Energy Consumption Of Nodes Research Articles

A smart WSNs node with sensor computing and unsupervised One-Class SVM classifier for machine fault detection

This paper proposed a novel machine fault detection method based on a smart WSNs node with sensor computing and unsupervised learning. Firstly, sensor computing mode, which achieves machine fault detection on the WSNs node, has been employed to address the limitations of raw data transmission mode, such as huge payload transmission data and node energy consumption. Secondly, a fault classifier based on unsupervised one-class support vector machine (OC-SVM) is designed to overcome the drawbacks of supervised learning, like the requirement of myriad labeled samples for model training. Finally, a smart WSNs node with sensor computing and an unsupervised OC-SVM classifier is developed and fabricated as test beds. A set of experiments has been conducted to verify the proposed method and smart node. The results show that they can reduce 99% of payload transmission data and about 5% of node energy consumption while maintaining acceptable detection accuracy (above 99.2%).

Analytical and Empirical Insights into Wireless Sensor Network Longevity: The Role MAC Protocols and Adaptive Strategies

Wireless Sensor Networks (WSNs) have become essential in various fields, such as corporate industries, environmental monitoring, military defense, and healthcare, due to their ability to monitor and transmit data from remote locations. However, the battery-powered nature of WSN nodes necessitates a strong emphasis on energy conservation to extend network lifespan. This paper evaluates the impact of different Medium Access Control (MAC) protocols on WSN performance, focusing on energy efficiency and data transmission reliability. We explore various MAC protocols, including contention-based protocols like Carrier Sense Multiple Access (CSMA), schedule-based protocols like Time Division Multiple Access (TDMA), and hybrid protocols that combine both approaches. The methodology integrates analytical modelling, empirical data collection, and expert interviews to comprehensively assess the performance and longevity of WSNs under different MAC protocols. Analytical models were developed to simulate WSN behavior, considering factors such as node density, traffic load, and energy consumption. Empirical data from real-world deployments and experimental setups were analyzed to validate the models and provide practical insights. Expert interviews further highlighted the challenges and considerations in MAC protocol design and implementation. This paper underscores the importance of context-specific MAC protocol selection and the potential of adaptive strategies, including machine learning, to enhance WSN efficiency and reliability. These insights can guide the development of more sustainable and high-performing WSN applications.

Charging Scheduling Method for Wireless Rechargeable Sensor Networks Based on Energy Consumption Rate Prediction for Nodes.

With the development of the IoT, Wireless Rechargeable Sensor Networks (WRSNs) derive more and more application scenarios with diverse performance requirements. In scenarios where the energy consumption rate of sensor nodes changes dynamically, most existing charging scheduling methods are not applicable. The incorrect estimation of node energy requirement may lead to the death of critical nodes, resulting in missing events. To address this issue, we consider both the spatial imbalance and temporal dynamics of the energy consumption of the nodes, and minimize the Event Missing Rate (EMR) as the goal. Firstly, an Energy Consumption Balanced Tree (ECBT) construction method is proposed to prolong the lifetime of each node. Then, we transform the goal into Maximizing the value of the Evaluation function of each node's Energy Consumption Rate prediction (MEECR). Afterwards, the setting of the evaluation function is explored and the MEECR is further transformed into a variant of the knapsack problem, namely "the alternating backpack problem", and solved by dynamic programming. After predicting the energy consumption rate of the nodes, a charging scheduling scheme that meets the Dual Constraints of Nodes' energy requirements and MC's capability (DCNM) is developed. Simulations demonstrate the advantages of the proposed method. Compared to the baselines, the EMR was reduced by an average of 35.2% and 26.9%.

Modernization of the method for predicting energy consumption of minigrid load nodes by using extrapolation of the trend line obtained using wavelet transformation of the electrical load graph

Modernization of the method for predicting energy consumption of minigrid load nodes by using extrapolation of the trend line obtained using wavelet transformation of the electrical load graph

A deep experimental analysis of energy‐proficient firewall policies and security practices for resource limited wireless networks

