Performance Of Wireless Sensor Networks Research Articles

Hybrid time synchronization protocol for large-scale wireless sensor networks

There are two common approaches to design a time synchronization protocol as reference-based and consensus-based. The first approach is mainly employed in protocols operating in large-scale wireless sensor networks (WSNs) because of its fast convergence. However, reference-based time synchronization protocols are vulnerable to node-failure in real-world applications. Although consensus-based time synchronization protocols do not experience the aforementioned problem, they are not suitable for operation in large-scale WSNs owing to their slow convergence. In this study, we propose a hybrid time synchronization protocol (HTSP) to utilize the advantages of both approaches. In particular, HTSP exploits a temporary reference node to significantly reduce the convergence time and employs average-based consensus during normal operation to cope with node failures. It is notable that each node in HTSP can seamlessly switch between the reference and consensus modes during its operation. Moreover, HTSP is a fully distributed protocol as it does not rely on any leader-election mechanism. Our simulation results demonstrated that HTSP outperforms flooding, gradient, and virtual link-based time synchronization protocols considering various types of network topologies and scales.

Impact of Evaporation on the Performance of Ant Colony Optimization-Based Routing Protocols on the Wireless Sensor Network

Wireless Sensor Network (WSN) has the characteristics limited computing, memory and energy, so a routing protocol that supports WSN network performance is needed. Routing protocols based Ant Colony Optimization (ACO) are very suitable for use in WSN for routing data packets. In this report, we will try out several kinds of parameter values that bear on the pheromone evaporation (ρ) on the ACO algorithms applied to the WSN routing protocol with grid topology on the number of nodes variables in the WSN network. Where this can increase the number of delivery packets on a busy WSN network and each node transmits a packet continuously. From the results, the value of ρ 0.75 makes WSN performance on the grid topology with the number of nodes 12 and 30 very good in terms of the delivery packet, but with the number of nodes 70 the value of ρ 0.70 makes WSN performance better than the delivery packet side.

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.

Energy Efficient Routing Protocol in Novel Schemes for Performance Evaluation

Wireless sensor networks (WSNs) are a comparatively new revolutionary technology that has the potential to revolutionize how we live together with the present system. To enhance data archiving, WSNs are frequently used in scientific studies. Many applications have proved the value of wired sensors; however, they are prone to wire cutting or damage. While preventing wire tangles and damage, wireless sensor networks provide autonomous monitoring. The WS network suffers from a number of fundamental restrictions, including insufficient processing power, storage space, available bandwidth, and information exchange. Consequently, energy-efficient strategies are necessary for maximizing the performance and lifespan of WSNs. As a result, the special cluster head relay node and energy balancing techniques will be applied to deal with WSN energy consumptions. This extends the life of the network. In wireless sensor networks, clustering is a smart approach to reduce energy consumption. Energy scarcity and consumption are serious issues that must be addressed with effective and dependable solutions. The proposed MGSA considers the distance between each node and its corresponding CHs, as well as the residual energy and delay, as important factors in the relay node selection. The proposed approach outperforms the current methods, such as low-energy adaptive clustering hierarchy, LEACH (in terms of data delivery rate), energy efficiency, and network longevity. The next level, which will boost the efficiency of wireless sensor networks, with two fitness functions, is proposed. The cluster head (CH) is in charge of collecting and transmitting data from all other cluster nodes. The flow of the consistency of the cluster head selection process will beat the improved data delivery rate, energy efficiency, recommended fuzzy clustering performance experiments, and assessments. As a result, energy-efficient operations are necessary to maximize the WSN performance and lifespan.

TADR-EAODV: A trust-aware dynamic routing algorithm based on extended AODV protocol for secure communications in wireless sensor networks

Compliance with security requirements in Wireless Sensor Networks (WSNs) is known as an important phenomenon in conserving energy consumption due to its dynamic topology. Energy consumption management in WSN can be achieved through secure routing protocols. Authentication, firewall and intrusion detection, and trust management are common ways to provide security on the WSNs. Meanwhile, trust management provides better performance due to ensuring distributed security through collaboration. In this paper, a Trust-Aware Dynamic Routing algorithm based on Extended AODV protocol for secure communications in the WSN (TADR-EAODV) is proposed. TADR-EAODV considers factors such as direct trust, recommended trust, connectivity strength, energy rate and worthiness score to measure the distributed safety level of nodes in routing. We use the AODV protocol for routing work, which is extended through a multi-route routing approach. In addition, a centralized ensemble clustering for node grouping is included in TADR-EAODV, which enables clustering-based routing to enhance WSN performance. Extensive experiments have been performed in the presence of denial-of-service attacks to verify our claims. The experimental results prove that high-performance TADR-EAODV has been able to identify attackers by detecting their anomalous behavior. Besides, TADR-EAODV has improved the average packet transfer rate by 6% to 17% compared to existing state-of-the-art algorithms such as LEACH-TM, TAAO-SDTIM and TAOSC-MHR.

