Showing papers on "Sensor node published in 2023"

Estimating Volumetric Water Content in Soil for IoUT Contexts by Exploiting RSSI-Based Augmented Sensors via Machine Learning

Abstract: This paper aims at proposing an augmented sensing method for estimating volumetric water content (VWC) in soil for Internet of Underground Things (IoUT) applications. The system exploits an IoUT sensor node embedding a low-cost, low-precision soil moisture sensor and a long-range wide-area network (LoRaWAN) transceiver sending relative measurements within LoRaWAN packets. The VWC estimation is achieved by means of machine learning (ML) algorithms combining the readings provided by the soil moisture sensor with the received signal strength indicator (RSSI) values measured at the LoRaWAN gateway side during broadcasting. A dataset containing such measurements was especially collected in the laboratory by burying the IoUT sensor node within a plastic case filled with sand, while several VWCs were artificially created by progressively adding water. The adopted ML algorithms are trained and tested using three different techniques for estimating VWC. Firstly, the low-cost, low-precision soil moisture sensor is calibrated by resorting to an ML model exploiting only its raw readings to estimate VWC. Secondly, a virtual VWC sensor is shown, where no real sensor readings are used because only LoRaWAN RSSIs are exploited. Lastly, an augmented VWC sensing method relying on the combination of RSSIs and soil moisture sensor readings is presented. The findings of this paper demonstrate that the augmented sensor outperforms both the virtual sensor and the calibrated real soil moisture sensor. The latter provides a root mean square error (RMSE) of 3.33%, a virtual sensor of 8.67%, and an augmented sensor of 1.84%, which improves down to 1.53% if filtered in post-processing.

Towards Sustainable Distributed Sensor Networks: An Approach for Addressing Power Limitation Issues in WSNs

Abstract: Distributed wireless sensor networks (WSNs) have been implemented in multiple applications. Those networks are intended to support the quality of operations and enhance applications’ productivity and safety. WSNs are constructed of a large amount of sensor nodes that are battery powered. Typically, wireless sensors are deployed in complex terrain which makes battery replacement extremely difficult. Therefore, it is critical to adopt an energy sustainability approach to enhance the lifetime of each sensor node since each node contributes to the lifetime of the entire WSN. In this work, we propose an approach to reduce power consumption in wireless sensors. The approach addresses power reduction in a sensor node at the sensing level, as well as the communication level. First, we propose configuring the microcontroller of the sensor to conserve energy based on the performed tasks. Then, we implement an interface to reduce consumed power by the radio module. Based on the approach, we carried out field experiments and we measure the improvement of power-consumption reduction. The results show that the approach contributes to saving up to 50% of the wasted energy at the sensor node and it improves communication reliability especially when the number of sensors in a network scales.

Low-cost, high-resolution and no-manning distributed sensing system for the continuous monitoring of fruit growth in precision farming

Abstract: Accurate, continuous and reliable data gathering and recording about crop growth and state of health, by means of a network of autonomous sensor nodes that require minimal management by the farmer will be essential in future Precision Agriculture. In this paper, a low-cost multi-channel sensor-node architecture is proposed for the distributed monitoring of fruit growth throughout the entire ripening season. The prototype presented is equipped with five independent sensing elements that can be attached each to a sample fruit at the beginning of the season and are capable of estimating the fruit diameter from the first formation up to the harvest. The sensor-node is provided with a LoRa transceiver for wireless communication with the decision making central, is energetically autonomous thanks to a dedicated energy harvester and an accurate design of power consumption, and each measuring channel provides sub-mm 9.0-ENOB effective resolution with a full-scale range of 12 cm. The accurate calibration procedure of the sensor-node and its elements is described in the paper, which allows for the compensation of temperature dispersion, noise and non-linearities. The prototype was tested on field in real application, in the framework of the research activity for next-generation Precision Farming performed at the experimental farm of the Department of Agricultural and Food Science of the University of Bologna, Cadriano, Italy.

