Network Energy Consumption Research Articles

Improving energy efficiency in wireless sensor networks (WSNs) using two-level fuzzy clustering and Artificial Bee Colony (ABC) optimization

ABSTRACT Improving network lifetime and energy efficiency in wireless sensor networks (WSNs) is a challenging but crucial issue. The ability to transport data over the network via a better route is a technology because of limitations on network lifetime and energy consumption. This paper presents a two-level fuzzy approach for optimising cluster formation and data routing in wireless networks. The approach focuses on determining cluster head nodes based on their unique properties, ensuring efficient cluster formation. The second level uses social traits to communicate among cluster nodes, calculating the cluster radius for better communication and data routing. The optimisation process also involves identifying optimal cluster head nodes and their corresponding radius for each cluster. Furthermore, we improved data routing by using the artificial bee colony technique, which included carefully selecting the best data transmission channels between the base station and the cluster head nodes. Thus, simulation results demonstrate that our technique outperforms other similar approaches in terms of energy usage, latency, packet transmissions, and average network lifespan. We tested the network performance from two perspectives: scalability and energy efficiency, in two stages. The experiments’ results demonstrated the superiority of the suggested approach over the methods that were compared.

A Hybrid Biologically-Inspired Optimization Algorithm for Data Gathering in IoT Sensor Networks

The wireless sensor networks are autonomous Ad hoc Networks that form the backbone of IoT technology. Their vital role is to collect data from their physical environments and transfer these data to the Base Station (BS). This task implies high energy consumption when a suitable data routing policy is not applied. Then, the data collection method determines the efficiency of WSNs. Moreover, the IoT-based applications that require the mobility of the IoT sensor nodes introduce a new challenge regarding network connectivity. The IoT sensor node’s mobility leads to regular changes in the network topology, resulting in high packet loss during communication. The management of network energy use in order to prolong the battery life of IoT sensor nodes is a crucial issue, as IoT sensor nodes possess limited battery capacity. This paper addresses these issues by proposing an efficient Hybrid Biologically-Inspired Optimization Algorithm, named HBIP, for Data Gathering in IoT Sensor Networks. The flagship idea of the HBIP algorithm is to incorporate the swarming stage of the Bacterial Foraging Optimization (BFO) into the Artificial Bee Colony (ABC) Optimization’s exploitation phase. Simulation results show that the HBIP protocol outperforms the LEACH protocol and ABC and BFO metaheuristics regarding network energy consumption, lifetime, and data packets received at the BS. In practice, we demonstrated that the HBIP protocol increases the data collection by 84.40% over LEACH, 19.43% over BFO, and 7.26% over ABC in the network scenario considered. Further on, we extend the study of the HBIP algorithm on Mobile IoT Sensor Networks by proposing a Multi-Objective Optimization-based Data Gathering Protocol (MOO-DGP). The MOO-DGP protocol employs the HBIP algorithm to determine the best solution that balances Velocity, Cluster Stability, Link Quality, and Energy Consumption. An extensive evaluation of the MOO-DGP protocol shows it outperforms the existing Mobility-Based Clustering (MBC) protocol regarding effective data packet delivery rate, average control overhead, network lifespan, and energy consumption.

Smart Grid Load Forecasting Models Using Recurrent Neural Network and Long Short-Term Memory

This paper presents two methods for managing electrical energy consumption and demand, with the objective of developing reliable and accurate forecasting models for smart electrical network energy consumption and optimization. The first method utilizes a recurrent neural network (RNN), while the second employs long short-term memory (LSTM) techniques. This approach builds upon previous studies that have explored the use of machine learning models for energy forecasting, but often with limited performance or the inability to capture long-term dependencies in the data. The study utilizes the Global Energy Forecast 2012 database - for the period from 2004 to 2008, with a focus on electricity consumption - to validate the performance of the proposed models. The R-squared (R2) score is used as the primary evaluation metric, with the LSTM model achieving a remarkable 90% R2 score, outperforming the RNN model's 80% R2 score. This is a significant improvement over previous studies, which have typically reported R2 scores in the range of 70-80% for energy forecasting models. Furthermore, the LSTM model demonstrates superior error rate performance, with a Mean Squared Error (MSE) of 4.345%, compared to the RNN model's 16.644% MSE. This highlights the ability of LSTM models to capture long-term dependencies in the data, which is crucial for accurate energy consumption forecasting, a limitation often observed in traditional RNN-based approaches. The findings of this study highlight the superior performance of the LSTM-based approach in accurately predicting energy consumption in smart grids, a crucial aspect for optimizing energy management and distribution. This contribution is particularly significant, as it showcases the advantages of LSTM models over traditional RNN techniques in the context of energy forecasting, providing valuable insights for researchers and practitioners in the field of smart grid optimization, where accurate forecasting is essential for efficient energy management and distribution.

