Wireless Sensor Network System Research Articles

Augmentation in performance and security of WSNs for IoT applications using feature selection and classification techniques

Energy consumption, secure connection of Sensor Nodes (SNs), and their performance analysis play a major role in Wireless Sensor Networks (WSNs) research. Increased case use of various Internet of Things (IoT) applications has led to the development of complicated networks. To address the threats and security issues faced in complicated WSNs, a novel combined feature selection method known as the Fast Correlation-based Feature Selection (FCBFS) with XG-Boost has been proposed for the NSL-KDD intrusion detection benchmark dataset. It helps in the selection of the best features in a cluster-based WSN before the classification step. Five popular machine learning-based classifiers, namely decision tree, random forest, Naïve Bayes, extra tree, and XG-Boost, are used for the development of a robust intrusion detection system in WSN and its IoT applications. The effectiveness and robustness of the developed ensemble methods have been checked using classification accuracy, precision, recall, and F-score. XG-Boost classifier along with the FCBFS process has performed best with a classic accuracy, precision, recall, and F-score up to 99.84%, 99.83%, 99.84%, and 99.82% on multiple runs. Upon comparison with the existing state-of-the-art work in this field, the proposed work has outperformed. Results show that, in contrast to the previous filter approaches, our proposed process can successfully minimize the number of features while maintaining a high classification precision and recognition rate. It further tends to lower the overall energy required by sensor nodes during attack detection, hence extending the network lifetime and usefulness to a sufficient time frame.

WOGRU-IDS — An intelligent intrusion detection system for IoT assisted Wireless Sensor Networks

One of the key mechanisms of the current electronic and wireless frameworks is the assistance of Wireless Sensor Networks (WSN) in Internet of Things (IoT) networks. A WSN typically consists of multipurpose sensor hubs for data sensing, processing, and communication. These networks are more suited to conveying medical data from various geographical regions and sending private medical data to the network owner. However, the worry about various attacks on health care data normally grows daily. These assaults could quickly have adverse impacts on the WSN-IoT (Internet of Things) nodes. Additionally, the low detection rate, significant processing overhead, and resource limitations of current intrusion detection systems all contribute to an increase in false alarm rates when trying to identify various attacks. The unique Whale Optimized Gate Recurrent Unit (WOGRU) Intrusion Detection System (IDS) for WSN-IoT networks is proposed in this research in light of the aforementioned issues in order to effectively identify various attacks. The whale algorithm was used in the proposed framework to tune the hyperparameters of the deep long short-term memory in order to achieve low computational overhead and great performance. Last but not least, validations are carried out using the WSN-DS dataset, and the performance of the suggested work is evaluated using the parameters accuracy, recall, precision, specificity, and F1-score. Additionally, the comparison study was conducted using the current frameworks. The data demonstrates that the suggested framework had an average performance of 99.85 percent for the detection of flooding, scheduling, black hole, and gray hole attacks.

High-performance, energy-efficient, and memory-efficient FIR filter architecture utilizing 8x8 approximate multipliers for wireless sensor network in the Internet of Things

IoT uses wireless sensor networks (WSN) to deploy many sensors to track environmental and physical parameters. The WSN measurements are frequently contaminated and altered by noise. The noise in the signal increases the sensor node’s computation and energy utilization, resulting in less longevity of the sensor node. The Finite Impulse Response (FIR) filter is commonly employed in WSN to pre-process sensed signals to remove noise from the sensed signals using delay elements, multipliers, and adders. Traditional multiplier-based FIR filter designs result in hardware-intensive multipliers that consume a lot of energy, and area and have low computation speed. These drawbacks make them unsuitable for IoT-based WSN systems with stringent power efficiency necessities. Approximate computing enhances the energy efficiency of an FIR filter. Arithmetic circuits utilizing approximate computing improve the hardware performance, with some loss of accuracy to save energy utilization and boost speed. A novel approximate multiplier architecture employing a fast and straightforward approximation adder is proposed in this study. Approximate multiplier M1 using OR gate and approximate multiplier M2 using proposed approximate adders are compared. The proposed approximate adder is suited for building an adder tree to accumulate partial product (PP) because it is less complicated than traditional adders. Compared to a one-bit-full adder, the critical path delay (CPD) is reduced significantly in the proposed methods. The accuracy comparison of M1. M2 and Wallace tree using the normalized mean error distance (NMED), the mean relative error distance (MRED), the maximum error (ME), and the error rate (ER) with the number of bits utilized for reducing error. For the area (delay) optimized circuit, when the bit used is 4, the delay is 0.4 ns for M1, 0.43 ns for M2, and 1.08 ns for the Wallace tree multiplier. For the delay (area) optimized circuit, when the bit used is 4, the delay is 0.16 ns for M1, 0.16 ns for M2, and 0.40 ns for the Wallace tree multiplier. To more accurately evaluate performance at the circuit level, the PDP and ADP are computed. The NMED, MRED, ME, and ER versus PDP and ADP are computed. The proposed multipliers M1 and M2 are compared with existing approximate multipliers. When an equivalent MRED, NMED, or ER is taken into account, M1 has the smallest ADP and PDP among other multipliers. The very low likelihood of a significant ED occurring is indicated by the small values of NMED and MRED in M1 and M2. The proposed solutions effectively reduce delay, area, and power while maintaining increased accuracy and performance.

