Wireless Sensor Network For Monitoring Research Articles

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.

Optimization of Ice and Snow Sports Industry Chain Structure Based on Sensor Network Communication and Artificial Intelligence

As a new tourism industry model, the ice and snow sports tourism industry has played an increasingly important role in the development of China’s entire tourism industry. This paper analyzes the structural optimization of the ice and snow sports industry chain based on sensor network communication and artificial intelligence and aims to fully apply sensor network communication and artificial intelligence technology to the development of the ice and snow sports industry. Let us look for an optimized method to promote the development of the ice and snow sports industry chain structure. This paper conducts a research on the optimization of the ice and snow sports industry chain structure based on sensor network communication and artificial intelligence. In the method part, this article explains the architecture of sensor nodes and networks, uses the long-term monitoring of wireless sensor networks and the flexibility of the data acquisition structure, combined with the superiority of artificial intelligence in information processing and analysis, to obtain and analyze the experimental data of this research, and introduces the specific content of artificial intelligence and the ice and snow sports industry. In the algorithm, the grey relational analysis method is introduced. In the part of the experimental results, experiments were carried out on the ice and snow city resources, competitive advantages, the quarterly average value of the ice and snow sports enterprise prosperity index, ice and snow sports venues, GDP, and the speed comparison and structure of the development of the sports industry, and simulation experiments were carried out from the perspective of sensors. On the whole, the development curve of China’s ice and snow sports industry basically conforms to the economic development curve. With the slowdown of GDP growth rate, the growth rate of the sports industry slowed down simultaneously. However, despite the continued decline in economic growth, the sports industry has ushered in a peak of development, with a growth rate of 89.45%, a year-on-year increase of 69.3 percentage points. This phenomenon shows that the sports industry has sufficient endogenous development momentum and huge development potential. It is an important driving force and emerging growth point of the economic society and modern services. It has a comprehensive driving role in stimulating urban consumption, promoting economic transformation and upgrading, and driving the development of the service industry.

A Robust UWSN Handover Prediction System Using Ensemble Learning

The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical.

Real time flood disaster monitoring based on energy efficient ensemble clustering mechanism in wireless sensor network

AbstractIn this article, energy efficient ensemble clustering method (EECM) with black widow optimization (EECM‐BWO) algorithm is proposed for effective data transmission with the help of real time flood disaster monitoring wireless sensor network (WSN). Initially, unified scalable ensemble clustering algorithm based on ensemble generation and consensus function is proposed for selecting the optimal routing path among the node using BWO algorithm. Then, biologically inspired routing black widow spiders optimization algorithm is proposed to trade off the nodes energy level, self‐organization, and self‐configuration in the WSN. The simulation is performed using NS2 simulator for validating the performance of the proposed EECM‐BWO method. Here, in node, low delay achieves 24.07%, 72.58%, 51.36%, 81.75%, 77.74%, high packet delivery ratio achieves 70.83%, 53.93%, 90.23%, 43.58%, 24.58%, low packet drop attains 77.93%, 72.76%, 61.56%, 51.87%, 34.35%, low energy consumption attains 75.9%, 52.94%, 65.81%, 58%, 41.2% compared with existing energy‐efficient clustering approach consolidated game theory as well as dual‐cluster‐head mode for WSNs energy‐aware clustering by cuckoo optimization approach (EECM‐COA), energy‐aware clustering‐based routing using multi‐path reliable transmission with routing and control board (EECM‐RCB‐MRT), adaptive repair algorithm with temporally ordered routing algorithms for flood control strategy (EECM‐AR‐TORA‐FCS), passive multi‐hop clustering algorithm (EECM‐PMC), dynamic source routing protocol based on genetic algorithm‐bacterial foraging optimization (DSR‐GA‐BFO).

Agricultural water management based on the Internet of Things and data analysis

ABSTRACT To improve the effect of agricultural water management, this paper builds an agricultural water management system based on the Internet of Things and data analysis, and designs an intelligent analysis model of the system using the method of time series forecasting. Moreover, this paper designs the software and hardware of the ZigBee wireless sensor network monitoring node, including the hardware circuit design of the ZigBee network monitoring node and the software acquisition program design to realise the data acquisition and short-distance transmission of the farmland environment. In addition, this paper designs a farmland irrigation system based on the Internet of Things, which can also realise real-time monitoring of agricultural water quality. Finally, this paper designs an experiment to analyse the performance of the system constructed in this paper. Judging from the performance of the agricultural water management system, it can be seen that its performance can meet the actual needs of agricultural water management.

