Wireless Sensor Network For Monitoring Research Articles

An Approach to Environmental Monitoring in Sparse Linear Wireless Sensor Networks for Energy Conservation Using Dual Sinks

An Approach to Environmental Monitoring in Sparse Linear Wireless Sensor Networks for Energy Conservation Using Dual Sinks

Implementation of Wireless Sensor Network for Continuous Seismic Monitoring of Isolated Cable-Stayed Bridge

Implementation of Wireless Sensor Network for Continuous Seismic Monitoring of Isolated Cable-Stayed Bridge

Energy-efficient collection scheme based on compressive sensing in underwater wireless sensor networks for environment monitoring over fading channels

Considering the energy-limited and fading channels of large underwater wireless sensor networks (UWSNs) in long-term environment monitoring, an energy-efficient collection scheme based on compressive sensing (CS) in UWSNs for environment monitoring over fading channels is proposed. In this paper, a CS-based UWSNs data collection model is established by exploiting the spatial sparsity of underwater environment data to reduce the number of sensor nodes required. By considering the impact of channel fading on instantaneous power, a packet transmission strategy is deduced to ensure the successful reception of given number of packets. Furthermore, a CS-based energy-efficient collection scheme is proposed based on the model and the strategy to realize an efficient monitoring of the target field and reduce the energy consumption of UWSNs. Performance analysis is conducted and real data example are provided to illustrate the validity of the proposed scheme.

A hybrid approach to enhance the lifespan of WSNs in nuclear power plant monitoring system

In recent years, the nuclear power plant has received huge attention as it generates vast amounts of power at a lower cost. However, its creation of radioactive wastes is a major environmental concern. Therefore, the nuclear power plant requires a reliable and uninterrupted monitoring system as an essential part of it. Monitoring a nuclear power plant using wireless sensor networks is a convenient and popular practice now. This paper proposes a hybrid approach for monitoring wireless sensor networks in the context of a nuclear power plant in Bangladesh. Our hybrid approach enhances the lifespan of wireless sensor networks reducing power consumption and offering better connectivity of sensors. To do so, it uses both the topology maintenance and topology construction algorithms. We found that the HGETRecRot topology maintenance algorithm enhances the network lifetime compared to other algorithms. This algorithm increases the communication and sensing coverage area but decreases the network performance. We also propose a prediction model, based on linear regression algorithm, that predicts the best combination of topology maintenance and topology construction algorithms.

A Novel RF Energy Harvester for Event-Based Environmental Monitoring in Wireless Sensor Networks

Perpetual energy source is the major research issue in the research community for energy-constrained wireless sensor network (WSN) and IoT applications. Radio-frequency (RF) energy harvesting has become a technology available round the clock due to increased communication activities. However, efficient RF energy harvesting for low-power regions [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PRF \leq (0 {\mathrm{ dBm}})$ </tex-math></inline-formula> ] is still a challenge. This article proposes a novel RF energy harvesting technique along with an intelligent dynamic energy flow control algorithm. The algorithm works with dedicated energy flow control hardware. It is also able to provide perpetual life time to the ultralow power sensor nodes. A close approximation of the size of the RF energy harvester array is proposed for the perpetual lifetime of the WSN node deployed with wake-up radio for event-based monitoring applications. The model is verified by mathematical analysis, simulation, and fabricated prototype results for the operability in the given system. It is found that the circuit provides improved dc output voltage over the previous benchmark at low power in the range of −20 to 0 dBm. The system can charge a 2.7 V, 350 F supercapacitor with the rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.16~\mu \text{V}$ </tex-math></inline-formula> /s at 0 dBm, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.125~\mu \text{V}$ </tex-math></inline-formula> /s at −6 dBm, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.08~\mu \text{V}$ </tex-math></inline-formula> /s at −20 dBm. To prove the utility of the proposed idea, an RF field survey is also conducted and found that the proposed circuit can harvest energy from high power region (≥ −6 dBm) for LTE850 and LTE 900 bands.