AbstractThe role of firewalls and security principles in resource‐limited wireless networks and Wireless Sensor Networks (WSN) is more expected than in any dedicated network environment. The advanced wireless technologies and emerging fashion of autonomous wireless network nodes are mostly expecting resilient firewall services with multi‐level security policies. Wireless networks are classified under sensor networks, Internet of Things (IoT) mobile networks, and so forth. According to the expectations, security frameworks are gradually invented around the communication platform using various ideologies. The novel ideology and respective implementation effort open better solutions against wireless network attacks. Anyhow, the minimal production of time complexity, energy complexity, and computation overhead from any novel security approach is always considered under the best practices. The complexity levels directly affect the wireless node's energy consumption ability and operational spans severely. The energy optimization techniques and load‐balancing techniques integrated into multi‐class firewall rules are extremely useful solutions for resource‐limited wireless networks. This article has been motivated to analyze the recent firewall techniques and secure data communication mechanisms used for securing wireless networks. Consequently, the practice of comparative literature analysis helps to improve the current limitations identified under the classified categories of security mechanisms such as energy‐optimized security principles and load‐balanced security principles. The establishment of secure and energy‐optimized multi‐class firewall rules in each wireless node assures a healthy focus on next‐generation networks. The experiment section shows the benefits of energy‐optimized secure network communication in terms of better accuracy and the benefits of load‐balanced secure network communication through minimal overhead in computing platforms.

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.

Levy flight based momentum search and Circle Rock Hyrax swarm optimization algorithms for energy effective clustering and optimal routing in wireless body area networks

In recent years, the connectivity of a new dimension has been brought to Wireless Body Area Networks (WBANs) through the concept of the Wearable Internet of Things (WIoT). WIoT offers valuable functionalities like data collection, analysis, and transmission for real-time health monitoring services. However, WBANs are characterized by a large number of battery-powered sensor nodes, making them resource-constrained. While keeping standard WSN abilities, WBAN's significant obstacles are data communication, social mobility, high interference in dense deployment, latency, and energy consumption of sensor nodes. As a result, the implementation and design of an application require dependable data transfer and an energy-efficient architecture. It proved critical to have as little delay as possible while delivering correct data. To overcome these issues, this study developed a novel energy-efficient clustered and optimized routing scheme in WBAN. Due to its effective scalability, this work employs an Improved Self-Executing Active Cross-Propagated (ISAC) clustering approach to form clusters in Wireless Body Area Networks (WBANs). Subsequently, the selection of the best Cluster Heads (CHs) is facilitated by the Levy Flight-based Momentum Search Algorithm (LFMSA). The Circle Rock Hyrax Swarm Optimization (CRH) algorithm is then utilized for energy-efficient routing. In determining the CH in each cluster, consideration is given to both residual energy and connectivity. The proposed framework minimizes the number of transmissions and overall transmission distance, optimizing inter and intra-cluster transmission distances. Additionally, the framework ensures energy-efficient packet delivery from CHs to the sink node. Performance comparisons, conducted using various statistical measures and simulated on the NS-2 platform, demonstrate that the proposed framework outperforms existing routing protocols and clustering methodologies. In comparison to previous methodologies, the proposed framework consistently achieves superior results.

Hybrid Boosted Chameleon and modified Honey Badger optimization algorithm-based energy efficient cluster routing protocol for cognitive radio sensor network

Single clustering protocols cannot meet the event-driven and time-triggered traffic requirements of Cognitive Radio Sensor Networks (CRSNs). The long wait between the completion of events and the process of clustering and searching for accessible routes results in increased time for information transmission. This paper proposed a Hybrid Boosted Chameleon and Modified Honey Badge optimization Algorithm-based Energy Efficient cluster routing protocol (HBCMHBOA) for handling the issues of traffic driven information transfer with energy efficiency in the CRSNs. This HBCMHBOA is proposed as one among few event-driven and time-triggered clustering protocol for the requirements of CRSNs. The integration of Boosted Chameleon and Modified Honey Badge optimization Algorithm is adopted for determining optimal number of clusters and constructs the structure of primitive clusters in an automated way to serve the time-triggered traffic in a periodic manner. It adopted priority-based schedule and its associated frame structure for guaranteeing reliable event-driven information delivery. It leveraged the merits of time-triggering for the construction of clustering architecture and confirmed than none of the cluster construction and selection of routes are facilitated after the emergent events. This characteristic helps in permitting only the nodes and their associated Cluster Heads (CHs) of CRSNs to discover emergent events. It facilitates the coverage of a fewer nodes, especially when sink is positioned in a corner to minimize the delay and node energy consumption. The simulation results of the proposed HBCMHBOA confirmed a reduction in total energy consumption and number of covered nodes on an average of 34.12 %, and 26.89 % than the prevailing studies.

