Cluster-based Wireless Sensor Networks Research Articles

Bridge Damage Detection with Support Vector Machine in Accelerometer-Based Wireless Sensor Network

Abstract Purpose This paper proposes an in-network vibration data processing using Wireless Sensor Network (WSN) leveraging Machine Learning (ML) for damage detection and localization. The study also presents the ML algorithms comparison that is suitable to be deployed in WSN and implemented the proposed cluster-based WSN topology on the bridge simulation test. Methods The bridge vibration data was acquired using accelerometer-based wireless sensor nodes. The data collected are transformed using Fast Fourier Transform (FFT) to obtain fundamental frequencies and their corresponding amplitudes. The machine learning method i.e., Support Vector Machine (SVM) with linear and Radial Basis Function (RBF) kernel was used to analyze the vibration data collected from the WSN. In-network data processing and cluster-based WSN topology is implemented and the programmable wireless sensor nodes is utilized in this study. Results The experiments were conducted using real programmable wireless sensor nodes and developed our test bed bridge which makes this work different from the previous studies. The classification and predicting results shows 97%, 96%, 97%, and 96% for accuracy, precision, recall rate, and f1-score, respectively. Conclusion Machine learning methods can potentially be combined with the vibration WSN for bridge damage detection and localization.

Light Weight Concurrent Scheduling of Mobile Sink routing protocol towards Minimization of Propagation Delay against Spatial and Temporal Uncertainties of Cluster based Wireless Sensor Network

Wireless Sensor Network highly exposed to certain security attacks due to various uncertainties of the network which lead to node compromise. Hence considerable attention has been made by researcher during scheduling of the mobile sink to data collection of sensed information from the sensor nodes. Many secure data aggregation mechanism has been proposed to withstand the network against these kind of attack. Despite of many advantageous, those architectures will lead to several limitations such as large propagation delay and high energy consumption and spatial and temporal uncertainties. In order to mitigate those challenges, Light Weight Concurrent Scheduling of Mobile Sink routing protocol towards Minimization of Propagation Delay against Spatial and Temporal Uncertainties of Wireless Sensor Network is designed. It should be capable achieving energy efficiency and collision avoidance due to uncertainty in transmission and reception. Proposed model eliminates the packet collision and poor channel utilization challenges. Initially, node is partitioned and cluster head is selected based on the highest energy density of the node in the particular location. Employing Full duplex channel easily examines the spatial and temporal characteristic of the node through utilization cache information of routing table. It further helps to build the light weight scheme with behavioral strategies of the nodes for secure propagation of the sensed data to the mobile sink. Light weight scheme deployed in mobile sinks estimate the node trustworthiness and simultaneously collects the secure aggregated data packets of the sensor node through cluster head against dynamic time slots concurrently .Comparing with traditional secure routing architecture, simulation results demonstrates that proposed architecture can reduce the propagation delay against various uncertainties and maximizes the life time of the network on increasing the networking performances with respect to packet delivery ratio, throughput and propagation delay. Finally proposed model obtains the cooperative communication of sensor node information to the base station through mobile sink on heterogeneous wireless sensor network environment. DOI: https://doi.org/10.52783/pst.387

Cluster-based wireless sensor network framework for denial-of-service attack detection based on variable selection ensemble machine learning algorithms

