Sensor Network Nodes Research Articles

Prediction and positioning of UWSN mobile nodes based on tidal motion model

As the node positioning of underwater wireless sensor networks is easily affected by tidal motion, ocean current motion and multipath effect, the node positioning accuracy is low. In order to better improve the positioning accuracy of moving nodes of underwater wireless sensor networks, a method of locating mobile nodes of underwater wireless sensor based on tidal motion model is proposed. Firstly, the Time Difference of Arrival (TDOA) localization optimized by niche genetic algorithm is used to initialize each node. The integration of niche technology can effectively find multiple excellent solutions in the solution space, thus providing more abundant solution choices. This algorithm has excellent performance in multi-modal optimization problems, and can avoid the algorithm falling into local optimal solutions, so as to obtain more comprehensive optimization results. The simulation results show that the proposed algorithm has better positioning accuracy than the traditional Chan algorithm and Taylor algorithm. Then, each node is updated in real time by the optimized tidal movement model formula predicted by Kalman filter algorithm. The prediction algorithm is used to compare the real-time predicted update position of the node with the actual position. The positioning distance error of the prediction algorithm is also enough to meet the practical application requirements.

Machine learning deployment for energy monitoring of Internet of Things nodes in smart agriculture

SummaryLow‐Power Wide‐Area Network technologies, such as LoRa, are gaining popularity in the agricultural sector for field deployment. The crucial factors in these devices are their range and power efficiency. The energy consumption of a LoRa wireless sensor network is predominantly affected by transmission parameters like carrier frequency, bandwidth, transmit power, spreading factor, and coding rate. Incorrect chosen transmission parameters can lead to a reduction in the battery life of end nodes, requiring frequent battery replacements—a situation undesirable for field deployment. This study introduces a machine learning deployment in the form of a web application designed to monitor the energy consumption of end nodes in LoRa wireless sensor networks. The research initially employs 12 regression models, including Linear, Random Forest, K‐Nearest Neighbours, Decision Tree, Support Vector, Lasso, Ridge, AdaBoost, Gradient Boost, XGBoost, CatBoost, and LightGBM models. The findings of the study reveal that the LightGBM model surpasses other models in accurately predicting the energy consumption of Internet of Things (IoT) nodes, leading to its selection for the web application. This machine learning web application can be implemented in a programmable Long Range Wide Area Network (LoRaWAN) gateway to effectively monitor the energy consumption of IoT end nodes in the agricultural sector.

An air-triggered contact-separation rotating triboelectric nanogenerator based on rotation-vibration-pressure conversion

Energy harvesting using triboelectric nanogenerators (TENGs) has demonstrated great potential in powering sensor network nodes and become a promising alternative for battery-free portable and wireless electronics. Unlike most existing rotational sliding-mode TENGs, an air-triggered contact-separation rotating triboelectric nanogenerator (AT-TENG) based on rotation-vibration-pressure conversion is proposed to reduce the violent collision of triboelectric materials and enhance the durability in this paper. The distinguishing feature of the AT-TENG was indirectly-excited contact separation mode along with the air-film interaction, where the rotational motion was converted into an elastic sheet vibration via the magnetic coupling and then the periodic contact or separation between the FEP film and aluminum plate was induced by the alternating air pressure. The feasibility of the presented AT-TENG was verified by theoretical analysis, fabrication and experiments. The results indicated that the distance of PVC to aluminum, radius ratio of the FEP to PVC, chamber volume (denoted by the acrylic ring height), magnet coefficient and rotational speed brought a significant effect on the output behaviors of the AT-TENG. There was an optimal PVC-aluminum distance of 0.1 mm as well as the maximum output voltage increased firstly and then decreased with the enhancement of radius ratio. Also, the output voltage was roughly enhanced with the increasing ring height except a maximum value occurred at h = 3.6 mm and demonstrated a reducing trend as the magnet coefficients was increased. In addition, the AT-TENG could achieve a maximum voltage of 367 V, maximum average power of 128.7 μW, and highest charging electricity of 14.45 mJ in the experiments. Besides, a parallel 200 LEDs could be efficiently powered by the AT-TENG. Furthermore, the AT-TENG demonstrated a long-term durability and stability through the 106444-excitation testing. It is expected that this work can offer a useful reference for further improvements of the rotational TENGs.