ViTool-BC: Visualization Tool Based on Cooja Simulator for WSN

Evaluation and monitoring of wireless sensor networks (WSN) and the parameters defining their operations and design, such as energy consumption, latency, and stability, is a complex task due to interaction with real devices. For greater control of these variables, the use of simulators arises as an alternative. Cooja is a WSN simulator/emulator which handles the devices’ controllers and multiple communication protocol implementations, such as RPL (RPL is one of the most used protocol in IoT). However, Cooja does not consider either the implementation of an energy model (it has infinite energy consumption) nor the visual behavior of the topology construction, although these aspects are crucial for effective network analysis and decision taking. This paper presents the design and the implementation of ViTool-BC, a software built on top of Cooja, which allows the creation of different energy estimation models and also to visualize in real time the behavior of WSN topology construction. In addition, ViTool-BC offers a heat map of energy consumption traces. Therefore, this tool helps researchers to monitor in real time the topology construction, node disconnection, and battery depletion, aspects to be considered in the analysis of the available routing protocols in Cooja.

IoT-Enabled Wireless Sensor Networks for Air Pollution Monitoring with Extended Fractional-Order Kalman Filtering.

This paper presents the development of high-performance wireless sensor networks for local monitoring of air pollution. The proposed system, enabled by the Internet of Things (IoT), is based on low-cost sensors collocated in a redundant configuration for collecting and transferring air quality data. Reliability and accuracy of the monitoring system are enhanced by using extended fractional-order Kalman filtering (EFKF) for data assimilation and recovery of the missing information. Its effectiveness is verified through monitoring particulate matters at a suburban site during the wildfire season 2019–2020 and the Coronavirus disease 2019 (COVID-19) lockdown period. The proposed approach is of interest to achieve microclimate responsiveness in a local area.

Integration of Machine Learning Techniques in Virtual Wireless Sensor Network for insect monitoring

Agriculture industries are comparatively slow in adopting emerging technologies than any other industries despite lot of exciting research. The use of Wireless sensor network (WSN) is very important role in agriculture for more productive and sustainable growth. The structure of WSN is tightly application dependent. Every WSN have sensors, processing unit, low frequency radio wave transmitter and power supply using battery. With increasing number of interconnected WSN devices, there is substantial increase in data generation. It contains both control messages as well as application dependent data, collected by the sensors. The collected data are frequently sent to the nearest centralized controller for further processing and decision making. For continuous functioning of WSN, uninterrupted power supply is needed. Many researches are carried out to overcome these challenges. In this manuscript we are proposing a simple and effective machine learning techniques combined with pause and play method to increase battery life of WSN. This could be achieved in three stages play-pause-play (PPP) model. First by gathering data for some time (play mode), Second by putting WSN to sleep (pause mode), in the backend, apply machine learning algorithm that helps to build model from training data to predict the future data. At the final stage, put WSN back to play mode. Compare the result with actual data and fine tune the model to reduce the error. Using this method WSN will get enough sleep time to increase overall life by simulating the normal behaviour of sensor node. The sleep time will be calculated dynamically.

WSN to detect real time terrain slides using Wi-GIM instrument

In this paper, the wireless sensor networks (WSN) were developed to detect terrain slides, including designing, developing and implementing WSNs for real time monitoring. Data were analyzed from the newly developed Wi-GIM (Wireless Ground Instability Monitoring Sensor Network) instrument. Landslides poses a significant geological threat in multiple geographical, geological and climate settings. In places that are vulnerable to sliding, Early Warning System-associated tracking systems provide a cost-effective way of minimizing damage with low economic and environmental effects. The device consists of a WSN composed of nodes to determine the distance between the flight times for the ultra-wide band impulses. Therefore, same signals are still used in the transmission range, so no sensors are used within the network.