An effective network intrusion detection and classification system for securing WSN using VGG-19 and hybrid deep neural network techniques

In today’s world, a Network Intrusion Detection System (NIDS) plays a vital role in order to secure the Wireless Sensor Network (WSN). However, the traditional NIDS model faced critical constraints with network traffic data due to growth in the complexity of modern attacks. These constraints have a direct impact on the overall performance of the WSN. In this paper, a new robust network intrusion classification framework based on the enhanced Visual Geometry Group (VGG-19) pre-trained model has been proposed to prolong the performance of WSN. Primarily, the pre-trained weights from the ImageNet dataset are utilized to train the parameters of the VGG-19. Afterward, a Hybrid Deep Neural Network based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) will be employed to extract the influential features from network traffic data to enlarge the intrusion detection accuracy. The proposed VGG-19 + Hybrid CNN-LSTM model exploits both binary classification and multi-classification to classify attacks as either normal or attacked. A network intrusion benchmark dataset is used to assess the performance of the suggested system. The results reveal that the proposed VGG-19 + Hybrid CNN-LSTM learning system surpasses other pre-trained models with a superior accuracy of 98.86% during the multi-classification test.

Modified Adaptive Mechanism for Optimising IEEE 802.15.4 WPANs for Wireless Sensor Networks

Different applications of wireless sensor networks (WSNs) have different expectations from the working of medium access control protocols. Some value reliability more than delay incurred while some demand a fair trade-off for the factors like: Throughput, bit error rate etc. This paper evaluates the performance of wireless personal area networks from 802.15.4 group for WSNs with modified algorithm which helps in reducing the medium access delay and delay in reaching of the packet from one end to another end. In this paper certain modifications to existing algorithm have been proposed for reducing the medium access delay and to reduce the number of packets dropped. The result comparisons on the performance parameters like: network output load, generated acknowledged traffic, media access delay, battery consumed and delay in packet transmission from one end to another end that the backoff number and exponent values used for transmission play vital role for improving the performance of WSNs as they directly affect the number of packets dropped, successfully acknowledged and medium access delay.

CH Selection Using the Sooty Tern Optimization with Dijkstra Algorithm for Wireless Sensor Networks

Wireless Sensor Network (WSN) consists of distributed and resources-restricted sensor devices. The main and crucial restriction is on the available energy for each sensor which drastically affects the network performance. Many clustering techniques have been proposed to save energy and consequently improves the performance of WSN. In this paper, the Sooty Tern Optimization Algorithm (STOA) is proposed to solve the Cluster Head (CH) selection problem in WSN. The used fitness function employs different network parameters that have been proved to affect significantly the performance of WSN. To achieve further enhancement, the Dijkstra algorithm is also implemented after the selection of the best CHs as a routing protocol to reduce the energy consumption by finding the best path from CHs to the BS. The proposed algorithm is subjected to extensive simulations and tests under many different conditions. The performance of the proposed algorithm is compared to that of many reported clustering algorithms. The comparison revealed that the proposed algorithm outperformed all other algorithms in terms of energy consumption, network lifetime, and packet count.

A Method of Selecting Optimal Control Nodes for WSNs Based on C-Means Clustering Algorithm

The wireless sensor networks (WSNs) require an optimal selection of control nodes for improving the operational performance of the overall network. The data are increasing day by day, and it is difficult to a handle huge amount of data. For speedy transmission of data, it is mandatory to deploy sophisticated methods for improving the operations of WSNs. There are many methods proposed by the researchers to improve the operations of WSNs, but the data are increasing and more methods are needed to be explored to handle the operations of WSNs to smoothly handle a huge amount of data. To cater to this need, this research is proposing a method of selecting optimal control nodes for WSNs based on the C-means clustering algorithm (CCA). The CCA is improved by the weighting mechanism in the cluster, and the remaining energy of the node is taken into account. If the node energy is more as compared to the average energy in the cluster in each round, it will have the chance to serve as the cluster head node (CHN) and the adaptive assignment of CHN is made according to the generated cluster size by WSN. Every node possesses the probability of becoming a CHN to save the energy utilization of the node and to obtain the optimal control for node selection in WSN. The experimental results reveal that the coverage rate of WSN is improved after applying the proposed method. The network energy utilization is optimized, which effectively prolongs the lifetime of WSN and improves the overall network output including throughput, energy consumption rate, and data transmission rate.

