Wireless Sensor Network Technology Research Articles

A new method based on swarm intelligence with encrypted data in wireless sensor networks

Wireless sensor networks (WSNs) technology is one of the most essential Internet of things technologies. It is utilized efficiently in a variety of real-world applications, including healthcare, environmental monitoring, tracking, etc. WSNs are composed of sensor nodes with restricted resources. However, the communication between WSN components is not secure. Therefore, it is necessary to build efficient and lightweight cryptographic algorithms to secure shared data. Our paper comprises proposes a secure protocol called grasshopper optimization algorithm routing protocol (GOARP) with a lightweight encryption method in each sensor called rivest cipher 5 (RC5) to enhance network efficiency and simulation in terms of power consumption, required memory space, and computational time. Subsequently, the network lifetime result achieved in the proposed method is about (70%) more than in GOA elliptic curve cryptographic and Diffie Hellman (GOA-ECCDH).

Data Fusion and Processing Technology of Wireless Sensor Network for Privacy Protection

Data fusion and privacy protection technologies are both the research focuses in the field of wireless sensor networks. When the sensor network is in a harsh environment, the sensor nodes will face the danger of malicious entity attack in the data fusion progress. The efficiency and privacy protection of sensor network data fusion are very important. The traditional data fusion privacy protection algorithm has the problems of low data fusion efficiency and low privacy protection level. These problems are to be solved in this study. An improved cluster-based privacy data aggregation (I-CPDA) is proposed, which combines data slicing and false interference data technology. The experimental results of the algorithm show that the data fusion accuracy of the I-CPDA algorithm increases faster than the traditional algorithm with the time interval increasing, and the highest value reaches 90.7%. The fusion accuracy of the traditional CPDA algorithm under the same environment is 68.7%. In the actual test, the interception success rate of the I-CPDA algorithm for data attacks reached 90.74%, while the traditional CPDA was only 76.66. In addition, when the number of nodes in the cluster is 15, the data traffic of the I-CPDA is 56, while the data traffic of the traditional CPDA algorithm in the same environment exceeds 200. Compared with the currently widely used traditional algorithms, the I-CPDA algorithm has obvious advantages in terms of fusion effect, privacy, and efficiency and can be put into practical application.

Design of Wireless Motion Sensor Nodes based on the Kalman Filter Algorithm

Background: In recent years, wireless sensor network technology has been the center of attention in the international community. It has been a key element in e-health to monitor bodies. This technology enables new applications in different domains, including medical, physical training, etc. Method: Some sensors in WSN (Wireless Sensor Nodes) capture physiological data such as temperature, heartbeat and moving distance. The ECG signal is obtained by an analog front-end chip ADS1292 and processed by an adaptive Kalman filter algorithm. The sensor LMT70 is used to gather the body temperature; the acceleration sensor MPU6050 is used to detect, capture, recognize and identify other body gestures and motions data and the STM32F103C8 microprocessor is used as the kernel control module. WSN sensor nodes provide wireless monitoring due to transceiver module NRF24L01for anybody, anywhere and anytime. Result: Each measurement result can meet physiological index demands. Conclusion: The system can be used to avoid sports accidents and injuries and to plan for future training and realized in LAB conveniently owing to the high cost-performance ratio.

Detection of Network Layer Attacks in Wireless Sensor Network

The Wireless Sensor Network (WSN) technology is being used in a huge number of monitoring applications. It consists of a large number of sensor nodes with limited battery life. These sensor devices are deployed randomly in a sensor zone to collect the data. But these are threatened and attacked by several malicious behaviors caused by some nodes, which result in security attacks. Several security attacks occur in different layers of the wireless sensor network. Due to these attacks, confidential information can be stolen by attackers or unauthorized users, which can cause several problems for authorized users. Cyber-attacks by sending large data packets that deplete computer network service resources by using multiple computers when attacking are called wormhole and Sybil attacks. It is important to identify these attacks to prevent further damage. To overcome these problems, we use a prediction module that consists of various machine learning algorithms to find the best-performing algorithm. we use XGBoost, Adaboost, Random Forest, and KNN algorithms. To train these algorithms, we have used the WHASA dataset which contains 10 different attacks of the VANET environment and benign (normal) class. By using these algorithms classification of attacks can be done which occur on the computer network service that is " normal " access or access under " attack " by Wormhole and Sybil attack as an output.

Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection

Wireless Body Area Network (WBAN) is a trending technology of Wireless Sensor Networks (WSN) to enhance the healthcare system. This system is developed to monitor individuals by observing their physical signals to offer physical activity status as a wearable low-cost system that is considered an unremarkable solution for continuous monitoring of cardiovascular health. Various studies have discussed the uses of WBAN in Personal Health Monitoring systems (PHM) based on real-world health monitoring models. The major goal of WBAN is to offer early and fast analysis of the individuals but it is not able to attain its potential by utilizing conventional expert systems and data mining. Multiple kinds of research are performed in WBAN based on routing, security, energy efficiency, etc. This paper suggests a new heart disease prediction under WBAN. Initially, the standard patient data regarding heart diseases are gathered from benchmark datasets using WBAN. Then, the channel selections for data transmission are carried out through the Improved Dingo Optimizer (IDOX) algorithm using a multi-objective function. Through the selected channel, the data are transmitted for the deep feature extraction process using One Dimensional-Convolutional Neural Networks (ID-CNN) and Autoencoder. Then, the optimal feature selections are done through the IDOX algorithm for getting more suitable features. Finally, the IDOX-based heart disease prediction is done by Modified Bidirectional Long Short-Term Memory (M-BiLSTM), where the hyperparameters of BiLSTM are tuned using the IDOX algorithm. Thus, the empirical outcomes of the given offered method show that it accurately categorizes a patient's health status founded on abnormal vital signs that is useful for providing the proper medical care to the patients.

Application of Wireless Sensor Network Computer Technology in Financial Management System

In the current enterprise competition, the core of the management information system is the financial management information system. Building a more comprehensive and efficient financial management information system can effectively establish a more efficient innovative enterprise and promote the transformation and development of the enterprise. This paper first designs a set of financial management system templates, including general ledger, accounts receivable, and accounts payable. Second, according to the characteristics of wireless sensor network technology, this paper proposes a financial management system based on the wireless sensor network and designs a load balancing topology control algorithm (LHTCA algorithm), which balances the energy consumption of network nodes and helps to improve the network life cycle. Finally, the management system is tested and validated. The test results show that the financial management system can realize the electronicization of financial data and paperless vouchers, improve the efficiency of financial work, and promote the sharing and integration of resources.

Sensor fault detection and isolation for smart irrigation wireless sensor network based on parity space

<p>In the recent years, wireless sensor network technology (WSN) has been widely adopted in precision agriculture for determining the needs of the soil in term of water by monitoring some environmental parameters. To do this, WSN is constructed using several sensor nodes; these small sensing devices are prone to failure and may produce erroneous measurements. To ensure good management of freshwater, the network service quality is necessary. To avoid the degradation of service, the detection of the faulty sensor in WSN is required. In this paper, a fault detection and isolation (FDI) algorithm derived from a parity space approach and based on direct redundancy is proposed toward detecting and isolating sensor fault in WSN. In laboratory experiments, the proposed FDI algorithm proved its effectiveness.</p>

Research on the Integration of Wireless Sensor Networks Based on IoT Technology

Wireless Sensor Network (WSN) is a wireless network formed by a group of sensors with wireless transceiver capabilities, connected in a multi-hop autonomous manner. It has several advantages: low traffic, greater stability, longer wireless communication, and higher coverage areas at a lower cost. The incorporation of new NB-IoT technologies in wireless sensor networks, combining the advantages of both and the establishment of NB-IoT-based wireless sensor networks, has a very promising application in defense and military, medical, intelligent transportation, and commercial applications. This study first investigates both WSN and NB-IoT technologies separately and then combines them to investigate the networking structure of NB-IoT and WSN as well as the related technologies based on the fusion. Then the coverage method of the traditional network node redeployment of wireless sensors is described, and many problems such as poor node connectivity and low coverage are caused by the traditional method due to the lack of local search of subgroup nodes. The initialization of the nodes is carried out. The global information exchange is combined with the subgroup to determine the detectable area of the wireless sensor, and the calculus method is used for the irregular area to obtain the optimal network node so that the nodes of the wireless sensor network can be redeployed.