Integrated energy-efficient and location-aware routing in wireless sensor networks

Sensor nodes in wireless sensor networks are commonly distributed randomly across a given landscape, and their placement may be randomized for specific applications, even extending to national deployments. The energy consumption associated with data transmission and reception by the cluster’s leader is notably higher compared to other nodes. To address this issue, it is recommended that wireless sensor networks adopt a more energy-efficient routing technique. This proposed technique assumes a spatial separation between different node types. Elevating the threshold enhances the likelihood that nodes with ample remaining power will endure as cluster leaders. Ultimately, a hybrid data transfer strategy is formulated, wherein data is directly exchanged between the base station and cluster heads among the super nodes containing advanced nodes. Most nodes employ a combination of single-hop and multi-hop approaches for data transport, aiming to minimize the power required for transmission between the cluster’s control node and the base station. According to simulation results, this proposed method surpasses the stable election protocol (SEP), demonstrating superiority over the improved threshold-sensitive stable election protocol in terms of the operational duration of a wireless sensor network.

Bayesian decision model based reliable route formation in internet of things

Security provisioning has become an important issue in wireless multimedia networks because of their crucial task of supporting several services. This paper presents Bayesian decision model based reliable route formation in internet of things (BDMI). The main objective of the BDMI approach is to distinguish unreliable sensor nodes and transmit the data efficiently. Active and passive attack recognition methods identify unreliable node sensor nodes. Remaining energy, node degree, and packet transmission rate parameters to observe their node possibilities for recognizing the passive unreliable nodes. In active recognition, the base station (BS) confirms every sensor node identity, remaining energy, supportive node rate, node location, and link efficiency parameters to detect active unreliable sensor nodes. The Bayesian decision model (BDM) efficiently isolates an unreliable sensor node in the multimedia network. Simulation outcomes illustrate that the BDMI approach can efficiently enhance unreliable node detection and minimize the packet loss ratio in the network.

Malicious Node Detection Using Machine Learning and Distributed Data Storage Using Blockchain in WSNs

Abstract: Wireless Sensor Networks (WSNs) present unique opportunities for a broad spectrum of applications such as industrial automation, situation awareness, tactical surveillance for military applications, environmental monitoring, chemical or biological detection etc., Wireless Sensor Networks (WSNs) consist of hundreds of tiny nodes having the capability of sensing, computation and wireless communications. Deployed in a hostile environment, individual nodes of a wireless sensor network (WSN) could be easily compromised by the adversary due to the constraints such as limited battery lifetime, memory space and computing capability. It is critical to detect and isolate the compromised nodes in order to avoid being misled by the falsified information injected by the adversary through compromised nodes. However, it is challenging to secure the flat topology networks efficiently because of the poor scalability and high communication overhead. Since security and performance issues are a big concern in the case of wireless sensor networks, emphasis has been given to the scheme based on Weighted-Trust Evaluation (WTE). Extensive simulation is performed using MATLAB, to verify performance and efficiency of WTE by varying various parameters

LSTM-DL: A Counting Optimization Approach for LSTM-Based DV-Hop Localization Model in Wireless Sensor Networks

The quest for precise localization of unknown nodes in Wireless Sensor Networks (WSN) garners significant academic attention, further intensified by the burgeoning domain of meta-heuristic algorithms. Traditional localization methods, such as the Least Squares Method (LSM), inherently yield lower accuracy, a limitation that this paper aims to transcend. We introduce a robust deep learning model grounded in Long Short-Term Memory (LSTM-DL) designed to enhance localization processes by effectively minimizing distance measurement errors in network topologies. This approach is augmented with an improved evaluation function, meticulously tailored to mitigate topological inaccuracies. Through rigorous experimental validation, the LSTM-DL model demonstrates a remarkable performance leap, surpassing the conventional DV-Hop method and outperforming existing meta-heuristic algorithms in accuracy and reliability. The findings decisively suggest that meta-heuristic approaches, and specifically the advanced LSTM-DL, hold substantial promise over traditional DV-Hop in the sphere of node localization within WSNs.