A Comprehensive Survey of Load Balancing Techniques in Multipath Energy-Consuming Routing Protocols for Wireless Ad hoc Networks in MANET

As technology progresses, networking undergoes continuous evolution to align with contemporary trends. Mobile Ad hoc Networks (MANETs) have emerged as a pivotal technology facilitating communication among mobile nodes sans a central administering node. MANETs find diverse applications spanning communication networks to battlefield scenarios. Nonetheless, MANETs encounter various challenges, including load balancing, energy efficiency, packet loss, and connection failures. Routing, among these challenges, assumes a pivotal role in defining network performance and connectivity capacity within MANETs. Moreover, the energy consumption of mobile nodes, often reliant on batteries with limited rechargeability, presents a significant concern in MANETs. This paper delves into diverse techniques devised to tackle the issues of energy efficiency and load balancing in MANETs. Additionally, it offers a comparative analysis to consolidate the survey findings. The outcomes of this study enrich our comprehension of energy-efficient and load-balanced routing protocols in MANETs, thereby facilitating the design and deployment of efficient networking solutions.

DESIGN AND INVESTIGATE THE STATE-OF-THE-ART WSN SOLUTIONS FOR THE CONGESTION CONTROL

Congestion in WSNs is an inescapable issue in today's reality, when data flow has exceeded the channel's aggregated capacity. Because retransmission of every unacknowledged packet is not an optimized solution in terms of energy for resource-restricted sensor nodes, the result is overflowing of the buffer at each receiving sensor node, which eventually reduces the packet delivery ratio, drops the packets, and degrades network throughput. Routing is one of the most popular ways for reducing node energy consumption and increasing throughput in WSNs since the routing issue has been proven to be NP-hard and it has been discovered that a heuristic-based approach outperforms their conventional equivalents. Therefore, it is need of optimized routing solutions for the future WSNs by considering the energy and traffic parameters. The scope of this study is limited to the WSNs.

An Energy-Efficient improved Grey Wolf Optimization Algorithm-Based Cluster Head and Shamir Secrets Sharing-Based WSNs with Secure Data Transfer

Introduction: Due to its self-configurability, ease of maintenance, and scalability capabilities, WSNs (Wireless Sensor Networks) have intrigued plenty of interest in a variety of fields. To move data within the network, WSNs are set up with more nodes. The security of SNs (sensing nodes), which are vulnerable to malevolent attackers since they are network nodes, is a crucial element of an IoT (Internet of Things)-based WSN. This study's primary objective is to provide safe routing and mutual authentication with IoT-based WSNs. Methods: The basic GWO algorithm's imbalances between explorations and mining, lack of population heterogeneity, and early convergences are all issues that this paper addresses by selecting energy-efficient CHs (cluster Heads) using EECIGWO algorithm, an upgraded version of the GWO, is used. Mean distances within clusters, well-spaced residual energies, and equilibrium of CHs are all factors that influence the choices of CHs. The average intra-cluster distances, sink distances, residual energies, and CHs balances are some of the criteria used to choose CHs. Results and Discussion: The proposed EECHIGWO-based clustering protocol's average throughput, dead node counts, energy consumption, and operation round counts have all been evaluated. Additionally, mutual authentication between the nodes is provided through SSS (Shamir Secret Sharing) mechanism. PDR (Packet Delivery Ratio) analysis is used to assess how well the EECHIGWO-IOT-WSNs are performing. Conclusion: The suggested proposed approach is assessed against existing methods like HHH-SS (Hybrid Harris Hawk and Salp Swarm), ESR (Energy-efficient and Secure Routing) protocol, and LWTS (Light Weight Trust Sensing) approaches in terms of AEED (Average End-to-End Delay), network overheads, and PLR (Packet Loss Ratio).

