Lifetime Of Wireless Sensor Networks Research Articles

Improvement of Teach Protocol for Enhancing Features of WSN

. Wireless Sensor Network (WSN) has several applications such as military, industry. and environment. The importance of WSNs in current applications makes the WSN technology highly relevant and significant to the field of communication and computing. However, WSN's performance deals with number of challenges. Energy consumption is the most considerable because nodes use energy to collect, treat, and send data, but they have restricted energy- For this reason. numerous efficient energy routing protocols have been dev eloped to save the consumption or power. Low energy adaptive clustering hierarchy (LEACH) is considered the most attractive one in WSNs, in this projects we evaluate the LEACH approach effectiveness in the cluster-head (CHJ choosing and in data transmission, then we propose an enhanced protocol. proposed algorithm aims to improve energy Consumption and prolong the lifetime of WSN through selecting CHs depending on the remaining power, balancing the number of nodes in clusters. determining abandoned nudes in order to send their data to the sink. Then CHs choose the optimal path to reach the sink, we propose a new approach to achieve better enhancement of WSN in terms of network lifetime and data transmission time represented by reducing the packet delay time. Then, we compare the simulated result of the proposed algorithm with the basic LEACH protocol.

Efficient malicious node detection by multi-objective energy trust aware hybrid optimization based maximizing lifetime of wireless sensor networks

Wireless sensor network (WSN) collect and detect data in real time, but their battery life limits their lifetime. The CH selection process increases network overhead and reduces lifetime, but it considers node processing and energy limitations. To solve that problem this research methodology proposed Multi Objective Energy trust - Aware Optimal Clustering and Secure Routing (MOETAOCSR) protocol. At first, the trust factors such as direct and indirect factors are calculated. Thus, the calculated values are given as input to the SDLSTM to detect the malicious node and normal node. Here, the network deployment process is initially carried out and then the cluster is formed by HWF-FCM. From the clustered sensor nodes, the cluster head is selected using Golden Jackal Siberian Tiger Optimization (GJSTO) approach. Then, the selection of CH the paths are learned by using the Beta Distribution and Scaled Activation Function based Deep Elman Neural Network (BDSAF-DENN) and from the detected paths the optimal paths are selected using the White Shark Optimization (WSO). From the derived path sensed data securely transferred to the BS for further monitoring process using FPCCRSA. The proposed technique is implemented in a MATLAB platform, where its efficiency is assessed using key performance metrics including network lifetime, packet delivery ratio, and delay. Compared to existing models such as EAOCSR, RSA, and Homographic methods, the proposed technique achieves superior results. Specifically, it demonstrates a 0.95 improvement in throughput, 0.8 enhancement in encryption time, and a network lifetime of 7.4.

Optimized Multi-objective Clustering using Fuzzy Based Genetic Algorithm for Lifetime Maximization of WSN

Background: Wireless Sensor Networks (WSNs) have gained significant attention due to their diverse applications, including border area security, earthquake detection, and fire detection. WSNs utilize compact sensors to detect environmental events and transmit data to a Base Station (BS) for analysis. Energy consumption during data transmission is a critical issue, which has led to the exploration of additional energy-saving techniques, such as clustering. Objective: The primary objective is to propose an algorithm that selects optimal Cluster Heads (CHs) through a fuzzy-based genetic approach. This algorithm aims to address energy consumption concerns, enhance load balancing, and improve routing efficiency within WSNs. Methods: The proposed algorithm employs a fuzzy-based genetic approach to optimize the selection of CHs for data transmission. Four key parameters are considered: the average remaining energy of CHs, the average distance between CHs and the BS, the average distance between member nodes and CHs, and the standard deviation of the distance between member nodes and CHs. Results: The algorithm's effectiveness is demonstrated through simulation results. When compared to popular models like LEACH, MOEES, and FEEC, it demonstrates an 8-20% improvement in the lifetime of WSNs. The proposed approach achieves enhanced efficiency, lifetime extension, and improved performance in CH selection, load balancing, and routing. Conclusion: In conclusion, this study introduces a novel algorithm that utilizes fuzzy-based genetic techniques to optimize CH selection in WSNs. By considering four key parameters and addressing energy consumption challenges, the proposed algorithm offers significant improvements in efficiency, lifespan, and overall network performance, as validated through simulation results.

