Network Lifespan Research Articles

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.

Dual-discriminator Conditional Generative Adversarial Network Optimized with Hybrid Momentum Search Algorithm and Giza Pyramids Construction Algorithm for Cluster-based Routing in WSN Assisted IoT

Wireless sensor network (WSN) efficiently sends and receives the data on the internet of things (IoT) environment. As a large-scale WSN's nodes are powered by batteries, it is essential to create an energy-efficient system to decrease energy consumption and increase the network's lifespan. The existing methods not present effectual cluster head (CH) selection and trust node computation. Therefore, dual-discriminator conditional generative adversarial network optimized with a hybrid Momentum search algorithm and Giza Pyramids Construction algorithm for Cluster Based Routing in WSN Assisted IoT is proposed in this manuscript, for securing data transmission by identifying the optimum CH in the network (DDcGAN-MSA-GPCA-CBR-WSN-IoT). Initially, the proposed method is acting routing process via cluster head. Therefore, Dual-Discriminator conditional Generative Adversarial Network (DDcGAN) is considered to select the CH depending on multi-objective fitness function. The multi-objective fitness function, such as energy, delay, throughput, distance among the nodes, cluster density, capacity, collision, traffic rate, and cluster density. Based on fitness function, CH is selected. After cluster head selection, a malicious node depends on three parameters: trust, delay, and distance. These three parameters are optimized by hyb MSA-GPCA for ideal trust path selection. The proposed DDcGAN-MSA-GPCA-WSN-IoT technique is activated in PYTHON and network simulator (NS2) tool. Its effectiveness is analyzed under performance metrics, such as number of alive nodes, dead nodes, delay, energy consumption, packet delivery ratio, a lifetime of sensor nodes, and total residual energy. The simulation outcomes display that the proposed method attains lower delay, higher packet delivery ratio and high network lifetime when comparing to the existing models.

Fuzzy based clustering and improved ant colony optimization for collecting data via mobile sink in wireless sensor networks

<span>Energy efficient routing of data from sensor to base station (BS) is attained utilizing the clustering of sensor nodes, thus minimizing the number of hops and circling the task of the cluster head (CH) sporadically. In addition, the clusters near to the BS take a substantial load over multi-hop communication. The hot spot problem affects wireless sensor networks (WSNs) with BS nodes, which is caused by sensor nodes close to the BS allowing for increased traffic load. So, the entire network lifespan is minimized owing to the element some nodes drain their energy resources much faster equated to the break. To solve these issues fuzzy based clustering and form the optimal route (FCOR) by mobile sink (MS) approach for efficient data collection is proposed. The fuzzy logic method is used for elected the CH by node remaining energy, node connectivity and node distance parameters. Discovering an optimal movable trajectory for the MS is serious so as to attain energy efficiency. Improved ant colony optimization (IACO) method is a better solution to discovering an optimal traversal route. Simulation results proves that FCOR increases the energy efficiency, throughput and minimized the network delay in the WSN.</span>

A QoS-Aware Energy-Efficient Chimp Optimization Routing Protocol with Efficient Sensor Node Deployment Strategy in Underwater Acoustic Sensor Network

As an extension of wireless sensor networks in the underwater environment, Underwater Acoustic Sensor Networks (UASNs) have led to a broad consideration of academicians. One of the problems that lower UWSN effectiveness in terms of network lifespan is premature energy depletion. It might be caused by the network nodes using different amounts of energy. In UASNs, the effectiveness and dependability of data transfer remain so adverse because of the intricate underwater environment in diverse ocean applications like surveilling atypical submarine oil pipelines. Inspired by the significance of UASNs’ quality of service in several implementations, this study proffers a metaheuristic optimization algorithm (AG) called Chimp Optimization-based Routing Protocol (CH-ORP) for UASNs obtaining intricate features of underwater (UW) medium into concern like 3D changing topology, high propagation delay, node mobility, and density, and, also, cluster head nodes’ rotation mechanism. Initially, the entire network (NW) paradigm has been considered as a three-dimensional cube out of a grid point of view, and the three-dimensional cube has been split into several little cubes by employing Delaunay Triangulation. The optimization has been carried out for lessening node failure rate and NW energy consumption rate by ideally placing the sensor nodes in UW acoustic communication. The NW topology’s steadiness has been assured by the AG, and this optimizes the node redeployment scheme by computing the fitness function for all nodes. The proffered AG’s simulation substantiations have been performed for exhibiting the CH-ORP’s efficiency that executes finer when compared to the advanced methodologies concerning energy efficiency, reliability, and end-to-end delay. It has been found that the proposed Ch-ORP achieves 698 packets received with 28% of energy consumption, 156-sec Network delay, 257 packet loss, 97.23% of PDR, and 1256 Mbps of Network throughput.