Node-Alive Index Driven Redundancy Elimination for Energy-Efficient Wireless Sensor Networks

Wireless Sensor Networks (WSNs) generate correlated and redundant data. This redundancy increases energy consumption during transmission and aggregation, which reduces the network lifespan. Eliminating data redundancy using appropriate data aggregation mechanisms in the dynamic environment is challenging. To address these issues, we designed the Data Aggregation with Redundancy Removal (DARR) protocol and implemented it in two phases. In Phase I, the DARR protocol identifies redundant nodes by calculating the spatial distance between the adjacent nodes. Over time, nodes may run out of energy and stop working after continuously sensing, aggregating, and transmitting the data. The dead nodes can obstruct data forwarding to intermediate nodes, so it is important to check periodically whether the nodes are alive or dead. The periodic time check identifies the status of each node, allowing the protocol to focus only on active nodes. It sets redundant nodes to sleep, which conserves network energy. In Phase II, the protocol reduces data redundancy at the source nodes using temporal correlation between data measurements. We enhanced the DARR protocol by incorporating a High Compression Temporal (HCT) mechanism, which further reduces data redundancy. Simulations show that the DARR protocol reduces data transmissions by 24% and lowers network energy consumption by up to 31% by eliminating redundant data at both the network and node levels.

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.

Sustainable Consensus Algorithms Applied to Blockchain: A Systematic Literature Review

In recent years, consensus algorithms have gained significant importance in the context of blockchain networks. These algorithms play a crucial role in allowing network participants to reach agreements on the state of the blockchain without needing a central authority. The present study focuses on carrying out a systematic mapping of these consensus algorithms to explore in detail their use, benefits, and challenges in the context of blockchain networks. Understanding consensus algorithms is essential to appreciating how blockchain networks achieve the reliability and integrity of their distributed ledgers. These algorithms allow network nodes to reach agreement on the validity of transactions and the creation of new blocks on the blockchain. In this sense, consensus algorithms are the engine that drives trust in these decentralized networks. Numerous authors have contributed to the development and understanding of consensus algorithms in the context of blockchain networks. This revolutionary concept paved the way for numerous cryptocurrencies and blockchain systems. Despite advances in this field, significant challenges remain: centralization, fair token distribution, scalability, and sustainability. The energy consumption of blockchain networks, particularly those using algorithms such as Proof of Work, Proof of Stake, Delegated Proof of Stake, Proof of Authority, and hybrid algorithms (Proof of Work/Proof of Stake), has raised concerns about their environmental impact, motivating the scientific and technological community to investigate more sustainable alternatives that promise to reduce energy consumption and contribute to climate change mitigation. Furthermore, interoperability between different blockchains and security in specific environments, such as IoT, are areas that still require significant research attention. This systematic mapping not only seeks to shed light on the current state of consensus algorithms in blockchain, but also their impact on sustainability, identifying those algorithms that, in addition to guaranteeing integrity and security, minimize the environmental footprint, promoting a more efficient use of energy resources, being a relevant approach in a context in which the adoption of sustainable technologies has become a global priority. Understanding and improving these algorithms are critical to unlocking the full potential of blockchain technology in a variety of applications and industry sectors.