Machine learning deployment for energy monitoring of Internet of Things nodes in smart agriculture

SummaryLow‐Power Wide‐Area Network technologies, such as LoRa, are gaining popularity in the agricultural sector for field deployment. The crucial factors in these devices are their range and power efficiency. The energy consumption of a LoRa wireless sensor network is predominantly affected by transmission parameters like carrier frequency, bandwidth, transmit power, spreading factor, and coding rate. Incorrect chosen transmission parameters can lead to a reduction in the battery life of end nodes, requiring frequent battery replacements—a situation undesirable for field deployment. This study introduces a machine learning deployment in the form of a web application designed to monitor the energy consumption of end nodes in LoRa wireless sensor networks. The research initially employs 12 regression models, including Linear, Random Forest, K‐Nearest Neighbours, Decision Tree, Support Vector, Lasso, Ridge, AdaBoost, Gradient Boost, XGBoost, CatBoost, and LightGBM models. The findings of the study reveal that the LightGBM model surpasses other models in accurately predicting the energy consumption of Internet of Things (IoT) nodes, leading to its selection for the web application. This machine learning web application can be implemented in a programmable Long Range Wide Area Network (LoRaWAN) gateway to effectively monitor the energy consumption of IoT end nodes in the agricultural sector.

FSLEdge: An energy-aware edge intelligence framework based on Federated Split Learning for Industrial Internet of Things

Federated Learning (FL) enabled edge computing has been widely used in training complex deep learning models by coordinating various heterogeneous resources in Industrial Internet of Things (IIoT). However, the existing FL solutions may cause long training time or huge energy consumption for resource-constrained terminal or edge nodes. To address this issue, we propose an adaptive edge intelligence offloading framework based on Federated Split Learning (FSL) and Reinforcement Learning (RL), named FSLEdge, for IIoT systems. To begin with, a Split Learning (SL) approach was added to FL to reduce the training time and energy consumption by allowing local devices to offload some training layers into the edge servers while ensuring the protection of private data. Additionally, FSLEdge adopts RL to find the optimal split point to adaptively identify which is the best layers of the deep learning models should be offloaded into the edge servers with the goal of reducing the training time and energy consumption. Experimental studies are conducted under two different datasets: one public, CIFAR10, and one private, GridDataset, a transmission line fault detection dataset in smart grid domain. And the results show that the training time and energy consumption of the system by our proposed FSLEdge can be reduced by 10% and 21%, respectively, compared to the classical FL, without sacrificing accuracy.

Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique

A wireless sensor network (WSN) is a wireless network composed of sensor nodes, widely used in military, industrial, and agricultural fields. In these practical application scenarios, due to the limited routing search ability of wireless sensor nodes and the small coverage range of a WSN, it faces the problem of a relatively short network lifecycle. Therefore, increasing a WSN’s coverage range and reducing node energy consumption are of high value. This article proposes a method of using the ant colony algorithm (ACOD) to calculate the optimal routing of a WSN, the K-means algorithm for clustering, and the whale algorithm (WOA) and a backpropagation (BP) neural network to increase the coverage range of a WSN. After multiple experiments, the total energy consumption of WSN nodes has been effectively reduced, the coverage range of the WSN has been increased, and the lifecycle of the WSN has been extended.

Intrusion Detection in Wireless Sensor Networks Based on IPSO-SVM Algorithm

To optimize node energy consumption and improve its security, this paper uses the DEEC algorithm to layer WSN and reduce the probability of channel information collision and uses the weighted probability of cluster head election to optimize node energy expenditure, so that WSN can obtain a longer lifecycle. Improved Particle Swarm Optimization-based Support Vector Machine (IPSO-SVM) algorithm is used for intrusion detection and experimental testing in WSN. The results showed that the IPSO-SVM algorithm exhibited good convergence, with a convergence step size of 5 steps, which converged earlier than the Support Vector Machine Algorithm based on Particle Swarm Optimization (PSO-SVM), which had a convergence step size of 10 steps. The IPSO-SVM algorithm performed best in WSN intrusion detection, with the highest detection rate of 96.20% in Probe attack data detection, which was 0.80% higher than the Support Vector Machine Algorithm based on Genetic Algorithm (GA-SVM). The PSO-SVM algorithm had the lowest detection rate of 95.20%. The IPSO-SVM algorithm had a minimum false positive rate of 1.54% in Dos attack data detection. In terms of average training time, the IPSO-SVM algorithm had a minimum average training time of 323.45 seconds. Compared to the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm, the Distributed Energy Efficient Clustering (DEEC) algorithm performs better, has less energy consumption, and retains more nodes. The method adopted in this study can make WSN have a longer life cycle and ensure its security.