“Clustering Protocol Based on Territorial Energy for Random Deployment of Wireless Sensor Networks"

Wireless Sensor Networks (WSNs) have become increasingly essential for monitoring and gathering data in diverse environments. The efficient utilization of energy resources is crucial for prolonging the network lifetime in WSNs. This paper presents a comparative analysis of three prominent clustering protocols: Low Energy Adaptive Clustering Hierarchy (LEACH), Residual Energy Aware Clustering (REAC), and Territorial Energy-Driven Clustering Protocol (TEDC). TEDC introduces a novel approach where cluster heads are elected based on territorial dominance and energy levels, aiming to enhance energy efficiency and prolong network lifetime. The performance of LEACH, REAC, and TEDC is evaluated under various scenarios, including node density, network size, and communication range. Key performance metrics such as energy consumption, network lifetime, and packet delivery ratio are analyzed to assess the effectiveness of each protocol. The results provide valuable insights into the comparative strengths and weaknesses of these protocols, aiding network designers and researchers in selecting the most suitable protocol for specific WSN deployments. Additionally, the paper discusses potential optimizations and future research directions to further enhance the performance of clustering protocols in WSNs.

Particle swarm optimization and fuzzy logic based clustering and routing protocol to enhance lifetime for wireless sensor networks

Particle swarm optimization and fuzzy logic based clustering and routing protocol to enhance lifetime for wireless sensor networks

Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization

Despite the deployment of wireless sensor networks in diverse fields (health, environment, military applications, etc.) for tracking or monitoring, several challenges, such as extending the lifetime of the network under energy constraints, still need to be resolved. Lifetime is the operational time of the network during which it can perform dedicated tasks and satisfy the application requirements. The energy constraints dictate that the energy consumption of sensors should be minimized since in most cases the sensors are battery-powered. Various methods have been proposed to work around this problem using scheduling approaches. In this paper, particle swarm optimization-based scheduling was designed and implemented to maximize the lifetime of wireless sensor networks formulated as a Non-Disjoint Sets Cover (NDSC) problem. The experimental findings show that the proposed approach is extremely competitive to the state-of-the-art algorithms, as it is able to find the optimal and best-known solutions in the instances investigated.

Optimal Drone Nodes Deployment to Maximize Coverage and Energy in WSNs Using Genetic Algorithms

Increasing coverage and reducing the energy consumed in wireless sensor networks is an interesting field for researchers since the discovery of wireless sensor networks and it is an open problem. Activating the lowest connectivity range for each node individually depending on its remaining power and the place it covers, while maximizing coverage by placing it in the optimal place will reduce the energy consumed and increase the lifetime of wireless sensor networks. In this paper, we find an approach to increase coverage by optimizing correlation and residual energy using genetic algorithms by placing each node in the optimal position to maximize coverage, assuming that each node has a different energy from the other nodes. We will use drones to carry and move sensors to optimal position. The aim of the proposed work is to cover the largest area of a given region by using the least range of connection and increasing the Lifetime. We ran a succession of simulations and found the proposed model better than the strategies we found in literature.

Green IoT Based on Tropical Weather: The Impact of Energy Harvesting in Wireless Sensor Network

Wireless Sensor Networks (WSNs) are a key component of Green IoT, as they play a critical role in many applications. However, a major challenge faced by WSNs is their limited energy capacity, which can impede their effectiveness. To address this issue, energy harvesting techniques are used to harness ambient energy and power the nodes, eliminating the need for frequent battery replacements or recharging. This study proposes a solar energy harvesting technique to prolong the lifespan of each wireless sensor node in a network. The aim of the research is to assess the impact of energy harvesting on wireless sensor networks with a focus on solar ambient light energy, as it is a critical component of achieving a Green Internet of Things (IoT). The study is adapted to the tropical weather of Malaysia and its forecasted conditions. Through the use of NetSim simulations, the energy efficiency and reliability of the network are evaluated using solar energy harvesting. The recharge rate for energy harvesting in a wireless sensor network is calculated to be 2.34W per day using a 55x70mm solar photovoltaic panel based on Malaysia weather forecasts and sun hours. This proposed method is successfully executed using NetSim, which utilizes real device parameters for a WSN in a simulated agriculture environment. The results show an overall improvement of 22.4 percent in performance parameters in all areas, including energy consumption, network layer, routing performance, and IEEE 802.15.4 metrics, representing a significant improvement. With the implementation of solar energy harvesting, traditional energy of WSNs can benefit from developing smart agricultural monitoring systems. This efficient method and calculation for solar energy harvesting can help to sustain the infinite lifetime of WSN by optimizing the operational time to 18% of the duty cycle. Results demonstrate that the proposed method can effectively increase the energy efficiency and reliability of the network, making it a promising solution for WSNs operating in tropical regions. This research contributes to the development of sustainable and energy-efficient WSNs, promoting the Green IoT concept.