Deep Reinforcement Learning and Enhanced Optimization for Real-Time Energy Management in Wireless Sensor Networks

Constraints are a major issue in radio-based communication in Wireless Sensor Networks, where each sensor node has a limited amount of power. Conventional clustering and optimization methods have been inappropriate for dynamic conditions which lead to timely energy drainage and reduce the network lifetime. In this research, the novel Deep Reinforcement Learning-Enhanced Hybrid African Vulture and Aquila Optimizer has been proposed that optimizes the dynamic clustering and energy-based parameters in real time. The proposed model is designed for optimizing the Wireless Sensor Networks, by including Deep Reinforcement Learning to adjust the dynamic formation of the base of the cluster on real-time data which leads to efficient energy utilization among all the sensor nodes. It combines the best properties of the Aquila and African Vulture Optimizer to optimize the network lifetime and energy consumption. The network lifetime, which is one of the most crucial characteristics, is optimized by using the global search algorithm of African Vulture Optimiser. In contrast, it is optimized by the localized search of Aquila optimizer to reduce energy consumption. The presented novel African Vulture and Aquila model outperforms the existing methods used convention-based optimization methods. It shows a 20% improvement in energy efficiency and faster convergence with better robustness while keeping the network scalability. The proposed approach is perfectly suited for the scalable WSNs which are mainly used in the environment such as smart cities and IoT systems where a timely adaptation process is inevitable.

A new enhanced TDEEC protocol for 3D HWSN

Wireless sensor networks (WSNs) are classified as three-dimensional (3D) when the elevation variation of deployed sensor nodes is significant relative to the length and breadth of the operating area. This paper introduces and evaluates a novel protocol, Enhanced Three-Dimensional Efficient TDEEC (ETDEEC-3D), which employs multidimensional scaling to optimize energy consumption in 3D heterogeneous wireless sensor networks (HWSNs) through an improved clustering approach. By excluding nodes closest to the base station from the clustering process, the protocol significantly extends the network lifetime. Performance evaluations indicate that ETDEEC-3D outperforms the TDEEC-3D algorithm in terms of packet delivery, energy efficiency, and the number of cluster heads (CH) formed per round. MATLAB simulations reveal a 24 % improvement in the First Node Dies (FND) metric, a 33 % reduction in energy consumption, and enhanced energy efficiency, achieved by strategically selecting CHs based on nodes' residual energy levels.

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.

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.

Positioning Sensor Data Mining and Correlation Analysis Based on Multi-mode Radio Frequency Identification

Real-time positioning and sensor data mining are critical for various Internet of things (IoT) applications, but current approaches face challenges such as limited accuracy, high cost, and energy inefficiency. This paper proposes a novel fusion intelligent structure that combines radio frequency identification (RFID) technology and wireless sensor networks (WSN) to enable accurate and efficient positioning and data mining. The proposed method consists of two key components: an energy heterogeneity strategy for optimizing network energy consumption, and a data association feature analysis technique for mining sensor data. The results demonstrated that the proposed approach achieved superior positioning accuracy compared to other algorithms, with an average error of less than 0.300 under various conditions. The data detection accuracy was also high, with a maximum false detection rate of 3%. Furthermore, the proposed energy heterogeneity strategy significantly improved network energy utilization and stability. Real-world scenario testing validated the practicality of the proposed approach, with an average positioning error of less than 0.500 meters. These results validate the effectiveness of the proposed RFID-WSN fusion structure for real-time positioning and sensor data mining, providing a promising solution for IoT applications.

Energy efficient mobile sink driven data collection in wireless sensor network with nonuniform data

In wireless sensor network, mobile sink is used to collect data from sensor nodes by periodically traversing the network to prevent hotspot problem. However, when sensor nodes generate data non-uniformly, the efficiency of data collection is constrained by rendezvous points and network topology. It becomes more challenging under network energy consumption constraint. Thus, this paper investigates non-uniform data generation in wireless sensor network and proposes an innovative approach: Optimal Clustering and Network Topology for Mobile Sink-Driven Data Collection, called OCNTMS. It focuses on determining optimal rendezvous points and their associated clusters. Through innovative methods such as weight-balanced clustering, cost function optimization, load-balanced link construction, and node forwarding selection, the OCNTMS can efficiently construct link sets within clusters and accurately plan the mobile sink’s traversal of rendezvous points. Simulation results show that the OCNTMS reduces energy consumption by 18% and increases network lifetime by 40% compared with existing approaches under the constraint of non-uniform data generation. This greatly improves the network energy efficiency and data transmission efficiency.

