Wireless Sensor Network Nodes Research Articles

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Machine Learning Diagnosis of Node Failures Based on Wireless Sensor Networks

Abstract Wireless sensors are widely deployed to harsh environments for information monitoring, as the sensor nodes are highly susceptible to various failures, resulting in erroneous monitoring data. Sensor fault diagnosis is the subject of research work in this paper. Sensor faults are categorized based on their causes and mechanisms. Secondly, the wavelet transform, tuned Q wavelet transform, and LSTM-based neural network model are utilized for equipment fault feature extraction and fault diagnosis. The structure of the LSTM neural network, as well as the parameter settings, are completed with an adaptive moment estimation algorithm for the model training, and simulations are carried out for verification. The diagnostic accuracy of the model in this paper is as high as 97%, and the root mean square error converges to 0.02 after 170 times of training, which shows the high accuracy of the model in this paper. The training time is very short, only 1.226s, which shows that the fault diagnosis model in this paper is very efficient and meets the requirements of practical applications, proving the effectiveness of this paper’s model in wireless sensor network node fault diagnosis.

Wireless sensor network deployment optimization for a smart farming application: comparison of two Multi-Objective Evolutionary Algorithms

As it is one of the main means of insuring food security, agriculture has employed different technologies such as the Internet of Things and wireless sensor networks (WSN), to improve the quality and quantity of agricultural products, while preserving natural resources. But unfortunately, in agricultural plots where we have large surfaces of interest, the optimization of node deployment in a WSN remains among the major problems to be solved. In this work, we proposed a WSN node deployment optimization model for an agricultural application according to classical constraints of coverage, over-coverage, connectivity and nodes number, in addition to the nodes separating distance constraint which affects the quality of physical parameters models. We have applied two variants of Multi-Objective Genetic Algorithms, the Non Sorting Genetic Algorithm II (NS-GA II) and the Strength Pareto Evolutionary Algorithm II (SPEA II). As a result, for a 100 m2 plot, both algorithms ensured a communication rate of 100% while SPEA 2 presented a lower sensor number and over-coverage rates with a smaller separating distance, and the execution time of NSGA II was shorter with 11 s. Besides, both of them were greedy in terms of computation time with the increase in the size of the plots.

Enhanced ACO in clustering algorithm for QoS in WSN enabled IoT

Various sensor nodes whose deployment is done in a random way are included in Wireless sensor networks (WSN) within a region for gathering the information out of the respective atmospheres as well as forwards it towards the Base Station (BS). Constrained sources of energy are allotted to the WSN nodes. The consumption of energy of nodes must be reduced for obtaining the enhanced lifetime of the network. Clustering is referred as a technique that reduced the consumption of energy in WSNs. By transmitting the gathered data to the sink through the Cluster Head (CH) nodes located within the clustered networks, the energy of the node can be saved. In WSNs, fault tolerance is a significant issue to consider. The entire data communication system can be rendered inoperable by the failure of just one cluster head. Within the scope of this research, a cluster-based routing method with fault tolerance is provided. The purpose of this study is to present an innovative technique for increasing tolerance of failure and data accumulation in clustered WSN by making use of backup CHs and improving the relay node selection mechanism by modifying ACO. The selection of the backup cluster head is what allows for fault tolerance in this case (BKCH). The method can be broken down into two distinct stages. In the first step, the network is categorized into clusters; in the second step, CHs and BCHs are chosen from the pool of candidates. Communication within the cluster takes place between the member nodes and the CH nodes. Aggregator nodes (AG) are utilised for the purpose of inter-cluster communication, and the modified ACO is utilised to determine which node serves as the most effective relay between CH and AG. When compared with other energy-conserving protocols, this technique uses less energy while increasing fault resilience and PDR.

An Efficient Electrothermal Model of a Thermoelectric Converter for a Thermal Energy Harvesting Process Simulation and Electronic Circuits Powering