Practice of Air Environment Quality Monitoring Data Visualization Technology Based on Adaptive Wireless Sensor Networks

This paper begins by outlining the evolution and current state of traditional wireless sensor networks, followed by an examination of the principle and architecture of wireless sensor networks, as well as the challenges that traditional wireless sensor networks face in environmental monitoring. This paper designs and develops a data visualization system for wireless sensor networks based on the visualization system and the advantages of other visualization systems, after analyzing and comparing the current visualization systems for monitoring wireless sensor networks. A real-time monitoring system for urban air quality based on wireless sensor network is designed to solve the problem that the traditional method of combining manual sampling with laboratory analysis for air quality monitoring is expensive and cannot carry out real-time online monitoring for multiple points at the same time. The system’s implementation is based primarily on air quality monitoring data collected in the Shenyang area for visual display and analysis. The air quality monitoring system proposed in this paper has a complete structure and independent modules and is simple to use. The dynamic adaptability of the monitoring system is greatly improved by utilizing the self-adaptability and self-organization of wireless sensor networks, which brings great convenience to air quality monitoring.

Safety Training Monitoring System of Ski Resort Based on Wireless Sensor Network

With the gradual rise in China’s ski industry, skiing is now the most common sport among winter outdoor sports, which is closely related to the origin of safety issues. Based on the ski resort, training is the key to safety detection, only to ensure the safety of training personnel and to maintain the momentum of high-speed development of skiing sports for a long time. The monitoring system plays an important role in the wireless sensor network monitoring system. Common monitoring systems are used for agriculture, smart home, and outdoor environment monitoring systems, most of which are professional fields. Based on the safety of ski resorts, this paper puts forward the research of ski training safety system based on the wireless sensor. In the research of ski training detection system based on the wireless sensor network, first, the safety system of domestic ski resort is analyzed and detailed suggestions are given. Then, this paper studies the routing protocol of wireless communication technology in detail and compares the service life and energy consumption of nodes under the use of two different routing protocols in wireless sensor networks. According to the data experiment in this paper, LEACH-MH protocol has great advantages over the LEACH protocol. The LEACH-MH protocol detects 185 more rounds than the LEACH protocol in the total detection lifetime. In the detection of energy consumption of 100 m ∗ 100 m, the LEACH protocol consumes 50J in 400 rounds, while the LEACH-MH protocol consumes only 50J in 760 rounds.

Development of young sago palm environmental monitoring system with wireless sensor networks

• Real-time monitoring of the environmental parameters (temperature, relative humidity, and light intensity) of young sago palms in mist chambers. • Establishment of a web-based monitoring system with wireless sensor networks using Zigbee protocol and GPRS gateway. • Reliability and performance of the system is discussed in terms of packet loss rate and mobile data consumption. • Effect of functional system with respect to the environmental factors monitored is analysed. Environmental monitoring plays a significant role in the agriculture field as it manages the critical factors required for sustainable growth of plants, and thus enhancing the quality and productivity of the crops. In this paper, the development of a wireless sensor network (WSN) monitoring system for young sago palms in mist chambers is presented. The effect of the functional system with respect to the environmental factors monitored is also discussed. The system was set up to monitor three environmental factors i.e., temperature, relative humidity, and light intensity. Instantaneous readings of the monitored parameters were made possible through a wireless network and a web portal connection. The system recorded the environmental reading at every 15-min interval and transmitted the data from node to base using the Zigbee network. The data was then stored in an online cloud platform through the general radio packet service (GPRS) gateway. Linear actuators were also installed to control the mist chamber doors opening for natural ventilation and they were automatically triggered by the threshold temperature. Besides, a smart alert system was added to the system prompting for actions via short messaging services (SMS) when triggered. The developed system achieved a reliable data transmission with a low packet loss rate (PLR) of 2.3%. The use of the GPRS gateway also enabled the system to operate at a lower cost as the mobile data consumption was low (10 MB per month). In conclusion, a WSN system for mist chambers in a sago palm greenhouse was successfully developed to real-time monitor the plant growing environment through data acquisition, data transmission and data visualisation. The developed WSN system had laid a foundation for smart farming. For future applications, the system could be further modified and developed for precision agriculture.