Mobility and Routing based Channel Estimation for Hybrid Millimeter-Wave MIMO WSNs

Recently, technological developments have enhanced, the use of Millimeter-wave (mm-Wave) Multiple Input Multiple Output (MIMO) system in various communication applications and wireless sensor networks as channel estimation efficiency can be immensely improved with the help of this technological developments in Millimeter-wave MIMO system and wireless sensor network as well. Moreover, they can improve quality of communication services to a great extent. However, cell interference in Millimeter-wave (mm-Wave) MIMO system can produce a massive impact on spectral efficiency. Therefore, a Routing Enabled Channel Estimation (RECE) technique is presented in this article to minimize interference between cells. The proposed Channel Estimation technique improves channel capacity as well as spectral efficiency. Moreover, Normalized Mean Square Error (NMSE) is minimized heavily using proposed RECE technique. Here, main aim of this article is to reduce cell interference and channel estimation inside a cell by using route selection, beam selection, and spatial frequency estimation. Here, different scenarios and parameters are considered to evaluate performance efficiency of proposed RECE technique in terms of spectral efficiency, NMSE and SNR and compared against varied traditional channel estimation techniques. Moreover, it is clearly evident from performance results that the proposed channel estimation technique performs better than the other two methods.

Energy Enhancement of WSN with Deep Learning based SOM Scheduling Algorithm

Energy efficiency is one of the primary requirements for designing a successful Wireless Sensor Network (WSN) model. The WSN systems are generally made with a group of nodes that are operated with a small size battery device. To improve the energy efficiency of such WSNs several methodologies like clustering approach, mobile node technique and optimal route planning designs were developed. Scheduling method is yet an efficient model that is widely used in WSN applications, that allows the nodes to be operated only for a certain prescribed time. The proposed work utilizes the Self Organizing Maps (SOM) approach for improving the performances of the scheduling algorithms to a certain limit. SOM is a kind of artificial neural network that analyzes the problem based on competitive learning rather than the backpropagation methods. The work compares the proposed algorithm with the traditional Ant Colony and Software Defined Network approaches, wherein the proposed approach has shown an improvement in terms of energy conservation and network lifetime.

IoT Sensor Network Using ESPAR Antenna Based on Beam Scanning Method for Direction Finding.

Wireless sensor networks (WSNs) systems based on Internet of Things (IoT) have developed rapidly in recent years. However, interference is a major obstacle to relatively long-distance communications in such networks. It is also very complicated and challenging to fix the exact location of tags in the IoT sensor networks. To overcome these problems, in this paper, an electronic steering parasitic array radiator (ESPAR) antenna used as a beamformer to handle the interference and extend the communication range from the sensors or tags is suggested. In addition, an efficient method, namely beam scanning (BS), is proposed to find the directions of tags. The beam scanning method (BSM) can be used for the selective beam switching (SBS) system by designing an ESPAR or array of ESPAR antennas with the help of CST studio. The antennas exhibit higher gain (8.17 dBi, 11.40 dBi) and proper radiation pattern at a particular direction. In addition, the MATLAB simulation findings indicate that the proposed BSM algorithm provides longer communication range, i.e., 25 m. In order to maximize range while avoiding interference, it is necessary to determine the direction and precise orientation of the tag in the WSN communication systems. Consequently, this work could be applied to an IoT sensor network such as an electrocardiogram system by providing better advantages such as higher localization accuracy and longer operating range.

