Wireless Sensor Network Applications Research Articles

An Effective Congestion and Interference Secure Routing Protocol for Internet of Things Applications in Wireless Sensor Network

AbstractThis paper provides an effective Wireless Sensor Network (WSN) routing solution for Internet of Things (IoT) applications cognizant of congestion, security, and interference. Because several sources try to deliver their packets to a destination simultaneously, which is a common case in IoT applications. The proposed congestion and interference aware safe routing protocol is claimed to work in networks with high traffic. The signal to interference ratio (SINR), congestion level, and survival factor is used in our suggested procedure to estimate the cluster head selection factor first. The adaptive fuzzy c-means clustering method clusters the network nodes based on the cluster head selection factor. After that, data packets are encrypted using Adaptive Quantum Logic-based packet coding. Finally, the Adaptive Krill Herd (AKH) optimization method identifies the least congested corridor, resulting in optimal data transmission routing. The exploratory findings show that the provided strategy outperforms previous methodologies in network performance, end-to-end delay, packet delivery ratio, and node remaining energy level.

A Priority based Self-Organised MAC Protocol for Real Time Wireless Sensor Network Applications

Wireless Sensor Networks (WSNs) are expressively utilized in various real-time control and monitoring applications. WSNs have been expanded considering the necessities in industrial time-bounded applications to support the dependable and time-bound delivery of data. Recently, Machine Learning (ML) algorithms have been used to address various WSN-related issues. The use of ML techniques supports dynamically modifying MAC settings based on traffic patterns and network conditions. In WSNs to control the communication between a large numbers of tiny, low-power sensor nodes while preserving energy and reducing latency, effective MAC protocols are essential. This paper addresses the ML centered priority-based self-organized MAC (ML-MAC) protocol to provide a priority-based transmission system to ensure the timely delivery of critical data packets. In this research, depending upon the predictions of the ML model, the MAC parameters are dynamically adjusted to find priority-based channel access and the optimal routing path to meet the deadline of critical data packets. From the result analysis, the average throughput and delay of the proposed ML-MAC algorithm outperforms the existing I-MAC protocol.

Utilizing minimum spanning trees for effective mobile sink routing in wireless sensor networks

With many practical applications, wireless sensor networks (WSNs) represent an important field of study. Real-world applications of WSNs include smart home automation, healthcare, agriculture, industrial automation, and environmental monitoring. WSNs present countless chances for creative solutions across various industries as they develop and become more sophisticated. But because they are unattended, we must devise ways to make them work better without using the sensor nodes’ most important resource—battery power. A unique sink mobility model from a deployed WSN is proposed in this paper, based on constructing a minimal Spanning tree. The proposed approach derives a controlled movement model for the mobile sink based on minimal spanning tree (MST) features. Consequently, fixed nodes will be scheduled and visited to save routing overhead and improve network efficiency. Using the properties of the minimal spanning tree, the moving sink node can visit immobile sensor nodes, which is the most effective approach to gather data and send it to the base station. The effectiveness of WSNs was examined when implementing this mobility model, and we used the NS-2 simulator to run simulations to assess how efficiently the suggested strategy performed. Our findings demonstrate that WSN performance can be significantly enhanced by implementing the proposed method.

Development of methods for monitoring and optimization of underground drainage systems using wireless sensor networks and ultra-wideband antennas