Trust secure data aggregation in WSN-based IIoT with single mobile sink

Wireless Sensor Networks (WSNs), as the fundamental infrastructure, are indispensable for the Industrial Internet of Things (IIoT). In particular, the security and effectiveness of WSNs in IIoT are universal and inevitable issues. In this paper, a WSN-based IIoT Model (WIM) is designed characterized by a single mobile sink. Based on WIM, a high robust aggregation tree algorithm and an outlier elimination scheme are proposed in a virtual grid network with the mobile sink, which can acquire data in a real-time and accurate manner. Moreover, a trust secure data aggregation mechanism is applied in WIM which takes both the direct trust and the indirect trust into consideration to perform the secure data aggregation without losing the effectiveness of network even if the sink moves randomly. The simulation results show that the proposed model and algorithms can promote the performances of WSNs in terms of accuracy, effectiveness and delay.

Intelligent and Secure Clustering in Wireless Sensor Network (WSN)-Based Intelligent Transportation Systems

Wireless Sensor Network (WSN) plays a vital role in dealing with the challenging tasks of information management in Intelligent Transportation Systems (ITS). Current research has shown that the ever growing number of vehicles on the roads is making congestion worse, and many safety concerns are being addressed comprehensively by the WSN-based ITS. However, the energy consumed by sensor nodes, their operational period, and ‘security compromise’ are ever growing concerns. Cluster-based routing strategies have potentially contributed in reducing the energy expenditure of sensor nodes, besides the selection of energy-efficient and secure Cluster Head (CH) is still seeking an optimized approach for acquiring the proliferated performance of WSN. To address these concerns, we propose an Intelligent Clustering approach for ITS (ICITS) which selects CHs based on a hybrid optimization method called GABAT that integrates the strengths of Genetic Algorithm (GA) and BAT Algorithm (BA). The proposed framework (ICITS) is targeted primarily to road transport in military areas due to their stringent requirements in terms of security and reliability while collecting the data from the deployed sensor nodes. The simulation results obtained with ICITS demonstrate that it performs well for various performance metrics that include stability period, network survival period and ‘number of packets sent’, which are improved by 54.7%, 19.6%, and 40.5%, respectively as compared to recently proposed Cluster-based Intelligent Routing Protocol (CIRP).

Implementation of energy-efficent routing protocol within real time clustering wireless sensor networks

Wireless sensor networks (WSNs) are characterized by huge sensors that are having a limited amount of energy and specifying their behavior. Sensor networks combine distributed sensing, processing, and communication capabilities to create a powerful system. There are issues with the length of sensors connected to the network since features inside the sensors frequently require energy, which is likely to limit WSN performance. In creating WSN application architectures, extending network longevity, scalability, and load balancing are important factors. Using clustering techniques, the challenge of prolonging overall network lifetime and increasing the first dead node duration in the network may be tackled. Clustering is a valuable approach for breaking a network into parts known as clusters and giving solutions for energy consumption concerns including data collection, aggregating, and routing to sink nodes by cluster heads (CHs). In this paper, we suggest optimum clustering in multi-path and multi-hop protocols as a feasible option for reducing energy consumption and extending the lifetime of wireless sensor networks. In compared to the low energy adaptive clustering hierarchy (LEACH) clustering method, simulation results show that the new technique, optimal real time clustering (ORTC), is promising in terms of extending network lifetime.

Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

AbstractImproving localization performance is one of the critical issues in Wireless Sensor Networks (WSN). As a range-free localization algorithm, Distance Vector-Hop(DV-Hop) is well-known for its simplicity but is hindered by its low accuracy and poor stability. Therefore, it is necessary to improve DV-Hop to achieve a competitive performance. However, the comprehensive performance of WSN is limited by computing and storage capabilities of sensor nodes. In this paper, we propose an algorithm with parallel and compact techniques based on Whale Optimization Algorithm (PCWOA) to improve DV-Hop performance. The compact technique saves memory consumption by reducing the original population. The parallel techniques enhance the ability to jump out of local optimization and improve the solution accuracy. The proposed algorithm is tested on CEC2013 benchmark functions and compared with some popular algorithms and compact algorithms. Experimental results show that the improved algorithm achieves competitive results over compared algorithms. Finally, simulation research is conducted to verify the localization performance of our proposed algorithm.