A Hybrid Charging Scheme for Minimizing the Number of Energy-Exhausted Nodes in Wireless Rechargeable Sensor Networks

A Hybrid Charging Scheme for Minimizing the Number of Energy-Exhausted Nodes in Wireless Rechargeable Sensor Networks

"Research on the Application of Visible Light Communication in IoT System Configuration"

The growing need for seamless connectivity on the internet of things (IoT) paradigm has fueled the development of innovative technologies. This research deeply focuses on the integration of visible light communications (VLC) to configure the wireless sensor network (WSN) nodes in an IoT system. VLC, an emerging technology that uses visible light to transmit data, is not limited in frequency and maximizes security in wireless data transmission. Furthermore, due to the use of light-emitting diodes (LEDs) in almost every aspect of our daily lives, VLC is providing massive connectivity for various types of large-scale IoT communications, from machine to machine, vehicle to infrastructure, infrastructure-to-vehicle, chip-to-chip as well as device to device. This article presents an investigation of the multifaceted applications of VLC, especially its role in configuring wireless fidelity Wi-Fi connectivity for smart devices and their location in the system within the IoT framework. In addition to conventional data transmission capabilities, VLC has emerged as a key technology for transmitting configuration data and location data, thereby supporting an accurate positioning system. This study shows the effective interaction between VLC and IoT, shedding light on the applications of VLC in IoT systems, especially in smart building

Improving the precision of time synchronization protocols in ultra-wideband networks estimating the time of flight of the radio signal

Modern applications of wireless sensor networks such as the conduction of collaborative tasks, the distributed detection of events or the alignment of data samples gathered by several nodes, require a precise time coordination of the nodes composing the network. Ultra Wide Band (UWB) technology has enabled the development of precise time synchronization protocols. The intrinsic properties of this technology allow an accurate estimation of the time of flight (ToF) of the radio signal in scenarios covering short distances. The integration of UWB transceivers in Wireless Sensor Networks (WSN) nodes is nowadays possible due to the availability of commercial chips implementing this technology. This combination facilitates the development of accurate time synchronization protocols that establish a global common clock throughout the network. In this context, the main contribution of this work is a protocol that allows the synchronization of the local clocks of these nodes with a common global reference kept in the master node. The master periodically broadcasts synchronization packets to keep the rest of the nodes synchronized. However, since the precision achieved in the time synchronization of the local clocks with UWB technology is comparable to the ToF of the RF signal for distances in the order of several meters, some considerations to estimate this delay are required. In this paper, a new algorithm is proposed that performs the estimation of the ToF of the UWB signal as well as the implementation of linear models to synchronize the local clocks of the network node, eliminating the dependence on the ToF. The experiments conducted with real devices reveal that an accuracy of tens of nanoseconds can be achieved regardless of the distance separating the network nodes.

Retraction Note: Pivot variable location-based clustering algorithm for reducing dead nodes in wireless sensor networks

Retraction Note: Pivot variable location-based clustering algorithm for reducing dead nodes in wireless sensor networks

A Distributed Deployment Method for Wireless Sensor Networks Based on Multi Agent Systems

This paper proposes a distributed deployment method for wireless sensor networks based on multi-agent systems, which addresses the issues of poor self adaptive deployment capability and high deployment costs caused by numerous and diverse types of nodes in the deployment process of wireless sensor networks, leading to blind spots in coverage and redundant nodes. Due to the characteristics of intelligent perception, intelligent communication, and clustering of nodes in wireless sensor network, sensor nodes can be seen as sensing agents, and sensor networks can be seen as distributed multi-agent systems composed of numerous sensing agents. Through the sensing agents' perception of the surrounding environment and communication interaction, a unified deployment model and distributed deployment algorithm with multiple nodes (cluster head nodes) as leaders are established, achieving effective integration of autonomous and leadership mechanisms, distributed management, and centralized management. Taking the problem of sensor network coverage as an example, simulation experiments were conducted, including 12 sensor agents and 1 20 * 20 monitoring areas, to verify how the sensor agents perceive the environment and optimize the migration and deployment process under the coordination of cluster head nodes. The experimental results confirmed the effectiveness of the above models and algorithms. The research results provide a theoretical model and practical approach for the distributed deployment and scheduling of sensor nodes in wireless sensor networks, and can be extended to intelligent Internet of Things systems, such as urban intelligent agent systems, logistics intelligent agent systems, etc., to improve the intelligence of wireless sensor networks deployment and scheduling.