This research focuses on optimizing ultra-wideband (UWB) antennas, which are critical in modern communication systems due to their wide frequency range (3.1–10.6 GHz) and high data transmission capabilities. The study addresses the challenge of optimizing key antenna parameters – such as return loss, peak gain, and radiation efficiency – while also ensuring energy efficiency and network longevity. Traditional optimization methods, such as LEACH-C, often fail to balance these factors, leading to suboptimal performance. To solve this problem, the researchers developed the Generalized Position-based Optimization Neural Network (GPON) for UWB antenna optimization. They also evaluated the Position-based Hybrid Neural Network (PAN) method, comparing its performance with existing algorithms including LEACH-C, Firefly Algorithm (FA), HFAPSO, FA-ANN, and HWOABCA. The GPON model reduced return loss to 25.5 dB at 3.5 GHz and improved peak gain to 4.2 dB i, while maintaining 92 % radiation efficiency. In contrast, PAN demonstrated a 15–25 % improvement in residual energy and extended network lifetime by 20 % compared to LEACH-C. These improvements were due to the integration of advanced neural network techniques in GPON and the effective use of positional data in PAN, enabling more precise and adaptive optimization. The ability to balance multiple performance metrics simultaneously – a challenge previous models struggled with – is a key feature. This balance is crucial for UWB antennas in communication systems where both performance and energy efficiency are vital. The findings are especially relevant for practical applications in wireless sensor networks, mobile communications, and radar systems, requiring long-term network reliability and optimal antenna performance

Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks.

Battery-powered sensor nodes encounter substantial energy constraints, especially in linear wireless sensor network (LWSN) applications like border surveillance and road, bridge, railway, powerline, and pipeline monitoring, where inaccessible locations exacerbate battery replacement challenges. Addressing these issues is crucial for extending a network's lifetime and reducing operational costs. This paper presents a comprehensive analysis of the factors affecting WSN energy consumption at the node and network levels, alongside effective energy management strategies for prolonging the WSN's lifetime. By categorizing existing strategies into node energy reduction, network energy balancing, and energy replenishment, this study assesses their effectiveness when implemented in LWSN applications, providing valuable insights to assist engineers during the design of green and energy-efficient LWSN monitoring systems.

An one-time pad cryptographic algorithm with Huffman Source Coding based energy aware sensor node design

Recently, the security-based algorithms for energy-constrained sensor nodes are being developed to consume less energy for computation as well as communication. For the mission critical wireless sensor network (WSN) applications, continuous and secure data collection from WSN nodes is an essential task on the deployed field. Therefore, in this manuscript, One-Time Pad Cryptographic Algorithm with Huffman Source Coding Based Energy Aware sensor node Design is proposed (EA-SND-OTPCA-HSC). Before transmission, the distance among transmitter and receiver is rated available in mission critical WSN for lessen communication energy consume of sensor node. For the mission critical WSN applications, continuous and secure data collection from WSN nodes is an essential task on the deployed field. The periodic sleep/wake up scheme with Huffman source coding algorithm is used to save energy at the node level. Then, one-time pad cryptographic algorithm in each sensor node, the vernam cipher encryption technique is applied to the compact payload. The proposed technique is executed and efficacy of proposed method is assessed using Payload Vs Energy consume for one sensor node, communication energy consume for one sensor node with different distances, energy consume for one sensor node under various methods, Throughput, delay and Jitter are analyzed. Then the proposed method provides 90.12 %, 89.78 % and 91.78 % lower delay and 88.25 %, 95.34 % and 94.12 % lesser energy consumption comparing to the existing EA-SND-Hyb-MG-CUF, EA-SND-PVEH and EA-SND-PIA techniques respectively.

Multi-Strategy Bald Eagle Search Algorithm Embedded Orthogonal Learning for Wireless Sensor Network (WSN) Coverage Optimization.

Coverage control is a fundamental and critical issue in plentiful wireless sensor network (WSN) applications. Aiming at the high-dimensional optimization problem of sensor node deployment and the complexity of the monitoring area, an orthogonal learning multi-strategy bald eagle search (OLMBES) algorithm is proposed to optimize the location deployment of sensor nodes. This paper incorporates three kinds of strategies into the bald eagle search (BES) algorithm, including Lévy flight, quasi-reflection-based learning, and quadratic interpolation, which enhances the global exploration ability of the algorithm and accelerates the convergence speed. Furthermore, orthogonal learning is integrated into BES to improve the algorithm's robustness and premature convergence problem. By this way, population search information is fully utilized to generate a more superior position guidance vector, which helps the algorithm jump out of the local optimal solution. Simulation results on CEC2014 benchmark functions reveal that the optimization performance of the proposed approach is better than that of the existing method. On the WSN coverage optimization problem, the proposed method has greater network coverage ratio, node uniformity, and stronger optimization stability when compared to other state-of-the-art algorithms.