This paper deals with an electrothermal model of a thermoelectric converter dedicated to performing simulations of coupled thermal and electrical phenomena taking place in harvesting processes. The proposed model is used to estimate the electrical energy gain from waste heat that would be sufficient to supply electronic circuits, in particular autonomous battery-less nodes of wireless sensor networks (WSN) and Internet of Things (IoT) devices. The developed model is not limited to low-power electronic solutions such as WSN or IoT; it can also be scaled up and applied to simulations of considerably higher thermal power conversion. In this paper, a few practical case studies are presented that show the feasibility and suitability of the proposed model for complex simultaneous simulation processes in both the electrical and thermal domains. The first example deals with a combined simulation of the electrothermal model of a thermoelectric generator (TEG) and an electronic harvester circuit based on Analog Devices’ power management integrated circuit LTC3108. The second example relates to the thermalization effect in heat sink-less harvesting applications that could be mitigated by a pulse mode operation. The unique contribution and advancement of the model is the hierarchical structure for scaling up and down, incorporating the complexity of the Seebeck effect, the Joule effect, heat conduction, as well as the temperature dependence of the used materials and the thermoelectric pellet geometries. The simulations can be performed in steady as well as transient states under changing electrical loads and temperatures.

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

SummaryWSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self‐attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi‐Objective Cluster Head Selection and Energy Aware Routing (SabGAN‐AqOA‐EgAwR‐WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN‐AqOA‐EgAwR‐WSN method is activated in NS2 simulator. Finally, the proposed SabGAN‐AqOA‐EgAwR‐WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.

Wireless sensor network security defense strategy based on Bayesian reputation evaluation model

AbstractIn order to solve the security problems caused by malicious nodes in wireless sensor networks, a TS‐BRS reputation model based on time series analysis is proposed in this paper. By using the time series analysis method, the matching analysis of two time series is carried out to reduce the interference of channel conflicts on the reputation evaluation model and improve the accuracy of model recognition. In order to improve the adaptability of the evaluation model, the adaptive maintenance function μ is introduced into the update of credit value, which aggravates the influence of node behaviour on credit value at the present stage. The simulation results show that the new reputation evaluation model can effectively improve the detection rate and detection speed of malicious nodes in the network. After the introduction of maintenance function, the reputation value of the captured malicious nodes in the network has a faster convergence speed.

Decentralized fault tolerant source localization without sensor parameters in wireless sensor networks

In this paper, we study the source (event) localization problem in decentralized wireless sensor networks (WSNs) under faulty sensor nodes without knowledge of the sensor parameters. Source localization has many applications, such as localizing WiFi hotspots and mobile users. Some works in the literature localize the source by utilizing the knowledge or estimates of the fault probability of each sensor node or the region of influence of the source. However, this paper proposes two approaches: the hitting set and feature selection for estimating the source location without any knowledge of the sensor parameters under faulty sensor nodes in WSN. The proposed approaches provide better or comparable source localization performances. For the hitting set approach, we also derive a lower bound on the required number of samples. In addition, we extend the proposed methods for localizing multiple sources. Finally, we provide extensive simulations to illustrate the performances of the proposed methods against the centroid, maximum likelihood (ML), fault-tolerant ML (FTML), and subtract on negative add on positive (SNAP) estimators. The proposed approaches significantly outperform the centroid and maximum likelihood estimators for faulty sensor nodes while providing comparable or better performance to FTML or SNAP algorithm. In addition, we use real-world WiFi data set to localize the source in comparison to the support vector machine based estimator in the literature, where the proposed methods outperformed the estimator.

Analysis and Improvement on an Authentication Scheme for Wireless Sensor Networks in Internet of Things Environment

Nowadays, Internet of Everything has become a major trend, and Internet of Things (IoT) has emerged. Wireless sensor networks (WSNs) are core technologies for IoT to sense the real world. Due to the unattended and resource-constrained characteristics of WSNs, it is a great challenge to design an efficient and secure authentication scheme for communication between users and sensor nodes in WSNs. Recently, Hu et al proposed an authentication scheme for WSNs in an IoT environment. They claimed that their scheme could maximize the balance between security and computational cost as well as efficiency, and be resistant to many known attacks. However, we find that the scheme is difficult to resist stolen smart card attack and denial-of-service attack. Moreover, during the login and key negotiation phase of the scheme, Gateway (GWN) is unable to extract key values for subsequent computation based on the messages sent by the sensor nodes, which in turn leads to the inability to achieve mutual authentication and key agreement. To overcome these shortcomings, we propose an improved scheme. The proposed scheme enables real-time data exchange and transmission as well as secure communication between users and sensor nodes.