Optimized Probabilistic Sink Node Deployment for Enhanced Performance in Wireless Sensor Networks

This paper presents an innovative mechanism to deploy sink nodes in Wireless Sensor Networks (WSN), which play an essential role in the gathering sensed data from sensor nodes across diverse applications. Our mechanism is different from existing mechanisms, which deploy sink nodes based on the residual status of buffer and energy of the sensor nodes. Instead, proposed mechanism’s sink node deployment is decided based on the optimal probabilistic metrics related to buffer and energy status during data processing; aim to extend the network performance with respect to risk of buffer overflow and energy consumption. We analyse and validate the performance of the proposed work through simulation and compared the results with existing residual-based sink node deployment mechanisms. The results indicate the signification extension in the performance in terms of energy, packet loss and lifetime by mitigating bottlenecks at nodes. The work contributes to the improvement of WSN design and deployment mechanisms by significantly enhancing the performance and reliability of the network.

An Efficient Metaheuristic Technique for Wireless Sensor Network Integrated Micro Grid Environment Using Improved Whale Optimization Algorithm (Iwoa)

Wireless Sensor Network (WSN) nodes require careful power management to maintain long term operation without regular battery changes. Battery replacement can be challenging and expensive in a microgrid scenario where sensors may be installed in remote or i naccessible areas. To extend the life of WSN nodes, energy efficient design and power management strategies should be used. A potential strategy for effectively monitoring, controlling, and managing the distributed energy resources (DERs) inside a microgri d is theincorporation of WSNs with microgrids. This research work uses Improved Whale Optimization Algorithm (WOA) for optimizing the routing and performance. The humpback whale's hunting style served as the inspiration for the WOA, a metaheuristic optimization algorithm. WOA is an optimization technique that is used with MG and WSNs. During the optimization phase, WOA establishes a balance between exploitation and exploration. To increase the lifespan of a WSN in a microgrid, effective energy management i s frequently needed. When considering the dynamic energy availability from renewable sources and the energy requirements of the microgrid components, WOA is used to optimise energy allocation and routing in WSNs. This may lead to increased energy efficiency and longer network longevity. The IWOA attains 97.5% PDR that is higher than the existing state of art techniques.

Malicious Code Propagation Model Based on Sleep Monitoring Mechanism in Wireless Sensor Networks

<p>Wireless sensor networks (WSNs) are characterized by high node density and finite energy storage. Each node exchanges information with all its neighboring nodes frequently, which makes it easy for nodes to run out of energy, leading to paralysis of the WSNs when facing network malicious code attacks. To address this problem, a malicious code propagation model based on sleep-monitoring technology for WSNs is proposed. As a Multi-compartment propagation, sleep nodes and monitoring nodes are introduced to the conventional SIR model. Sleep nodes can turn the infected node into a dormant state and stop the dissemination of information to save energy. Monitoring nodes can contain malicious codes spread in wireless sensor networks by sharing prevention information in real time. Additionally, by calculating the equilibrium point and propagation threshold of the new feedback model, the corresponding Lyapunov function is constructed to prove the local stability and global stability of the equilibrium point. Finally, the results of numerical simulation experiments show that when the sleep rate is 0.3 and feedback rate is 0.0000005, the number of infected nodes in the wireless sensor network decreases by 43.45%. Therefore, adding sleep-monitoring technology can effectively control the spread of malicious code in the networks.</p> <p> </p>

The effectiveness of reference nodes in reputation-based ad-hoc sensor networks

Ad-hoc sensor networks (known also as citizen or resident networks) are gaining in popularity. They tend to utilize inexpensive sensors; they are deployed and operated in ad-hoc, opportunistic manner, consequently often delivering results of higher uncertainty. Reputation-based algorithms are used for data reconstruction to contain the input of faulty, or unreliable, nodes on the network. This paper discusses the question whether it is feasible to introduce high quality, expensive reference nodes to the network to significantly decrease its uncertainty, in situations where the reconstruction algorithm run by the network is not altered. This paper uses five leading cases, subsequently analyzed with the help of a simulator that executes the reputation-based EWMA algorithm. It identifies that the simple introduction of reference nodes alone does not help in addressing uncertainty without making alteration to the algorithm or to the design of the network.

