Wireless Sensor Network For Monitoring Research Articles

An Agricultural Monitoring System Based on Wireless Sensor and Depth Learning Algorithm

The rise and development of the Internet of Things (IoT) have given birth to the frontier technology of the agricultural IoT, which marks the future trend in agriculture and the IoT. The agricultural IoT can be combined with Zigbee, a short-range wireless network technology for monitoring systems, to solve the excessively large planting area and other defects in agricultural production. Meanwhile, the modernization of planting and harvesting has set the stage for deep learning. Unlike the artificial neural network, the deep learning is an important intelligent algorithm, capable of solving many real-world problems. Therefore, this paper probes into the problems of modern automatic agriculture. First, the routing allocation technology and transmission mode were optimized to solve the energy consumption problem. Second, the classification model based on deep learning algorithm was put forward according to the application of the Wireless Sensor Network (WSN) in continuous monitoring of soil temperature and humidity. Despite the lack of upper soil humidity sensor in agriculture, the model can still classify the soil moisture of the nodes, and derive the main soil moisture content. Finally, a solution was presented based on agricultural ZigBee WSN technology. In addition to cheap cost and low power consumption, the solution has the functions of reminding and recognition due to the adoption of artificial intelligence algorithm. Suffice it to say that the solution is a successful attempt to integrate artificial intelligence and sensor technology into agricultural modernization.

Wireless sensor networks: A survey on monitoring water quality

Abstract Diseases related to poor water and sanitation conditions have over 200 million cases reported annually, causing 5–10 million deaths world-wide. Water quality monitoring has thus become essential to the supply of clean and safe water. Conventional monitoring processes involve manual collection of samples from various points in the distribution network, followed by laboratory testing and analysis. This process has proved to be ineffective since it is laborious, time consuming and lacks real-time results to promote proactive response to water contamination. Wireless sensor networks (WSN) have since been considered a promising alternative to complement conventional monitoring processes. These networks are relatively affordable and allow measurements to be taken remotely, in real-time and with minimal human intervention. This work surveys the application of WSN in environmental monitoring, with particular emphasis on water quality. Various WSN based water quality monitoring methods suggested by other authors are studied and analyzed, taking into account their coverage, energy and security concerns. The work also compares and evaluates sensor node architectures proposed the various authors in terms of monitored parameters, microcontroller/microprocessor units (MCU) and wireless communication standards adopted, localization, data security implementation, power supply architectures, autonomy and potential application scenarios.

On enhancing energy harvesting performance of the photovoltaic modules using an automatic cooling system and assessing its economic benefits of mitigating greenhouse effects on the environment

The performance of photovoltaic (PV) modules under outdoor operation is greatly affected by their location and environmental conditions. The temperature of a PV module gradually increases as it is exposed to solar irradiation, resulting in degradation of its electrical characteristics and power generation efficiency. This study adopts wireless sensor network (WSN) technology to develop an automatic water-cooling system for PV modules in order to improve their PV power generation efficiency. A temperature estimation method is developed to quickly and accurately estimate the PV module temperatures based on weather data provided from the WSN monitoring system. Further, an estimation method is also proposed for evaluation of the electrical characteristics and output power of the PV modules, which is performed remotely via a control platform. The automatic WSN-based water-cooling mechanism is designed to avoid the PV module temperature from reaching saturation. Equipping each PV module with the WSN-based cooling system, the ambient conditions are monitored automatically so that the temperature of the PV module is controlled by sprinkling water on the panel surface. The field-test experiment results show an increase in the energy harvested by the PV modules of approximately 17.75% when using the proposed WSN-based cooling system.