Secure and Energy-Based STEERA Routing Protocol for Wireless Sensor Networks

In wireless sensor networks (WSN), the multifunctional low-power sensor nodes are linked to base stations and are highly responsible for sensing various information. However, maintaining the network’s lifespan is a major issue because of limited battery capacity and node failures. Thus, the proposed study introduced a new Scalable Trust-based Energy Efficient Routing Algorithm (STEERA) with Adaptive Grasshopper Algorithm (AGA) to enhance the network lifespan. At first, the trust values (Tv) are generated for each node to eliminate the malicious node with the help of penalty factor and volatilization. To make efficient routing process, the nodes are formed into cluster groups. A suitable cluster head is selected for each cluster to mitigate the energy consumption problem through the AGA approach by optimizing the parameters like Tv, distance and residual energy. Finally, the data packets are effectively transmitted through a multi-hop routing process by selecting an appropriate path and satisfying certain parameters through Fire Hawks Optimization (FHO). To simulate the proposed techniques, NS2 software is employed, and the performance is measured over various metrics like energy consumption, residual energy, the lifespan of a network, packet loss ratio and average end-to-end delay. The performance analysis shows that the proposed protocol design has obtained higher results than conventional routing protocols.

Performance Analysis of Sink Mobility Models for Wireless Sensor Networks: A Comparative Study

Wireless sensor networks (WSNs), deployed in the area of interest to gather data unattended, comprise numerous tiny, ponderous, and battery-operated sensor nodes (SNs). Numerous research publications presented strategies for extending the lifespan and performance of wireless sensor networks because SNs lifetime depends on limited battery life. One strategy for enhancing the performance of wireless sensor networks is to deploy an energy-rich sink capable of mobility to gather data sensed by stationary SNs. Therefore, several mobility models (MMs) were suggested. The primary objective of this investigation is to compare the effectiveness of wireless sensor networks using two MMs for mobile sinks (MSs): Kohonen’s self-organizing map-based model and the genetic algorithm-based model, in order to find the most suitable conditions under which each one of them can be used. As a result, network performance is investigated using the NS-2 simulator under various scenarios and MS speeds. Additionally, throughput, packet delivery ratio (PDR), and end-to-end (E2E) delay are the metrics used to analyze performance. Finally, messages are forwarded from their sources to the MS using the AODV routing protocol. The results show that the Kohonen-based model is suitable for small networks with moderate speeds of the mobile sink. On the other hand, the genetic algorithm-based model is suitable to be used with medium-sized networks with low speeds of the mobile sink.

Energy aware multilevel clustering scheme with Wake-Up Sleep Algorithm in underwater wireless sensor networks

For a wide range of reasons, researchers are interested in underwater wireless sensor networks (UWSNs). These include water quality and environment monitoring, disaster and earthquake prediction, surveillance by the military, and underwater navigation. Nevertheless, there are a lot of drawbacks and difficulties that come with the aquatic medium, including a high multipath delay, high levels of interference and noise, a harsh atmosphere, poor bandwidth, and short battery life for the sensor nodes. Yet, there are several obstacles to the successful deployment of underwater wireless sensor networks. The issue of sensor node energy depletion is the main cause of concern in the underwater sensor network. These problems require the use of good research methodologies and strategies to solve them. In this study, an Energy-Aware Multilevel Clustering with Wake-up Sleep Algorithm is developed to prolong the lifespan of the underwater wireless sensor network. The oceanic network region is thought to be a series of multi-level, 3D cylinders. High water pressure directed at the sea floor creates communication problems that are resolved by many layers of differing heights. The effectiveness of the suggested method is demonstrated by simulations, which demonstrate that it performs better in terms of an extended network lifespan, average residual energy, and many more factors. In comparison to the results of the simulation with current ones, the network lifespan has significantly increased.