IWindCr field trial and electrochemical analysis for corrosion detection and monitoring offshore wind turbine’s MP-TP steel components

A Proof-of-Concept (POC) low power-low current wireless sensor network (WSN) for corrosion detection and monitoring of offshore wind turbine (OWT)’s component, entitled iWindCr, has been developed and field trialled in one of the OWTs in the UK south region. This paper reports on the setting up and outcomes of this field trial. The trial has successfully demonstrated the working functionality of the WSN by measuring changes of the Open Circuit Potential (OCP) and Zero Resistance Ammeter (ZRA) electrochemical parameters over a period of time. The state of corrosion and estimated life of an M72 galvanised steel stud, part of the Monopile (MP)-transition piece (TP) flanged connection were evaluated using the real-time data from the WSN with reference to the material’s corrosion thresholds from the iWindCr database. The paper details the electrochemical analysis processes in relation to the Electrochemical Impedance Spectroscopy (EIS), Potentiodynamic Polarisation Curve (PPC) in addition to the OCP and ZRA techniques. The electrochemical parameters and corrosion threshold values from seawater immersion tests of the steel alloys SS316L and S355, the typical MP-TP materials are presented.

Analysis of different cylindrical magnet and coil configurations for electromagnetic vibration energy harvesters

Electromagnetic vibration energy harvesting is a relatively new technology that transforms kinetic energy from mechanical vibrations into electrical energy, allowing the substitution of batteries or cables for powering ultra-low-power devices like wireless sensor networks for structural health monitoring. For this aim, different magnet and coil configurations have been proposed for the design of these harvesters by several researchers. In this paper, four cylindrical “Magnet in-line coil” configurations with back steel, which include a typical single-magnet, a double-magnet array, and two proposed cylindrical Halbach magnet arrays of three and five magnets, are analyzed using the finite element method and compared in terms of their magnetic flux linkage and transduction factor. The numerical simulations are conducted in all cases with the same materials properties, coil parameters, and geometrical boundaries, the latter consisting of the total cross-sectional area of the magnets and the coil, the air gaps, and the total volume of the transducer mechanism. Furthermore, the design that provides the best performance is analyzed with two different coil configurations. It is finally found that the proposed cylindrical Halbach magnet array with three magnets and one-center coil presents the best results, reaching an average transduction factor of 95.83 Vs/m and a normalized power density of 19.72 mW/cm3g2.

Analysis of different cylindrical magnet and coil configurations for electromagnetic vibration energy harvesters

Electromagnetic vibration energy harvesting is a relatively new technology that transforms kinetic energy from mechanical vibrations into electrical energy, allowing the substitution of batteries or cables for powering ultra-low-power devices like wireless sensor networks for structural health monitoring. For this aim, different magnet and coil configurations have been proposed for the design of these harvesters by several researchers. In this paper, four cylindrical “Magnet in-line coil” configurations with back steel, which include a typical single-magnet, a double-magnet array, and two proposed cylindrical Halbach magnet arrays of three and five magnets, are analyzed using the finite element method and compared in terms of their magnetic flux linkage and transduction factor. The numerical simulations are conducted in all cases with the same materials properties, coil parameters, and geometrical boundaries, the latter consisting of the total cross-sectional area of the magnets and the coil, the air gaps, and the total volume of the transducer mechanism. Furthermore, the design that provides the best performance is analyzed with two different coil configurations. It is finally found that the proposed cylindrical Halbach magnet array with three magnets and one-center coil presents the best results, reaching an average transduction factor of 95.83 Vs/m and a normalized power density of 19.72 mW/cm3g2.

Non-uniform clustering routing protocol of wheat farmland based on effective energy consumption

