Distributed Vibration Monitoring System for 10 kV-400 kVA 3D Wound Core Transformer under Progressive Short-Circuit Impulses.

As large-scale, high-proportion, and efficient distribution transformers surge into the grids, anti-short circuit capability testing of transformer windings in efficient distribution seems necessary and prominent. To deeply explore the influence of progressively short-circuit shock impulses on the core winding deformation of efficient power transformers, a finite element theoretical model was built by referring to a three-phase three-winding 3D wound core transformer with a model of S20-MRL-400/10-NX2. The distributions of internal equivalent force and total deformation of the 3D wound core transformer along different paths under progressively short-circuit shock impulses varying from 60% to 120% were investigated. Results show that the equivalent stress and total deformation change rate reach their maximum as the short-circuit current increases from 60% to 80%, and the maximum and average variation rate for the equivalent stress reach 177.75% and 177.43%, while the maximum and average variation rate for the total deformation corresponds to 178.30% and 177.45%, respectively. Meanwhile, the maximum equivalent stress and maximum total deformation reach 29.81 MPa and 38.70 µm, respectively, as the applied short-circuit current increased to 120%. In light of the above observations, the optimization and deployment of wireless sensor nodes was suggested. Therefore, a distributed monitoring system was developed for acquiring the vibration status of the windings in a 3D wound core transformer, which is a beneficial supplement to the traditional short-circuit reactance detection methods for an efficient grid access spot-check of distribution transformers.

Towards Mass-Scale IoT with Energy-Autonomous LoRaWAN Sensor Nodes.

By 2030, it is expected that a trillion things will be connected. In such a scenario, the power required for the trillion nodes would necessitate using trillions of batteries, resulting in maintenance challenges and significant management costs. The objective of this research is to contribute to sustainable wireless sensor nodes through the introduction of an energy-autonomous wireless sensor node (EAWSN) designed to be an energy-autonomous, self-sufficient, and maintenance-free device, to be suitable for long-term mass-scale internet of things (IoT) applications in remote and inaccessible environments. The EAWSN utilizes Low-Power Wide Area Networks (LPWANs) via LoRaWAN connectivity, and it is powered by a commercial photovoltaic cell, which can also harvest ambient light in an indoor environment. Storage components include a capacitor of 2 mF, which allows EAWSN to successfully transmit 30-byte data packets up to 560 m, thanks to opportunistic LoRaWAN data rate selection that enables a significant trade-off between energy consumption and network coverage. The reliability of the designed platform is demonstrated through validation in an urban environment, showing exceptional performance over remarkable distances.

Automated Sensor Node Malicious Activity Detection with Explainability Analysis.

Cybersecurity has become a major concern in the modern world due to our heavy reliance on cyber systems. Advanced automated systems utilize many sensors for intelligent decision-making, and any malicious activity of these sensors could potentially lead to a system-wide collapse. To ensure safety and security, it is essential to have a reliable system that can automatically detect and prevent any malicious activity, and modern detection systems are created based on machine learning (ML) models. Most often, the dataset generated from the sensor node for detecting malicious activity is highly imbalanced because the Malicious class is significantly fewer than the Non-Malicious class. To address these issues, we proposed a hybrid data balancing technique in combination with a Cluster-based Under Sampling and Synthetic Minority Oversampling Technique (SMOTE). We have also proposed an ensemble machine learning model that outperforms other standard ML models, achieving 99.7% accuracy. Additionally, we have identified the critical features that pose security risks to the sensor nodes with extensive explainability analysis of our proposed machine learning model. In brief, we have explored a hybrid data balancing method, developed a robust ensemble machine learning model for detecting malicious sensor nodes, and conducted a thorough analysis of the model's explainability.

Multipurpose Modular Wireless Sensor for Remote Monitoring and IoT Applications.

Today, maintaining an Internet connection is indispensable; as an example, we can refer to IoT applications that can be found in fields such as environmental monitoring, smart manufacturing, healthcare, smart buildings, smart homes, transportation, energy, and others. The critical elements in IoT applications are both the Wireless Sensor Nodes (WSn) and the Wireless Sensor Networks. It is essential to state that designing an application demands a particular design of a WSn, which represents an important time consumption during the process. In line with this observation, our work describes the development of a modular WSn (MWSn) built with digital processing, wireless communication, and power supply subsystems. Then, we reduce the WSn-implementing process into the design of its modular sensing subsystem. This would allow the development and launching processes of IoT applications across different fields to become faster and easier. Our proposal presents a versatile communication between the sensing modules and the MWSn using one- or two-wired communication protocols, such as I2C. To validate the efficiency and versatility of our proposal, we present two IoT-based remote monitoring applications.

Design and Implementation of an Event-Driven Smart Sensor Node for Wireless Monitoring Systems.

