Sensor Network Applications Research Articles

Enhancing Cybersecurity in Wireless Sensor Networks: AI Solutions to Simulated Attacks

Owing to the real time applications of Wireless Sensor Networks (WSNs) including: industrial automation and remote environment monitoring, WSNs have revolutionized today’s infrastructure. While implementing WSNs in strategic areas, security threats have become increasingly prevalent. Security enhancement in WSN by adopting advanced techniques in machine learning is the major focus of this research work. In an effort to discover possible use of Random Forest and Isolation Forest algorithms on them to detect and prevent the attacks, we look into depth of the attack. In this paper, the dataset is pulled from different repositories that are freely available to the public as an initial step followed by various preprocessing techniques. Data cleaning, feature selection, normalization, and categorical variable encoding have been applied as a part of preprocessing. We then observed a general increase in the detection of malicious flows together with the improvement of the tolerance to the simulated attacks. Moreover, we observed how ML enhances the security of WSNs with the combined use of ensemble learning and anomaly detections showing promising approaches and foundations for theoretical and experimental studies. The carried-out experiment proves the efficacy of the Random Forest Classifier (RFC), while maintaining a high level of accuracy, which is 99. 86% compared to 99. 72% before the attack.

A review of graph-powered data quality applications for IoT monitoring sensor networks

A review of graph-powered data quality applications for IoT monitoring sensor networks

ОЦІНЮВАННЯ НАДІЙНОСТІ БЕЗДРОТОВИХ СЕНСОРНИХ МЕРЕЖ СИСТЕМ МОНІТОРИНГУ ЛІСОВИХ ПОЖЕЖ З УРАХУВАННЯМ ФАТАЛЬНИХ КОМБІНАЦІЙ МНОЖИННИХ ВІДМОВ СЕНСОРІВ

Based on the analyzed sources, it is established that the main aspects of the application of wireless sensor networks (WSNs) for fire detection include reliability, energy efficiency, routing methods, sensor deployment, and detection technologies. A software tool for generating WSN configurations and evaluating its reliability through simulation modeling is presented. An example of using the developed software tool to generate a map of coverage of a given forest area by sensors for monitoring forest fires and a given number of sensor failures, as well as to determine at each iteration the fatal combinations of failed sensors (FCFS), is given. The influence of the number of sensor failures, the number of FSCS, and the number of inoperable sensors in the FSCS on the probability of failure of a WSN is investigated. Keywords: wireless sensor network, forest fires, fatal combinations of failed sensors, reliability, simulation modeling, software tool.

Different approaches for testing body sensor network applications

Different approaches for testing body sensor network applications

Vertex Coloring and Eulerian and Hamiltonian Paths of Delaunay Graphs Associated with Sensor Networks

In this paper, we explore the connection between sensor networks and graph theory. Sensor networks represent distributed systems of interconnected devices that collect and transmit data, while graph theory provides a robust framework for modeling and analyzing complex networks. Specifically, we focus on vertex coloring, Eulerian paths, and Hamiltonian paths within the Delaunay graph associated with a sensor network. These concepts have critical applications in sensor networks, including connectivity analysis, efficient data collection, route optimization, task scheduling, and resource management. We derive theoretical results related to the chromatic number and the existence of Eulerian and Hamiltonian trails in the graph linked to the sensor network. Additionally, we complement this theoretical study with the implementation of several algorithmic procedures. A case study involving the monitoring of a sugarcane field, coupled with a computational analysis, demonstrates the performance and practical applicability of these algorithms in real-world scenarios.

A Robust Approach for Energy‐Aware Node Localization in Wireless Sensor Network Using Fitness‐Based Hybrid Heuristic Algorithms

ABSTRACTIn wireless sensor network (WSN) applications, the Received Signal Strength Indicator (RSSI) value from the original signal is determined for computing the distance between the unidentified and beacon nodes in the WSN. However, several factors including noise, diffraction, scattering, and some obstructions affect the precision of the localization techniques. This paper aims to implement a smart node localization scheme in WSNs by estimating the shortest distance between beacon nodes and unknown nodes using the RSSI factor. Initially, the beacon node is positioned at a known position, and the exact location of the unknown node is computed by the hybrid optimization concept. The objective of the proposed node localization method is to reduce the average localization error, and it is derived for assigning the unknown nodes to each beacon nodes. Optimization plays a vital role in providing clear communication among sensor nodes without any hindrance. The hybridized algorithm named as Fitness‐aware Hybrid One‐to‐One with Archery Optimizer (FHOOAO) is used for the positioning of the unknown nodes to each beacon node. After assigning unknown nodes, their best positions are identified by considering the maximum number of hops. Finally, the experimentation is done in three different forms of node positioning in WSN such as S‐shape, H‐shape, and. C‐shape. The simulation experiments demonstrate superior outcomes of the proposed model compared to alternative methods, and it also enhances communication efficiency among sensor nodes.