Wireless sensor network (WSN) can achieve real-time data collection and transmission of environment, soil, meteorology, crop physiology and other information in agriculture. The data provided by WSN could be used for decision making and management, which is very important in precision agriculture. Wheat farmland wireless sensor network has the characteristics of wide coverage area, long planting period, inconvenient energy supply, and serious impact of crop environment on wireless signal transmission. Routing protocol is an important method to achieve long-term WSN monitoring by selecting an appropriate path with low energy consumption for data transmission. According to the phenomenon of uneven environment and channel parameters caused by intensive crop growth in farmland, a non-uniform clustering routing protocol based on effective energy consumption (UCEEC) was proposed in this work. The method combined with the characteristics of multi-path fading of farmland environment signals. The idea of image segmentation was introduced. Nodes with high similarity were divided into a cluster area by the dissimilarity between nodes in order to improve the intracluster communication performance. Meanwhile, a multi-hop path selection method between cluster-heads based on the estimation of two-hop effective energy consumption is designed. The energy consumption cost factor is calculated by the effective energy consumption and the average energy consumption within the cluster to achieve the minimum and balance of the overall energy consumption of the network. Simulation results show that, compared with the existing Maximum Residual Energy Based Routing (MREBR) protocol, minimum Energy Consumption Based Routing (MEC) routing protocols, UCEEC improves the energy balance effect between nodes, prolongs the network life cycle, and realizes efficient energy utilization of wireless sensor network data collection in the complex environment of wheat field. Keywords: farmland, wireless sensor networks, multi-path fading channel, routing protocol, energy optimization DOI: 10.25165/j.ijabe.20211403.6540 Citation: Miao Y S, Zhao C J, Wu H R. Non-uniform clustering routing protocol of wheat farmland based on effective energy consumption. Int J Agric & Biol Eng, 2021; 14(3): 163–170.

A novel iterative identification based on the optimised topology for common state monitoring in wireless sensor networks

Power consumption and data redundancy of wireless sensor networks (WSN) are widely considered for a distributed state monitoring network. For reducing the energy consumption and data amount, we propose a topology optimisation and an iterative parameter identification method for estimating the common model factors in WSN. The former method optimises the decentralised topology such that all the leaf nodes in a community connect to the head node directly. A circle topology is built to enable the remote leaf nodes to link to the head node through two adjoining relay nodes to reduce the whole communication distance and power consumption. Based on the optimised topology, an iterative identification method is proposed to minimise the information capacity by transmitting the processed results instead of raw data to reduce the data amount for calculation and storage. Then, we prove the consensus and convergence of the proposed identification method. Finally, two simulations verify the effectiveness of the proposed method and the comparative results present the data reduction for the on-board calculation, communication, and storage in the practical use of WSN.

Breadth-first search tree integrated vertex cover algorithms for link monitoring and routing in wireless sensor networks

Wireless sensor networks (WSNs) are infrastructureless networks of tiny sensor motes which can sense from the environment and can transmit the sensed data through wireless communication. Generally, the transmission ranges of sensor nodes are limited and obstacles are present in the sensing area, so multi-hop communication by utilizing routing protocols is of utmost importance. Since the wireless transmission is prone to vulnerabilities such as eavesdropping and spoofing, monitoring the network traffic by secure points is a critical mission. In this paper, we propose breadth-first search tree (BFST) based vertex cover (VC) algorithms for routing and link monitoring in WSNs. BFST provides a shortest hop routing tree for WSNs and VC is a set of nodes for covering edges (communication links) of the network where it perfectly fits for network traffic monitoring. Unlike most existing algorithms which find minimal VC in a separate procedure, the proposed algorithms construct VC during the BFST establishment phase which leads to lower energy consumption for routing and link monitoring operations. We analyze the correctness proof of the algorithms along with the time, message, space, and bit complexities. We implement the proposed algorithms with their counterparts on a testbed of IRIS motes to evaluate their performances. We find through extensive evaluations from the IRIS testbed experiments and TinyOS simulations that the proposed algorithms find smaller VC sets with up to 3.1 times lower energy consumption than the existing distributed algorithms.

Evaluation and Analysis for Maximum Lifespan of Wireless Sensor Networks by Energy-Efficient Design

Wireless Sensor Networks (WSNs) have used worldwide in the past few years and are now being used in health monitoring ,disaster management, defense, telecommunications, etc. Such networks are used in many industrial and consumer applications such as industrial process and environment monitoring, among others. A WSN network is a collection of specialized transducers known as sensor nodes with a communication link distributed randomly in any locations to monitor environmental parameters such as water level, and temperature. Each sensor node is equipped with a transducer, a signal processor, a power unit, and a transceiver. WSNs are now being widely used to monitor environmental parameters, including the amount of gas, water, temperature, humidity, oxygen level, dust, etc. The WSN for environment monitoring can be equivalently replaced by a multiple-input multiple-output (MIMO) relay network. Multi-hop relay networks have attracted significant research interest in recent years for their capability in increasing the coverage range. The network communication link from a source to a destination is implemented using the amplify-and-forward (AF) or decode-and-forward (DF) schemes. The AF relay receives information from the previous relay and simply amplifies the received signal and then forwards it to the next relay. On the other hand, the DF relay first decodes the received signal and then forwards it to the next relay in the second stage if it can perfectly decode the incoming signal. For analytical simplicity, in this thesis, we consider the AF relaying scheme and the results of this work can also be developed for the DF relay.