A forest fire monitoring and detection system based on wireless sensor networks

Global climate change is affecting Earth in various ways, including glacial detachment, sea-level rise, and more intense heat waves, which in turn contribute to fires in areas rich in flora and fauna, causing erosion and changes in plant and animal ranges. Despite international efforts to mitigate climate change, the problem remains uncontrolled in many areas. In this study, a forest fire monitoring and early warning system is proposed. It is based on a wireless sensor network (WSN) that measures the environment to detect and prevent fires in rural areas. The WSN uses open-access technologies and the Zigbee standard for radiofrequency communication and sends collected data to a web server via GPRS and TCP/IP protocol. The WSN's performance is evaluated using two metrics: data transmission rate and time delay. The WSN achieved an average success rate of data transmission greater than 86% with an average time delay of less than 500 milliseconds in all tests, demonstrating the potential of WSNs as near real-time forest fire detection systems.

Towards People Crowd Detection Using Wireless Sensor Networks

Objective: The objective of this study was to examine and propose the use of wireless sensor networks for people crowd detection in resource constrained environments such as developing economies.
 Methodology: A systematic review was carried out on current technological trends and application of Wireless Sensor Networks (WSNs) in crowd detection. For this study, focus was on WSN implementation in developing economies, where infrastructure is underdeveloped and people crowds are dynamic and spontaneous. Based on a requirement analysis and knowledge of the inherent challenges of WSNs, a WSN implementation for people crowd detection was proposed.
 Findings: Most studies in crowd detection using WSNs, have been in the area of non-people crowds. However issues critical to deployment of WSNs for people crowd detection in developing countries include: the uncontrollable nature of people crowds, under developed physical infrastructure and the inherent challenges of power, computational capacity and broadcast communication characterizing WSNs. Achieving people crowd detection using WSNs therefore, calls for special attention.
 Recommendation: To ensure effective people crowd detection, requires taking into consideration connectivity, scalability, performance, security, accuracy and resource utilization of WSNs.

Real-Time Data Transmission Scheduling Algorithm for Wireless Sensor Networks Based on Deep Q-Learning

In the industrial environment, the data transmission of Wireless Sensor Networks (WSNs) usually has strict deadline requirements. Improving the reliability and real-time performance of data transmission has become one of the critical issues in WSNs research. One of the main methods to improve the network performance of WSNs is to schedule the transmission process. An effective scheduling algorithm can meet the requirements of a strict industrial environment for network performance, which is of great research significance. Aiming at the problem of concurrent data transmission in WSNs, a real-time data transmission scheduling algorithm based on deep Q-learning is proposed. The algorithm comprehensively considers the influence of the remaining deadline, remaining hops, and unassigned time-slot nodes in the data transmission process, defines the reward function and action selection strategy of Q-learning, and guides the system state information transfer process. At the same time, deep learning and Q-learning are combined to solve the problem of disaster maintenance caused by the large scale of the system state. A multi-layer Stacked Auto Encoder (SAE) network model establishes the state-action mapping relationship, and the Q-learning algorithm updates it. Finally, according to the trained SAE network model, the data transmission scheduling strategy of the system in different states is obtained. The network performance of the proposed data transmission scheduling algorithm is analyzed and evaluated by simulation experiments. The simulation results show that compared with the commonly used heuristic algorithms, the proposed algorithm improves real-time performance and can better meet the data transmission requirements of high reliability and real-time WSNs.

Performance research on ZigBee wireless sensor network self-organizing network for 220 kV four-circuit transmission lines on the same tower

With the integration of massive power transmission equipment and the Internet of Things (IoT), it is particularly important to choose a suitable low-power sensor networking structure, when facing a large amount of on-site sensing data. To select a suitable ZigBee network structure with a large number of embedded sensing nodes, for 220 kV four-circuit transmission lines on the same tower, this paper uses OPNET simulation software to conduct three different ZigBee wireless sensor network (WSN) models, respectively with star, tree and mesh topologies. Then, the performance of the Media Access Control (MAC) layer (delay and throughput performance), application layer (end-to-end delay and traffic received performance), and network layer (number of hops and packets dropped performance) is further investigated and compared for these three different ZigBee WSNs under 220 kV transmission scenario. Considering that quantity of sensing nodes would influence the ZigBee WSN performance greatly, thus, the simulation results under two application scenarios, 220 kV four-circuit transmission lines on the same tower and 500 kV double-circuit transmission lines on the same tower, were thoroughly compared and analyzed in this paper.