Research on the Application of Wireless Sensor Networks in Smart Home Environments: Challenges, Strategies, and Prospects

Wireless Sensor Networks (WSNs) have become a crucial component in the telecommunications sector, with significant potential for innovation, particularly in smart home environments. This paper provides an overview of the research progress and current status of WSNs in smart home applications, emphasizing their background, significance, and underlying theories. The study analyzes recent literature, highlighting the latest methods and trends in WSNs for smart homes, and identifies key challenges such as ethical privacy concerns, technological standards, and interoperability issues. The application of WSNs in smart homes encompasses remote control, monitoring, activity recognition, and security, with researchers continuously seeking breakthroughs to optimize these networks. However, challenges remain, including the integration of various wireless protocols, energy efficiency management, and the safeguarding of security and privacy. Addressing these challenges is critical for advancing the application of WSNs in smart homes, and further research is necessary to overcome the existing limitations and enhance their effectiveness in this domain.

Acoustic Sensors data transmission integrity and endurance with IoT-enabled location-aware framework.

Environmental monitoring and disaster mitigation are critical applications of underwater acoustic sensor networks (UASNs). However, UASNs face significant challenges, including high latency, limited bandwidth, and energy constraints. This study introduces an Internet of Things (IoT)-driven location-aware framework (ILAF) designed to enhance UASN performance by utilizing non-GPS geographic coordinates for determining the location of sensor and sink nodes, identifying their neighbors based on coordinates and transmission range, and optimizing node placement and routing without the need for GPS modems. The framework is compared with several state-of-the-art protocols, including Bald Eagle Search inspired optimized energy efficient routing protocol (BES-OEERP) and IoT-enabled depth-based routing technique (IDBR), demonstrating superior performance. Specifically, ILAF achieved a packet delivery ratio (PDR) of 99%, which outperforms energy-efficient region-based source distributed routing algorithm (EERSDRA) (98%) and energy-efficient geo-opportunistic routing protocols (EEGORP) (96%). Additionally, ILAF reduced energy consumption by 20% compared to these existing protocols. These improvements result in a more energy-efficient network with fewer dead nodes (12 after 1,000 rounds) and higher throughput (5.7 kbps at 1,000 rounds), making ILAF suitable for real-time underwater applications. Future research will explore integrating lightweight IoT protocols like Message Queue Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP) to enhance the framework's performance and reliability further.

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.

Low-Cost Sensor Network for Air Quality Assessment in Cabo Verde Islands.

This study explores the application of low-cost sensor networks for air quality monitoring in Cabo Verde islands, utilizing Clarity Node-S sensors to measure fine particulate matter with diameters equal to or smaller than 10 µm (PM10) and 2.5 µm (PM2.5) and nitrogen dioxide (NO2) gasses, across various locations. The sensors were strategically placed and calibrated to ensure coverage of the whole archipelago and accurate data collection. The results consistently revealed seasonal patterns of dust variation across the archipelago, with concentrations of particulate matter exceeding World Health Organization (WHO) limits in all regions. However, Praia frequently exhibits the highest levels of air pollution, exceeding a 200 µg/m3 daily average, particularly during the dry season. Seasonal variations indicated that pollutants are significantly higher from November to March due to Saharan dust flux (a phenomenon locally know as Bruma Seca). Other cities showed more stable and lower pollutant concentrations. This study highlights the potential of low-cost sensors to provide extensive and real-time air quality data, enabling better environmental assessment and policy formulation. However, the variability in equipment accuracy and the limited geographical coverage remain the main limitations to be overcome. Future research should focus on these issues, and a sensor network integrated with reference methods could be a great asset to enhance data accuracy and improve outcomes of air quality monitoring in the country.

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.