Accurate Prediction of Tunnel Face Deformations in the Rock Tunnel Construction Process via High-Granularity Monitoring Data and Attention-Based Deep Learning Model

Monitoring and predicting the deformation of surrounding rocks in the rock tunnel construction process is of great significance. This study implemented a wireless sensor network (WSN), including gateway transmission, relay point, and sensor nodes, to obtain high granularity deformation data during construction. A transformer model is proposed, which considers the construction sequence into the positional embedding and has an attention module to deeply learn the high dimensionality correlation between the nearby deformation data and the tunnel face deformation. The attention-enhanced LSTM model and the LSTM model are also constructed to compare them with the performance of the transformer model. A site study conducted on a shallow buried tunnel section suggested an excellent performance of the proposed WSN system. The transformer model shows the best performance in terms of the model prediction results, which can extract more information from the time sequence data than the attention-enhanced LSTM and LSTM models. The proposed system has great value as guidance and reference for the construction of rock tunnel projects in complex and unfavourable geological conditions.

A Survey on Settling Gridlock Utilizing Picture Handling Methods and Improvement of an Android Application

Our current situation makes this a severe issue. Traffic congestion on both major and minor routes is constant. A strong traffic control system is necessary in order to address these problems. Human and material resources are lost as a result of poor traffic management. Due to these recurring issues, interest is growing in finding new ways to address the congestion issue. Inadequate capacity, unrestrained demand, and protracted delays caused by traffic signals are only a few of the factors that contribute to traffic congestion. Magnetic loop detectors, infrared sensors, wireless sensor networks, and other devices are all used in modern technology. As opposed to These frequently occurring problems are generating interest in solving the congestion problem through various methods and methods. Traffic congestion occurs for a variety of reasons, including insufficient capacity, unrestricted demand, and long delays due to traffic signals. Modern technology uses wireless sensor network systems, magnetic loop detectors, infrared sensors and more. On the other hand, video and image processing methods offer many advantages over traditional systems. This article summarizes an overview of all available existing methods compared to those used in this field of research. We summarize all the various research papers based on different methods for the development of intelligent traffic detection systems using image and video processing methods. A comparison and overview of all methods is presented in this overview document.

Comparison of 920 MHz and 2.4 GHz Near Ground Electromagnetic Wave Pathloss Propagation Model for Wireless Sensor Network in Forest Environment Application

A Wireless Sensor Network (WSN) system that uses wireless communication technologies occasionally experiences data loss when undertaking wireless data communication. This problem happens because of the WSN system being placed on top of the ground and surrounded by vegetation that adds more loss to the transmission. In order to avoid this problem, the wireless system design must meet its best performance. To build the best performing WSN system, electromagnetic wave behavior in the forest environment needs to be studied well. This paper investigates the electromagnetic wave behavior transmitted and propagated by a WSN node at less than 30 cm from the ground using a 920 MHz frequency. We have analyzed that low height (30 cm) and vegetation environments can also add more loss at about 30.96 dB to the free space pathloss model. The new 920 MHz (that adds 30.96 dB loss) model shows identical behavior to 2.4 GHz with an average difference of 12.24 dB. However, the 920 MHz model performs better, achieving an average RMSE of 1.06 compared with the 2.4 GHz model, which can only achieve an average RMSE of 4.92 compared with the 920 MHz measurement.

Study of Wireless Sensor Networks Topological Protocol and Its Applications

In this paper we studied about the wireless sensor networks and topology protocols and applications of wireless sensor networks (WSN). Many applications of RFID and Wireless Sensors Networks (WSN) in diverse areas including intelligent transport vehicles, defense, environmental monitoring and forecasting such as air pollution monitoring, structural health monitoring, intelligent home, and warehouse management were offered [1-4]. The integration infrastructure of WSN and RFID system with a network was dedicated. Furthermore, the architecture framework of Electronic Production Code (EPC) global sensor network was proposed aiming to establish the infrastructure of global network which is able to aggregate various data [6]. However, RFID and WSN integration was developed by [7]. This research strives to elaborate accurate traceability of different objects by means of RFID which are not conveniently identifiable by applying ordinary sensors. Notwithstanding the fact that the condition of items cannot be monitored by RFID, the environmental condition as well as condition of items can be achieved by sensor nodes. To address this issue, both RFID system and WSN are applied together to overcome some difficulties in industrial environment.