A Cluster-Based Wireless Sensor Network (CBWSN) is a system designed to remotely control and monitor specific events or phenomena in areas such as smart grids, intelligent healthcare, circular economies in smart cities, and underwater surveillance. The wide range of applications of technology in almost every field of human activity exposes it to various security threats from cybercriminals. One of the pressing concerns that requires immediate attention is the risk of security breaches, such as intrusions in wireless sensor network traffic. Poor detection of denial-of-service (DoS) attacks, such as Grayhole, Blackhole, Flooding, and Scheduling attacks, can deplete the energy of sensor nodes. This can cause certain sensor nodes to fail, leading to a degradation in network coverage or lifetime. The detection of such attacks has resulted in significant computational complexity in the related works. As new threats arise, security attacks get more sophisticated, focusing on the target system's vulnerabilities. This paper proposed the development of Cluster-Based Wireless Sensor Network and Variable Selection Ensemble Machine Learning Algorithms (CBWSN_VSEMLA) as a security threats detection system framework for DoS attack detection. The CBWSN model is designed using a Fuzzy C-Means (FCM) clustering technique, whereas VSEMLA is a detection system comprised of Principal Component Analysis (PCA) for feature selection and various ensemble machine learning algorithms (Bagging, LogitBoost, and RandomForest) for the detection of grayhole attacks, blackhole attacks, flooding attacks, and scheduling attacks. The experimental results of the model performance and complexity comparison for DoS attack evaluation using the WSN-DS dataset show that the PCA_RandomForest IDS model outperforms with 99.999 % accuracy, followed by the PCA_Bagging IDS model with 99.78 % accuracy and the PCA_LogitBoost model with 98.88 % accuracy. However, the PCA_RandomForest model has a high computational complexity, taking 231.64 s to train, followed by the PCA_LogitBoost model, which takes 57.44 s to train, and the PCA_Bagging model, which takes 0.91 s to train to be the best in terms of model computational complexity. Thus, the models surpassed all baseline models in terms of model detection accuracy on flooding, scheduling, grayhole, and blackhole attacks.

Multi-objective Evolutionary Algorithms for Coverage and Connectivity Aware Relay Node Placement in Cluster-Based Wireless Sensor Networks

Multi-objective Evolutionary Algorithms for Coverage and Connectivity Aware Relay Node Placement in Cluster-Based Wireless Sensor Networks

Enhancing energy efficiency in intrusion detection: a cluster-based wireless sensor network protocol using PSO

Enhancing energy efficiency in intrusion detection: a cluster-based wireless sensor network protocol using PSO

An Energy Efficient Cluster Head Selection in WSN Based on Enhanced Chicken Swarm Optimization

Clustering of data are ab effective energy saving method for wireless sensor networks (WSN). However, they incur additional costs in data collections from sensor nodes and sending them to base station (BS) after aggregations. Cluster heads (CHs) in hierarchical cluster-based WSNs use more energy. Therefore, choosing a suitable CHs is essential to conserve energy for sensor nodes and prolong the life of the WSN. Provide energy-saving cluster head selection techniques in this work. where enhanced fuzzy means clustering is used to perform the first cluster construction. Enhanced Chick Swarm optimization (ECSO) selects CHs which is evaluated strictly for varying WSN scenarios and counts of CHs and nodes for values of remaining energies of sensor nodes, sink distances, and distances within clusters. To demonstrate that the proposed technique is superior, the results are compared with those of several other currently used algorithms.

Optimal selection of cluster head in wireless sensor networks using particle swarm optimization (PSO)

In a wide range of applications, such as the military, healthcare, and environmental monitoring, wireless sensor networks (WSNs) have emerged as a key player. Cluster-based WSNs are a viable method for enhancing the life of the sensor network. Choosing the proper cluster head for wireless sensor networks (WSNs) is a key undertaking that affects the network's performance. Current approaches for selecting the cluster head have a number of drawbacks, such as nodes dying too quickly, uneven energy utilization, and shorter network lifespan. Additionally, conventional techniques like fixed Cluster Head and randomized Clustering are ineffective at extending the network lifetime. In the proposed method, Particle swarm optimization was used to create an optimal cluster head selection that addresses the problem of intra-cluster communication and lowers SN energy consumption. The simulation result shows that the performance improvement of the developed algorithm PSO in terms of network lifetime is 10% against Improved Cuckoo Search Algorithm (ICSA) and 25% against Hybrid Crow Search Algorithm (HCSA), energy consumption is 15% against ICSA and 20% against HCSA, and number of alive node is 4% against ICSA and 6% against HCSA respectively. Therefore, our developed algorithm PSO outperforms ICSA and HCSA in terms of the aforementioned parameters.

Novel Clustering Techniques in Wireless Sensor Networks – A Survey

A study of Wireless Sensor Networks has been growing tremendously these days. Wireless Sensor Networks play a major role in various fields ranging from smart homes to health care. WSN’s operate independently in remote places. Because of tiny size of the nodes in such type of networks, they have a limited number of resources in terms of energy and power. Basically, sensor networks can be classified into flat and cluster based Wireless Sensor Networks. But, Clustering based Sensor Networks play a major role in reducing the energy consumption in Wireless Sensor Networks. Clustering also focuses on solving the No.s that arise during transmission of data. Clustering will group nodes into clusters and elects Cluster Heads for all clusters in the network. Then the nodes sense data and send that data to cluster head where the aggregation of data will take place. This paper focuses on various novel clustering techniques that improve the network’s lifetime.