Enhancing wireless sensor network security and efficiency with CNN-FL and NGO optimization

In this study, we discuss how Wireless Sensor Networks (WSN) are susceptible to malicious assaults from malfunctioning nodes, which jeopardise the security of data transmission in crucial applications. We use a Convolutional Neural Network coupled with Fuzzy Logic (CNN-FL) for improved deep learning, introducing a unique method, effectively identifying and categorizing trustworthy and malicious nodes. This is combined with a routing strategy enhanced by the Neuro Genetic Optimizer (NGO), built on the LEACH routing protocol and based on a Roulette wheel selection mechanism. The WSN's lifetime is increased by our suggested routing method, which not only provides safe data transmission but also dramatically reduces latency and energy consumption. Simulation findings show that our technique outperforms current protocols as ASNGSRA, DMCNN, and FRCSROD in terms of packet delivery ratio, energy economy, and latency analysis.

Towards optimal path planning for sustainable data collection in real-time wireless sensor networks

ABSTRACT Data collection using mobile sink(s) has proven to reduce energy consumption and enhance the network lifetime of wireless sensor networks. Generally speaking, a mobile sink (MS) traverses the network region, sojourning at rendezvous points (RPs) and gathering data there from nearby sensor nodes (SNs). However, determining the optimal number and location of RPs is a hard problem. Metaheuristic algorithms are well suited to solve such kind of problems. In this paper, we propose a tour planning scheme for the MS by a combined approach that uses K-means clustering and Particle Swarm Optimization algorithm. The proposed scheme first determines a set of near-optimal RPs and then uses them to schedule the tour of the MS. Herein, the energy consumption of the overall scheme is reduced due to the low computational cost of the proposed algorithm. At last, the proposed scheme is shown to outperform the comparative algorithms through simulation.

Vector Based Genetic Lavrentyev Paraboloid Network Wireless Sensor Network Lifetime Improvement

Vector Based Genetic Lavrentyev Paraboloid Network Wireless Sensor Network Lifetime Improvement

Optimal Stable Cluster Head Selection Method for Maximal Throughput and Lifetime of Homogeneous Wireless Sensor Network

Optimal Stable Cluster Head Selection Method for Maximal Throughput and Lifetime of Homogeneous Wireless Sensor Network

An energy consumption optimization strategy for Wireless sensor networks via multi-objective algorithm

Deploying relay nodes is a significant mechanism to prolong the network lifetime of wireless sensor networks (WSNs). However, most existing studies overlook the energy consumption of relay nodes, leading to imperfections in the optimization process. Additionally, there is also a lack of analysis of conflicts between different optimization objectives. In this regard, a multi-objective antlion with fuzzy clustering algorithm (MOALO-FCM) is designed to obtain a better trade-off between different optimization objectives. And an adaptive membership function revision strategy is introduced to improve the energy balance of relay nodes. To verify the performance of the proposed algorithm, simulation experiments are set in 2-dimensional and 3-dimensional scenes with correlative algorithms, respectively. The main evaluation indexes include performance of Pareto optimal solution sets, the life cycle of network, the energy consumption of sensor nodes, the energy consumption of relay nodes, the number of living nodes, and the running time of algorithms. The results indicate that the proposed algorithm has better performance in various indexes.