Targeting the high frequency tail of wave spectra for energy harvesting in marine sensor networks

While the conventional philosophy of wave energy conversion is to target the large amounts of power in the peak of the input wave spectrum, this study proposes that for the application of powering marine sensor networks (MSNs), it is advantageous to target the high frequency tail of the wave spectrum. This strategy is predicated on two primary advantages: the spatial and temporal persistence of the wave energy resource in the high-frequency region and its compatibility with the resonance characteristics of smaller MSN devices. To identify the optimal frequency range for energy harvesting, we conducted a detailed analysis of wave spectra across multiple coastal locations. This involved calculating and comparing the power spectra at different frequencies, using data from long-term wave measurements. The high-frequency tail was defined by determining the frequency above which the energy content showed consistent temporal and spatial stability across all study sites. The quantification of available power was achieved by integrating the wave power spectrum over the identified frequency range. Our case study, focusing on the coast of Queensland, Australia, reveals that frequencies above 2.5 rad/s consistently offer a stable and persistent energy resource. The available power in this range is quantified, totalling an average of 60 W/m, with additional analysis provided within narrower sub-bandwidths to address the inherent narrow-bandedness of wave energy harvesters. This research provides critical insights for the design of efficient wave energy harvesters tailored to the needs of diverse marine environments.

Survey broach for clustering in wireless sensors protocols and types

Sensor network applications have become part of everyday life applications. Creating such networks needs to meet its own constraints, such as: the energy consumption, scalability, fault tolerance, estimated cost, and the possibility of changing the network topography and the environment. In this work, a survey on hierarchical routing protocols in Wireless Sensor Networks (WSN) was performed. WSNs hold tiny nodes with ability of sensing, communications, and computation. Due to the power limitation of the sensor unit, several hierarchical routing protocols have been proposed for minimizing power exhaustion. Much research labor has been implemented to overcome weakness and to upgrade the performance of hierarchical routing protocols. So, a universal review is needed which can review latest technologies, construe functional and performance sides, and focus on issues and challenges of hierarchical routing protocol in WSNs. lastly, the paper focus on issues and challenges in current routing protocols of WSNs, which can aide in outlook research for election of appropriate research field and supply guidance in selection of energy awareness techniques in the design of improving energy of routing protocols for WSNs.

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.

Developing Wireless Communication Technique for Usage in IoT Applications

The Internet of Things (IoT) is another registering worldview that imagines standard ordinary articles being changed into significant items. In view of its benefits over wired advancements, like simpler arrangement, less expensive structures, compact help, flexibility, and straightforwardness of association, remote innovations will be the best option for interfacing IoT gadgets. There are assortments of remote advances that can be utilized for IoT; these innovations cover a wide scope of distances, from a couple of centimeters to numerous kilometers. The Internet Engineering Task Force (IETF) introduced the 6LowPAN convention in this article, and the ZigBee association set up the ZigBee convention over the low-power IEEE802.15.4 standard. Sensors are gathered in remote sensor networks applications to illuminate sensor centers. Commonly, battery power is utilized to drive these center points. In IoT applications, these centers should keep going for quite a long time without waiting be re-energized. IoT helps with settling on choices in view of genuine information gathered from a colossal number of ordinary gadgets that have been improved with information through the expansion of detecting, handling, and correspondence capacities. Remote correspondence is for the most part utilized by IoT gadgets to speak with different gadgets.

CLARA: A cluster-based node correlation for sampling rate adaptation and fault tolerance in sensor networks

Recently, wireless sensor networks (WSNs) have been proven as an efficient and low-cost solution for monitoring various kind of applications. However, the massive amount of data collected and transmitted by the sensor nodes, which are mostly redundant, will quickly consume their limited battery power, which is sometimes difficult to replace or recharge. Although the huge efforts made by researchers to solve such problem, most of the proposed techniques suffer from their accuracy and their complexity, which is not suitable for limited-resources sensors. Therefore, designing new data reduction techniques to reduce the raw data collected in such networks is becoming essential to increase their lifetime. In this paper, we propose a CLuster-based node correlation for sAmpling Rate adaptation and fAult tolerance, abbreviated CLARA, mechanism dedicated to periodic sensor network applications. Mainly, CLARA works on two stages: node correlation and fault tolerance. The first stage introduces a data clustering method that aims to search the correlation among neighboring nodes. Then, it accordingly adapts their sensing frequencies in a way to reduce the amount of data collected in such networks while preserving the information integrity at the sink. In the second stage, a fault tolerance model is proposed that allows the sink to regenerate the raw sensor data based on two methods: moving average (MA) and exponential smoothing (ES). We demonstrated the efficiency of our technique through both simulations and experiments. The best obtained results show that the first stage can reduce the sensor sampling rate, and accordingly the sensor energy, up to 64% while the second stage can accurately regenerate the raw data with an error loss less than 0.15.