NDSC based methods for maximizing the lifespan of randomly deployed wireless sensor networks for infrastructures monitoring

Abstract This paper addresses the problem of wireless sensor network (WSN) lifetime maximization, under limited available energy constraint. The investigations have shown that the valid amount of energy was not used by the existing disjoint set covers (DSC) based scheduling method for WSNs lifetime maximization, because of the DSC constraints. Instead, we suggest in this paper to schedule non-disjoint sets covers (NDSC) for maximizing WSNs lifetime. Thus, we have formulated this problem, using the integer linear programming (ILP) mathematical model, then we developed an approach based on genetic algorithm GA to find the maximal lifespan. As main contributions, we investigated and designed a new method using the NDSC instead of the DSC. This approach removes the latter’s constraint and gives the opportunity to a sensor to participate in more than one cover, and thereby improves significantly the WSNs’ lifetime. We proposed an exact method and a genetic algorithms (GA) for the NDSC efficient scheduling for the WSN lifetime maximization. The exact method lies on the integer linear programming (ILP). For the GA based heuristics, we used a specific arrangement of chromosomes combining several crossover and mutation strategies for encoding the solutions. We provided experimental results for different instances involving sensors with non-identical amount of initial energy and power consumption. In addition, we provided comparative analysis results between the solutions obtained by our both methods and the existing methods based on DSC. The comparisons of the run times and the solutions’ quality revealed the dominance of the solutions yielded by our methods based on the NDSC compared to those based on the DSC.

A statistical analysis on the leak detection performance of underground and overground pipelines with wireless sensor networks through the maximum likelihood ratio test

Leaks in pipelines of the oil and gas industry are an economic and environmental problem that needs to be detected early and effectively. Wireless sensor networks (WSNs) have been researched as one of those technologies to be used in the remote monitoring of pipeline infrastructure. The idea of using tiny sensor nodes on pipelines seemingly provides industries with effective and reliable real-time monitoring, and better coverage density per area. The benefits are apparent in the deployment of WSNs for pipeline monitoring. However, what really lacks is an actual comparison in the detection performance between deployment in overground pipelines and underground pipelines. Extensive research has been going on the use of wireless underground sensor networks for a number of applications. This paper attempts to provide a statistical insight on the concepts of leak detection performance of WSNs when deployed on overground and underground pipelines. The approach in the study employs the hypothesis testing problem to formulate a solution on the detection plan. Through the hypothesis test, the maximum likelihood ratio scheme is used to provide an optimal performance analysis of the detection idea. The test also takes into consideration the signal to noise ratio performance of the two settings of underground and overground and is crucial in bringing up a conjecture on the performance of detection. As would be shown in the paper, thresholds, determined by probability, are the key in ensuring a good detecting performance for the WSN.

Developing residential wireless sensor networks for ECG healthcare monitoring

Wireless technology development has increased rapidly due to it’s convenience and cost effectiveness compared to wired applications, particularly considering the advantages offered by Wireless Sensor Network (WSN) based applications. Such applications exist in several domains including healthcare, medical, industrial and home automation. In the present study, a home-based wireless ECG monitoring system using Zigbee technology is considered. Such systems can be useful for monitoring people in their own home as well as for periodic monitoring by physicians for appropriate healthcare, allowing people to live in their home for longer. Health monitoring systems can continuously monitor many physiological signals and offer further analysis and interpretation. The characteristics and drawbacks of these systems may affect the wearer’s mobility during monitoring the vital signs. Real-time monitoring systems record, measure, and monitor the heart electrical activity while maintaining the consumer’s comfort. Zigbee devices can offer low-power, small size, and a low-cost suitable solution for monitoring the ECG signal in the home, but such systems are often designed in isolation, with no consideration of existing home control networks and smart home solutions. The present study offers a state of the art review and then introduces the main concepts and contents of the wireless ECG monitoring systems. In addition, models of the ECG signal and the power consumption formulas are highlighted. Challenges and future perspectives are also reported. The paper concludes that such mass-market health monitoring systems will only be prevalent when implemented together with home environmental monitoring and control systems.

산업 무선 센서 네트워크에서 실시간 모니터링을 위한 신뢰성 향상 기법

산업 무선 센서 네트워크에서 실시간 모니터링을 위한 신뢰성 향상 기법

A Study on Piezoelectric Energy-Harvesting Wireless Sensor Networks Deployed in a Weak Vibration Environment

Wireless sensor network (WSN) is an interestingly promising technology with many applications in intelligent environment-monitoring. Its energy consumption is, however, essentially challenged after it is deployed. In this paper, we developed a low-resonant-frequency piezoelectric energy harvesting device as an alternative to power WSN deployed in a weak vibration environment. An energy management module, equipped with a full-bridge rectifier (FBR), a fully self-powered voltage controller, and a logic-level protection circuit was designed to enable a regulated voltage to intermittently drive WSN for temperature monitoring. The power transfer efficiency of FBR reached 42%, presenting a good performance/cost ratio in the case of a relatively high open-circuit voltage of 8 V and a low rectified voltage of 3.3 V. The experimental results have demonstrated that the proposed WSN was successfully driven at an interval time of less than 1 min, and fully self-powered by the scavenged energy from the environmental vibration source of a 0.15-g acceleration and a 40-Hz vibration frequency.