Survey on Wireless Sensor Network Structure Attacks and Energy Optimization

A Wireless Sensor Network (WSN) is a novel form of embedded real-time device that can be used for a variety of applications that make conventional networking impractical. Concerning the energy production of the nodes, WSN has significant issues that may affect the system's stability. Researchers have developed a range of methods and strategies to decrease wireless sensor network energy consumption. This paper has brief WSN structure for the optimization of communication with routing techniques. As network is vulnerable hence attacks were also present. This paper has list attacks as per number of layers. Further paper has list some of latest research work that not only optimize the network energy but also increases the robustness. Index Terms— Adhoc Network, Wireless Sensor Network, Communication Attacks, Virtual machines.

An improved energy saving clustering method for IWSN based on Gaussian mutation adaptive artificial fish swarm algorithm

The current industrial wireless sensor network (IWSN) cluster routing methods suffer from energy inefficiency. Designing efficient cluster-based routing protocols is crucial for improving network performance and energy efficiency. Therefore, this paper first designs a new clustering model to achieve efficient cluster head (CH) selection and data transmission performance by comprehensively considering multiple key factors such as CH energy, base station (BS) distance, packet loss rate, and data delay. Based on this clustering model, a novel cluster routing protocol based on Gaussian mutation adaptive artificial fish swarm algorithm (GAAFSA) is proposed. At the same time, a new Gaussian mutation strategy and an adaptive strategy were introduced to effectively promote the protocol to avoid local optima and prevent premature convergence. The GAAFSA based cluster routing protocol was experimentally compared with five popular schemes, namely CMSTR, D2CRP, EEHCHR, ESCVAD and BAFSA. The results showed that the proposed protocol outperformed the other four schemes in terms of network energy consumption, system lifetime, data transmission reliability, and latency. Specifically, GAAFSA has improved network lifespan by at least 15.68%, BS received packets by at least 7.46%, and reduced packet loss rate by at least 15.28%. Therefore, GAAFSA effectively optimizes network performance and extends network lifespan, greatly reducing energy loss within the network and significantly improving network quality of service (QoS).

NARA: Network-Aware Resource Allocation mechanism for minimizing quality-of-service impact while dealing with energy consumption in volunteer networks

A large-scale volunteer computing system is a type of distributed system in which contributors volunteer their computing resources, such as personal computers or mobile devices, to contribute to a larger computing effort. Volunteer resources are connected over the Internet and together form a powerful computing system capable of providing a service without depending on a service provider. Volunteer network resource allocation is the process of assigning computing tasks or services to a network of volunteer resources. The allocation process includes identifying the needed resources, selecting appropriate volunteers, and assigning tasks or services based on their capabilities. Volunteer computing systems consist of a large number of heterogeneous resources - in terms of processing power, storage, and availability - belonging to different authorities - users or organizations - and exhibiting uncertain behavior in terms of connection, disconnection, capacity, and failure. All of this makes resource allocation a challenging task in terms of ensuring a minimum quality of service, requiring complex algorithms and optimization techniques to ensure that services are efficiently allocated while respecting the constraints of the available resource. This paper introduces the Network-Aware Resource Allocation mechanism, which leverages the location, connectivity, and network latency of volunteer nodes to minimize the time a service runs with degraded quality of service and aims to deal with the energy consumption resulting from data replication requirements. This resource allocation mechanism applies to both the initial deployment of the service in the network and to the reallocation of nodes in the event that one of the allocated nodes fails or becomes unavailable. Our method has been validated in a simulation environment of a realistic volunteer system. The analysis of the results shows how our mechanism meets the quality requirements of users while minimizing the synchronization and replication times of service data replicas, as well as the time that services run with degraded quality of service, reducing the times by more than 70% in the service deployment phase and by more than 60% in the service execution phase. It also helps to reduce overall energy consumption.

EDLIoT: A method for decreasing energy consumption and latency using scheduling algorithm in Internet of Things

Decreasing energy consumption in networks with limited resources, such as the Internet of Things, has always been one of the main challenges in guaranteeing network performance. In this article, cooperative game theory is employed to improve the cooperation patterns of fog computing resources. The EDLIoT method consists of two main steps: “Topology Construction” and “Determining Optimal Fog Computing Resources to Process IoT Object Tasks”. In the first step of the proposed method, the set of reliable communications in the network is identified to establish connections between IoT objects and fog computing resources in the form of a tree structure. Then, in the second step, a model based on cooperative game theory and the cost function is used to determine the optimal computing resources in the fog layer for outsourcing the processing tasks of IoT objects. In EDLIoT, active IoT objects perform computation in the fog layer instead of locally, to conserve energy. This is done so that IoT objects, if possible, discover the most suitable processing resources in the fog based on characteristics such as energy consumption, delay, and processing power of the computing resource. The efficiency of the proposed method has been evaluated in a simulated environment, and the results have been compared with those of previous algorithms. The results demonstrate that using the EDLIoT method, in addition to decreasing energy consumption and delay, more computing tasks can be processed through fog resources, thereby increasing the quality of service for IoT users.