Mobile agents assisted data collection in wireless sensor networks utilizing ZigBee technology

Wireless sensor networks (WSNs) are being utilized widely in many different industries, including agriculture, medicine, and the military. They contain many distributed sensors to monitor physical or environmental factors, such as temperature, humidity, pressure, etc. and use various communication technologies, including WiFi, radio frequency (RF), Bluetooth, and ZigBee. ZigBee is always a preferred choice for applications in WSNs. ZigBee has remarkable capabilities, such as saving energy and transmitting data over long distances. ZigBee end devices, as well as a ZigBee coordinator (ZC) and a ZigBee router (ZR), are crucial components of the WSNs. This article discusses the fundamentals of the ZigBee network, one of the most popular data transmission technologies in wireless sensor networks (WSNs). Additionally, we want to discuss the ZigBee communication technologies and their applications, particularly in the networks. Different scenarios for mobile agents including their routing protocols in WSNs are considered. Simulation results of different scenarios demonstrate how easily scalability can be achieved and provide a foundation for further ZigBee application development. At last, some conclusions and ideas are presented for considerations.

Variance of total dissolved solids and electrical conductivity for water quality in Sabak Bernam

<span lang="EN-US">Water pollution is one of the most serious environmental problems in Malaysia. The most notable occurrence of pollution happened in Selangor. Currently, there are various water quality monitoring (WQM) methods to observe the quality of water. One of the methods used is the internet of things (IoT) for wireless sensor network technology to obtain real-time data measurement. In this study, the developed WQM system is equipped with a sensor that can measure total dissolved solid (TDS) and electrical conductivity (EC). Arduino UNO was used in this system as a microcontroller to interact with the sensor. The Wi-Fi module, ESP8266, was used to transfer the collected data to ThingSpeak, which acts as a cloud to store all the data. The results showed that both sample populations can be discriminated since the p-value is greater than 0.05 in the normality test, while in the paired sample t-test, the p-value is less than 0.05. In conclusion, this research provides an easier way to monitor water quality by taking up less time at less cost, as well as being reliable in giving real-time data reading.</span>

Energy-saving street lamp system based on wireless sensor network

The purpose of this paper is to design a smart street lamp controscheme that uses wireless sensor networks to meet the needs of smartcity construction and to create energy-saving street lamps. First, the traditional neural network algorithm is enhanced to make it simple to dynamically adjust the brightness of the lamp node with ambient light. Secondly, the wireless sensor network technology based on Zigbee communication and the LabVIEW host computer program were combined to create the intelligent street lamp system. Finally, the software and hardware tests were carried out to realize the dynamic adjustment, remote control and data transmission of street lights. The results show that the lamps controlled by neural network can flexibly adjust the light intensity according to the ambient lighting and traffic flow. This method enables intelligent adjustment and energy saving.

THE DEVELOPMENT OF GAMMA RADIATION DOSIMETERS REAL-TIME NETWORK-BASED SENSOR WIRELESS IEEE 802.15.4 PROTOCOL

Several studies have been carried out on the development of real-time gamma radiation dosimeter based on the wireless sensor network (WSN) protocol IEEE 802.15.4. WSN technology can be adopted to develop a gamma radiation dose monitoring system on a large scale due to its real-time, centralized, efficient, and relatively low-cost attributes. This study aims to develop a personal radiation dosimeter that can collect data from the measurement results of several sensor nodes or multi-sensors. The measurement results of radiation dose values from many radiation workers are sent to a coordinator node consisting of the Xbee module and Arduino as a microcontroller. The system test is compared with a calibrated dosimeter reading and is expected to be one of the instruments for determining radiation dose in real-time. It can also reduce the risk of receiving a radiation dose that exceeds the threshold allowed by the ionizing control agency and a program in radiation protection. Changes in the intensity of radiation emitted by Cs-137 (gamma radiation) are carried out by varying each detector's distance to the source, which indirectly varies the value of the dose rate received by the detector. This relationship is shown from the value of R2 = 0.99, which indicates a strong correlation between changes in the dose given to the detector's response being developed. The system built is efficient, inexpensive, and collecting radiation exposure data can monitor radiation exposure received by workers in real-time. This technique can help speed up reporting of radiation exposure received by radiation workers.