In this paper, an event-driven wireless sensor node is proposed and demonstrated. The primary design objective is to devise a wireless sensor node with miniaturization, integration, and high-accuracy recognition ability. The proposed wireless sensor node integrates two vibration-threshold-triggered energy harvesters that sense and power a threshold voltage control circuit for power management, a microcontroller unit (MCU) for system control, a one-dimensional convolutional neural network (1D-CNN) environment data analysis and vibration events distribution, and a radio frequency (RF) digital baseband transmitter with IEEE 802.15.4-/.6 protocols. The dimensions of the wireless sensor node are 4 × 2 × 1 cm3. Finally, the proposed wireless sensor node was fabricated and tested. The alarming time for detecting the vibration event is less than 6 s. The measured recognition accuracy of three events (knock, shake, and heat) is over 97.5%. The experimental results showed that the proposed integrated wireless sensor node is very suitable for wireless environmental monitoring systems.

Raspberry Pi Platform Wireless Sensor Node for Low-Frequency Impedance Responses of PZT Interface.

A wireless impedance monitoring system, called SSeL-Pi, is designed to have cheap, mobile, and handy practical features as compared to wired commercial impedance analyzers. A Raspberry Pi platform impedance sensor node is designed to measure signals at a low-frequency range of up to 100 kHz. The low-frequency impedance measurement via the proposed node has been combined with a new PZT interface technique for measuring local responses sensitive to structural damage. The new PZT interface can work as a surface-mounted or embedded sensor, and its local dynamic characteristics are numerically analyzed to pre-determine an effective impedance resonant frequency range of less than 100 kHz. Next, a software scheme was designed to visualize the input/output parameters of the proposed SSeL-Pi system (i.e., Raspberry Pi platform and PZT interface) and automate signal acquisition procedures of the impedance sensor node. The calibration for impedance signals obtained from the proposed system was performed by a series of procedures, from acquiring real and imaginary impedance to adjusting them with respect to a commercial impedance analyzer (HIOKI-3532). The feasibility of the wireless impedance monitoring system was experimentally evaluated for PZT interfaces that were subjected to various compressive loadings. The consistent results analyzed from signals measured by the SSeL-Pi and HIOKI 3532 systems were observed. Additionally, the strong relationships between impedance features (frequency shift and RMSD index) and compressive stresses of the PZT interfaces showed the potential for axial force/stress variation monitoring in real structures using the Raspberry Pi platform impedance sensor node and developed PZT interface.

A Light-Tolerant Wireless Neural Recording IC for Motor Prediction With Near-Infrared-Based Power and Data Telemetry.

Miniaturized and wireless near-infrared (NIR) based neural recorders with optical powering and data telemetry have been introduced as a promising approach for safe long-term monitoring with the smallest physical dimension among state-of-the-art standalone recorders. However, a main challenge for the NIR based neural recording ICs is to maintain robust operation in the presence of light-induced parasitic short circuit current from junction diodes. This is especially true when the signal currents are kept small to reduce power consumption. In this work, we present a light-tolerant and low-power neural recording IC for motor prediction that can fully function in up to 300 µW/mm2 of light exposure. It achieves best-in-class power consumption of 0.57 µW at 38° C with a 4.1 NEF pseudo-resistorless amplifier, an on-chip neural feature extractor, and individual mote level gain control. Applying the 20-channel pre-recorded neural signals of a monkey, the IC predicts finger position and velocity with correlation coefficient up to 0.870 and 0.569, respectively, with individual mote level gain control enabled. In addition, wireless measurement is demonstrated through optical power and data telemetry using a custom PV/LED GaAs chip wire bonded to the proposed IC.

Improved pig behavior analysis by optimizing window sizes for individual behaviors on acceleration and angular velocity data.

This paper presents the application of machine learning algorithms to identify pigs' behaviors from data collected using the wireless sensor nodes mounted on pigs. The sensor node attached to a pig's back senses the acceleration and angular velocity in three axes, and the sensed data are transmitted to a host computer wirelessly. Two video cameras, one attached to the ceiling of the pigpen and the other one to a fence, provided ground truth for data annotations. The data were collected from pigs for 131 h over 2 mo. As the typical behavior period depends on the behavior type, we segmented the acceleration data with different window sizes (WS) and step sizes (SS), and tested how the classification performance of different activities varied with different WS and SS. After exploring the possible combinations, we selected the optimum WS and SS. To compare performance, we used five machine learning algorithms, specifically support vector machine, k-nearest neighbors, decision trees, naive Bayes, and random forest (RF). Among the five algorithms, RF achieved the highest F1 score for four major behaviors consisting of 92.36% in total. The F1 scores of the algorithm were 0.98 for "eating," 0.99 for "lying," 0.93 for "walking," and 0.91 for "standing" behaviors. The optimal WS was 7 s for "eating" and "lying," and 3 s for "walking" and "standing." The proposed work demonstrates that, based on the length of behavior, the adaptive window and step sizes increase the classification performance.