Multi-Channel Data Aggregation Scheduling Based on the Chaotic Firework Algorithm for the Battery-Free Wireless Sensor Network

The emergence of battery-free wireless sensor networks (benefiting from the ability to collect energy from the surroundings) has broken through the energy and lifetime limitations of traditional wireless sensor network systems, but also brings challenges to the sharing of network resources. In the multi-channel wireless communication environment, in particular, how to coordinate the communication time and occupied channels of a large number of sensor nodes from the perspective of optimizing the global network has become a research problem that must be solved. To reduce the transmission delay and the usage of wireless channels, a new multi-channel data aggregation scheduling method based on the chaotic firework algorithm is proposed in this paper. With the help of the generation function of feasible solutions, one scheduling set and a firework individual can be rapidly converted to each other. By the operations of firework explosions, the Gaussian mutation, and chaotic exploration, a sub-optimal scheduling set could be found during an acceptable time period. Finally, simulation results show that the new scheduling method has advantages in aggregation delay and occupied channels when compared with existing methods.

Low Budget Respirometer Chamber Design Based on Wireless Sensor Network

Fresh fruit respiration information is essential optimizing food storage systems. Meanwhile, respiration is defined as the process of oxygen production and carbon dioxide release during storage in a closed respiratory chamber. Therefore, this study aims to design a low-budget computerized respiratory chamber for enhancing fruit packaging and storage system. Real-time fruit respiration can be measured by applying wireless gas sensors network. The respirometer consisted of 3,600 mL glass jar with a screw stainless lid, while the electrochemical and non-dispersive infrared sensors were mounted on the cover of the glass jar for collecting data on the oxygen, carbon dioxide, and temperature during mangoes’ respiration. Arduino USB port was used to record all measured parameters consisting of oxygen (%) and carbon dioxide concentrations (ppm, as well as temperature in the respiration chamber. Additionally, a controlled cooling chamber was applied to maintain the temperature during storage, while data communication was supported by Xbee S2C based on radio frequency. According to the respirometer real-time reading, there was a decrease in oxygen concentration caused by increasing carbon dioxide release with temperature. The low-budget respirometer was used to measure the respiration rate and record the data through a wireless sensor network system. The data plot shows that the respiration rate increased as the storage temperature and the respiratory quotient ranged from 0.32-0.44.

Image Detection System Based on Smart Sensor Network and Ecological Economy in the Context of Fine Agriculture

In this paper, an in-depth study and analysis of the ecological economy of fine agriculture are carried out using image detection methods of smart sensor networks. The analog signal output from the wireless sensor network is filtered and thresholder to convert into a digital signal to complete the sensor monitoring data preprocessing for digital information analysis. In this paper, with the objectives of good environmental adaptability, low power consumption, low cost, and standardization, the key technologies of wireless sensor networks for fine agriculture are studied, including network structure, networking method, node positioning method, data fusion method, rapid energy self-sufficiency, and energy-saving strategy, and the performance evaluation method of wireless sensor network system, IoT-oriented middleware design method, generic node software and hardware design method, and typical application system. Firstly, a convolutional layer is used instead of a fully connected layer, which makes the network more flexible in terms of input image requirements and enables the calculation of the target rice region. Not only will many complex operations be generated, but it will also limit the generalization ability of the model. Then, by introducing a flexible connection layer based on unit and optimizing the loss function of the network, a crop convolutional neural network (Crop-Net) is finally proposed for training and testing rice images at different growth stages to improve the detection accuracy. In this paper, a network quality of service goal-driven evaluation strategy and evaluation method for agricultural wireless sensor network systems is designed to provide a reference for the establishment of industry standards for wireless sensor network systems for fine agriculture.