Ant Colony Optimization with Levy-Based Unequal Clustering and Routing (ACO-UCR) Technique for Wireless Sensor Networks

Wireless Sensor Networks (WSN) became a novel technology for ubiquitous livelihood and still remains a hot research topic because of its applicability in diverse domains. Energy efficiency treated as a crucial factor lies in the designing of WSN. Clustering is commonly applied to increase the energy efficiency and reduce the energy utilization. The proper choice of cluster heads (CHs) and cluster sizes is important in a cluster-based WSN. The CHs which are placed closer to base station (BS) are affected by the hot spot issue and it exhausts its energy faster than the usual way. For addressing this issue, a new unequal clustering and routing technique using ant colony optimization (ACO) algorithm is presented. Initially, CHs are chosen and clusters are constructed based on several variables. Next, the ACO algorithm with levy distribution is applied for the selection of optimal paths between two nodes in the network. A comprehensive validation set takes place under diverse situations under the position of BS. The experimental outcome verified the superiority of the presented model under several validation parameters.

Optimal Selection of the Cluster Head in Wireless Sensor Networks by Combining Particle Swarm Optimization and Efficient Genetic Algorithm

Wireless Sensor Networks (WSNs) have become a crucial component of numerous applications, including the military, healthcare, and environmental monitoring. A promising approach to increasing the lifespan of the sensor network is cluster-based WSNs. In WSNs, choosing the best cluster head is a crucial task that has an impact on the network's performance and energy efficiency. There are various issues with current methods for choosing the cluster head, including nodes dying too soon, uneven energy usage, and shorter network lifetimes. Moreover, traditional methods such as Randomized Clustering and Fixed Cluster Head are not effective in prolonging the network lifetime as they do not consider the energy consumption and residual energy of nodes. In this paper, an optimal selection of cluster head is presented where we combine the Particle Swarm Optimization (PSO) and Efficient Genetic Algorithm (EGA). Firstly, PSO is used to randomly select the cluster head and update the position of each cluster. Thereafter, EGA invokes its fitness values to select the best cluster head that transmits information to the base station. The simulation result shows that the performance improvement of the proposed method PSO_EGA in terms of network lifetime is 0.10% against Improve Cuckoo Search Algorithm (ICSA) and 0.20% against Hybrid Crow Search Algorithm (HCSA), packet to cluster head is 7% against ICSA and 16% against HCSA, packet to sink is 11% against ICSA and 22% against HCSA and number of alive node is 28% against ICSA and 48% against HCSA. Therefore, our proposed method outperforms ICSA and HCSA in terms of the aforementioned parameters.

DGTTSSA: Data Gathering Technique Based on Trust and Sparrow Search Algorithm for WSNs

Wireless Sensor Networks (WSNs) have been successfully utilized for developing various collaborative and intelligent applications that can provide comfortable and smart-economic life. This is because the majority of applications that employ WSNs for data sensing and monitoring purposes are in open practical environments, where security is often the first priority. In particular, the security and efficacy of WSNs are universal and inevitable issues. One of the most effective methods for increasing the lifetime of WSNs is clustering. In cluster-based WSNs, Cluster Heads (CHs) play a critical role; however, if the CHs are compromised, the gathered data loses its trustworthiness. Hence, trust-aware clustering techniques are crucial in a WSN to improve node-to-node communication as well as to enhance network security. In this work, a trust-enabled data-gathering technique based on the Sparrow Search Algorithm (SSA) for WSN-based applications, called DGTTSSA, is introduced. In DGTTSSA, the swarm-based SSA optimization algorithm is modified and adapted to develop a trust-aware CH selection method. A fitness function is created based on the nodes’ remaining energy and trust values in order to choose more efficient and trustworthy CHs. Moreover, predefined energy and trust threshold values are taken into account and are dynamically adjusted to accommodate the changes in the network. The proposed DGTTSSA and the state-of-the-art algorithms are evaluated in terms of the Stability and Instability Period, Reliability, CHs Average Trust Value, Average Residual Energy, and Network Lifetime. The simulation results indicate that DGTTSSA selects the most trustworthy nodes as CHs and offers a significantly longer network lifetime than previous efforts in the literature. Moreover, DGTTSSA improves the instability period compared to LEACH-TM, ETCHS, eeTMFGA, and E-LEACH up to 90%, 80%, 79%, 92%, respectively, when BS is located at the center, up to 84%, 71%, 47%, 73%, respectively, when BS is located at the corner, and up to 81%, 58%, 39%, 25%, respectively, when BS is located outside the network.