An efficient reconfigurable geographic routing congestion control algorithm for wireless sensor networks

<span lang="EN-US">In recent times, huge data is transferred from source to destination through multi path in wireless sensor networks (WSNs). Due to this more congestion occurs in the communication path. Hence, original data will be lost and delay problems arise at receiver end. The above-mentioned drawbacks can be overcome by the proposed efficient reconfigurable geographic routing congestion control (RgRCC) algorithm for wireless sensor networks. the proposed algorithm efficiently finds the node’s congestion status with the help queue length’s threshold level along with its change rate. Apart from this, the proposed algorithm re-routes the communication path to avoid congestion and enhances the strength of scalability of data communication in WSNs. The proposed algorithm frequently updates the distance between the nodes and by-pass routing holes, common for geographical routing. when the nodes are at the edge of the hole, it will create congestion between the nodes in WSNs. Apart from this, more nodes sink due to congestion. it can be reduced with the help of the proposed RgRCC algorithm. As per the simulation analysis, the proposed work indicates improved performance in comparison to conventional algorithm. By effectively identifying the data congestion in WSNs with high scalability rate as compared to conventional methods</span>

High uncertainty aware localization and error optimization of mobile nodes for wireless sensor networks

<p>The localization of mobile sensor nodes in a wireless sensor network (WSN) is a key research area for the speedy development of wireless communication and microelectronics. The localization of mobile sensor nodes massively depends upon the received signal strength (RSS). Recently, the least squared relative error (LSRE) measurements are optimized using traditional semidefinite programming (SDP) and the location of the mobile sensor nodes was determined using the previous localization methods like least squared relative error and semidefinite programming (LSRE-SDP), and approximate nonlinear least squares and semidefinite programming (ANLS-SDP). Therefore, in this work, a novel high uncertainty aware-localization error correction and optimization (HUA-LECO) model is employed to minimize the aforementioned problems regarding the localization of mobile sensor nodes and enhance the performance efficiency of root mean square error (RMSE) results. Here, the position of target mobile sensor nodes is evaluated based on the gathered measurements while discarding faulty data. Here, an iterative weight updation approach is utilized to perform localization based on Monte Carlo simulations. Simulation results show significant improvement in terms of RMSE results in comparison with traditional LSRE-SDP and ANLS-SDP methods under high uncertainty.</p>

Blockchain Based Wireless Sensor Networks for Detecting Nodes

Abstract The versatility of Wireless Sensor Networks (WSNs) and other unique qualities inspired by real-time collaboration between the sensor nodes make them widely used for a range of applications in monitoring and surveillance. As solutions for malicious node identification involve a single, centralized decision-making process, security in WSNs is becoming more and more important. Errors are difficult to prevent with this paradigm, and repeatability and traceability are problems. Therefore, the detecting ability and integrity of the developed detection method cannot be guaranteed by malicious node identification technology in ordinary WSNs. This article provides insights into this innovative idea as well as a thorough investigation of the use of blockchain technology to WSNs (Blockchain-Based WSN), including a full analysis of a blockchain-based method for identifying rogue nodes. The Blockchain-Based WSN architecture, business-specific applications, and use are all included in this study. The Blockchain-Based WSN architecture model for tracking malicious nodes and the smart contract features of malicious node detection is both included in the explanation of harmful node identification based on Blockchain-Based WSN in this paper. The second part of this study investigates the use of blockchain technology to the management of WSN data, which necessitates online information aggregation and may also require auditing, event logging, and storage for information analysis and offline query processing. The traditional WSN data management approaches are discussed first in this study, followed by the blockchain-based WSN data management choices. The benefaction of blockchain to WSN security management is also included in this survey. Before discussing blockchain-based WSN security management solutions, such as those that provide access control, protect data integrity, ensure anonymity, and prolong the life of WSN nodes, it first looks at the security vulnerabilities of centralized WSN models.

Dragon fly algorithm based approach for escalating the security among the nodes in wireless sensor network based system