An Intelligent Bio-Inspired Autonomous Surveillance System Using Underwater Sensor Networks.

Energy efficiency is important for underwater sensor networks. Designing such networks is challenging due to underwater environmental traits that hinder network lifespan extension. Unlike terrestrial protocols, underwater settings require novel protocols due to slower signal propagation. To enhance energy efficiency in underwater sensor networks, ongoing research concentrates on developing innovative solutions. Thus, in this paper, an intelligent bio-inspired autonomous surveillance system using underwater sensor networks is proposed as an efficient method for data communication. The tunicate swarm algorithm is used for the election of the cluster heads by considering different parameters such as energy, distance, and density. Each layer has several clusters, each of which is led by a cluster head that continuously rotates in response to the fitness values of the SNs using the tunicate swarm algorithm. The performance of the proposed protocol is compared with existing methods such as EE-LHCR, EE-DBR, and DBR, and results show the network's lifespan is improved by the proposed work. Due to the effective fitness parameters during cluster head elections, our suggested protocol may more effectively achieve energy balance, resulting in a longer network lifespan.

Fuzzy-Based Efficient Healthcare Data Collection and Analysis Mechanism Using Edge Nodes in the IoMT.

The Internet of Things (IoT) is an advanced technology that comprises numerous devices with carrying sensors to collect, send, and receive data. Due to its vast popularity and efficiency, it is employed in collecting crucial data for the health sector. As the sensors generate huge amounts of data, it is better for the data to be aggregated before being transmitting the data further. These sensors generate redundant data frequently and transmit the same values again and again unless there is no variation in the data. The base scheme has no mechanism to comprehend duplicate data. This problem has a negative effect on the performance of heterogeneous networks.It increases energy consumption; and requires high control overhead, and additional transmission slots are required to send data. To address the above-mentioned challenges posed by duplicate data in the IoT-based health sector, this paper presents a fuzzy data aggregation system (FDAS) that aggregates data proficiently and reduces the same range of normal data sizes to increase network performance and decrease energy consumption. The appropriate parent node is selected by implementing fuzzy logic, considering important input parameters that are crucial from the parent node selection perspective and share Boolean digit 0 for the redundant values to store in a repository for future use. This increases the network lifespan by reducing the energy consumption of sensors in heterogeneous environments. Therefore, when the complexity of the environment surges, the efficiency of FDAS remains stable. The performance of the proposed scheme has been validated using the network simulator and compared with base schemes. According to the findings, the proposed technique (FDAS) dominates in terms of reducing energy consumption in both phases, achieves better aggregation, reduces control overhead, and requires the fewest transmission slots.

Increasing efficiency for routing in internet of things using Binary Gray Wolf Optimization and fuzzy logic

In the field of information and communication technology, the Internet of Things is regarded as a brand-new and important technology. The introduction of new protocols in this area is caused by the presence of devices in these networks with constrained resources and relatively low computing power. One of the most well-known routing protocols for low-power devices is the RPL protocol. This algorithm cannot take into account all of the required routing goals at once. This article introduces a proposed data-oriented RPL algorithm that divides data during routing according to their content. This can decrease the amount of duplicate data transferred through the network, shorten the communication system's delay, conserve the node's limited energy, and prolong the network's lifespan. The effectiveness of RPL can be increased by selecting the best route utilizing the Binary Gray Wolf Optimization. The best parent node in the routing procedure is chosen using an objective function during the tree construction phase. This objective function is built using fuzzy logic and the Binary Gray Wolf Optimization in the suggested technique. The results of Matlab 2022a and OMNET environment tests have shown that the proposed method has increased the efficiency of energy consumption and reduced the period of instability and end-to-end delay. that the ratio of the instability period in the proposed method is much less than the other three methods, so that the ratio of the instability period is 57% for the proposed method in the ORPL and Qos RPL methods, it is equal to 80%, and in the RPL method it is equal to 89%. This problem shows that the proposed method is more stable, or, in other words, it has been active for a longer period of time with the maximum number of nodes.