An energy efficient prediction based protocol for target tracking in wireless sensor networks

Target tracking is one of the most attractive applications of wireless sensor networks, used for estimating the moving target's position accurately. A primary challenge in this domain is achieving precise target path estimation while conserving energy resources. This paper introduces an energy-efficient target tracking protocol in wireless sensor networks, considering both accuracy and reduced energy consumption. The protocol uses the Kalman filter to estimate the target's position and predict the subsequent step of its path. In each step of target tracking, a selected sensor, named the ‘leader’ performs computations for position estimation and path prediction, while two other sensors, known as ‘assistants’ help the leader in the tracking process. Leader selection within the protocol is performed in two phases: an initial phase occurring upon the target's entry to the network and a subsequent phase named the forced handoff phase. The forced handoff phase performs the selection of a new leader when either the target exits the sensing range of the current leader or the leader's energy decreases significantly. Although the proposed protocol is a new work, it can be considered as an improvement of the PPCP protocol by adding several changes and also replacing the binary variational filter with the Kalman filter. The efficiency of the proposed protocol is evaluated through simulations in Matlab. Results demonstrate the protocol's ability to achieve high-precision target tracking while maintaining low energy consumption. Comparative analysis shows its energy efficiency, which significantly increases the network lifetime.

Analytical and Empirical Insights into Wireless Sensor Network Longevity: The Role MAC Protocols and Adaptive Strategies

Wireless Sensor Networks (WSNs) have become essential in various fields, such as corporate industries, environmental monitoring, military defense, and healthcare, due to their ability to monitor and transmit data from remote locations. However, the battery-powered nature of WSN nodes necessitates a strong emphasis on energy conservation to extend network lifespan. This paper evaluates the impact of different Medium Access Control (MAC) protocols on WSN performance, focusing on energy efficiency and data transmission reliability. We explore various MAC protocols, including contention-based protocols like Carrier Sense Multiple Access (CSMA), schedule-based protocols like Time Division Multiple Access (TDMA), and hybrid protocols that combine both approaches. The methodology integrates analytical modelling, empirical data collection, and expert interviews to comprehensively assess the performance and longevity of WSNs under different MAC protocols. Analytical models were developed to simulate WSN behavior, considering factors such as node density, traffic load, and energy consumption. Empirical data from real-world deployments and experimental setups were analyzed to validate the models and provide practical insights. Expert interviews further highlighted the challenges and considerations in MAC protocol design and implementation. This paper underscores the importance of context-specific MAC protocol selection and the potential of adaptive strategies, including machine learning, to enhance WSN efficiency and reliability. These insights can guide the development of more sustainable and high-performing WSN applications.

Connected Dominating Set Construction Using the Hunger Games Search Optimisation Algorithm in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a significant role in monitoring changes in the physical environment, relying on a subset of designated nodes known as the Connected Dominating Set (CDS) to facilitate communication and serve as the network's virtual backbone for routing and clustering. Despite the various algorithms for CDS construction, the challenges persist in terms of construction cost and scalability. In this context, this paper introduces the utilisation of the Hunger Games Search Optimisation Algorithm (HGSOA) for Connected Dominating Set Construction in Wireless Sensor Networks (HGSOA-CDS-WSN). The primary intention of this paper is “to develop a technique capable of constructing small-sized CDS with reduced construction costs, particularly suitable for uniformly or randomly distributed nodes”. The proposed HGSOA-CDS-WSN method is implemented in MATLAB based on various metrics. The results are compared with those of existing techniques, such as the Parallel Structure from Motion for UAV Pictures using Weighted CDS (PSfM-CDS-WSN), Polyhedra Associated with Locating-Dominating, Open Locating-Dominating, and Locating Total-Dominating Sets in Graphs (PAL-CDS-WSN), and Sustainable Maintenance of Connected Dominating Set by Solar Energy Harvesting for Internet of Things Networks (SHE-CDS-WSN). The proposed technique achieves lower delay by 13.24%, 25.64%, and 9.96%, and higher throughput by 18.34%, 24.55%, and 23.04% compared to the existing methods. This systematic evaluation underscores the efficacy and superiority of the proposed HGSOA-CDS-WSN technique in terms of connected dominating set size, construction time, energy efficiency, network stability, and overall performance, highlighting its potential to advance wireless sensor network applications.