Congestion-Free Cluster Formation and Energy Efficient Path Selection in Wireless Sensor Networks using ButPCNN

Today, network congestion is a common occurrence that needs to be focused on and effectively addressed, particularly in Wireless Sensor Networks (WSN) for packed type networks. The main causes of congestion in WSN are a lack of channel capacity and energy waste. This study's major goal is to develop Energy Efficient Congestion Free Path Selection Protocol (ECFPSP) protocol, which aims to reduce network congestion. By selecting the most appropriate main cluster head (PCH) and secondary cluster head (SCH), the ECFPSP protocol is proposed to decrease end-to-end delay time and extend the network lifetime. The suggested protocol implements a routing protocol that provides security by avoiding hostile nodes and reducing data loss. It also routes the nodes. Hence, a Congestion-Free Cluster Formation is provided to increase the lifetime of the network by proposed ButPCNN approach. To decrease packet loss and conserve energy, this research also uses brand-new cluster-based WSNs. In comparison to other standard protocols, the simulation results reveal that ButPCNN has a reduced packet drop rate, which increases the ratio of packet distribution, network life, and residual energy. As a result, the suggested method enhances congestion control performance while using less energy and a recently developed strategy is suggested to successfully enhance network performance. The proposed ButPCNN gives 25 percent improvement to optimize traffic on overloaded node than the other traditional approaches.

Compatibility Analysis of Cluster-Based WSN Framework for IoT Applications

Compatibility Analysis of Cluster-Based WSN Framework for IoT Applications

Efficient Data Collection in UAV-Assisted Cluster-Based Wireless Sensor Networks for 3D Environment: Optimization Study

Unmanned aerial vehicles (UAVs) have been recently employed in combination with wireless sensor networks (WSNs) to collect data efficiently and improve surveillance effectiveness. This integration enhances the WSN infrastructure where UAVs are used as aerial base stations from which to access wireless sensors in hard-to-reach places within surveillance area. Consequently, the UAVs have become a promising solution to maintain reliability for the communication between wireless sensors and base station particularly in cases where infrastructure becomes unavailable such as hilly terrains and emergencies. However, UAVs encounter many challenges which mainly focus on their lifespan and efficient placement that improves the coverage and data collection. In this paper, a novel optimization study is presented to improve the lifespan of UAV-assisted cluster-based WSNs deployed in 3D environment. This optimization study is based on two algorithms: (1) Particle Swarm Optimization (PSO) which is employed to address the clustering problem in the WSN and (2) Genetic Algorithm (GA) which is employed to locate an efficient UAV placement to maximize the lifetime. The UAV-WSN system is evaluated by considering two metrics: lifetime and throughput. The simulation results show that varying UAV altitude has significant impact on both lifetime and throughput especially in the presence of different terrain. With increasing altitude, lifetime and throughput decrease as this loss can be as high as 94%. However, the proposed optimization plays a major role in combating these losses by redirecting the UAV to efficient placement corresponding to the new altitude level to maintain maximum lifetime and throughput. Moreover, the system lifetime concerning efficient UAV placement outperforms the one concerning centered placement at lower altitude, while the difference between two cases becomes less at higher altitude. Thereby, these outcomes may provide interesting measures for designing such integrated systems to achieve efficient data collection.

Design of Soil Moisture Telemetry System Using cluster-based Wireless Sensor Networks

Agriculture is an art and science used to cultivate the land, grow crops, and raise livestock. Agricultural land in Indonesia has different soil quality in each region. Soil quality for agriculture can have an impact on crops. Therefore, a Wireless Sensor Network (WSN) was created to make it easier to determine soil moisture. This system can help to monitor the condition of soil moisture content. WSN applies a Cluster-Based Algorithm, and the protocol used in WSN is a Low Energy Adaptive Clustering Hierarchy (LEACH). WSN will search for the cluster head, and then the sensor node will send the results of soil moisture sensing data to the cluster head for further data aggregation. Then, the results of the aggregation data will be sent to the sink node and then forwarded to the cloud. Data aggregation can make the sink stay longer than 30 minutes than the sink node that receives data without aggregation. Sink nodes that do not receive aggregation data also use more load current than sinks that receive aggregation data. The test results of the average response time to find new cluster heads have an average time of 6023.4 milliseconds. The maximum distance between nodes to transmit data to each other is 100 meters.