Enhanced Throughput and Power for Forth level of Heterogeneity using Sector based clustering Protocol (SBCP)

Over the course of the last two decades, a considerable body of research has been devoted to devising methodologies aimed at prolonging the operational longevity of networks and enhancing data transfer rates. Notably, LEACH, PEGASIS, HEED, TEEN, and SEP have emerged as prominent clustering algorithms, each offering potential for augmenting network stability and throughput, thus concurrently extending the lifespan of camera sensor networks. Within this academic investigation, we present the Sector Based Clustering Protocol (SBCP) tailored explicitly to Third Level Heterogeneity, posited as an innovative approach fostering amplified data throughput and heightened power efficiency. Our proposed protocol attains a noteworthy 70% increase in data transfer rates, while rigorously upholding the quintessential requisites of network stability and sustained existence. The empirical evaluation, conducted through the implementation of sector-based clustering in MATLAB, includes a comparative analysis against LEACH, SEP, and ZSEP. The results obtained from this rigorous comparison underscore the superior performance of the proposed SBCP in terms of optimal data throughput, accompanied by marked enhancements in network stability vis-à-vis the aforementioned clustering algorithms.

Application of Artificial Intelligence Technology in the Optimal Deployment of Wireless Sensor Network Nodes

WSN (Wireless Sensor Network) is a network composed of a large number of sensor nodes self-organizing through wireless communication technology. Nodes are prone to failures due to environmental impact and energy depletion, and environmental interference and node failures can easily cause changes in the network topology. This article conducts research on the application of AI (Artificial Intelligence) technology in the optimal deployment of WSN nodes. Using AI technology to optimize the deployment of hybrid WSN mobile nodes, ensuring that the node density of each sub target area reaches its expected node density to ensure effective coverage of the entire target monitoring area. Given the target area range, the number and position of sensor nodes are determined through AI technology, which is the network layout for a given target area. Effectively reducing the data flow of the entire network. Due to the limited energy of sensor nodes, minimizing energy consumption and maximizing network lifespan are the main goals of designing WSN. Deployment strategies must optimize the deployment strategies of sensor nodes, intermediate nodes, and base stations to ensure coverage, connectivity, and robustness.

Improved Accurate Localization using PSO and the Weighted Dijkstra Algorithm in Software Defined Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are crucial in various fields, including monitoring the environment, surveillance, and healthcare. They rely on localization services for accurate data transfer and optimal network performance. Traditional WSN techniques struggle to adapt to dynamic environmental changes beyond the intended task scope. A synergy between Software-Defined Networking (SDN) and WSN has been suggested to address this issue. This research paper presents proposed approach for machine learning-based localization in Software Defined Wireless Sensor Networks (SDWSNs) using Particle Swarm Optimization (PSO) technique and the Weighted Dijkstra algorithm. PSO technique is used for clustering, the weighted Dijkstra algorithm (WDA) for finding the shortest path and sending data packets, and machine learning algorithms like AdaBoost and Naïve Bayes for data classification. The effectiveness of the proposed approach is measured using energy consumption, throughput, network lifespan, and packet delivery ratio, outperforming existing models like OEERP, LEACH, DRINA, and BCDCA. The machine learning algorithms' performance is also evaluated, with Naïve Bayes achieving the highest accuracy of 78.24% and AdaBoost 98.90%.

Feature Selection and Energy Management in Wireless Sensor Networks using Deep Learning

In wireless sensor networks, when the available energy sources and battery capacity are extremely constrained, energy efficiency is a major issue to be adressed. One of the main goals in the design of wireless sensor networks (WSNs) is to maximize longevity of battery life. Designers can benefit from the use of intelligent power utilization models to accomplish this goal. These models seek to decrease the number of chosen sensors used to record environmental measures in order to minimize power utilization while retaining the acceptable level of measurement accuracy. In order to simulate wireless sensor networks, we looked at real world datasets. Our simulation findings demonstrate that the suggested strategy can be used to accomplish significant goals by using the right number of sensors using deep learning, extend the lifespan of the wireless sensor networks.

Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning.