A new technology that is gaining popularity today is the Wireless Sensor Network. Smart sensors are being used in a variety of wireless network applications, including intruder detection, transportation, the Internet of Things, smart cities, the military, industrial, agricultural, and health monitoring, as a result of their rapid expansion. Sensor network technologies improve social advancement and life quality while having little to no negative impact on the environment or natural resources of the planet are examined in sensor networks for sustainable development. Real-world applications face challenges ensuring Quality of Service (QoS) due to dynamic network topology changes, resource constraints, and heterogeneous traffic flow. By enhancing its properties, such as maintainability, packet error ratio, reliability, scalability, availability, latency, jitter, throughput, priority, periodicity, deadline, security, and packet loss ratio, the optimized QoS may be attained. Real-world high performance is difficult to attain since sensors are spread out in a hostile environment. The performance parameters are divided into four categories: network-specific, deployment phase, layered WSN architecture, and measurability. Integrity, secrecy, safety, and security are among the privacy and security levels. This article leads emphasis on the trustworthiness of the routes as well as the nodes involved in those routes from where the data has to pass from source to destination. First of all, the nodes are deployed and cluster head selection is done by considering the total number of nodes and the distance from the base station. The proposed work uses AODV architecture for computing QoS parameters that are throughput, PDR and delay. K-means clustering algorithm is used to divide the aggregated data into three possible segments viz. good, moderate and bad as this process does not involve the labelling of aggregated data due to its supervised behavior. The proposed trust model works in two phases. In first phase, data is divided into 3 segments and labelling is done. In second phase, uses generated class objects are to be applied viz. the route records to publicize the rank of the routes followed by the rank of nodes. The proposed technique employed the statistical machine learning and swarm intelligence strategy with dragon fly algorithm in order to address the issues related effective rank generation of nodes and improving the network lifetime. Deep learning concepts can be combined with fuzzy logics approach for resolving issues like secure data transmission, trustworthiness of ranking nodes and efficient route discovery.

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.

Effect of Energy-Efficient Routing for Packet Splitting using Chinese Remainder Theorem (CRT) for Wireless Sensor Networks (WSNS)

The most popular Wireless Sensor Network (WSN) protocol utilized for energy optimization of WSN nodes is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol, which is a basic energy-efficient routing protocol of hierarchical routing protocol’s family, where the sensor nodes remain static. There is a need to extend the lifetime with respect to reducing the energy consumption of nodes, and this was accomplished using the Chinese Remainder Theorem (CRT. In this research large data packets are broken into smaller packets. The focus of the study is to develop an energy routing for splitting packets in Wireless Sensor Networks (WSNs) using Chinese Remainder Theorem (CRT) techniques. A further improvement in WSN lifetime is by utilizing fuzzy logic. Factoring node parameters such as residual energy of nodes, their centrality within clusters, and their distance to the base station and improving upon the selection of nodes as cluster heads are required. The developed application can be used to reduce the communication cost of Energy efficiency of WSN. The forwarding technique for this research is to split the packet sent by the source node of a WSN so that the maximum number of bits per packet that a node has to transmit is significantly minimized.

Patterned Coating of Ionic Diode Arrays Toward Flexible Moist‐Electric Generators to Power Wireless Sensor Nodes

AbstractUbiquitous moisture is attractive for developing sustainable mobile power sources. However, how to endow moisture‐electric generators (MEGs) with fine design, mass customization, high power output, and foldability is still a largely unsolved problem. In this work, based on the Patterned Coating method, use is made of modulated carbon nanotube, nano‐Al2O3, and liquid metal inks to design and fabricate MEGs with properties required for their applications. A single MEG of the ionic diode type thus fabricated can generate an open‐circuit voltage (VOC) of 1.03 V and a short‐circuit current density (JSC) of 47.77 µA cm−2. Through large‐area integrated fabrication, an array containing 160 MEGs can generate a VOC of 154.1 V, and an array containing 100 MEGs can produce a short‐circuit current of 1.95 mA. In addition, the excellent wearability of MEG is verified by analyzing its electrical output performance after subjecting it to 800 cycles of bending deformations. The simple and cost‐saving fabrication technology issued from the work opens new perspectives for MEG applications and paves a path to their industrialization. For example, the customizable ionic diode arrays can meet the on‐demand power supply requirements of wireless sensor network nodes in the Internet of Things.

A Novel Incentive Routing Protocol with Virtual Projection for Mobile Packet Forwarding Nodes in Wireless Sensor Networks

The size of the Packet Forwarding Nodes (PFNs) is becoming very small as the technology advances in Wireless Sensor Networks (WSNs). The node has an additional parameter with low energy levels. The PFNs are distributed in a square cross-sectional area with each node acting as a Sensing Point (SP) that can be used for various kinds of applications like temperature, atmospheric humidity, acoustic, and pressure measurements. The packet is divided into several fragments where each fragment is considered as fixed or variable length. Each of these packets is sent over multiple PFNs toward the data center using PFNs. The selection of PFNs in the path is picked based on the trust level. In the network even special PFNs are placed which are responsible to deliver the packets toward the data center without losing the data during the transmission. The selection of special PFNs is done by computing the meeting probability, remaining energy computation, computation of data weight, and security value computation. The proposed Incentive Routing Protocol with Virtual Projection (IRPVP) method is compared with the conventional approaches concerning the parameters like delay, link count, resource energy, healthy PFNs, non-healthy PFNs, health ratio computation, remaining energy, control to data ratio, and balancing factor. The simulation outcomes show that the performance of the proposed IRPVP algorithm is better than the other conventional algorithms.