Design of Monitoring System Temperature And Humidity Using DHT22 Sensor and NRF24L01 Based on Arduino

Abstract: The monitoring system is used for know or get an information on a situation or condition in a certain place or area, with various methods used, so that many monitoring systems are developed and applied to assist human tasks in carrying out monitoring. The hardware design consists of 2 nodes namely the base node and the client node. At the base node there are 2 hardware components used, namely: Arduino Uno and the wireless communication module nRF24L01. The use of the NRF24L01 module is a reliable data communication module so that the data received is in accordance with the data sent by testing the module in different rooms and the same range with a distance of ± 800 m. Buzzer works well with temperature performance results ≥ 32 °C buzzer on and with ≤ 65% humidity buzzer on. The sensor network system can run well, because the ratio of the DHT22 sensor with the thermohygro, the average error is 1.4% for temperature and 3.8% for humidity from the results of data collection.

UAV-Based Wireless Data Collection from Underground Sensor Nodes for Precision Agriculture

Abstract: In precision agriculture, information technology is used to improve farm management practices. Thereby, productivity can be increased and challenges with overfertilization and water consumption can be addressed. This requires low-power and wireless underground sensor nodes for monitoring the physical, chemical and biological soil parameters at the position of the plant roots. Three ESP32-based nodes with these capabilities have been designed to measure soil moisture and temperature. A system has been developed to collect the measurement data from the sensor nodes with a drone and forward the data to a ground station, using the LoRa transmission standard. In the investigations of the deployed system, an increase in the communication range between the sensor node and the ground station, from 300 m to 1000 m by using a drone, was demonstrated. Further, the decrease in the signal strength with the increasing sensor node depth and flight height of the drone was characterized. The maximum readout distance of 550 m between the sensor node and drone was determined. From this, it was estimated that the system enables the readout of the sensor nodes distributed over an area of 470 hectares. Additionally, analysis showed that the antenna orientation at the sensor node and the drone influenced the signal strength distribution around the node due to the antenna radiation pattern. The reproducibility of the LoRa signal strength measurements was demonstrated to support the validity of the results presented. It is concluded that the system design is suitable for collecting the data of distributed sensor nodes in agriculture.

Remote Data Acquisition Using UAVs and Custom Sensor Node Technology

Abstract: Precision agriculture technology has advanced rapidly in the 21st century. Despite this, the vast majority of US farmers do not employ any form of precision agriculture. Reasons for this include the high initial cost, lack of internet connectivity in rural areas, and complex setup and operation. The basis of this project was to create a low-cost, energy-efficient data collection system using an unmanned aerial vehicle (UAV) as a mobile sink node in a local wireless system. This was accomplished through the design and manufacture of custom sensor nodes and a custom drone-mounted wireless receiver node. The sensor node and drone node enclosures were 3D printed and assembled using low-cost materials and internal components. The system was successfully tested in a field where it collected soil data, including soil moisture, soil temperature, and electrical conductivity. The cost and scalability of the system are discussed, as well as potential improvements and comparisons with existing technologies. The system was concluded to have many potential applications in its current state but with room to expand and improve its operation and features.

Evaluation of Wireless Sensor Networks Module using IoT Approach

Abstract: Microcomputers and medical devices with signal transceivers that operate on a specific radio display constitute the backbone of wireless sensor networks (WS Ns) that monitor environmental conditions (temperature, pressure, light, vibration levels, location). It is widely used in WAN sensor networks because of its flexible design and low setup fees. The u200b touch network allows for the connection of up to 65,000 devices, while the Intelligent sensors on other wireless networks are used to transfer data ports and assign wireless networks. Since the price of wireless solutions has been decreasing, and their functional capabilities have been growing, they are gradually replacing wired ones in telemetry data gathering systems and long- distance detecting communication. A deep learning model was used in this investigation to prevent the sensor nodes from manipulating data. Sensor nodes include a lot of parameters and estimations. If these projected data values are altered, network performance will suffer, and the node's lifetime will be reduced. Data security became a priority when the sensor nodes were distributed. This new method is 98.82% more efficient than the previous one.