Fruchterman–Reingold Hexagon Empowered Node Deployment in Wireless Sensor Network Application

Internet of Things (IoT) and Big Data technologies are becoming increasingly significant parts of national defense and the military, as well as in the civilian usage. The proper deployment of large-scale wireless sensor network (WSN) provides the foundation for these advanced technologies. Based on the Fruchterman–Reingold graph layout, we propose the Fruchterman–Reingold Hexagon (FR-HEX) algorithm for the deployment of WSNs. By allocating edges of hexagonal topology to sensor nodes, the network forms hexagonal network topology. A comprehensive evaluation of 50 simulations is conducted, which utilizes three evaluation metrics: average moving distance, pair correlation diversion (PCD), and system coverage rate. The FR-HEX algorithm performs consistently, the WSN topologies are properly regulated, the PCD values are below 0.05, and the WSN system coverage rate reaches 94%. Simulations involving obstacles and failed nodes are carried out to explore the practical applicability of the FR-HEX algorithm. In general, the FR-HEX algorithm can take full advantage of sensors’ hardware capabilities in the deployment. It may be a viable option for some IoT and Big Data applications in the near future.

Spatio-Temporal Graph for Improvement of Decision-Making in Risks Treatment

Failures that hinder the proper functioning of complex systems are characterised by frequencies of occurrence, rates of evolution and variables periodicities. Indeed, a good design of this kind of systems requires the elaboration of an adapted analysis and modelling approach, which takes into account the spatial-temporal dynamics of the systems elements dysfunction. This paper proposes a Spatio-temporal modelling approach integrated into a risk management methodology. This modelling allows both to describe the evolution of the system in space and in time, and to follow the spatial and temporal scope of a failure of the system in order to improve decision making in choosing the appropriate corrective action. The elements of the methodology are illustrated and validated by a case study of a wireless sensor network system.

Development of a Wireless Sensor Network and IoT-based Smart Irrigation System

This study proposes a smart irrigation system (SIS) using the drip method, which was designed and implemented using wireless sensor networks and an open-source Internet of Things (IoT) cloud computing platform (“Thingspeak.com”) for data collection, storing, data analytics, and visualization. The methodology incorporates the integration of hardware and software components to make irrigation decisions based on web resources like the weather forecast from “weather.com” and sensor values from soil samples. The data collected are then analyzed at the edge server and updated every 15 minutes. Based on the threshold value, the system starts pumping water or stops the irrigation process depending on the irrigation schedule. A web application was developed to display the result so that we could monitor and control the system using an android application edge or a web browser. Based on the data recorded and measured, the data are in comma-separated values (CSV) format and contain 143731 entries with 10 columns. The sample size used contains 5722 rows and 6 columns from the result of our machine learning algorithms using Microsoft Excel and Jupyter Notebook to process and evaluate the performance of the drip SIS, considering the soil moisture, soil temperature, sunlight, rain, and pump. The results confirm the threshold metrics classification evaluation, and some of the metrics computed from our confusion matrix are shown in the classification summary results, showing the accuracy to be 89%, the misclassification rate (error rate) is equal to 10%, the sensitivity is equal to 79%, the specificity is equal to 93%, and the precision of the model is 81%. The evaluation, when compared to K-nearest neighbours using K = 6 and K = 1, shows the prediction accuracy to be 97% and 98%. The results indicate the system is highly efficient and reliable in performing irrigation and managing water resources and can be adopted in rural areas to boost agricultural productivity.

Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network.

With the increasing openness and development of network technology, the network based on the wireless sensor network system has increasingly become an important tool for human social life and production, but it also brings some network security problems. Among them, focus is on network privacy disclosure and foreign intrusion and the research of intrusion detection and privacy protection has increasingly become an important topic of network security. This paper deeply studies the wireless sensor network system based on neural network on the basis of traditional privacy protection and intrusion detection system. Firstly, it applies particle swarm optimization algorithm and constructs a wireless sensor network intrusion detection system based on particle swarm optimization algorithm. The system includes important modules such as data extraction, data analysis, data feedback, and auxiliary decision-making. Compared with other algorithms, particle swarm optimization algorithm does not rely on problem information. It mainly uses real numbers to solve, so the algorithm has strong universality. At the same time, its corresponding principle is simple and easy to implement and less parameters need to be adjusted. Compared with other algorithms, particle swarm optimization algorithm has fast convergence speed and little memory requirement for the computer. At the same time, this paper uses the leap of particle swarm optimization algorithm to make it easier to find the global optimal solution. At the corresponding level of wireless sensor network privacy protection, based on the original data aggregation privacy protection scheme, this paper proposes a privacy protection scheme based on polynomial regression and a user privacy protection scheme based on the same state encryption, which further improves the security of privacy protection and facilitates the management of information. To realize the integrity of user privacy information protection, this paper realizes the decryption of data based on the correlation between binary metadata and compares the corresponding decrypted data with aggregated data, so as to complete the integrity of privacy data protection. The experimental results show that the binary metadata correlation decryption method proposed in this paper and the introduction of the corresponding particle swarm optimization algorithm improve the stability of the system by about 10%, the corresponding system security by a positive proportion, and the integrity of private data by about 10%; therefore, the algorithm proposed in this paper has obvious advantages.