Q-Learning and Efficient Low-Quantity Charge Method for Nodes to Extend the Lifetime of Wireless Sensor Networks

With the rapid development of the Internet of Things (IoT), improving the lifetime of nodes and networks has become increasingly important. Most existing medium access control protocols are based on scheduling the standby and active periods of nodes and do not consider the alarm state. This paper proposes a Q-learning and efficient low-quantity charge (QL-ELQC) method for the smoke alarm unit of a power system to reduce the average current and to improve the lifetime of the wireless sensor network (WSN) nodes. Quantity charge models were set up, and the QL-ELQC method is based on the duty cycle of the standby and active times for the nodes and considers the relationship between the sensor data condition and the RF module that can be activated and deactivated only at a certain time. The QL-ELQC method effectively overcomes the continuous state–action space limitation of Q-learning using the state classification method. The simulation results reveal that the proposed scheme significantly improves the latency and energy efficiency compared with the existing QL-Load scheme. Moreover, the experimental results are consistent with the theoretical results. The proposed QL-ELQC approach can be applied in various scenarios where batteries cannot be replaced or recharged under harsh environmental conditions.

An enhanced node segmentation and distance estimation scheme with a reduced search space boundary and improved PSO for obstacle-aware wireless sensor network localization

This paper proposes an enhanced method for localizing sensor nodes in wireless sensor networks with obstacles. Such environment settings lead to lower localization accuracy because locations are estimated based on detour distances circumventing the obstacles; we, therefore, improve the segmentation technique to address the issue as they divide the whole area into multiple smaller ones, each containing fewer or no obstacles. Nevertheless, when radio transmissions between sensor nodes are obstructed (as simulated by the radio irregularity model), the signal-strength variation tends to be high, reducing localization accuracy; thus, we provide a method for accurately approximating the distances between pairs of an anchor node (whose location is known) and an unknown node by incorporating the related error into the approximation process. Additionally, when the nodes with unknown locations are outside the polygon formed by the anchor nodes, the search area for localization is relatively large, resulting in lower accuracy and a longer search time; we then propose a method for reducing the size of approximation areas by forming boundaries based on the two intersection points between the ranges of two anchor nodes used to localize an unknown node. However, these reduced search areas could still be large; we further increase the accuracy of the PSO location estimation method by adaptively adjusting the number of particles. In addition, with PSO, the accuracy of unknown node location estimation depends on having a properly selected fitness function; therefore, we incorporate appropriate variables to reflect the distance approximation accuracy between each anchor-unknown node pair. In experiments, we measure performance in sensor node deployment areas of three different shapes: C-shaped, with 1 hole, and with 2 rectangular holes. The results show that our method provides higher localization accuracy than others in small-, medium-, and large-scaled WSNs. Specifically, our proposed method is 27.46%, 49.28%, 50.33%, and 74.62% more accurate on average than IDE-NSL, PSO–C, min-max PSO, and niching PSO, respectively.

A multi-time-scale framework for state of energy and maximum available energy of lithium-ion battery under a wide operating temperature range

Lithium-ion batteries are one of the best choices as energy storage devices for self-powered nodes in wireless sensor networks (WSN) due to their advantages of no memory effect, high energy density, long cycle life, and being pollution-free after being discarded, ensuring that the sensor nodes maintain high power operation for a long time. An improved co-estimation framework for SOE and maximum available energy has been established, considering the problem of maximum available energy decay caused by temperature and battery charge-discharge rate, which can update the maximum available energy in real-time and reduce SOE errors caused by fixed energy values. A multi-timescale SOE and maximum available energy co-estimation framework is proposed to address the asynchronous and coupled characteristics of maximum available energy and SOE estimation, effectively reducing the algorithm's computational complexity. The proposed algorithm is experimentally verified according to the designed dynamic stress test conditions of lithium-ion batteries in WSN nodes. The experimental results show that smaller time scales can provide more accurate and reliable maximum available energy correction and higher SOE estimation accuracy, but the computational time cost is higher than that of larger time scales. To balance SOE estimation accuracy and algorithm computational complexity, the appropriate time scale should be selected based on the SOE estimation accuracy and time cost in practical battery management system working conditions.