Enhancing network lifetime in wireless sensor networks through coverage-aware optimized sensor activation

Wireless sensor networks (WSNs) are a pivotal technology in the modern era, enabling the monitoring and sensing of environmental conditions across vast areas with unprecedented precision and flexibility. At the heart of WSNs lie crucial challenges such as optimizing coverage, extending network lifetime, and strategizing node deployment to ensure efficient operation while conserving energy. This paper introduces the coverage-aware optimized sensor activation and deployment (CAOSAD) Strategy, a novel methodology designed to address these challenges. By integrating advanced node placement algorithms and scheduling techniques, the EcoNet lifespan maximization (ELM) strategy significantly enhances area and target coverage, minimizes energy consumption, and thereby prolongs the network’s operational lifespan. We present a comprehensive framework that dynamically adjusts node activity based on a predictive model, ensuring robust coverage and connectivity with minimal energy expenditure. Through a series of simulations, the ELM strategy demonstrates a substantial improvement in network sustainability compared to existing methodologies, offering a promising approach for the development of future WSNs. By focusing on the synergy between coverage optimization, energy-efficient node deployment, and innovative scheduling algorithms, this paper contributes a ground-breaking perspective to the research and application of WSNs, setting a new benchmark for the design of eco-friendly and durable sensing infrastructures.

Wireless sensor networks protocols, applications, and network-on-chip communications

A wireless sensor network (WSN) is a network consisting of self-governing sensors that are deployed in space and communicate with each other using wireless technology to monitor physical or environmental variables. These networks generally include compact, inexpensive sensor nodes equipped with sensing, processing, and communication functionalities. WSNs are specifically engineered to gather data from their immediate environment, do local data processing, and subsequently communicate pertinent information either to a central hub or to other interconnected nodes within the network. Continuous research in the domain of WSNs is devoted to advancing security concerns, developing novel sensing technologies, and optimizing communication protocols. The advancements in these domains enhance the ongoing development and efficiency of WSNs. The WSNs are very important for getting information from the real world in many situations. WSNs are flexible tools for keeping an eye on and controlling different environments because they have sensor nodes, wireless communication, and distributed processing. WSNs use network-on-chip (NoC) communication architecture to connect sensor nodes. The article explains the introduction to WSN, the background of wireless communication, motivation, ZigBee protocol, and WSN applications.

Wearable Devices based on Wireless Sensor Network and Speech Synchronization Overlay Algorithm Application in Sports Training Data Simulation

Wearable Devices based on Wireless Sensor Network and Speech Synchronization Overlay Algorithm Application in Sports Training Data Simulation

Design of Microstrip Patch Antenna for WSN Application

Abstract: This paper focuses on the design and simulation of a microstrip patch antenna for Wireless Sensor Network (WSN) applications, utilizing HFSS (High-Frequency Structure Simulator) software and an FR-4 epoxy substrate. The objective is to develop an efficient and cost-effective antenna tailored for the 2.4 GHz frequency band, commonly used in WSNs due to its favourable propagation characteristics. The choice of FR-4 epoxy substrate, with a relative dielectric constant (ࡾࢿ (of 4.4 provides a balance of performance, availability, and cost-effectiveness. These properties are crucial in determining the antenna's resonant frequency and overall efficiency. The design process involves optimizing the dimensions of the rectangular microstrip patch antenna to achieve the desired frequency and performance metrics. HFSS simulation tools are employed to model the antenna's behaviour, allowing for precise adjustments and performance predictions. Key parameters analysed include return loss (S11), gain, radiation pattern, and bandwidth.