Development of a Wireless Sensor Network for Structural Health Monitoring

The importance of wireless sensor networks for monitoring structural health is constantly developing in response to the rising need for security and safety in urban areas. As wireless technology has improved, wireless sensor networks have become an integral part of structural monitoring systems. When compared to traditional wired systems, structural health monitoring systems based on wireless sensor networks provide novel technology with tempting advantages, such as reduced installation and maintenance costs. Due to structural health monitoring, wireless sensor networks now have to deal with even more challenging network design problems. This report summarizes the extensive knowledge gained by researchers using wireless sensor networks for structural health monitoring. We look into the design, functioning, communication methods, and well-known operating systems of wireless sensor nodes, as well as the technologies used in both wired and wireless sensor systems. The most up-to-date summaries of academic and commercial wireless platform technologies are then tallied and evaluated in the context of testbed and field deployments for applications related to structural health monitoring. After this, the primary obstacles connected with using wireless sensor networks for structural health monitoring are outlined, and the feasibility of applying wireless technology for such applications is discussed at length. Finally, the issues that still need fixing in wireless sensor networks for structural health monitoring are analyzed.

Energy Efficient Data Aggregation and Density-Based Spatial Clustering of Applications with Noise for Activity Monitoring in Wireless Sensor Networks

Energy Efficient Data Aggregation and Density-Based Spatial Clustering of Applications with Noise for Activity Monitoring in Wireless Sensor Networks

Modeling and Simulation of Abnormal Behavior Detection Through History Trajectory Monitoring in Wireless Sensor Networks

Data security is becoming increasingly important with the growing popularity of smart management systems combining wireless sensor networks (WSNs) and intelligent systems in industrial, agricultural, and construction fields. Traditional security methods for WSN have focused on data integrity and the identification of outliers in sensor data and are therefore vulnerable to attacks such as false positive attacks (FPAs) and false negative attack (FNAs) using massively compromised nodes. these attacks significantly compromise the sensor nodes because the correlation between node verification behaviors is not considered in the communication process until the intercepted sensor data are used as input into the system. This study introduces an FPA and FNA detection method using a spatiotemporal historical data-based knowledge base. The main contribution of the present study is the recognition of abnormal correlations using behavior monitoring through the discrete event system specification model. By recognizing abnormal correlations, the proposed method prevents the inflow of false data generated as a result of widespread damage. Furthermore, a new strategy is proposed to maximize the lifetime of the network by blocking any compromised nodes. The proposed spatiotemporal data-based detection approach is applicable to a wide variety of fields owing to its use of a shared security model. The proposed method was shown to reduce the energy consumption by 46.737% and 41.927% in comparison to statistical en-route filtering and cluster-based false-data filtering, respectively.

Air Pollution Prediction Using Dual Graph Convolution LSTM Technique

In current scenario, Wireless Sensor Networks (WSNs) has been applied on variety of applications such as targets tracking, natural resources investigation, monitoring on unapproachable place and so on. Through the sensor nodes, the information for the applications is gathered and transferred. The physical coordination of these sensor nodes is determined, and it is called as localization. The WSN localization methods are studied widely for recent research with the study of small proportion of the sensor node called anchor nodes and their positions are determined through the GPS devices. Sometimes sensor nodes can be a IoT device in the network. With despite this, among the various applications, air pollution and air quality monitoring having many issues on how to place the sensor network in a wide area to monitor the air pollutants level such as carbon dioxide (CO2), nitrogen dioxides (NO2), particulate matter (PM), sulphur dioxide (SO2), ammonia (NH3) and other toxic gases involved in human and industrial activities. The responsibility of the WSN in air quality monitoring is to be positioning the sensor nodes in the large area with low cost and also gather the real time data and produce the monitoring system as an accurate one. In this proposed work, deep learning-based approach called dual graph convolution and LSTM (Long Short-Term Memory) network based (air quality index) AQI predictions were performed. This uses the infrared based technology to measure the CO2, temperature and humidity, Geo statistic method and low power wireless networking. Accuracy of the proposed system is maximum of 95% which is higher than existing techniques.

A software-defined multi-modal wireless sensor network for ocean monitoring

The software-defined networking paradigm enables wireless sensor networks as a programmable and reconfigurable network to improve network management and efficiency. However, several challenges arise when implementing the concept of software-defined networking in maritime wireless sensor networks, as the networks operate in harsh ocean environments, and the dominant underwater acoustic systems are with limited bandwidth and high latency, which render the implementation of software-defined networking central-control difficult. To cope with the problems and meet demand for high-speed data transmission, we propose a radio frequency–acoustic software-defined networking-based multi-modal wireless sensor network which leverages benefits of both radio frequency and acoustic communication systems for ocean monitoring. We first present the software-defined networking-based multi-modal network architecture, and then explore two examples of applications with this architecture: network deployment and coverage for intrusion detection with both grid-based and random deployment scenarios, and a novel underwater testbed design by incorporating radio frequency–acoustic multi-modal techniques to facilitate marine sensor network experiments. Finally, we evaluate the performance of deployment and coverage of software-defined networking-based multi-modal wireless sensor network through simulations with several scenarios to verify the effectiveness of the network.