Wireless Sensor Network Technology for Vibration Condition Monitoring of Mechanical Equipment

Wireless Sensor Network Technology for Vibration Condition Monitoring of Mechanical Equipment

Timestamp-Free Synchronization Under Delays With Arbitrary Distribution in Wireless Sensor Networks

Clock synchronization is a key technology in wireless sensor networks (WSNs). It is significant and challenging to track clock parameters under arbitrary delay distributions. This letter proposes an Adaptive Gaussian mixture extended Kalman particle filter (AGMEKPF), which is robust to arbitrarily distributed delays in practical WSNs and can jointly track clock skew and offset. It is combined with the timestamp-free synchronization, which is a clock synchronization protocol that can effectively reduce energy consumption without exchanging timestamps. Simulation results show that the proposed method outperforms similar methods in terms of accuracy and complexity.

Soil Energy Harvester for Batteryless Wireless Sensor Network Node using Redox Method

The Wireless Sensor Network technologies has great advantage that provide us with cheap solution to deal with telecommunication infrastructure problem that don’t exist in extreme and isolated area. However, the biggest problem exist within wireless sensor network was WSN node limited power. In this paper we try to provide battery less power sources for Wireless Sensor Network Node using Redox method. Using 9 combinations of electrodes circuits, it can provide 6.53 volt and turn on Arduino Mini Pro microcontroller. However, the second it turns on Arduino Mini Pro the voltage drops to 1.73 Volts. Hence this energy harvester can provide power to the Arduino Mini Pro microcontroller with unstable power supply.

Energy Efficient Routing Protocol for an IoT-Based WSN System to Detect Forest Fires

Around a third of the world’s surface area is covered with forests. This is about 4–5 billion hectares of land. We are losing an additional 3 million hectares of forest cover each year than we did in 2001. Hence, researchers worldwide have been working on the identification and prevention of forest fires more aggressively. The wireless sensor network technology for forest fire detection was established as a result of the shortcomings of standard forest fire detection techniques in real time. This paper proposes an energy-aware Internet of Things (IoT) based Wireless Sensor Network (WSN) model with an efficient clustering and routing technique for forest fire detection. The proposed model is named Energy Efficient Routing Protocol (EERP). The model decreases the energy utilization in sensor nodes by minimizing idle listening in cluster heads. EERP also minimizes the transmission of redundant data by allowing only the sensor nodes close to an event to report the event. Furthermore, the model ensures that sensor nodes with low energy levels do not become cluster heads. EERP uses multi-hop routes to send data from source nodes to the Base Station. The performance of EERP is compared with that of some existing Medium Access Control (MAC) protocols in various scenarios. The simulation results prove that the proposed model reduces energy consumption in sensor nodes significantly.

Mental-state estimation model with time-series environmental data regarding cognitive function

It is expected that understanding and estimating the human mental state will be helpful for mental health measures, improving learning and labor work efficiency, and preventing human error. Our research group has been developing a methodology and model for estimating the mental state of humans in their environment from indoor environmental data regarding cognitive function obtained by wireless sensor network technology. This study constructed a model to estimate mental states from multidimensional time-series indoor environmental data such as temperature, humidity, and illumination. The experiment results show that the proposed system shows higher estimation accuracy. In particular, the CNN-LSTM model with multidimensional time-series indoor environmental data shows about 90% estimation accuracy. The results obtained in this study, showing that mental states can be determined with high accuracy from environmental data, are helpful for future research approaches. It may also contribute to the creation of a less stressful environment.