Structural Health Monitoring of Bridges Using WSNs

Wireless sensor networks (WSNs) for Structural health monitoring (SHM) has earned immense attention in research as it has potential to decrease expenses associated with the maintenance cum installation of these monitoring systems. SHM systems, traditionally are being utilized to screen perilous substructures such as bridges, tall buildings, fields and is capable to increase structure lifetime and safety of public. Earlier wired networks were used by SHM. But this resulted in heavy cost in terms of maintenance and deployment. This project introduces a wireless sensor network using Bluetooth nodes for detection of damage if any in any type of structure in which the data is processed locally in real time and helps in sending alerts once the damage is identified.

Experiments of an IoT-based wireless sensor network for flood monitoring in Colima, Mexico

Abstract Urban flooding is one of the major issues in many parts of the world, and its management is often challenging. One of the challenges highlighted by the hydrology and related communities is the need for more open data and monitoring of floods in space and time. In this paper, we present the development phases and experiments of an Internet of Things (IoT)-based wireless sensor network for hydrometeorological data collection and flood monitoring for the urban area of Colima-Villa de Álvarez in Mexico. The network is designed to collect fluvial water level, soil moisture and weather parameters that are transferred to the server and to a web application in real-time using IoT Message Queuing Telemetry Transport protocol over 3G and Wi-Fi networks. The network is tested during three different events of tropical storms that occurred over the area of Colima during the 2019 tropical cyclones season. The results show the ability of the smart water network to collect real-time hydrometeorological information during extreme events associated with tropical storms. The technology used for data transmission and acquisition made it possible to collect information at critical times for the city. Additionally, the data collected provided essential information for implementing and calibrating hydrological models and hydraulic models to generate flood inundation maps and identify critical infrastructure.

Using wireless sensor network for monitoring and controlling of farm

Agriculture is most important business for any country. Agriculture has to face many complications like changing soil quality, water shortage, changing climate, etc. Due to the increasing in population demand for cereals and other crops for daily usage by the customers is also increased so there is a need for smarter implementation of irrigation and also develop farming methods that lessen human efforts and alter the basic eco systems this is less harmful to other creatures too. India is one of highest farming dependent county and generating countries all over the world. At the same time, India is facing the challenge of farmers committing suicide because of crop failure and liability. Most of the land in India are reserved for farming. Still there exists several places that uses primitive methods for irrigation, fertilization etc. This paper discusses the design and development of an IoT based module that assist farmers to improvise their methods of farming and also make the best use of their land for a better income. The main goal line of this IoT module is to sense agriculture parameter and advice farmers to properly grow and treat the crops. Also, in this assignment we are going to implement a irrigation control i.e. motor control through android app. To avoid animal intrusion flasher are also going to implement in the field. This will reduce human efforts.

IoT-Based Epidemic Monitoring via Improved Gated Recurrent Unit Model

During the Coronavirus Disease 2019 (COVID-19) pandemic, non-contact health monitoring and human activity detection by various sensors have attracted tremendous attention. Robot monitoring will result in minimizing the life threat to health providers during the COVID-19 pandemic period. How to improve the performance and generalization of the monitoring model is a critical but challenging task. This paper constructs an epidemic monitoring architecture based on multi-sensor information fusion and applies it in medical robots’ services, such as patient-care, disinfection, garbage disposal, etc. We propose a gated recurrent unit model based on a genetic algorithm (GA-GRU)to realize the effective feature selection and improve the effectiveness and accuracy of the localization, navigation, and activity monitoring for indoor wireless sensor networks (WSNs). By using two GRU layers in the GA-GRU, we improve the generalization capability in multiple WSNs. All these advantages of GA-GRU make it outperform other representative algorithms in a variety of evaluation metrics. The experiments on the WSNs verify that the proposed GA-GRU leads to successful runs and provides optimal performances. These results suggest the GA-GRU method may be preferable for epidemic monitoring in medicine and allied areas with particular relation to the control of the epidemic or pandemic such as COVID-19 pandemic.