequences for population diversification during initialization. Additionally, it incorporates Lévy flight and Gaussian perturbation-based position updates to prevent trapping in local optima. Benchmark experiments validate QPSOFL's efficacy compared to Harris Hawks Optimization (HHO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Quantum Particle Swarm Optimization (QPSO), focusing on accuracy, search capability, and convergence speed. Within QPSOFL, a fuzzy logic system determines the best next-hop cluster head based on descriptors such as residual energy, energy deviation, and relay distance. Extensive simulations compare QPSOFL's performance in terms of network lifetime, throughput, energy consumption, and scalability against existing protocols E-FUCA, IHHO-F, F-GWO, and FLPSOC, demonstrating its superior performance over these counterparts.

A mobile data collection method for balancing energy consumption and delay in strip-shaped wireless sensor networks with branches

Strip-shaped Wireless Sensor Networks (WSNs) with branches are commonly used in various long and narrow applications, such as mines, factories, subways, and pipelines, and they face serious energy hole problems caused by multi-hop communication. The Mobile Data Collector (MDC) can alleviate the energy hole problem. Current solutions have two limitations: one is the balance between energy consumption and delay, and the other is the overly ideal network model, e.g., the square region or circular area. This paper focuses on strip-shaped networks and proposes a novel mobile data collection method to find a trade-off between energy preservation and data delivery delay. Firstly, the MDC path is planned by solving the diameter of the tree in the network, resulting in reduced delay. Secondly, the network energy consumption is further reduced by clustering and optimal transmission distance adjustment. Then, a network lifetime balancing mechanism is designed to balance network energy between backbone and branches. Finally, the performance of the algorithm proposed in this paper has been studied in four types of strip-shaped WSNs and compared with four existing MDC methods with evaluation metrics of maximum node energy consumption, network delay and weighted sum of both. The simulation results demonstrate that the proposed algorithm is applicable to different types of strip-shaped WSNs with branches and achieves excellent network performance, which can effectively balance network energy consumption and data collection delay.

Mitigating Energy Consumption in Heterogeneous Mobile Networks Through Data-Driven Optimization

Mitigating Energy Consumption in Heterogeneous Mobile Networks Through Data-Driven Optimization

Energy saving performance analysis for future fifth generation millimetre-wave cellular networks

The deployment of fifth generation (5G) millimetre-wave (mmWave) base stations (BSs) will consume more energy over time due to the limited time available, despite the increasing interest in developing 5G mmWave wireless communication technology. Constructing 5G mmWave cellular network infrastructure can improve energy efficiency, which is a challenge to implement in heterogeneous networks. This paper presents analytical frameworks for monitoring the effectiveness of 5G mmWave cellular networks. Based on the state management of BS, a system model for 2-tier heterogeneous networks is developed, and particle swarm optimization (PSO) is then used to compute the total energy consumption of the heterogeneous networks. Energy consumption was compared and analysed by leveraging state switching and the aggregate delay for three methods: fundamental separation, conventional separation, and a proposed energy-saving method that introduced a sleep state. Simulation shows that the proposed energy-saving method, which is a combination of conventional separation approaches, has the lowest total energy consumption and offers a 9% reduction compared to other related works. The results validate the accuracy of the power usage used in the 5G mmWave cellular network of the proposed method.

Cost-efficient microgeneration renewable energy provision dimensioning for sustainable 5G heterogeneous network

The deployment of mobile networks has imposed an urgent requirement for the pursuit of low-carbon communication infrastructures. The increasing energy consumption of mobile networks has brought about challenges of techno-economic and environmental sustainability. Renewable energy-enabled mobile networks have received a lot of attention due to their capability to evade greenhouse gas emissions and easy availability. Microgeneration-based renewable energy provision is a feasible and effective solution for 5G networks. Dimensioning of microgeneration renewable energy power supply is an essential issue to make the system operate for a long period cost-effectively with a minimum amount of grid energy consumption. For effective deployment of microgeneration renewable energy system, it is essential to provision it with adequate PV panel capacity and storage devices. This work attempts to identify the cost-effective, energy-efficient, and emissions-aware sizing of PV panels and storage for 5G HetNet. An energy-saving strategy based on an optimal policy of advanced sleep modes and traffic-aware load offloading is developed and the interaction of the energy-saving strategy on the system dimensioning is explicitly examined. The proposed solution aims to ensure the communication quality of service whilst keeping the optimal cost-effective deployment and network operation. The system performance in terms of grid energy consumption, empty storage probability, emission performance, and total cost of the system is extensively assessed through experiments for a range of operational scenarios. The numerical results demonstrated that the proposed sustainable energy system dimensioning and operation integrated with an energy-saving strategy is energy-efficient and cost-effective.

