Algorithm For Wireless Sensor Networks Research Articles

GWO-SMSLO: Grey wolf optimization based clustering with secured modified Sea Lion optimization routing algorithm in wireless sensor networks

GWO-SMSLO: Grey wolf optimization based clustering with secured modified Sea Lion optimization routing algorithm in wireless sensor networks

Efficient Sink Node Position Estimation using Harris Hawks Optimization Algorithm in Wireless Sensor Networks

Wireless sensor network (WSN) was utilized widely in numerous areas owing to their accessibility in data collection, processing, and transmission, and the strength and reliability of data processing and transmission are based on the accuracy of the positions of sensor nodes (SNs) in the WSN. Sink node location estimation in WSN is a vital task intended to define the geographical position of the sink node in the network area of coverage. This procedure normally includes using numerous localization techniques that trust data like received signal strength, arrival time, time variance of arrival, or angle of arrival from adjacent SNs. The accuracy of sink node localization directly influences the efficiency of data aggregation, routing procedures, and complete performance of the network in tasks like environmental monitoring, target tracking, and event recognition. As WSNs are frequently used in remote environments where physical involvement is unusable, an effective and accurate sink node localization model plays a vital part in certifying the network's longevity and reliability. This study develops an Efficient Sink Node Position Estimation using the Harris Hawks Optimization (SNPE-HHO) Algorithm in WSN. The main intention of the SNPE-HHO technique is to recognize the optimal position of the sink node in the network. To achieve this, the SNPE-HHO technique employs the HHO system which gets inspiration from the hunting tactics of Harris Hawk. Moreover, the SNPE-HHO technique computes a fitness function that can drive the searching direction of the HHO algorithm and enhance the node estimation performance. The performance analysis of the SNPE-HHO method is verified by utilizing distinct metrics. The experimentation values confirmed the improved estimation performance of the SNPE-HHO technique over other existing methods

Enhancing K-Nearest Neighbors Algorithm in Wireless Sensor Networks through Stochastic Fractal Search and Particle Swarm Optimization

The utilization of wireless sensor networks (WSNs) holds significant importance in diverse data collection applications. Efficient operation of computers, especially in predictive tasks, is imperative for obtaining accurate results within WSNs. This research introduces an innovative approach employing Stochastic Fractal Search-Particle Swarm Optimization (SFS-PSO) to enhance the performance of the K-Nearest Neighbors (KNN) algorithm. The proposed methodology initiates with the establishment of a particle population, dynamically adjusting their positions and velocities and integrating a diffusion process. Through an iterative process of incremental adjustments and evaluations, the algorithm fine-tunes its parameters, resulting in a refined KNN regression model. The enhanced model exhibits substantial improvements, as indicated by the notable reduction in root mean square error (RMSE) and mean absolute error (MAE), accompanied by a strengthened correlation between variables. The favorable outcomes underscore the efficacy of the SFS-PSO optimization technique in augmenting the KNN algorithm's performance within wireless sensor networks. In simpler terms, the application of SFS-PSO in conjunction with KNN leads to a significant decrease in RMSE, reaching a value as low as 0.00894, demonstrating the notable effectiveness of this optimization approach in refining the predictive capabilities of the KNN algorithm in the context of WSNs.

A whale swarm based energy efficient routing algorithm for wireless sensor networks

A whale swarm based energy efficient routing algorithm for wireless sensor networks

A Situation Based Predictive Approach for Cybersecurity Intrusion Detection and Prevention Using Machine Learning and Deep Learning Algorithms in Wireless Sensor Networks of Industry 4.0

A Situation Based Predictive Approach for Cybersecurity Intrusion Detection and Prevention Using Machine Learning and Deep Learning Algorithms in Wireless Sensor Networks of Industry 4.0

HarborSync: An Advanced Energy-efficient Clustering-based Algorithm for Wireless Sensor Networks to Optimize Aggregation and Congestion Control

HarborSync: An Advanced Energy-efficient Clustering-based Algorithm for Wireless Sensor Networks to Optimize Aggregation and Congestion Control

Performance Analysis of Gossip Algorithms for Large Scale Wireless Sensor Networks

Performance Analysis of Gossip Algorithms for Large Scale Wireless Sensor Networks

Clustering routing algorithm of wireless sensor network based on swarm intelligence

Clustering routing algorithm of wireless sensor network based on swarm intelligence