EATMR: an energy-aware trust algorithm based the AODV protocol and multi-path routing approach in wireless sensor networks

Rapid developments in radio technology and processors have led to the emergence of small sensor nodes that provide communication over Wireless Sensor Networks (WSNs). The crucial issues in these networks are energy consumption management and reliable data exchange. Due to the limited resources of sensor nodes, WSNs become a vulnerable target against many security attacks. Thus, energy-aware trust-based techniques have become a powerful tool for detecting nodes’ behavior and providing security solutions in WSN. Clustering-based routings are one of the most effective methods in increasing the WSN performance. In this paper, an Energy-Aware Trust algorithm based on the AODV protocol and Multi-path Routing approach (EATMR) is proposed to improve the security of WSNs. EATMR consists of two main phases: firstly, the nodes are clustered based on the Open-Source Development Model Algorithm (ODMA), and then in the second phase, clustering-based routing is applied. In this paper, the routing process follows the AODV protocol and multi-path routes approach with considering energy-aware trust. Here, the optimal and safe route is determined based on various parameters, namely energy, trust, hop-count, and distance. In this regard, we emphasize the evaluation of node trust using direct trust, indirect trust, and a multi-objective function. The simulation has been performed in MATLAB software in the presence of a Denial of Service (DoS) attack. The simulation results show that EATMR performs better than the state-of-the-art methods in terms of successfully detecting malicious nodes and enhancing network lifetime, energy consumption, and packet delivery ratio. As a conclusion, EATMR shows an average of 4.3 and 6.1% superiority over M-CSO and SQEER in different scenarios, respectively.

A Framework for Wireless Sensor Network Optimization Using Fuzzy-Based Fractal Clustering to Enhance Energy Efficiency

Wireless sensor networks’ energy consumption is the major challenge to be handled. Clustering is one of the techniques majorly used for reducing energy consumption. During the course of time, many methodologies are being proposed and the existing ones are hybrid. Still, the energy can be reduced more. The scope of optimization is always there. Existing approaches either reduce energy consumption or work on routing or only on data gathering capabilities. But the technique proposed increases the lifetime of the wireless sensor network (WSN) by reducing energy consumption and improves routing efficiency. This paper proposes an approach based upon Fractal Clustering to improve the lifetime of the sensor nodes. The proposed approach named Enhanced Energy Efficient Fuzzy-based Fractal Clustering (EEFFC) algorithm optimizes the performance of WSN. First, fractal clustering is used on sensor nodes to find the location of the sensors. Then, a fuzzy inference system (FIS) is applied to results produced by fractal clustering. Applying FIS on cluster heads generated will optimize the results. As a result, the cost of data transmission will reduce, and hence, the lifetime of the network will improve. FIS generates multi-level clustering, which will result in a better routing path for sensor nodes. Hence, routing will also be improved. MATLAB 2020 is the simulation tool. The results of the simulation depict that EEFFC shows optimized results and it works better than LEACH, LEACH-SF, TEEN and DEEC. The energy consumption is being reduced by reducing the listening time of a node and by reducing the communication distance, for which clustering is optimized. The energy consumption has been reduced by 2% as compared to the algorithms it is compared with. Also, the node’s time of death has been delayed by 3% in total.

An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm

Improving wireless communication and artificial intelligence technologies by using Internet of Things (Itoh) paradigm has been contributed in developing a wide range of different applications. However, the exponential growth of smart phones and Internet of Things (IoT) devices in wireless sensor networks (WSNs) is becoming an emerging challenge that adds some limitations on Quality of Service (QoS) requirements. End-to-end latency, energy consumption, and packet loss during transmission are the main QoS requirements that could be affected by increasing the number of IoT applications connected through WSNs. To address these limitations, an effective routing protocol needs to be designed for boosting the performance of WSNs and QoS metrics. In this paper, an optimization approach using Particle Swarm Optimization (PSO) algorithm is proposed to develop a multipath protocol, called a Particle Swarm Optimization Routing Protocol (MPSORP). The MPSORP is used for WSN-based IoT applications with a large volume of traffic loads and unfairness in network flow. For evaluating the developed protocol, an experiment is conducted using NS-2 simulator with different configurations and parameters. Furthermore, the performance of MPSORP is compared with AODV and DSDV routing protocols. The experimental results of this comparison demonstrated that the proposed approach achieves several advantages such as saving energy, low end-to-end delay, high packet delivery ratio, high throughput, and low normalization load.