Stratified Sampling Based Compressed Sensing for Structured Signals

Structured compressed sensing takes signal structure into account, thereby outperforming earlier compressed sensing methods. However, results are usually based on sampling in the Fourier domain, such as in Magnetic Resonance Imaging. In the time domain, the benefits of structured compressed sensing are still unknown. This paper introduces concepts that incorporate the signal structure into both the acquisition and reconstruction of compressed sensing in time and image domain applications. First, a stratified-random sampling pattern is proposed to improve the recovery of the dominant low-frequency range of natural signals. A heuristic <i>decay of primes</i> criterion is developed to evaluate the properties of the sensing matrix and is used to optimize the sampling pattern. Second, the sparsity of the Fourier transform as the representation domain is improved by estimating the signal structure in a preprocessing step, and then adapting the grid of the Fourier transform. In contrast to existing methods, <i>grid stretching</i> is integrated into the fast Fourier transform to reduce computational complexity. Both structured acquisition and reconstruction are evaluated using simulations, as well as two real-world applications: wireless sensor networks in structural health monitoring and electron microscopy. Results show that both reconstruction errors and robustness can significantly be improved by incorporating structure into the acquisition and reconstruction.

An Anchor-Based Localization in Underwater Wireless Sensor Networks for Industrial Oil Pipeline Monitoring

Industries need solutions that can automatically monitor oil leakage from deployed underwater pipelines and to rapidly report any damage. The location prediction of mineral reservoirs like oil, gas, or metals in deep water is a challenge during the extraction of these resources. Moreover, the problem of ores and mineral deposits on the seafloor comes into play. The abovementioned challenges necessitate for the deployment of underwater wireless sensor networks (UWSNs). Anchor-based localization techniques are segregated into range-free and range-based processes. Range-based schemes depend on various techniques like angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), and received signal strength indicator (RSSI). In this article, the localization of these leakages is performed by using range-based metrics for calculating the distance among anchor nodes (ANs) and target nodes (TNs). This estimated distance is further optimized to minimize the estimation error. A multilateralism procedure is used to estimate the optimal position of each TN. The results exhibit that the proposed algorithm shows a high performance when compared to previous works, in terms of minimum energy consumption, lower packet loss, rapid location estimation, and lowest localization error. The benefit of using the proposed methodology greatly impacts on identifying the leakage area in mobility-assisted UWSN, where rapid reporting helps to lower the loss of resources.

Solar Powered Water Quality Monitoring System Using Zig-bee

This work presents a solar power based water quality monitoring wireless sensor network (WSN) technology, to screen water quality over various destinations as an ongoing application, an astounding framework engineering comprised by disseminated sensor nodes and a base station is proposed. The model and base station are associated utilizing WSN innovation like Zig-Bee. Modeling and implementation of a proposed model using one node powered by solar cell and WSN technology is the challenging work. Data collection by different sensing element at the node side like pH, turbidity conductivity, saltiness and temperature is sent by means of WSN to the base station. Information gathered from the distant site can be shown in visual organization too as it very well may be examined utilizing diverse reproduction apparatuses at base station. This original framework has benefits like no fossil fuel byproduct, low power utilization, more adaptable to send data at far off site, etc.

An efficient coverage method for SEMWSNs based on adaptive chaotic Gaussian variant snake optimization algorithm.