Design and Experimental Investigation of a Thermoelectric Conversion Device with Power Management for Forest Fire Monitoring

Forest fires have long been a significant global problem. How to reliably accomplish the early warning and real-time monitoring of forest fires has become a pressing issue in order to limit the damage caused by forest fires. A novel technological approach for forest fire monitoring has been made possible by the quick development of wireless sensor network (WSN) technology. Currently, batteries are the primary source of power for WSNs used in forests, but frequent battery replacement will compromise the network for monitoring. As a result, the power supply is the key limit to its application in forest areas. This paper puts forward the thermoelectric conversion based on the Seebeck effect. Its notable feature is to convert heat energy into electric energy through the temperature difference between shallow soil and air. In the process of testing the device, the maximum voltage was 803.36 mV. At the same time, a power management system (PMS) for a thermoelectric conversion device was designed. The main feature of this system is that there is no need for an external control module. In the laboratory test, the minimum input power of this system was 200 mV. When the load resistance was 8 KΩ, the output power was 0.55 mW, and the maximum efficiency could reach 65.38% when the input was 500 mV, which fulfills the requirements of low cost and high reliability, providing a feasible solution for solving the energy limitation problem of WSNs in a forest area.

Dynamic collaborative optimization of end-to-end delay and power consumption in wireless sensor networks for smart distribution grids

Wireless sensor network (WSN) technology is poised to be widely adopted in the smart distribution grid (SDG), which has strict requirements regarding communication delays. However, delays in WSNs are easily affected by dynamic interference factors (such as channel access competition, transmitting power, and node failure), and these dynamic characteristics make the traditional offline optimization methods unsuitable. Besides, the reinforcement learning (RL) based online optimization methods have dimension explosion and convergence problems. In this paper, we propose a dynamic collaborative optimization of the end-to-end delay and power consumption of the WSNs based on grouped RL. In particular, we first build an environment model for evaluating the values of the optimization objective. Those values are used to calculate the rewards for the RL algorithm. To accelerate the convergence of RL swamped by the dimensions of the action space, a novel grouped RL is proposed. Then iterative learning is performed to balance the end-to-end delay and power consumption by adjusting the transmitting power of each node. The simulation results show that the proposed algorithm is able to meet the SDG delay requirements with low power consumption when the communication is dynamically affected. The developed algorithm achieves a maximum end-to-end delay reduction of 20.3% and a computational cost reduction of 6.2% to 52.7% compared with the other two RL algorithms.

A Perfect Security Key Management Method for Hierarchical Wireless Sensor Networks in Medical Environments

Wireless sensor networks (WSNs) have developed during the past twenty years as a result of the accessibility of inexpensive, short-range, and simple-to-deploy sensors. A WSN technology sends the real-time sense information of a specific monitoring environment to a backend for processing and analysis. Security and management concerns have become hot topics with WSN systems due to the popularity of wireless communication channels. A large number of sensors are dispersed in an unmonitored medical environment, making them not safe from different risks, even though the information conveyed is vital, such as health data. Due to the sensor′s still limited resources, protecting information in WSN is a significant difficulty. This paper presents a hierarchical key management method for safeguarding heterogeneousWSNs on hybrid energy-efficient distributed (HEED) routing. In the proposed method, the Bloom scheme is used for key management and a pseudo-random number generator (PRNG) to generate keys in an efficient method to keep sensor resources. In addition, using cipher block chaining-Rivest cipher 5 (CBC-RC5) in this method achieved cryptography goals such as confidentiality. A comparison is made between the proposed and existing methods such as dynamic secret key management (DSKM) and smart security implementation (SSI) under the same circumstance to determine the performance of the new method. The data transmission in WSN consumes about 71 percent of a sensor′s energy, while encryption computation consumes only 2 percent. As a result, our method reduces the frequency with which data transmissions are made during the key management process. The simulation findings demonstrated that, in comparison to earlier techniques, the proposed method is significantly more secure, flexible, scalable, and energy-efficient. Our proposed method is also able to prevent classifications of node capture attacks.