WiSensor: Passive Sensor Data Transmission by Way of Ambient Wi-Fi Channels

Abstract: Recent developments in wireless sensor networks have empowered low-power, intelligent sensor nodes to be distributed over a large area. However, wireless sensor nodes that actively emit electromagnetic waves for energy-hungry communication are limited by the volumes and capacities of batteries, requiring periodic maintenance in widespread applications. To solve this problem, we propose a passive wireless sensor platform, WiSensor, which makes wireless sensor nodes take use of ambient Wi-Fi waves instead of actively emitting electromagnetic waves to transmit sensor data. By affecting the Wi-Fi channels through backscattering, the sensor node modulates the sensor data onto the Wi-Fi channels established by a Wi-Fi transmitting node and a Wi-Fi receiving node. The receiving node acts as the sensor receiver and the sensor data are extracted from the Wi-Fi channel state information. We define the signal-to-noise ratio at the sensor data receiver and analyze the WiSensor performance dependencies, providing guidance for the system design and implementation. A prototype of WiSensor is established, in which sensor data can be transmitted at 200 bps with a bit error rate of 0.01% while the sensor node is placed as far as 5 m from the Wi-Fi transmitting node and 2 m from the Wi-Fi receiving node at 2.37- $\mu \text{W}$ power consumption for the wireless connection. WiSensor is a novel and general wireless sensor platform that embeds the utilization of Wi-Fi channels to practical sensing tasks without the need for wireless sensor nodes to follow Wi-Fi protocols, empowering ubiquitous commodity Wi-Fi devices in our daily life with general sensor connectivity. We believe WiSensor demonstrates promising potential in the field of next-generation smart homes and other applications that urgently require ultralow-power sensor data transmission.

Solar Powered Wireless Sensor Network for Water Quality Monitoring and Classification

Abstract: Water is essential for human being, also for animals and plants. In Indonesia, there are a lot of residential living in the riverbank which have poor water conditions. People frequenty use water from the river for daily activities. To determine the quality of water, samples are usually taken and tested in the laboratory. This method is less efficient in time and also cost. In order to determine and monitor the quality of water, this paper discuss the Wireless Sensor Network (WSN) to monitor the quality of water from a distance with the self powered sensor node. One of the issue in developing the WSN is the energy. Since this is implemented in outdoor, therefore it is possible to use solar panel to produce the energy. In this study three indicators; pH, TDS, and turbidity; were used to determine water quality based on the Indonesian Minister of Health Regulation. The results examine the WSN performance, and also the analysys of the solar energy supply for each sensor node. The WSN successfully works in detect and clasify tha water quality category and display it in the monitoring center or user. The sensors are calibrated and works with tolerable error of sensor reading of 5,1%. The WSN node is embedded with solar panel to supply the energy for node component. Therefore it able to extend the lifetime of the networks devices with renewable energy to implement the Green WSN.

Energy security of sensor networks

Abstract: The article focuses on the problem of extending the lifetime of wireless sensor network (WSN), which depends on the operation time of the power supply source embedded into the sensor node. The approaches to the energy conservation of the sensor node power supply source discussed in the Russian-language literature and their shortcomings are considered. It is proposed to use the system of recharging or replacing batteries of WSN sensor nodes with the help of unmanned aerial vehicles (UAV). UAV with recharging signal transmitters allow covering all sensor nodes located at some distance one from another on terrains with any kind of accessibility. The servicing sequence of the sensor nodes is selected in the frameworks of “digital twins” concept. A digital model of the network power supply is used as the digital twin. The servicing process is optimized using the digital model, periodically entering the current parameters of the network status into it. The main parameters are described and main correlations determining the indicated parameters of the network digital model are introduced. The algorithm of recharging or replacing batteries of network nodes of the wireless sensor network using unmanned aerial vehicles is demonstrated and the result of its software implementation for the wireless sensor network with five sensor nodes and one servicing UAV is shown.