Malicious Nodes Detection and Avoidance Using Trust-based Routing in Critical Data Handling Wireless Sensor Network Applications

Malicious Nodes Detection and Avoidance Using Trust-based Routing in Critical Data Handling Wireless Sensor Network Applications

Artificial Intelligence Image Processing Based on Wireless Sensor Networks Application in Lake Environmental Landscape

Artificial Intelligence Image Processing Based on Wireless Sensor Networks Application in Lake Environmental Landscape

Application of Wireless Sensor Network in Monitoring Quality of Irrigation Water

Application of Wireless Sensor Network in Monitoring Quality of Irrigation Water

Wireless sensor localization based on distance optimization and assistance by mobile anchor nodes: a novel algorithm.

Wireless sensor networks (WSNs) have wide applications in healthcare, environmental monitoring, and target tracking, relying on sensor nodes that are joined cooperatively. The research investigates localization algorithms for both target and node in WSNs to enhance accuracy. An innovative localization algorithm characterized as an asynchronous time-of-arrival (TOA) target is proposed by implementing a differential evolution algorithm. Unlike available approaches, the proposed algorithm employs the least squares criterion to represent signal-sending time as a function of the target position. The target node's coordinates are estimated by utilizing a differential evolution algorithm with reverse learning and adaptive redirection. A hybrid received signal strength (RSS)-TOA target localization algorithm is introduced, addressing the challenge of unknown transmission parameters. This algorithm simultaneously estimates transmitted power, path loss index, and target position by employing the RSS and TOA measurements. These proposed algorithms improve the accuracy and efficiency of wireless sensor localization, boosting performance in various WSN applications.

A Novel Energy-Efficient Routing Protocol for Industrial WSN Using Hybrid Coot-Ls Algorithm with LSTM-Based Dom Prediction

In industrial Wireless Sensor Networks (WSNs), energy efficiency and reliable data transmission are critical challenges that need to be addressed to ensure sustainable and robust network operations. This paper proposes a novel energy-efficient routing protocol that integrates a Hybrid COOT-LS (Coot- Levy Search) algorithm with Long Short-Term Memory (LSTM)-based Dominant Object Motion (DOM) prediction. The routing protocol leverages the strengths of Hybrid Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to enhance routing efficiency and reduce energy consumption. The Hybrid PSO and ACO algorithms are employed to optimize routing paths by balancing exploration and exploitation, considering multiple factors such as energy levels, node distance, and reliability. The COOT-LS algorithm further refines these paths by incorporating a Levy flight mechanism to enhance the search process. Additionally, the LSTM-based DOM prediction provides accurate forecasts of network conditions, enabling dynamic adjustments to routing strategies in real time. Simulation results demonstrate that the proposed protocol significantly improves network lifetime, reduces energy consumption, and enhances data transmission reliability compared to traditional routing protocols. This approach provides a robust and scalable solution for industrial WSN applications, ensuring efficient and reliable network performance in dynamic and complex industrial environments.

A COMPREHENSIVE ANALYSIS OF ENERGY EFFICIENT WIRELESS SENSOR NETWORK (WSN) PROTOCOLS

Wireless Sensor Networks (WSNs) include many tiny and low energy and cheaper nodes in different areas for measuring the environmental parameters like temperature and humidity. Hence, the usage of WSNs has become an adaptable means of data transfer across different fields due to the advancement of wireless technology. It is noted that these networks,which use nodes for event detection and identification, gather information by means of simple calculations and send it to a particular Base Station (BS) via gateway nodes. The applications of WSNs can be classified into monitoring and tracking,which are used in areas such as environmental monitoring, health diagnostics, military surveillance, and many others.nergy consumption is still a major issue of concern, sensor nodes in particular, use energy in sensing data and transmitting it. Energy savings techniques like clustering and routing protocols are important for increasing the life time of the network.hus, this research aims at analysing the different aspects of improving energy efficiency and network performance,especially in the context of the clustering algorithms, such as the Multi-level Stable and Energy Efficient Clustering (MSEEC) protocol. The paper discusses major issues and suggests some new combined routing approaches for WSNs with the problems connected with fault tolerance, topology, scalability, and power consumption.