Node fault diagnosis algorithm for wireless sensor networks based on BN and WSN

Wireless sensor networks, as an emerging information exchange technology, have been widely applied in many fields. However, nodes tend to become damaged in harsh and complex environmental conditions. In order to effectively diagnose node faults, a Bayesian model-based node fault diagnosis model was proposed. Firstly, a comprehensive analysis was conducted into the operative principles of wireless sensor systems, whereby fault-related features were then extrapolated. A Bayesian diagnostic model was constructed using the maximum likelihood method with sufficient sample features, and a joint tree model was introduced for node diagnosis. Due to the insufficient accuracy of Bayesian models in processing small sample data, a constrained maximum entropy method was proposed as the prediction module of the model. The use of small sample data to obtain the initial model parameters leads to improved performance and accuracy of the model. During parameter learning tests, the limited maximum entropy model outperformed the other two learning models on a smaller dataset of 35 with a distance value of 2.65. In node fault diagnosis, the diagnostic time of the three models was compared, and the average diagnostic time of the proposed diagnostic model was 41.2 seconds. In the node diagnosis accuracy test, the proposed model has the highest node fault diagnosis accuracy, with an average diagnosis accuracy of 0.946, which is superior to the other two models. In summary, the node fault diagnosis model based on Bayesian model proposed in this study has important research significance and practical application value in wireless sensor networks. By improving the reliability and maintenance efficiency of the network, this model provides strong support for the development and application of wireless sensor networks.

Sinkhole Attack Defense Strategy Integrating SPA and Jaya Algorithms in Wireless Sensor Networks.

A sinkhole attack is characterized by low difficulty to launch, high destructive power, and difficulty to detect and defend. It is a common attack mode for wireless sensor networks. This paper proposes a sinkhole attack detection and defense strategy integrating SPA and Jaya algorithms in wireless sensor networks (WSNs). Then, combined with the SPA trust model, the trust values of suspicious nodes were calculated, and the attack nodes were detected. The Jaya algorithm was adopted to avoid the attacked area so that nodes can find the route to communicate with the real Sink, and attack nodes are isolated in the network to improve the capabilities of network directional defense. The simulation results show that the improved detection algorithm can effectively detect malicious nodes in the network, and the defense strategy implemented in the attacked area can improve the packet delivery rate, reduce network delay and energy consumption, and improve the security and reliability of wireless sensor networks.

An efficient reconfigurable geographic routing congestion control algorithm for wireless sensor networks

<span lang="EN-US">In recent times, huge data is transferred from source to destination through multi path in wireless sensor networks (WSNs). Due to this more congestion occurs in the communication path. Hence, original data will be lost and delay problems arise at receiver end. The above-mentioned drawbacks can be overcome by the proposed efficient reconfigurable geographic routing congestion control (RgRCC) algorithm for wireless sensor networks. the proposed algorithm efficiently finds the node’s congestion status with the help queue length’s threshold level along with its change rate. Apart from this, the proposed algorithm re-routes the communication path to avoid congestion and enhances the strength of scalability of data communication in WSNs. The proposed algorithm frequently updates the distance between the nodes and by-pass routing holes, common for geographical routing. when the nodes are at the edge of the hole, it will create congestion between the nodes in WSNs. Apart from this, more nodes sink due to congestion. it can be reduced with the help of the proposed RgRCC algorithm. As per the simulation analysis, the proposed work indicates improved performance in comparison to conventional algorithm. By effectively identifying the data congestion in WSNs with high scalability rate as compared to conventional methods</span>

Energy efficient data transmission using multiobjective improved remora optimization algorithm for wireless sensor network with mobile sink

<span lang="EN-US">A wireless sensor network (WSN) is a collection of nodes fitted with small sensors and transceiver elements. Energy consumption, data loss, and transmission delays are the major drawback of creating mobile sinks. For instance, battery life and data latency might result in node isolation, which breaks the link between nodes in the network. These issues have been avoided by means of mobile data sinks, which move between nodes with connection issues. Therefore, energy aware multiobjective improved remora optimization algorithm and multiobjective ant colony optimization (EA-MIROA-MACO) is proposed in this research to improve the WSN’s energy efficiency by eliminating node isolation issue. MIRO is utilized to pick the optimal cluster heads (CHs), while multiobjective ant colony optimization (MACO) is employed to find the path through the CHs. The EA-MIROA-MACO aims to optimize energy consumption in nodes and enhance data transmission within a WSN. The analysis of EA-MIROA-MACO’s performance is conducted by considering the number of alive along with dead nodes, average residual energy, and network lifespan. The EA-MIROA-MACO is compared with traditional approaches such as mobile sink and fuzzy based relay node routing (MSFBRR) protocol as well as hybrid neural network (HNN). The EA-MIROA-MACO demonstrates a higher number of alive nodes, specifically 192, over the MSFBRR and HNN for 2,000 rounds.</span>