Hybrid Interval Type-2 Fuzzy AHP and COPRAS-G-based trusted neighbour node Discovery in Wireless Sensor Networks

Abstract: In Wireless Sensor Networks (WSNs), reliable and rapid neighbour node discovery is considered as the crucial operation which frequently needs to be executed over the entire lifecycle. Several neighbour node discovery mechanisms are proposed for reducing the latency or extending the sensor nodes’ lifetime. But majority of the existing neighbour node discovery mechanisms failed in addressing the critical issues of real WSNs related to energy consumptions, constraints of latency, uncertainty of node behaviors, and communication collisions. In this paper, Hybrid Interval Type-2 Fuzzy Analytical Hierarchical Process (AHP) and Complex Proportional Assessment using Grey Theory (COPRAS-G)-based trusted neighbour node discovery scheme (FAHPCG) is proposed for better data dissemination process. In specific, Interval Type 2 Fuzzy AHP is applied for determining the weight of the evaluation criteria considered for neighbour node discovery, and then Grey COPRAS method is adopted for prioritizing the sensor nodes of the routing path established between the source and destination. It adopted the merits of fuzzy theory for handling the uncertainty and vagueness involved in the change in the behavior of sensor nodes during the process of neighbour discovery. It is proposed with the capability of exploring maximized number of factors that aids in exploring the possible dimensions of sensor nodes packet forwarding potential during the process of neighbour node discovery. The simulation results of the proposed FAHPCG scheme confirmed an improved neighbour node discovery rate of 23.18% and prolonged the sensor nodes lifetime to the maximum of 7.12 times better than the baseline approaches used for investigation.

Distributed Matrix-Weighted Fusion Consensus Filtering With Two-Stage Filtering for Sensor Networks

Abstract: This article is concerned with a distributed consensus filtering problem in a sensor network (SN). In the SN, each sensor node plays a local fusion center. By diffusion and fusion of information among sensor nodes, estimates of all nodes in the SN tend to consensus. At each sensor node, a distributed consensus filtering algorithm with a two-stage filtering structure is devised. At each time, the first stage filtering is that each sensor node produces its local filter estimate based on its own measurement and previous fused estimate, and then transmits it to its neighbor nodes; and the second stage filtering is that each sensor node produces a fused estimate by using a matrix-weighted optimal fusion algorithm in the linear unbiased minimum variance (LUMV) criterion to iteratively fuse estimates of sensor itself and neighbor nodes, where cross-covariance matrices (CCMs) between sensor nodes at different fusion times are derived. Its estimation accuracy is gradually improved with the increase of fusion times and approximates a centralized fusion filter (CFF). To avoid calculation of CCMs between sensor nodes, a suboptimal distributed consensus filtering algorithm is also presented by minimizing an upper bound of the fusion filtering error covariance at each fusion in the second stage. It has reduced computational cost at the expense of accuracy loss. The performance of the proposed distributed consensus filters is analyzed. A tracking system is used to verify the effectiveness of the algorithms.

Modelling and Analysis of a Sigfox-Based IoT Network Using uppaalsmc

Abstract: Wireless sensor nodes are usually powered by batteries that have limited energy capacity. In many applications, the nodes are installed in inaccessible locations where they are problematic to replace or recharge. Therefore, energy optimization is crucial for increasing the node’s lifetime. This study presents a method for the analysis and prediction of the energy consumption of Sigfox-based wireless sensor nodes. The method is illustrated in a use case where the nodes monitor the water level in drainage lines in cities to improve surface and wastewater management. We propose a formal model-based technique using the UPPAAL statistical model checker tool to model and analyze the node’s lifetime. Statistical model checking (SMC) provides a highly scalable technique for the performance analysis of complex cyber-physical systems. The model captures the energy-related behavior of the node, the Sigfox radio specification, and the sensor, each parameterized with values from the device’s datasheets. Furthermore, we calibrate the model using measurements obtained from real-world hardware. Finally, we evaluate a collection of strategies to optimize the battery lifetime of the node. We simulate the model with a 10000-mAh battery, and the results indicate that we can extend the node’s lifetime from 202 days to 2.71 years using our most optimized transmission strategy.