Improvement of Low Energy Adaptive Clustering Hierarchical Protocol Based on Genetic Algorithm to Increase Network Lifetime of Wireless Sensor Network

Improvement of Low Energy Adaptive Clustering Hierarchical Protocol Based on Genetic Algorithm to Increase Network Lifetime of Wireless Sensor Network

Energy Efficient Clustering Using Improved Particle Swarm Optimization in Wireless Sensor Networks

The Wireless Sensor Network (WSN) includes many low-cost nodes that have the capacity to perceive, operate, and communicate wirelessly. WSN can spread the information to all around through a cooperative node approach. It also has many advantages in terms of both cost and cooperative intelligence. In a wireless sensor network, nodes have limited energy resources, so their life cycle is considered as one of the main concerns about wireless sensor networks. Energy efficiency grouping and routing are two well-known issues in optimization that have been widely studied in order to increase the lifetime of wireless sensor networks. In this paper, an improved particle swarm optimization (IPSO) clustering algorithm for energy efficiency network management is introduced in order to find a route for creating optimal clusters. To evaluate the efficiency of the proposed clustering algorithm, this algorithm is simulated and compared with the particle swarm optimization(PSO) algorithm based on parameters such as network energy, number of live nodes and network life.

Enhancing Wireless Sensor Network Lifetime through Energy-Efficient Data Clustering and Compressed Forwarding in video Processing

Wireless Sensor Networks (WSN) play a crucial role in diverse data gathering applications, but face a significant challenge in the form of limited energy reserves within sensor nodes. Enhancing the network's Quality of Service, particularly its lifetime, is paramount. Prolonging the network's operational span hinges on mitigating energy consumption, with communication accounting for a substantial portion of nodal power usage. By reducing data transmission, not only can energy consumption be curtailed, but also bandwidth requirements and network congestion can be minimized. In the context of Wireless Sensor Networks, the Distributed Similarity-based Clustering and Compressed Forwarding (DSCCF) approach strives to construct data-similar iso-clusters with minimal communication overhead. This technique involves extracting trend and magnitude components from lengthy data series using an LMS filter, resulting in what is termed data projection. Data similarity between nodes is assessed by measuring the Euclidean distance between these data projections, thereby facilitating efficient and low-overhead iso-cluster formation. To further economize intra-cluster communication, an adaptive-nLMS-based dual prediction framework is employed. During each data collection round, the cluster head holds instantaneous data for each cluster member, using either prediction or direct data communication. Furthermore, inter-cluster data is reduced via a multi-level lossless compressive forwarding technique. Impressively, this proposed approach has achieved an 80% reduction in data while maintaining optimal data accuracy for the collected information. The transmission of inter-cluster data exclusively occurs through a network backbone comprised solely of cluster heads. Initially, the cluster heads establish this network backbone. Each cluster head dispatches a link request query towards the sink through the backbone, receiving a link reply message containing path length and the weakest link of the path. The cluster head repeats this process for each available path, subsequently selecting the most optimal path based on the acquired information and its reliability in terms of link quality

Energy efficient gateway based routing with maximized node coverage in a UAV assisted wireless sensor network.

Ad-hoc wireless sensor networks face challenges of optimized node deployment for maximizing coverage and efficiently routing data to control centers in post disaster events. These challenges impact the outcome for extending the lifetime of wireless sensor networks. This study presents a uav assisted reactive zone based EHGR (energy efficient hierarchical gateway routing protocol) that is deployed in a situation where the natural calamity has caused communication and infrastructure damage to a major portion of the sensor network. EHGR is a hybrid multi layer routing protocol for large heterogeneous sensor nodes (smart nodes, basic nodes, user handheld devices etc.) EHGR is tailored to meet two important concerns for a disaster hit wsn ie. optimized deployment and energy efficient routing. The first part of EGHR focuses on maximized coverage during node deployments. Maximized coverage is an important aspect to be considered during the event of disaster since most of the nodes loose coverage and are detached from the wireless sensor network. The first part of EHGR uses state of the art game theory approach to build a model that maximizes the coverage of nodes during the deployment phase from all participating entities i.e. nodes and uavs. Rather than fixing the cluster head as is the case in traditional cluster-based approaches EHGR uses the energy centroid nodes. Energy centroid nodes evolve on the basis of aggregated energy of the zone. This approach is superior to simply electing cluster head nodes on the basis of some probability function. The nodes that fail to achieve any successful outcome from the game theory matching model fail to get any association. These nodes will use multi hop d2d relay approach to reach the energy centroid nodes. Gateway relay nodes used with the game theory approach during the deployment of the uav assisted wsn improves the overall coverage by 25% against traditional leach based hierarchical approaches. Once the optimum deployment phase is completed the routing phase is initiated. Aggregated data is sent by the energy centroid nodes from the ECN nodes to the servicing micro controller enabled un manned aerial vehicles. The routing process places partial burden of zone formation and data transmission to the control center for each phase on the servicing uavs. Energy centroid nodes engage only in the data aggregation process and transmission of data to servicing uav. Servicing-uavs reduce energy dissipated of the entire zone which result in gradual decrease of energy for the zone thus increasing the network lifetime. Node deployment phase and the routing phase of EHGR utilize the computations provide by the mirco controller enabled unmanned aerial vehicles such that the computationally intensive calculations are offloaded to the servicing uav. Experiment results indicate an increase in the first dead node report, half dead node report, and last dead node report. Network lifetime is extended to approximately 1800 rounds which is an increase by ratio of 100% against the traditional leach approach and increase by 50% percent against the latest approaches as highlighted in the literature.