Hybrid optimization driven deep residual network for attack detection and attack mitigation in wireless sensor networks

Wireless sensor network (WSN) comprises small sensor nodes that have the ability of sensing platform, store and send data. In addition, the sensor nodes pose limited resource such as energy, computing power. Clustering is an effective manner for diminishing energy usage utilized by sensor nodes for sending data from source to base station (BS). The multi-path routing is utilized for routing data amongst nodes. However, the WSN are susceptible to several types of security issues which can degrade network performance. WSN are susceptible to various types of attacks, which degraded the performance of entire network. Hence, the primary purpose of this research is to design and develop attack detection and attack mitigation system in WSN for detecting the attackers. This paper devises an energy efficient and optimization aware deep model for attack detection and mitigation in WSN. Here, the first step is the simulation of WSN nodes. The overall procedure of the proposed model includes simulation of WSN, cluster head selection, routing to BS, Sybil attack detection in BS and finally attack mitigation in BS. The first step is WSN simulation and then the selection of energy efficient cluster head is done using Lower Energy Adaptive Clustering Hierarchy (LEACH) protocol.

FPGA-Based Antipodal Chaotic Shift Keying Communication System

The current work presents a novel digital chaotic communication system with antipodal chaotic shift keying modulation, implemented in a field-programmable gate array (FPGA). Such systems provide high security on the physical communication level and can be used in wireless sensor network systems. A modified Chua circuit chaos generator and error linear feedback chaotic synchronization are implemented in FPGA and used to develop a chaotic communication system with digital transmitter and receiver an analog in-between signal transmission. Additionally, a validated mathematical model of the communication system prototype is created in the Simulink environment, which is used to compare the performance of the prototype and its nodes with the simulation and simplify its development. The performance is evaluated using simulation with the additive white Gaussian noise channel and analyzing the bit error ratio.

Application of Arduino Devices in various IOT Application

In this paper, v segribe the wireless sensor network system is the next open source hardware we've graded or raspberry arduino refers to an open source electronic operating system or board and the software used to program it. Arduino is designed to give electronic devices access to artists, designers, entertainment enthusiasts and those interested in creating interactive objects or contexts. Arduino boards input sensor in light, a finger or twitter message read on a button -and turn it into a release- running a motor, turning on the led, posting anything online. To the microcontroller on board a set of instructions what to do by sending. Little education in this article run the mobile robot complete of systems provides overview. The arduino is easy to use and a greater number named for the sensors. In fact, in every application new skills are added, also this hardware, hardware services i / o bespoke with target, point-to-point relationships for protocols are defined primarily. Voltage is measured using voltage. Separator, because generated by the solar panel voltage for arduino larger than the receiver. Finally, created by the solar panel current sensor current is measured using volume. These parameters are arduino and as the input value for the output the liquid crystal display (LCD) is displayed on the screen. LCD screen temperature, light intensity, shows voltage and current value output. Arduino analog of the parameter the purpose of the input is to quickly convert to digital output, LCD screen displays via arduino, it is one of the most popular microcontrollers one will be used in robotics .different types of arduino there are microcontrollers, they are about design and features not only that, but the size and processing skills also vary. Completely different chips, however there are only two models in use: fixed and mega. Arduino board, this is yours when creating products of the hardware you work with is a part of. Is the arduino IDE software running on your computer? A sketch you upload on the arduino board (a small computer program) create IDE are using. More than a decade with intensive research and development, wireless sensor network for many innovative applications technology a emerges as a viable solution