Optimisation of the hybrid grey wolf method in cluster-based wireless sensor network using edge computing

Optimisation of the hybrid grey wolf method in cluster-based wireless sensor network using edge computing

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in large-scale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network.

Predictive Model Techniques with Energy Efficiency for IoT-Based Data Transmission in Wireless Sensor Networks

Wireless sensor networks are limited by the vast majority of goods with limited resources. Power consumption, network longevity, throughput, routing, and network security are only a few of the research issues that have not yet been addressed in sensor networks based on the Internet of Things. Prior to becoming widely deployed, sensor networks built on the Internet of Things must overcome a variety of technological obstacles as well as general and specific hazards. In order to address the aforementioned problems, this research sought to improve rogue node detection, reduce packet latency/packet loss, increase throughput, and lengthen network lifetime. Wireless energy harvesting is suggested in the proposed three-layer cluster-based wireless sensor network routing protocol to extend the energy lifespan of the network. For the purpose of recognising and blacklisting risky sensor node behaviour, a three-tier clustering architecture with an integrated security mechanism is suggested. This clustering approach is cost-based, and the sink node selects the cluster and grid heads based on the cost function’s value. With its seemingly endless potential across a wide range of industries, including intelligent transportation, the Internet of Things (IoT) has gained prominence recently. To analyse the nodes and clustering strategies in IoT, the suggested method PSO is applied. A plethora of new services, programmes, electrical devices with integrated sensors, and protocols have been produced as a result of the Internet of Things’ explosive growth in popularity.

Augmentation in performance and security of WSNs for IoT applications using feature selection and classification techniques

Energy consumption, secure connection of Sensor Nodes (SNs), and their performance analysis play a major role in Wireless Sensor Networks (WSNs) research. Increased case use of various Internet of Things (IoT) applications has led to the development of complicated networks. To address the threats and security issues faced in complicated WSNs, a novel combined feature selection method known as the Fast Correlation-based Feature Selection (FCBFS) with XG-Boost has been proposed for the NSL-KDD intrusion detection benchmark dataset. It helps in the selection of the best features in a cluster-based WSN before the classification step. Five popular machine learning-based classifiers, namely decision tree, random forest, Naïve Bayes, extra tree, and XG-Boost, are used for the development of a robust intrusion detection system in WSN and its IoT applications. The effectiveness and robustness of the developed ensemble methods have been checked using classification accuracy, precision, recall, and F-score. XG-Boost classifier along with the FCBFS process has performed best with a classic accuracy, precision, recall, and F-score up to 99.84%, 99.83%, 99.84%, and 99.82% on multiple runs. Upon comparison with the existing state-of-the-art work in this field, the proposed work has outperformed. Results show that, in contrast to the previous filter approaches, our proposed process can successfully minimize the number of features while maintaining a high classification precision and recognition rate. It further tends to lower the overall energy required by sensor nodes during attack detection, hence extending the network lifetime and usefulness to a sufficient time frame.

Residue Number System-Based Approach to Minimise Energy Consumption in Wireless Sensor Networks

This study harnesses the useful number properties of the residue number system (RNS) to minimise energy consumption in a wireless sensor network. In a traditional cluster-based wireless sensor network, large bit representations of aggregated packets are transmitted to the base station. However, large bit patterns of packets are slower compared to smaller bits. The proposed approach splits aggregated data into a pre-specified number of transmission channels using a moduli set. Cheap energy cost routes from the cluster heads are computed to deliver the chunked aggregated data to the base station. Forward and reverse converters are proposed to encode data into RNS and decode the RNS data that reaches the base station. MATLAB simulation is used to implement the proposed data splitting method and to evaluate network performance. The experimental results suggest that the proposed method is more effective at minimising transmission energy when compared with traditional approaches in which complete packets are transmitted.