A Poisson Hidden Markov Model and Fuzzy based Chicken Swarm Optimization algorithm for efficient fault node detection in wireless sensor network

Abstract: Abstract Wireless Sensor Networks (WSN) is built with miniature sensor nodes (SN) which are deployed into the geographical location being sensed to monitor environmental condition which transfer the sensed physical information to the base station for further processing. The sensor nodes frequently experience node failure as a result of their hostile deployment and resource limitations. In WSN, node failure can cause a number of issues, namely Wireless Sensor Networks topology changes, broken communications links, disconnected portions of the network, and data transmission errors. An important concern of WSN is the detecting, diagnosing and recovering of sensor node failures. In the course of this effort, an effective strategy for sensor node failure detection algorithm using Poisson Hidden Markov Model (PHMM) and the Fuzzy based Chicken Swarm Optimization (F-CSO) is proposed for efficient detection of sensor nodes fault in the WSN. The proposed work offers optimal false alarm, false positive, energy consumption, detection accuracy, network lifetime, and least delay rates. Moreover, the F-CSO provides improved localization to locate the defective sensor nodes which are present in the WSN. The proposed work is implemented in the NS2 simulator with realistic simulation parameters and the simulation result demonstrate that the proposed work is more effective in terms of false alarm rate, false positive rate, detection accuracy, delay, energy consumption and network lifetime when it is compared with other existing state of art systems.

Multi-Modal Wireless Flexible Gel-Free Sensors with Edge Deep Learning for Detecting and Alerting Freezing of Gait in Parkinson's Patients

Abstract: Freezing of gait (FoG) is a debilitating symptom of Parkinson's disease (PD). This work develops flexible wearable sensors that can detect FoG and alert patients and companions to help prevent falls. FoG is detected on the sensors using a deep learning (DL) model with multi-modal sensory inputs collected from distributed wireless sensors. Two types of wireless sensors are developed, including: (1) a C-shape central node placed around the patient's ears, which collects electroencephalogram (EEG), detects FoG using an on-device DL model, and generates auditory alerts when FoG is detected; (2) a stretchable patch-type sensor attached to the patient's legs, which collects electromyography (EMG) and movement information from accelerometers. The patch-type sensors wirelessly send collected data to the central node through low-power ultra-wideband (UWB) transceivers. All sensors are fabricated on flexible printed circuit boards. Adhesive gel-free acetylene carbon black and polydimethylsiloxane electrodes are fabricated on the flexible substrate to allow conformal wear over the long term. Custom integrated circuits (IC) are developed in 180 nm CMOS technology and used in both types of sensors for signal acquisition, digitization, and wireless communication. A novel lightweight DL model is trained using multi-modal sensory data. The inference of the DL model is performed on a low-power microcontroller in the central node. The DL model achieves a high detection sensitivity of 0.81 and a specificity of 0.88. The developed wearable sensors are ready for clinical experiments and hold great promise in improving the quality of life of patients with PD. The proposed design methodologies can be used in wearable medical devices for the monitoring and treatment of a wide range of neurodegenerative diseases.

Quantifying the Impact of Soil Moisture Sensor Technology on Crop Productivity in Smart Gardening Systems

Abstract: Water is a valuable resource, but not all communities can afford to use it liberally. It is now crucial to use the water that is available as effectively as possible, particularly in agriculture. An unattended ground moisture sensor can be used to monitor the present moisture level in the soil surrounding the plants in order to decrease overwatering of crops. So, a farmer will be able to decide when to water and when to stop. The user should receive the moisture data information wirelessly for convenience. It is detailed how to develop a wireless communication and user interface system for an unattended ground moisture sensor. The Wheat stone bridge for determining il is followed by a differential amplifier in the resistance of the sensor design. Transforming the resistance that was measured into a voltage. This is carried out because resistance and moisture are correlated. A Lora communication transmitting node sends this voltage together with a Lora communication receiving node, where a microcontroller interprets it as moisture data. The receiving node then transmits the data to a PC host so the user can access it. a power circuit that uses a linear regulator and a battery.