IMPLEMENTATION OF ENERGY EFFICIENT SECURITY MECHANISMS FOR WIRELESS SENSOR NETWORKS

Designing energy-efficient security mechanisms is critical for WSNs due to their limited power resources. Techniques like low-power cryptography, duty cycling, and energy-aware security protocols can help in achieving security without significantly impacting the energy consumption of sensor nodes. In this paper, we propose an energy aware geographical multipath routing scheme for WSNs. The distance to the destination location, remaining battery capacity, and queue size of candidate sensor nodes in the local communication range are taken into consideration for next hop relay node selection, and Analytical Hierarchy Process (AHP) and Geographical Routing Algorithm (GRA) are applied for decision making. Simulation results show that these schemes can extend the network lifetime longer than the original geographical routing scheme which only considers distance to the destination location. KEYWORDS: WSN, linear discriminate packet flow analysis system, optimized rout path switch, Analytical Hierarchy Process (AHP) and Geographical Routing Algorithms(GRA).

Investigation on optimization strategy of IoT operation mode based on blockchain

Under the call of the national scientific and technological power, the research in the field of sensor technology has made rapid progress. Many smart devices integrating various technical heights have entered the homes of ordinary people. Making these smart devices increasingly intelligent has greatly improved people’s lifestyles and even changed their lives. With the development of blockchain technology and the development of 5G communication equipment and applications, the industrial model of Blockchain and the Internet of Things can also become an important guide for the development of the Internet of Things. Therefore, this paper studies the optimization strategy of the IoT operation mode based on Blockchain. The research results have shown that the IoT operation mode based on Blockchain is superior to the traditional IoT operation mode in terms of node energy consumption, life cycle, and flux performance. Under the IoT operation mode of the Blockchain, the node energy consumption is reduced by about 1.19J on average, and the flux performance is increased by about 21 on average. This showed that the IoT operation model based on Blockchain is feasible.

An efficient dual layer data aggregation scheme in clustered wireless sensor networks

AbstractIn wireless sensor network (WSN) monitoring systems, redundant data from sluggish environmental changes and overlapping sensing ranges can increase the volume of data sent by nodes, degrade the efficiency of information collection, and lead to the death of sensor nodes. To reduce the energy consumption of sensor nodes and prolong the life of WSNs, this study proposes a dual layer intracluster data fusion scheme based on ring buffer. To reduce redundant data and temporary anomalous data while guaranteeing the temporal coherence of data, the source nodes employ a binarized similarity function and sliding quartile detection based on the ring buffer. Based on the improved support degree function of weighted Pearson distance, the cluster head node performs a weighted fusion on the data received from the source nodes. Experimental results reveal that the scheme proposed in this study has clear advantages in three aspects: the number of remaining nodes, residual energy, and the number of packets transmitted. The data fusion of the proposed scheme is confined to the data fusion of the same attribute environment parameters.

An Enhanced Sector Low Energy Adaptive Clustering Hierarchy (S-LEACH) using Modified Grey Wolf Optimisation Algorithm and Game Theory

Wireless Sensor Networks (WSN) and other sensing and communication technologies have given man the ability to keep tabs on his surroundings. Distributed sensors in the form of WSNs are used to keep tabs on environmental or physics-related variables. These sensors operate together to send information via the internet to a central location, where it may be used to inform decisions that have real consequences for people's lives. As detecting, processing, and transmitting all take a lot of energy, WSNs are powered by batteries that cannot be replaced during data transmission. Therefore, it is essential to increase the network's durability by reducing the energy consumption of each sensor node. Power consumption problems exist in network's communication routes between the sensor nodes might have disastrous effects on a network that relies on timely data transmission. Since the sensor nodes need electricity to function, losing that power disrupts data transmission and, in most situations, kills the network. A serious issue with WSNs is their rapid loss of energy. Studies have shown that switching the node from active to sleep mode while it is not in use may extend the lifespan of WSNs. Some have argued that a mobile charger should be made available instead of a sensor node with a changeable battery. Even though energy harvesting the practice of drawing power from the surroundings and transforming it into electrical energy has the potential to address this issue, there are situations in which this is not possible. The objective of this study is to design a more energyefficient algorithm for usage by sensor nodes in order to decrease their power consumption. By combining game theory and Grey Wolf Optimisation with the existing Sector Low Energy Adaptive Clustering Hierarchy (LEACH) routing system, a more efficient and effective algorithm was developed called Enhance Game and Grey Wolf Algorithm (EG-GWA).

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.