Enhancing sustainability in irrigation networks: A multicriteria method for optimizing flow distribution and reducing environmental impact

Irrigation systems significantly enhance agricultural productivity but are also substantial consumers of water, energy, and natural resources. The need to optimize their design encouraged agronomic engineering to develop various methods for improving the design and management of these irrigation networks. This development focuses on creating a tool to define the optimal flow distribution according to the system's irrigation or consumption needs, thereby determining the design flows. The aim is to optimize the design of pipe diameters to improve sustainability (i.e., reducing CO2 emissions, minimizing service pressure, and maximizing recoverable energy within the system). These principles ensure a better evaluation of sustainable development goals within agricultural production. The proposed procedure develops a strategy to define the best-fitting distribution using a multicriteria solution. As novel, the research develops a tool, which characterizes flow distributions deviating from the classic Clement's formulation used in irrigation systems. The proposed method was applied in a Mediterranean irrigation system in Spain, achieving a correlation coefficient above 0.9 in the model. This methodology addresses design criteria in terms of sustainability and reduces energy consumption in networks. It achieved material savings of 6.01 % compared to the observed network, reducing CO2 emissions between 5.61 and 5.72 TnCO2/ha over its lifecycle.

Review of Underwater Wireless Sensor Networks Deployment Techniques and Challenges

Underwater Wireless Sensor Networks (UWSNs) have become increasingly important due to their critical roles in marine life monitoring, communication, ocean data collection, sampling, and military security operations. The success of UWSNs largely depends on efficient node deployment techniques that ensure optimal coverage, connectivity, cost-effectiveness, network lifetime, and energy utilization. This paper presents a comprehensive review of various node deployment types and techniques specifically designed for UWSNs. It covers depth-adjustment, movement-assisted, self-movement, and soft-computing techniques, highlighting their advantages, limitations, and application scenarios. Each technique is evaluated based on key performance metrics such as network coverage, connectivity, energy consumption, network lifetime, and deployment cost. Additionally, the paper discusses the challenges and identifies open research directions in the field, providing valuable insights for researchers and practitioners in selecting appropriate node deployment techniques for UWSNs.

Towards a Sustainable Blockchain: A Peer-to-Peer Federated Learning based Approach

In the rapidly evolving digital world, blockchain technology is becoming the foundation for numerous applications, ranging from financial services to supply chain management. As the usage of blockchain is becoming more prevalent, the energy-intensive nature of this technology has raised concerns about its long-term sustainability and environmental footprint. To address this challenge, we explore the potential of Peer-to-Peer Federated Learning (P2P-FL), a distributed machine learning approach that allows multiple nodes to collaborate without sharing raw data. We present a novel integration of P2P-FL with blockchain technology, aimed at enhancing the sustainability and efficiency of blockchain networks. The basic idea of our approach is the use of distributed learning mechanisms to find the optimal performance parameters of blockchain without relying on centralized control. These parameters are then used by a load-balancing mechanism that prioritizes energy efficiency to distribute loads on different blockchains. Furthermore, we formulate a non-cooperative game theory model to align the individual node strategies with the collective objective of energy optimization, ensuring a balance between self-interest and overall network performance. Our work is exemplified through a case study in the renewable energy sector, demonstrating the application of our model in creating an efficient marketplace for energy trading. The experimentation and results indicate a significant improvement in the execution times and energy consumption of blockchain networks. Therefore, the overall sustainability of the network is enhanced, making our framework practical and applicable in real-world scenarios.