Signal-Power-Based Combination Weight Rule for Distributed Blind Equalization in WSN Model

Abstract: In this paper, we consider how to improve the performance of distributed estimation over a diffusion network in order to estimate the unknown parameter of interest from noisy measurements. Specifically, the distributed blind equalization based on the single-input multiple-output channel model over a wireless sensor network (WSN) is discussed in this paper. We propose a new combination weight rule that the weight of each sensor node is assigned by the signal power of the sensor (received) signal instead of only the degree of each sensor node. We assume that each channel from the common transmitter to the sensor nodes in the WSN is common and that noises, the variances of which are different, are included at each channel. The average mean square error and symbol error rate performance characteristics of the distributed blind equalization are investigated. Simulations show that a significant performance improvement is obtained by employing the proposed combination weight rule compared with using conventional combination weight rules.

Pitfalls in Measuring Ultra Low Power Energy Harvesting Wireless Sensor Networks

Abstract: Applications and evaluations in the area of ultra-low power energy harvesting Wireless Sensor Networks (WSNs) verge toward utilizing state-of-the-art hardware with extremely low power consumption. For the evaluation of these networks, real-world deployments are a common approach where special performance logging hardware is often used in conjunction with the sensor nodes. This hardware needs to be kept simple and cost-effective to allow for scalability. At the same time, it needs to be ensured that the measurement hardware does not interfere with the performance of the sensor node, thereby distorting the evaluation results. Herein lies an especially prominent challenge with ultra-low power sensor nodes. We outline some pitfalls that should be considered when designing or selecting this hardware.

Wireless Sensors with Edge Deep Learning for Detecting and Alerting the Freezing of Gait Symptoms in Parkinson's Patients^*

Abstract: This paper presents the design of a wireless sensor network for detecting and alerting the freezing of gait (FoG) symptoms in patients with Parkinson's disease. A novel button pin type sensor node design was developed for easy attachment. Three sensor nodes, each integrating a 3-axis accelerometer, can be placed on a patient at their ankle, thigh, and truck. Each sensor node can independently detect FoG using an on-device deep learning (DL) model, featuring a convolutional neural network (CNN). The DL model outputs from the three sensor nodes are processed in a central node using a majority voting algorithm. In a validation using a public dataset, the prototype developed achieved an FoG detection sensitivity of 88.8% and an F1 score of 85.34%, using less than 20k trainable parameters per sensor node. Once FoG is detected, an auditory signal will be generated to alert users, and the alarm signal will also be sent to mobile phones for further actions if needed. The sensor node can be easily recharged wirelessly by inductive coupling. The system is self-contained and processes all user data locally without streaming data to external devices or the cloud, thus eliminating the cybersecurity risks and power penalty associated with wireless data transmission. The developed methodology can be used in a wide range of applications.

Towards a next generation Ocean Bottom Node: Incorporating a 6C motion sensor

Abstract: Summary In this presentation a new single station rotational seismic sensor is introduced. The new sensor enables simultaneous rotational and translational sensing in three orthogonal axes, representing 6 component (or 6C) measurements. Data examples and early results from an offshore field trial are presented, where the new sensor has been deployed in an ocean bottom node (OBN). Data from the new sensor are compared to reference data from adjacent conventional ocean bottom nodes containing standard 3C geophones. Our findings are that the new sensor is fully deployable in an OBN setting and provides results consistent with reference data obtained by conventional nodes. These capabilities in a single station sensor also means there is potential for significant efficiency gains in OBN acquisition.