FDPA internet of things system for forest fire detection, prediction and behaviour analysis

AbstractThe escalating issue of forest fires poses severe risks to ecosystems and human habitats, primarily due to the greenhouse effect and sudden climate changes. These fires, mostly occurring naturally, necessitate prompt detection and control. Addressing this, the authors introduce the Forest Fire Detection, Prediction, and Behaviour Analysis (FDPA) system, an innovative Internet of Things (IoT) solution. The FDPA system leverages a wireless sensor network to efficiently detect and analyse fire behaviour, providing real‐time data on fire spread, speed, and direction. Uniquely, it can anticipate natural fires hours before they occur by monitoring ecological parameters such as humidity, temperature, and using the Chandler Burning Index (CBI) for quantifying fire danger. Designed for the challenging forest environment, the FDPA system prioritises minimal power usage and simple components, crucial in areas with limited power resources. Its resilient design ensures the wireless sensor network and sensor nodes withstand harsh weather and fire conditions, maintaining functionality and reliability. Field tests of the FDPA system in various Jordanian forest locations, including Burgish–Ajloun, have demonstrated its effectiveness. The trials revealed the system's capability in early fire detection, low latency response, predicting fire behaviour, and determining fire spread direction. Continuous monitoring of the forest ecosystem and rapid detection allow authorities to act swiftly, preventing potential fires from escalating. Furthermore, the system tracks ecological changes within the forest, offering insights into imminent natural fires. This feature enables proactive measures to mitigate fire spread, safeguarding the environment and nearby communities. The strategic placement of sensor nodes and the use of durable yet straightforward components reduce the risk of system damage due to environmental extremities. Overall, the FDPA system emerges as a promising tool for forest fire management. Its ability to detect, predict, and analyse forest fires in real‐time positions it as a vital asset in minimising the detrimental impacts of forest fires on the environment and human settlements.

Search and Integration of Nodes in Hybrid Wireless Sensor Networks by Exploring the Connectivity Frontier

Search and Integration of Nodes in Hybrid Wireless Sensor Networks by Exploring the Connectivity Frontier

Designs of Energy Harvester for Wireless Sensor Networks

An essential part of the IoT architecture are wireless sensor networks. There has been extensive study done to identify alternative methods of powering wireless sensor networks because the nodes of wireless sensor networks are mostly power constrained. This study mainly introduces three energy collector solutions that can power wireless sensors. They are an electromagnetic vibration energy harvesting system that powers the light source of the fiber Bragg grating sensor, a solar energy collection system that uses ambient photovoltaic energy, and a wind energy harvester that uses gas elastic vibration to convert wind energy into electrical energy. The solar energy collection system is based on maximum power point tracking, in which the solar panels can provide 3W of power with a high efficiency of up to 96%; the wind energy harvester is composed of a wind generator and a power management unit, capable of operating at wind speeds of 2-9m/s, providing 70mW of power. Besides, the electromagnetic vibration energy harvesting system provides an average power output of approximately 40mW when subjected to acceleration levels greater than 0.05g. These results can serve as solutions for sustainable power output.

Application of voice enhancement based on virtual sensor network in enterprise financial management

Based on virtual sensor network, this paper explores the application of voice enhancement technology in enterprise financial management. Virtual sensor network is a low cost, easy to expand and flexible network system based on simulation and simulation technology. In this network system, the virtual voice sensor node can collect and process the voice information inside the enterprise. Speech enhancement technology uses signal processing and machine learning algorithms to reduce noise, enhance and clarify speech signals, thereby improving the accuracy and reliability of speech data. By constructing virtual voice sensor nodes in virtual sensor networks and applying voice enhancement technology, the research results show that enterprises can collect and process voice data more accurately in the process of financial management. Voice enhancement technology can reduce the impact of environmental noise on financial data and improve the clarity of voice signals, so as to effectively improve the quality of data. Through the voice enhancement technology based on virtual sensor network, high-quality voice data acquisition and processing can be realized in the financial management of enterprises, improve the accuracy and effect of financial decision-making, and reduce the cost and complexity of enterprises.