Hybrid energy balancer for clustering and routing techniques to enhance the lifetime and energy-efficiency of wireless sensor networks

Hybrid energy balancer for clustering and routing techniques to enhance the lifetime and energy-efficiency of wireless sensor networks

On the Best Fitness Function for the WSN Lifetime Maximization: A Solution Based on a Modified Salp Swarm Algorithm for Centralized Clustering and Routing

Wireless sensor networks (WSNs) have become increasingly important in recent years due to their ability to monitor and collect data in various environments. However, WSNs are often limited by their energy resources, making energy efficiency a critical concern in designing WSNs. Clustering and multi-hop routing are effective methods for maximizing the lifetime of WSNs. The energy efficient clustering and routing problem in WSNs can be formulated as a multi-dimensional knapsack problem (d-kp). This formulation can generalize all clustering and routing protocols in regard to energy efficiency. In this paper, we investigate the hardness of the energy-efficient clustering and routing problem in WSNs based on the d-kp formulation and introduce a modified Salp Swarm Algorithm (SSA) as a method to solve this problem. SSA is a metaheuristic algorithm inspired by the behavior of salps in the ocean. Moreover, we prove that it is impossible to design a polynomial time fitness function to maximize a long-term metric such as FND (time of first node death), which is a commonly used metric for network lifetime. Our results have implications for the design of energy-efficient WSNs and call for the development of more efficient algorithms that can handle the complexity of this problem. furthermore, we evaluate our proposed algorithm using simulation and compare it to existing algorithms. Our results show that using SSA with a carefully designed fitness function based on the theoretical findings and a unique one-to-one routing protocol to mitigate the energy hole problem, outperforms many of the existing algorithms in terms of energy efficiency and network lifetime.

Swarm Intelligence-Optimized Energy Management for Prolonging the Lifetime of Wireless Sensor Networks

Recent technological and industrial progress has enabled the development of small, high-performing, energy-saving, affordable sensor nodes that possess the potential to adapt, be self-aware, and self-organize. These nodes are designed for versatile communications applications. Sensor networks for sustainable development focus on the ways in which sensor network technology can enhance social development and improve living standards without causing harm to the environment or depleting natural resources. Wireless sensor networks (WSNs) offer undeniable benefits in various fields, including the military, healthcare, traffic monitoring, and remote image sensing. Given the constraints of sensor networks, varying degrees of security are necessary for these critical applications, posing difficulties in the implementation of conventional algorithms. The issue of security has emerged as a primary concern in the context of IoT and smart city applications. Sensor networks are often regarded as the fundamental building blocks of IoTs and smart cities. The WSN encompasses a routing algorithm, network strength, packet loss, energy loss, and various other intricate considerations. The WSN also addresses intricate matters such as energy usage, a proficient approach for picking cluster heads, and various other concerns. The recent growth of Wireless Sensor Networks (WSNs) has made it increasingly difficult to ensure the trustworthiness and reliability of data due to the distinct features and limitations of nodes. Hostile nodes can easily damage the integrity of the network by inserting fake and malicious data, as well as launching internal attacks. Trust-based security is employed to detect and identify rogue nodes, providing a robust and adaptable protection mechanism. Trust evaluation models are crucial security-enhancement mechanisms that enhance the reliability and collaboration of sensor nodes in wireless sensor networks. This study recommends the use of DFA UTrust, a unique trust technique, to effectively satisfy the security requirements of WSNs.