A Distributed Clustering Scheme for Dense Wireless Sensor Networks

Abstract: Abstract: A sensor node, also known as an Alfeo (mainly in North America), is a sensor network node capable of processing, collecting sensor data, and communicating with other connected nodes in the network. Alpheus is a node, but a node is not always a point. Each sensor node has a microprocessor and some memory. In addition, each sensor node has one or more sensor devices such as acoustic sensors, microphone arrays, and video cameras, infrared, seismic or magnetic sensors. However, dead batteries in sensor nodes are difficult to replace. A typical sensor node consumes part of its energy during wireless communication. This research paper proposes to develop a well estimated distributed clustering model for dense wireless sensor network fields that provides improved performance over the existing clustering algorithm LEACH. The two robbery concepts behind the proposed system are the hierarchical distributed clustering mechanism and the threshold concept. Energy consumption is significantly reduced, which significantly extends the life of sensor nodes

Energy-Constrained UAV Data Acquisition in Wireless Sensor Networks with the Age of Information

Abstract: This paper considers a wireless sensor network (WSN) assisted by the unmanned aerial vehicle (UAV) in the Internet of Things (IoT). The UAV departs from the data center to the ground node to collect sensor node data as a relay. Under the constraints of battery energy, the UAV will travel to and from the data center repeatedly and transmit the collected sensor node data. The freshness of the node data received by the data center is measured by the Age of Information (AoI) as a performance metric. A genetic algorithm is used to plan the flight trajectory of the UAV. To ensure the data’s integrity and accuracy in a single sensor node, the UAV continuously collects sensor node data when the distance from the sensor node is less than the minimum acquisition distance. Through simulation experiments, we analyzed the influence of changing acquisition distance, the initial battery capacity, acquisition success probability, and transmission power on the peak age of information and the average age of information.

Low-Cost Civil Structure Health Monitoring Using Wireless Sensor Network

Abstract: This research presents a low-cost, real-time, reliable, and scalable solution for Structural Health Monitoring.(SHM) using an IoT-based multi-node system. The study's objective is to detect and identify damages timely andweaknesses in civil structures such as bridges, buildings, and dams, which environmental factors, overuse, or natural phenomena may cause. Each sensor node, fabricated using NodeMCU, is equipped with sensors for measuring vibration, shock, tilt, strain, humidity, and temperature. The data collected by multiple sensor nodes is transmitted to Amazon Web Services (AWS) cloud platform via Wi-Fi and analyzed for damage detection and predictive maintenance. The field testing on a pedestrian steel bridge showed over 99% success in data transmission with a frequency of 1 second per reading, resulting in 18,000 readings over 5 hours. With a fabrication cost of $40 per sensor node and scalable AWS resources, the proposed solution is highly scalable and can operate for 15 days without human intervention.

IOT Platform for Vital Signs Detection Using Node Microcontroller

Abstract: It is possibly possible to stop the spread of the coronavirus by utilizing IoT technology to monitor the vital signs of patients. As a consequence, a proposal has been developed for an efficient and affordable remote monitoring system that measures vital signs. This system makes use of a temperature sensor, a dual heart rate and oxygen level sensor, a temperature sensor, two microcontrollers, a UNO as a power supply, and a Node as the main controller. The vital signs are monitored in a non-invasive manner using photoplethysmography technique, and the information is transmitted wirelessly to the individual involved via the Blynk platform, making use of a Wi-Fi device. It also demonstrates how the system can connect to the internet anywhere in the world, which enables its utilization in a variety of clinical trials.