System for monitoring building heat consumption

Relevance. Energy consumption and efficient use of district heating networks affect the environment, society and the economy. Solutions are needed that can significantly reduce specific thermal energy losses. Using a technically and economically feasible way to assess energy efficiency allows these decisions to be made. Currently, assessing the energy efficiency of buildings raises significant questions among specialists. There is not much modern specialized literature in this area. The problem of energy efficiency requires more rigorous attention to urban planning issues. Thus, refined methods for assessing the energy efficiency of buildings make it possible to simultaneously save capital investments and ensure efficient consumption of thermal energy. Aim. To develop a system for assessing the real heat consumption of buildings to ensure optimization of dispatching of centralized heat supply systems in real time. Methods. Computer modeling of the state of heat consumption of buildings with various characteristics and schemes for their connection to centralized heat supply networks; methods of geographic information analysis. Results. The authors have proposed the system for monitoring the heat consumption of buildings. This system allows assessing the energy efficiency of buildings through the maximum use of computer and software technologies. It is shown that the calculated heat flows of buildings used to predict savings when modernizing buildings do not always correspond to the actual heat loads, which can lead to an incorrect assessment of investment projects. The authors obtained graphical dependences of the quantities influencing specific heat losses on various primary independent factors. Conclusions. The constructed system for monitoring the heat consumption of buildings allows, together with the use of geoinformation analysis methods, obtaining a fairly complete picture of the state of heat consumption for city areas and an assessment of the energy characteristics of buildings.

Energy efficiency and interoperability through O-RAN Rapid Transition Protocol (ORTP)

Mobile network traffic is increasing accompanied by increased energy consumption. Traditional Radio Access Networks (RANs) have been used for decades as the foundation of mobile communications, providing mobile customers with reliable voice and data services. However, they encounter obstacles in terms of scalability, adaptability, and cost-effectiveness. Next-generation wireless networks need efficient Radio Access Network (RAN) solutions to achieve low latency and high throughput. The Open RAN (O-RAN) architecture is a potential solution for 5G and beyond networks due to its open interfaces, disaggregated network entities and services, network hardware and software virtualization, and intelligent control. Traditionally standard RAN accounts for the bulk of mobile network energy consumption, leading towards higher Operational expenditure (OPEX) costs. In the context of cellular networks. Handover strategies need careful development to cater for efficient resource usage as well as overall system energy efficiency. O-RAN Rapid Transition Protocol (ORTP) presented in this paper employs minimum value of handover margin (HOM) and is aimed at increased energy efficiency. 5G based System level simulations have been performed to investigate the efficacy of ORTP for key performance indicators including handover probability, Radio Link Failure, connection density, ping pong effect and dynamic power consumption. It is found that the usage of lower HOM values provide noticeable achievement in terms of power consumption, thereby rendering ORTP around 20 % more efficient compared to 5G networks, while keeping it interoperable.

BEFF-SIGS: blockchain-enhanced fog framework- securing IoT data integrity and green sustainability through scalable authentication-authorization

This paper introduces a comprehensive framework designed to address security concerns in the Internet of Things (IoT) environment while contributing to the global Green Environment Initiative. The proposed mechanism utilizes a secure and scalable Authentication-Authorization system, employing JSON Web Token (JWT) and Casbin to ensure that only authorized entities access critical data. Data processing is strategically offloaded to the fog layer to enhance scalability and reduce latency, optimizing IoT data handling. The framework actively supports environmental sustainability by promoting eco-friendly practices within industrial setups. Industries are incentivized to embrace environmentally responsible approaches through tax incentives, fostering a greener future. Our model is a robust mechanism to guarantee the consistency and authenticity of the data stored in the cloud environment, offering assurance against potential tampering attempts by securing data integrity. This holistic approach demonstrates how governments can leverage cutting-edge technology to incentivize industries and promote a sustainable and eco-friendly future. Utilizing blockchain technology ensures unparalleled transparency, robust security, and tamper-resistant record-keeping. We evaluate the impact on network consumption, latency, and energy consumption for analyzing the performance of the proposed system. The proposed framework offers better latency, network efficiency, and distributed computational power as major benefits over depending exclusively on cloud resources in IoT deployments.

A deep belief network-based energy consumption prediction model for water source heat pump system

To achieve optimal energy efficiency in buildings, accurately forecasting the energy consumption of air conditioning systems is crucial. This study develops an energy consumption prediction model based on a deep belief network, which is constructed according to the principles of a restricted Boltzmann machine. Actual experimental data from a water source heat pump system are collected, and feature variables are selected. The study discusses the impact of model parameters and training set sizes on the performance of energy consumption prediction model. Additionally, the trend in model prediction performance is analyzed through parameter adjustments. The results show that the coefficient of determination (R2) for the optimized model has increased to 0.585. The mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) have been reduced to 6.311, 2.512, and 1.625, respectively. The deep belief network energy consumption prediction model outperforms other common machine learning models for water source heat pump systems.