Delay-aware optimized scheduling algorithm for high performance wireless sensor networks

Because of the phenomenal expansion of Internet of Things (IoT) devices around the world, Wireless Sensor Networks (WSN) have become increasingly important among the technical community, and research in this area has been growing exponentially. Researchers have used a variety of WSN technologies to address issues such as processing power constraints, bandwidth-limited connections, delays and energy consumption outlines that arise with sensor networks. However, in terms of delay optimization, affordability and effective energy consumption, WSN is the most suitable and alluring technology. This paper uses an Enhanced Scheduling Algorithm (ESA) with a probabilistic approach called Random Classical Game Theory (RCGT) to reduce the delay in WSN. Retransmissions are minimized when ESA and RCGT are used, which improve WSN delay. The idea is to improve the scheduling algorithm by using RCGT to lengthen the lifespan of the entire network. It has been demonstrated that the improved technique outperforms the existing algorithms in terms of throughput, energy consumption, hop count, delay and lifespan ratio.

EN‐DADA: Node task assignment algorithm for energy harvesting wireless multimedia sensor networks

AbstractToday, a directional wireless multimedia sensor network is a popular environment for solving the task assignment problem. Achieving long‐term frontal monitoring of moving objects is a crucial challenge for scholars in this field. Utilizing directional sensors equipped with energy harvesting is an effective technique to enhance network performance. In this way, the energy of nodes is no longer limited to batteries and can result in better frontal monitoring. In this method, each sensor categorizes tasks based on its own energy, allowing the determination of task execution nodes through bidding. The present study proposes a distributed algorithm for directional task assignment, EN‐DADA, based on energy harvesting. The task was first classified to determine the candidate node set that could execute the task, and then the task assignment was determined according to the monitoring income of each node in the candidate node set. The comparative analysis confirmed that the proposed method had advantages in terms of task revenue and network lifetime when using the same energy harvesting model.

Secure Clustering and Routing using Adaptive Decision and Levy Flight based Artificial Hummingbird Algorithm for Wireless Sensor Networks

Wireless Sensor Network (WSN) receives huge attention from various remote monitoring applications because of its self configuration, ease of maintenance and scalability features. But, the sensors of the WSNs vulnerable to malicious attackers due to the energy constraint, open deployment and lack of centralized administration. Therefore, the secure routing is established for achieving the secure and reliable data broadcasting in the WSN. In this paper, an Adaptive Decision and Levy Flight based Artificial Hummingbird Algorithm (ADLFAHA) is proposed for performing an effective secure routing under the blackhole and Denial of Service (DoS) attacks. The ADLFAHA is developed to perform Secure Cluster Head (SCH) selection and secure path identification according to the trust, energy, load and communication cost. An adaptive decision strategy and levy flight incorporated in the ADLFAHA is used to enhance exploration and achieves global optimization capacity that helps to enhance the searching process. Moreover, the developed ADLFAHA helps to avoid the congestion among the nodes by balancing the load in network. The ADLFAHA is analyzed using End to End Delay (EED), throughput, Packet Delivery Ratio (PDR) and overhead. The existing researches such as Firebug Optimized Modified Bee Colony (FOMBC) and Lightweight Secure Routing (LSR) are used to compare the ADLFAHA. The PDR of the ADLFAHA for the simulation time of 100 s is 98.21 that is high than the FOMBC and LSR.

Enhancing Energy Efficiency in Cluster Based WSN using Grey Wolf Optimization

Wireless sensor networks (WSNs) are typically made up of small, low-power sensor nodes (SNs) equipped with capability for wireless communication, processing, and sensing. These nodes collaborate with each other to form a self-organizing network. They can collect data from their surrounding environment, such as temperature, humidity, light intensity, or motion, and transmit it to a central base station (BS) or gateway for additional processing and analysis. LEACH and TSEP are examples of cluster-based protocols developed for WSNs. These protocols require careful design and optimization of CH selection algorithms, considering factors such as energy consumption, network scalability, data aggregation, load balancing, fault tolerance, and adaptability to dynamic network conditions. Various research efforts have been made to develop efficient CH selection algorithms in WSNs, considering these challenges and trade-offs. In this paper, the Grey Wolf Optimization (GWO) algorithm is employed to address the problem of selecting CHs (CHs) in WSNs. The proposed approach takes into account two parameters: Residual Energy (RE) and the distance of node (DS)s from the BS. By visualizing and analyzing the GWO algorithm under variable parameters in WSNs, this research identifies the most appropriate node from all normal nodes for CH selection. The experimental results demonstrate that the proposed model, utilizing GWO, outperforms other approaches in terms of performance.