The Comparative Analysis between Distance DEEC and IOT DEEC Based on Network Lifetime and Energy Consumption

The Comparative Analysis between Distance DEEC and IOT DEEC Based on Network Lifetime and Energy Consumption

Optimizing IOT Network Performance: A Study on Low-Power Wide-Area Network (LPWAN) Technologies for Smart City Applications

Abstract: This research explores with a specific focus on smart city applications, how to optimise the execution of Web of Things (IoT) networks by utilising breakthroughs in Low-Powered Wide Area Networks (LPWAN). As IoT devices proliferate and generate vast amounts of data, having a competent network becomes essential, especially when dealing with low device thickness and limited power availability. This analysis provides a detailed energy consumption model tailored to LoRaWAN, based on a deeper understanding of its functional components in Internet of Things scenarios. The review uses MATLAB for replication and looks at two clear scenarios: the standard allocation of spreading factors without optimisation and an improved method that uses particle swarm optimisation (PSO) for dynamic spreading factor work. The results show that the network energy consumption dropped significantly, from 6.3997×10^ (- 17) Joules to 2.5230×10^(- 17) Joules, along with increased throughput, decreased parcel misfortune, and decreased idleness. Through a detailed analysis of several parameters, such as network density and data transfer efficiency, this study demonstrates the feasibility of the suggested optimisation model in terms of improving battery efficiency and overall network performance. Finally, the findings demonstrate how efficient LPWAN systems may provide robust and useful IoT environments, which are essential for the successful implementation of smart city initiatives.

Rethinking max-min planning on energy-efficient software-defined networking for 5G networks.

Energy efficiency plays an important role in intelligent networking for 5G networks, which concerns environmental, financial, and performance aspects of intelligent networking for 5G networks. To this end, network designers propose energy-efficient approaches to reduce energy consumption of networks and to raise network performance by switching off the links/nodes with low loads or at idle status. The existing energy-efficient approaches can be formulated as a max-min optimal problem, namely maximizing network/node/port throughput via minimum energy consumption. The max-min planning investigates energy efficiency only from the links/nodes perspective. The max-min planning for energy-efficient networking, if not carefully designed from the network-wide standpoint, can lead to lower energy efficiency for the whole network due to lack of global planning, which in turn results in the degraded performance due to network un-connectivity after closing the nodes/links. In this paper we rethink the max-min planning framework on energy-efficient software-defined networking for intelligent networking of 5G networks, which takes in account combining network connectivity and maximum network flow with minimum energy consumption. Our framework aims at how to deliver dynamic end-to-end traffic demands with the appropriate network topology by building data forwarding plane with maximum network flow and control plane with network connectivity. We discuss the associated challenges and implementation issues. A dynamic max-min planning framework depending on dynamic end-to-end traffic demands is presented to achieve network-wide energy efficiency. Numerical results show the improved energy efficiency performance for the whole network.

Design of Routing Algorithm for Communication of Power Wireless Sensor Networks Based on Improved Harmony Search

In this paper, a routing algorithm for the communication of power wireless sensor networks based on improved harmony search is proposed to reduce the communication routing delay and energy consumption of power wireless sensor networks. Firstly, according to the composition of communication routing delay and energy consumption of power wireless sensor networks, the delay model and energy consumption model are constructed respectively, and the objective function is constructed by assigning different weights. Then, artificial bee colony algorithm and Levy flight mechanism improved harmony search algorithm are adopted for solving the objective function. Experiment results reveal that the algorithm can meet different users’ different demands on delay and energy consumption. If the user needs low delay, set the low delay weight to 1 and the power consumption weight to 0, then the average delay is 7s, and the average power consumption is 3203J. When user demand is low energy consumption, set the delay weight to 0 and the energy consumption weight to 1, so that the average energy consumption is 2667J and the average delay is 15s, which can meet the needs of users.