Microwave-sensor-node integrated into a short-range wireless sensor network

Abstract: This paper presents the first microwave-sensor-node integrated into a short-range wireless sensor network based on ZigBee technology. The node includes an analog front-end circuit, a Frequency Modulated Continuous Wave generator, an Analog-to-Digital-Converter module, a transceiver, a power unit, a processing unit and a new one-port dielectric permittivity sensor which is able to measuring the separation of structural cracks by the reflection coefficient measured in microwave frequencies. The analog front-end is composed of a pair of power dividers, an isolator and a mixer. The dielectric permittivity sensor is based on a patch antenna of variable length. The processing unit and transceiver are implemented with an Arduino UNO and an XBee module respectively. Additionally, the methodology for data processing is presented and the results of the measurement of a synthetic crack are presented. The results show that the system was successfully implemented with a sensitivity of 0.07 GHz/mm, for an opening range of between 0 and 5 mm and for a frequency range ranging from 2.782 GHz to 3.131 GHz. It is important to mention that the measurement was done remotely, placing the sensor 3 m from the client PC.

A Communication Protocol for Online Monitoring of Power Quality in Distribution System

Abstract: The wireless sensor network (WSN) can be used in many applications, especially electrical distribution systems (EDS). The power transmitted on the distribution line has a huge impact by environmental changes like temperature, humidity etc., leading to problems like voltage sag, voltage swell, and changes in the power frequency. These effects affect the devices that are driven by power, for large scale industries, the impact is so heavy. To monitor the imbalance in the transmitting power, the sensor nodes are used to monitor the PQ issues. Then the monitored information needs to be transmitted to the monitoring centre; in this flow, communication plays an important role in minimising the cost of the WSN. For monitoring the PQ, first, the sensor nodes are deployed in the appropriate area. Deploying the sensor node anywhere on the sensor node is impossible. Therefore, the node in this article is used on the feed circuit, which is mounted on the insulator for protection from the power supply. Then the bald eagle search (BES) optimisation based ZigBee communication protocol (ZBES) is designed to transmit the retrieved data from the sensor node to the monitoring station. The designed protocol will make the cost efficient and energy saving path to transmit data to the edge node. The performance of the proposed routing protocol is compared with the ZigBee and other routing protocols.

Relay Awake Feature-based Efficient Route Formation in Mobile Wireless Sensor Network

Abstract: Mobile Wireless Sensor Networks (MWSNs) comprise mobile sensor nodes that energetically exchange information between themselves. MWSN is self-configuring because of its dynamic nature, and each node functions with inadequate energy. Thus, energy depletion and link weight are significant challenges since links are unreliable. Uneven link strength initiates through the mobility in MWSN reasons network function. To solve this concern, this introduces Relay Awake Feature-based Efficient Route Formation (RAFE) in MWSN. RAFE approach uses a communication key function that isolates the malicious nodes in the MWSN. This measures the link weight through the sensor node packet obtained rate, loss rate, and delay factors. RAFE chooses the relay sensor node by the highest remaining energy and the greatest link weight. This approach reduces unwanted energy utilization and improves the network lifetime. Simulation results demonstrate that this approach improves the network energy efficiency and minimizes the network delay. In addition, it improves the network throughput in the network.

A Battery-Less Non-Hybrid Six-Port RFID-Based Wireless Sensor Architecture for IoT Applications

Abstract: This article introduces a novel battery-less wireless sensor architecture, which is based on the principle of direct frequency conversion. The proposed architecture uses a non-hybrid six-port structure to integrate a UHF radio frequency identification (RFID) chip with sensing elements to create a sensor node. The RFID chip provides a unique identification to the sensor node and the sensing element enables the reading of environmental conditions. The non-hybrid six-port structure unequally divides an incoming RFID interrogator signal into an in-phase and quadrature branch. Using the novel unequal distribution in a six-port allows higher power being directed to the RFID chip to ensure a longer read range. The I and Q signals reflected by the RFID chip and the sensing element, respectively, are mixed without the use of a lossy or an active mixer. The mixed signal’s amplitude and phase are directly dependent on the values of the attached sensing element. To read the value of the sensing element wirelessly at a reader, an IQ demodulator is used, which determines the phase of the backscattered signal. As a result, various sensed parameters such as light intensity, voltage, or force may be read wirelessly without requiring a battery at the node. To demonstrate the performance, a p-i-n diode is used as a sensing element to read voltages wirelessly at a distance of up to 2.45 m.