Regional Link Routing Algorithm for Wireless Sensor Networks based on Improved Grey Wolf Optimization Algorithm

In order to improve the survival time, connectivity and the active number of nodes in the wireless sensor network, this paper proposes a regional chain wireless sensor network routing algorithm (RWA) based on improved grey Wolf optimization algorithm. The algorithm uses the method of dividing regions into chains, and in each region, chains are formed to select cluster heads before communication. The simulation results show that RWA has an improvement in avoiding too long chain, balancing energy consumption and prolonging the network life cycle.

Energy Saving Routing Algorithm for Wireless Sensor Networks Based on Minimum Spanning Hyper Tree

With the rapid development of wireless sensor networks (WSNs), designing energy-efficient routing protocols has become essential to prolong network lifetime. This paper proposes a minimum spanning tree-based energy-saving routing algorithm for WSNs. First, sensor nodes are clustered using the LEACH protocol and minimum spanning trees are constructed within clusters and between cluster heads. The spanning tree edge weights are optimized considering transmission energy, residual energy, and energy consumption rate. This avoids channel competition and improves transmission efficiency. An energy-saving routing model is then built whereby deep reinforcement learning (DRL) agents calculate paths optimizing the energy utilization rate. The DRL reward function integrates network performance metrics like energy consumption, delay, and packet loss. Experiments show the proposed approach leads to 10-15W lower average switch energy consumption compared to existing methods. The throughput is high since overloaded shortest paths are avoided. The average path length is close to shortest path algorithms while maintaining energy efficiency. In summary, the proposed minimum spanning tree-based routing algorithm successfully achieves energy-saving goals for WSNs while guaranteeing network performance. It provides an efficient and adaptive routing solution for resource-constrained WSNs.

An enhanced MSU‐TSCH scheduling algorithms for industrial wireless sensor networks

SummaryThe reliability and latency requirements of wireless sensor network‐based smart grid communications are met in large part by MAC protocols. Developed by IEEE, time slotted channel hopping (TSCH), a method that is effective, dependable, and predictable. However, the TSCH standard does not enable mobility and does not offer any solution for scheduling, especially in IEEE 802.15.4 TSCH, the most recent generation of extremely dependable and low‐power MAC protocols. Recently, MSU‐TSCH a time‐frequency communication schedule was proposed to provide mobility to TSCH. Despite its distinctive qualities, MSU‐TSCH has the drawback of computing the TSCH schedule at each node independently of its traffic load, which can significantly increase the communication delay. Due to this limitation, MSU‐TSCH is not suitable for some delay‐sensitive smart grid applications. In this article, we provide an improved MSU‐TSCH‐based TSCH protocol, called Mobility‐TSCH, that dynamically adjusts time slot assignments based on traffic volume and latency requirement. Moreover, the protocol Mobility‐TSCH adapts to topology changes and supports mobility. Additionally, it optimizes time slot allocation by prioritizing nodes closest to the sink. The performance and evaluation analysis of Mobility‐TSCH compared to the original MSU‐TSCH, reveal that the communication delay is greatly reduced, decreases the average end‐to‐end latency, the high packet delivery ratio (PDR), Increase the performance of both metrics, parent connectivity ratio (PCR) and convergence time (CT), while maintaining a minimal overhead signaling.

An On-Demand Partial Charging Algorithm without Explicit Charging Request for WRSNs

Wireless rechargeable sensor networks provide an effective solution to the energy limitation problem in wireless sensor networks by introducing chargers to recharge the nodes. On-demand charging algorithms, which schedule the mobile charger to charge the most energy-scarce node based on the node’s energy status, are one of the main types of charging scheduling algorithms for wireless rechargeable sensor networks. However, most existing on-demand charging algorithms require a predefined charging request threshold to prompt energy-starved nodes with energy levels lower than this threshold to submit an explicit charging request to the base station so that the base station can schedule the mobile charger to charge these nodes. These algorithms ignore the difference in importance of nodes in the network, and charging requests sent by nodes independently can interfere with the mobile charger’s globally optimal scheduling. In addition, forwarding charging requests in the network increases the network burden. In this work, aiming to maximize the network revenue and the charging efficiency, we investigate the problem of scheduling the mobile charger on-demand without depending on explicit charging requests from nodes (SWECR). We propose a novel on-demand partial charging algorithm that does not require explicit charging requests from nodes. Our algorithm accounts for the differences in importance between nodes and leverages the deep reinforcement learning technique to determine the target charging node and each node’s charging time. The simulation results demonstrate that the proposed algorithm significantly improves the charging performance and maximizes the network revenue and the charging efficiency.