Soil element monitoring wireless sensor networks (SEMWSNs) are widely used in soil element monitoring agricultural activities. SEMWSNs monitor changes in soil elemental content during agriculture products growing through nodes. Based on the feedback from the nodes, farmers adjust irrigation and fertilization strategies on time, thus promoting the economic growth of crops. The critical issue in SEMWSNs coverage studies is to achieve maximum coverage of the entire monitoring field by adopting a smaller number of sensor nodes. In this study, a unique adaptive chaotic Gaussian variant snake optimization algorithm (ACGSOA) is proposed for solving the above problem, which also has the advantages of solid robustness, low algorithmic complexity, and fast convergence. A new chaotic operator is proposed in this paper to optimize the position parameters of individuals, enhancing the convergence speed of the algorithm. Moreover, an adaptive Gaussian variant operator is also designed in this paper to effectively avoid SEMWSNs from falling into local optima during the deployment process. Simulation experiments are designed to compare ACGSOA with other widely used metaheuristics, namely snake optimizer (SO), whale optimization algorithm (WOA), artificial bee colony algorithm (ABC), and fruit fly optimization algorithm (FOA). The simulation results show that the performance of ACGSOA has been dramatically improved. On the one hand, ACGSOA outperforms other methods in terms of convergence speed, and on the other hand, the coverage rate is improved by 7.20%, 7.32%, 7.96%, and 11.03% compared with SO, WOA, ABC, and FOA, respectively.

Energy-efficient and balanced routing in low-power wireless sensor networks for data collection

Cost-based routing protocols are the main approach used in practical wireless sensor network (WSN) and Internet of Things (IoT) deployments for data collection applications with energy constraints; however, those routing protocols lead to the concentration of most of the data traffic on some specific nodes which provide the best available routes, thus significantly increasing their energy consumption. Consequently, nodes providing the best routes are potentially the first ones to deplete their batteries and stop working. In this paper, we introduce a novel routing strategy for energy efficient and balanced data collection in WSNs/IoT, which can be applied to any cost-based routing solution to exploit suboptimal network routing alternatives based on the parent set concept. While still taking advantage of the stable routing topologies built in cost-based routing protocols, our approach adds a random component into the process of packet forwarding to achieve a better network lifetime in WSNs. We evaluate the implementation of our approach against other state-of-the-art WSN routing protocols through thorough real-world testbed experiments and simulations, and demonstrate that our approach achieves a significant reduction in the energy consumption of the routing layer in the busiest nodes ranging from 11% to 59%, while maintaining over 99% reliability. Furthermore, we conduct the field deployment of our approach in a heterogeneous WSN for environmental monitoring in a forest area, report the experimental results and illustrate the effectiveness of our approach in detail. Our EER based routing protocol CTP+EER is made available as open source to the community for evaluation and adoption.

Development of a Wireless Corrosion Detection System for Steel-Framed Structures Using Pulsed Eddy Currents

Structural health monitoring (SHM) can be more efficient with the application of a wireless sensor network (WSN). However, the hardware that makes up this system should have sufficient performance to sample the data collected from the sensor in real-time situations. High-performance hardware can be used for this purpose, but is not suitable in this application because of its relatively high power consumption, high cost, large size, and so on. In this paper, an optimal remote monitoring system platform for SHM is proposed based on pulsed eddy current (PEC) that is utilized for measuring the corrosion of a steel-framed construction. A circuit to delay the PEC response based on the resistance–inductance–capacitance (RLC) combination was designed for data sampling to utilize the conventional hardware of WSN for SHM, and this approach was verified by simulations and experiments. Especially, the importance of configuring sensing modules and the WSN for remote monitoring were studied, and the PEC responses caused by the corrosion of a specimen made with steel were able to be sampled remotely using the proposed system. Therefore, we present a remote SHM system platform for diagnosing the corrosion condition of a building with a steel structure, and proving its viability with experiments.

Integration of Wireless Sensor Network and IoT for Smart Environment Monitoring System

Air contamination, water waste, and radioactive contamination are significant environmental issues. Adequate supervision is needed to ensure economic sustainability through the preservation of a good society. Environmental tracking has become a Smart environment monitoring (SEM) system in recent times, with developments in the Internet of Things and the creation of advanced detectors. This situation evaluates substantial achievements and scientific studies on SEM, including air quality control, water management, radiation emissions, and agricultural practices. A Wireless sensor network and IoT integrated system for Smart environment monitoring (WSN-IoT-SEC) framework are proposed in this research. The analysis will be divided employing SEM techniques applications, and each aim will then be further studied in terms of the detectors, machine learning models, and classifiers operated. A systematic study was carried out based on the study’s evaluated results and patterns, indicating important suggestions and the SEM analysis’s influence. The researchers have discussed objectively how the advancements in mobile technologies, IoT, and wireless sensor networks allow the control of the atmosphere an intelligent monitoring device. Eventually, the concept of rigorous machine learning techniques has been proposed, denouncing techniques and developing appropriate WSN specifications.