A Survey on Data Quality for Dependable Monitoring in Wireless Sensor Networks.

Wireless sensor networks are being increasingly used in several application areas, particularly to collect data and monitor physical processes. Non-functional requirements, like reliability, security or availability, are often important and must be accounted for in the application development. For that purpose, there is a large body of knowledge on dependability techniques for distributed systems, which provide a good basis to understand how to satisfy these non-functional requirements of WSN-based monitoring applications. Given the data-centric nature of monitoring applications, it is of particular importance to ensure that data are reliable or, more generically, that they have the necessary quality. In this survey, we look into the problem of ensuring the desired quality of data for dependable monitoring using WSNs. We take a dependability-oriented perspective, reviewing the possible impairments to dependability and the prominent existing solutions to solve or mitigate these impairments. Despite the variety of components that may form a WSN-based monitoring system, we give particular attention to understanding which faults can affect sensors, how they can affect the quality of the information and how this quality can be improved and quantified.

A case study of an autonomous wireless sensor network system for environmental data collection

This article presents a case study of the design of an autonomous wireless sensor network for environmental monitoring to provide data for understanding the relationships among food, energy, and water systems in suburban areas in Long Island New York. The study focuses on the existing water quality data for the two counties on Long Island, Nassau and Suffolk Counties, to understand the impact of energy and food production on the coastal waters of Long Island Sound and Peconic Bay. The analysis of existing data from the US Environmental Protection Agency database reveals the challenges in using such data to understand the relationships between water quality, weather events and identified point sources of pollutants. Maintaining on-going data collection programs is expensive, time-consuming and personnel intensive which is why this project examines new ways to obtain and utilize environmental data. The researchers propose the design of an autonomous wireless sensor network environmental monitoring system with an optimal sensor placement strategy to identify targeted locations for water quality monitoring. The goal is to provide environmental data that can facilitate a better understanding of the interactions of farming activities and energy production resulting in undesirable impacts on water quality. The proposed design can be used to provide real-time data to help stakeholders improve efficiency in resource management and advance early detection of potential hazardous events. © 2017 American Institute of Chemical Engineers Environ Prog, 2017

Sleep Scheduling in Industrial Wireless Sensor Networks for Toxic Gas Monitoring

Toxic gas leakage that leads to equipment damage, environmental effects, and injuries to humans is the key concern in large-scale industries, particularly in petrochemical plants. Industrial wireless sensor networks (IWSNs) are specially designed for industrial applications with improved efficiency, and remote sensing for toxic gas leakage. Sleep scheduling is a common approach in IWSNs to overcome the network lifetime problem due to energy constrained nodes. In this article, we propose a sleep scheduling scheme that ensures a coverage degree requirement based on the dangerous levels of the toxic gas leakage area, while maintaining global network connectivity with minimal awake nodes. Unlike the previous sleep scheduling algorithm, for example, the connected k-neighborhood (CKN)-based approach that wakes up the sleep nodes over the entire sensing field by increasing the k-value, our proposed scheme dynamically wakes up the sleep nodes only in the particular toxic gas leakage area. Simulation results show that our proposed scheme outperforms the CKN-based sleep scheduling scheme with the same required coverage degree for the toxic gas leakage area. In addition, the proposed scheme considers multiple hazardous zones with various coverage degree requirements. We show that at the expense of a slight extra message overhead, energy consumption in terms of totally awake nodes over the entire sensing field is reduced compared to the other approaches, while maintaining network connectivity.