In high-speed railway operational monitoring network systems targeting railway infrastructure as its monitoring objective, there is a wide variety of sensor types with diverse operational requirements. These systems have varying demands on data transmission latency and network lifespan. Most of the previous research focuses only on prolonging network lifetime or reducing data transmission delays when designing or optimizing routing protocols, without co-designing the two. In addition, due to the harsh operating environment of high-speed railways, when the network changes dynamically, the traditional routing algorithm generates unnecessary redesigns and leads to high overhead. Based on the actual needs of high-speed railway operation environment monitoring, this paper proposes a novel Double Q-values adaptive model combined with the existing reinforcement learning method, which considers the energy balance of the network and real-time data transmission, and constructs energy saving and delay. The two-dimensional reward avoids the extra overhead of maintaining a global routing table while capturing network dynamics. In addition, the adaptive weight coefficient is used to ensure the adaptability of the model to each business of the high-speed railway operation environment monitoring system. Finally, simulations and performance evaluations are carried out and compared with previous studies. The results show that the proposed routing algorithm extends the network lifecycle by 33% compared to the comparison algorithm and achieves good real-time data performance. It also saves energy and has fewer delays than the other three routing protocols in different situations.

Congestion Detection and Mitigation Technique for Multi-Hop Communication in WSN

The primary function of a network system is to gather information from the observation region and transmit it to the base station. The network life span and congestion are the two major concerns in wireless networks. To enhance the lifespan of the sensor system; multi-hopping has been proved as best in class. Congestion is an important factor to be taken, where multiple nodes forward data to one another in the process of communication. Hence to overcome the issue of congestion in WSN, we proposed a congestion detection and mitigation method along with the multi-hop concept. In this technique, we have considered different routes among communication units that were classified on distance, relative attainment rate (RAR) and node storage occupancy. A utility function (U) has been proposed and calculated using the above illustrated factors for every node that acts as a neighbour to the transmitting node. Neighbour node with highest U-valued will be considered as the packet forwarding node's next hop. In this manner congestion free nodes are selected for data transmission.

Analysis of Long-Range Wireless Area Network Enabled Smart Agriculture for Sustainable Food Production

Technology advancements and increased connection are bolstering the movement toward digital transformation in agricultural and food systems, which might lead to the proliferation of robots, fully automated farms and tractors, and spraying drones. The next-generation (4G) wireless network connection, known as 5G, will be necessary provide the massive data transmission volumes and low latency that may have many positive effects on IoT and blockchain applications in the agriculture and food industries. This study makes use of LoRaWAN (Long Range Wireless Area Network) for its energy-harvesting capabilities in remote agriculture monitoring. According to the findings of the experiments, LoRaWAN is the best technology to utilize in a system for monitoring agriculture when network lifespan and power consumption are important factors. Following application criteria, the experimental findings may be utilized to pick wireless technology for agricultural monitoring.

Particle swarm optimization based artificial neural network (PSO-ANN) model for effective k-barrier count intrusion detection system in WSN

Wireless Sensor Networks (WSN) offers an extensive array of possibilities and finds application to nearly every wake of human lifetime. Two of the methods are intrusion detection and monitoring in dangerous environments. Generally, WSN are extremely vulnerable to various kinds of network attacks, lifespan of whole network is reduced by interfering with data transmission and communication operations. A novel approach, provides accurate measurement of several obstacles to achieve rapid identification and avoidance of disruptions. In order to more accurately estimate k-barrier number for efficient intrusion detection and prevention, a Particle Swarm Optimization based Artificial Neural Network (PSO-ANN) is developed. The region's size, sensor's detecting field, area where sensor transmits its signals, several sensors constitute the four important features utilized for learning and evaluating PSO-ANN model. The extraction of the different attributes is achieved with the help of simulation through Monte Carlo. It has been inferred from the research that the approach is accurate in predicting number of invasions, correlation coefficient, root mean square error, accuracy, precision are achieved to be excellent in contrast to the remaining approaches.

Research on Resource Allocation Strategies for Wireless Powered Sensor Network

Wireless powered sensor network (WPSN) solves the problem of difficult battery replacement of sensor nodes in some environments. The unreasonable distribution of network resources in the energy transmission environment will lead to a sharp decline in the energy collected by sensor nodes, reduce the network life, and cause resource waste. In this paper, by introducing hybrid relay nodes to transmit energy to passive terminals and forward terminal information, the distance between passive terminals and final receiving nodes of information is reduced, and the contradiction between passive terminals’ data transmission requirements and “double path loss” of radio frequency energy transmission in WPSN is alleviated. At the same time, in the relay-powered WPSN, the life span of the relay node is equal to the life span of the whole network. This paper studies the energy efficiency transmission scheme of the relay node to maximize the energy utilization efficiency of the relay node and improve the life span of the WPSN.

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.