Decentralized Control Algorithm for Fast Monitoring and Efficient Energy Consumption in Energy Harvesting Wireless Sensor Networks

The paper proposes a fully decentralized algorithm to control nodes transmission radii for simultaneously coping with energy-type requirement (e.g., network power consumption) and application-type requirement (e.g., fast monitoring and estimation of variables of interest). The stability of the closed loop network under the proposed control law is proved. Simulation validations show the effectiveness of the proposed approach in nominal scenario as well as in the presence of uncertain power requirements and harvesting system supply, outperforming a well known related approach existing in the literature.

Adaptive Neuro-Fuzzy Location Indicator in Wireless Sensor Networks

Indoor localization is a basic process in Wireless Sensor Networks (WSN) monitoring. This paper presents a new approach for localization of mobile nodes in WSNs. The proposed approach is based on the design of an adaptive fuzzy localization system. First proposed contribution is to consider the rooms of the target environment as a fuzzy sets made by adjacent zones described by a Fuzzy Location Indicator (FLI). FLI provides a fuzzy linearization of the building map hence the creation of a fuzzy linguistic model of the system. Fingerprints of the Radio Signal Strength Indicators (RSSI) are collected from different anchors according to each FLI. A Sugeno type-0 fuzzy inference system is proposed and submitted to a supervised learning through the neuro-fuzzy ANFIS algorithm. Simulation results as well as experimentations in Cynapsys company premises have proved that a good learning process leads to high success rate. Finally, a comparative study with two fuzzy localization systems proved the lower localization error average of the proposed approach.

A prototype IOT based wireless sensor network for traffic information monitoring

An Internet of things (IOT) based wireless sensor system, solely using wireless accelerometers, is developed for traffic volume and vehicle classification monitoring in this paper. A series of laboratory tests, field tests as well as numerical simulation were performed to validate the feasibility and accuracy of the monitoring system. Besides, in order to eliminate the impacts of noises in the output signals, an advanced algorithm is developed to analyze the test data. The findings based on the test results indicate that the system is capable of reliably detecting axles and calculating axle spacing in both laboratory and field tests. In addition, compared with the actual measurements, the numerical simulation further validates the feasibility of the integrated wireless sensor system for traffic information monitoring.

An energy based cluster head selection unequal clustering algorithm with dual sink (ECH-DUAL) for continuous monitoring applications in wireless sensor networks

The essential sections of the hot spot problem are network lifetime improvements and uniform residual energy distribution in wireless sensor networks (WSN). Clustering of sensor nodes is a significant process that improves network lifetime and energy efficiency of WSN. Usage of equal cluster sizes in WSN causes more energy to be consumed by the cluster heads when the data is routed to sink thus resulting in hot spot problems. Hence, recent research papers focus on unequal clustering where cluster size increases as the distance to the sink increases. In this paper, cluster heads are selected by modifying energy efficient unequal clustering mechanism (EEUC). This process is done in two ways. Firstly, in EEUC, final cluster heads are selected based on the residual energy of the randomly selected tentative cluster heads. In our algorithm, tentative cluster head is selected based on energy based timer, residual energy, node IDs and trust value. Final cluster head selection approach selects final CHs based on competition range, node degree and head count. Secondly, in applications like continuous monitoring, usage of static sink causes the clusters near the sink to die out faster, as the cluster heads in these clusters form the fixed path for data routing, hence resulting in hot spot problems. In this work, an energy based cluster head selection unequal clustering algorithm (ECH-DUAL) using dual (static and mobile) sink is proposed. The simulation shows that proposed system (ECH-DUAL) improves network lifetime of continuous monitoring wireless sensor networks significantly over EEUC.

Efficient Routing with Compressive Sensing for Wireless Sensor Network

A wireless sensor network is a collection of nodes cooperative to each other and organized into a certain network. It is used in large applications to monitor the physical or environmental conditions. Agriculture and farming is one of the most important fields which uses wireless sensor network for monitoring and gathering data such as underground water resources in new cultivated lands. The energy saving is the most important parameter in wireless sensor network. In this paper, the compressive sensing with LEACH routing protocol is investigated for the purpose of increasing the lifetime of the nodes by reducing the transmitted data size. Simulation results show that node life time achieved by the proposed LEACH routing protocol with compressive sensing can be increased approximately to 1.7 times that achieved by conventional LEACH protocol.

Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring

A UAV-bouy hybried Wireless Sensor Network, with LoRa communications, is presented for the monitorization of marine-coastal environments. This implementation offers a low-power, long-range and cost-effective solution to current marine monitoring applications, as well as a safer method to observing natural protected areas and reservoirs.

Design and Implementation of Low-Power Analog-to-Information Conversion for Environmental Information Perception

Because sensing nodes typically have limited power resources, it is extremely important for signals to be acquired with high efficiency and low power consumption, especially in large-scale wireless sensor networks (WSNs) applications. An emerging signal acquisition and compression method called compressed sensing (CS) is a notable alternative to traditional signal processing methods and is a feasible solution for WSNs. In our previous work, we studied several data recovery algorithms and network models that use CS for compressive sampling and signal recovery. The results were validated on large data sets from actual environmental monitoring WSNs. In this paper, we focus on the hardware solution for signal acquisition and processing on separate end nodes. We propose the paradigm of an analog-to-information converter (AIC) based on CS theory. The system model consists of a modulation module, filtering module, and sampling module, and was simulated and analyzed in a MATLAB/Simulink 7.0 environment. Further, the hardware design and implementation of an improved digital AIC system is presented. We also study the performances of three different greedy data recovery algorithms and analyze the system power consumption. The experimental results show that, for normal environmental signals, the new system overcomes the Nyquist limit and exhibits good recovery performance with a low sampling frequency, which is suitable for environmental monitoring based on WSNs.

Design and Application of Wireless Sensor Network Monitoring Software Based on LABVIEW

Wireless multi-point real-time environmental monitoring system based on LabVIEW is composed of three parts: wireless environment information collection node, communication gateway, and host computer monitoring system. The purpose of this study is to explore the application of wireless monitoring technology in industry. Since that it is at the stage of research feasibility, various sensors used for the applications select the level limited to the ordinary teaching using sensor. AGV uses vehicle manipulator developed by Wuhan Depushi Technology Company and some sensors are simulated. The results of simple basic experiments showed that the wireless module can achieve point to multipoint communication. It is concluded that the monitoring interface can simultaneously process the data of a simulated pipeline terminal node with a simulated AGV data and make them work in a closed loop state.

QoS Analysis of Wireless Sensor Networks for Temperature and Humidity Monitoring and Control of Soybean Seed Storage Based IOT Using NodeMCU

Based on data from Central Bureau of Statistics in 2016 - 2020 soybean production deficit, the main factor is the decline in the quality of soybean seeds temperature and humidity sensitive. And so we need a system of monitoring and control of temperature and humidity container store soya beans. Wireless sensor network wireless technology that consists of a collection of sensor nodes distributed on a given area can support the communication between the sensor nodes using the system and sensor NodeMCU DHT11. This research was to analyze the WSN QoS monitoring system and temperature and humidity control soybean seed store container-based IOT using NodeMCU clients and coordinators are connected to an access point for sending data to the server in realtime. Tests carried out by putting the sensor node at 3 points with a distance variation of the coordinator and a data packet transmission interval. The test results obtained using a star topology indoor NLOS conditions optimal distance sensor node 3 which is 4 meters and the delivery interval 40s with packet loss value of 0-20%, delay 1.154 - 5,92s, jitter 0.241 to 7.57 ms, and throughput 66.32 bits / s. WSN protocol IOT uses MQTT NodeMCU and goes well with a low throughput value is 529.81 bps to 544.85 bps can still generate a delay kualiatas 200.33 to 270.83 ms and packet loss from 0 to 1.284% which is good. 92s, jitter 0.241 to 7.57 ms, and the throughput of 66.32 bits / s. WSN protocol IOT uses MQTT NodeMCU and goes well with a low throughput value is 529.81 bps to 544.85 bps can still generate a delay kualiatas 200.33 to 270.83 ms and packet loss from 0 to 1.284% which is good. 92s, jitter 0.241 to 7.57 ms, and the throughput of 66.32 bits / s. WSN protocol IOT uses MQTT NodeMCU and goes well with a low throughput value is 529.81 bps to 544.85 bps can still generate a delay kualiatas 200.33 to 270.83 ms and packet loss from 0 to 1.284% which is good.