Analyzing the behavior of pigs provides great insights into animal welfare and health. Technologies that enable automatic, continuous, and real-time behavior monitoring have emerged as alternative solutions and have received considerable attention. Using sensor-based animal monitoring technology, we could provide objective and quantitative assessments of the health and continuous care of pigs. We extracted distinct characteristics/features of different activities over given segments of acceleration data to boost classification performance. Pigs have various behavior patterns with different durations; treating behaviors with a small duration the same as a long duration could ignore the minority behaviors in the window frame. Our study showed that by finding the adaptive window sizes customized for individual behaviors, we could reduce the chance of mixing activities and compute the feature for better classification performance.

Battery-Free Wireless Light-Sensing Tag Based on a Long-Range Dual-Port Dual-Polarized RFID Platform.

Radio frequency identification (RFID) represents an emerging platform for passive RF-powered wireless sensing. Differential Multi-port RFID systems are widely used to enable multiple independent measurands to be gathered, or to overcome channel variations. This paper presents a dual-port/dual-integrated circuit (IC) RFID sensing tag based on a shared aperture dual-polarized microstrip antenna. The tag can be loaded with different sensors where the received signal strength indicator (RSSI) of one IC is modulated using a sensor, and the other acts as a measurand-insensitive reference, for differential sensing. The 868 MHz tag maintains a minimum unloaded read range of 14 m insensitive to deployment on metals or lossy objects, which represents the longest reported range of a multi-port RFID sensing tag. The tag is loaded with a light-dependent resistor (LDR) to demonstrate its functionality as a battery-less wireless RFID light sensor. Following detailed RF characterization of the LDR, it is shown that the impedance, and consequently the RSSI, of the sensing tag are modulated by changing the light intensity, whereas the reference port maintains a mostly unchanged response for a correlated channel. The proposed tag shows the potential for channel variations-tolerant differential RFID sensing platforms based on polarization-diversity antennas.

A Multifunctional Battery-Free Bluetooth Low Energy Wireless Sensor Node Remotely Powered by Electromagnetic Wireless Power Transfer in Far-Field.

This paper presents a multifunctional battery-free wireless sensing node (SN) designed to monitor physical parameters (e.g., temperature, humidity and resistivity) of reinforced concrete. The SN, which is intended to be embedded into a concrete cavity, is autonomous and can be wirelessly powered thanks to the wireless power transmission technique. Once enough energy is stored in a capacitor, the active components (sensor and transceiver) are supplied with the harvested power. The data from the sensor are then wirelessly transmitted via the Bluetooth Low Energy (BLE) technology in broadcasting mode to a device configured as an observer. The feature of energy harvesting (EH) is achieved thanks to an RF-to-DC converter (a rectifier) optimized for a low power input level. It is based on a voltage doubler topology with SMS7630-005LF Schottky diode optimized at -15 dBm input power and a load of 10 kΩ. The harvested DC power is then managed and boosted by a power management unit (PMU). The proposed system has the advantage of presenting two different power management units (PMUs) and two rectifiers working in different European Industrial, Scientific and Medical (ISM) frequency bands (868 MHz and 2.45 GHz) depending on the available power density. The PMU interfaces a storage capacitor to store the harvested power and then power the active components of the sensing node. The low power digital sensor HD2080 is selected to provide accurate humidity and temperature measurements. Resistivity measurement (not reported in this paper) can also be achieved through a current injection on the concrete probes. For wireless communications, the QN9080 system-on-chip (SoC) was chosen as a BLE transceiver thanks to its attractive features: a small package size and extremely low power consumption. For low power consumption, the SN is configured in broadcasting mode. The measured power consumption of the SN in a deep-sleep mode is 946 µJ for four advertising events (spaced at 250 ms maximum) after the functioning of sensors. It also includes voltage offset cancelling functionality for resistivity measurement. Far-field measurement operated in an anechoic chamber with the most efficient PMU (AEM30940) gives a first charging time of 48 s (with an empty capacitor) and recharge duration of 27 s for a complete measurement and data transmission cycle.

Energy Autonomous Wireless Sensor Nodes for Freight Train Braking Systems Monitoring.

Nowadays, railway freight transportation is becoming more and more crucial since it represents the best alternative to road transport in terms of sustainability, pollution, and impact on the environment and on public health. Upgrading the potentiality of this kind of transportation, it would be possible to avoid delays in goods deliveries due to road accidents, traffic jams, and other situation occurring on roads. A key factor in this framework is therefore represented by monitoring and maintenance of the train components. Implementing a real time monitoring of the main components and a predictive maintenance approach, it would be possible to avoid unexpected breakdowns and consequently unavailability of wagons for unscheduled repair activities. As highlighted in recent statistical analysis, one of the elements more critical in case of failure is represented by the brake system. In this view, a real time monitoring of pressure values in some specific points of the system would provide significant information on its health status. In addition, since the braking actions are related to the load present on the convoy, thanks to this kind of monitoring, it would be possible to appreciate the different behavior of the system in case of loaded and unloaded trains. This paper presented an innovative wireless monitoring system to perform brake system diagnostics. A low-power system architecture, in terms of energy harvesting and wireless communication, was developed due to the difficulty in applying a wired monitoring system to a freight convoy. The developed system allows acquiring brake pressure data in critical points in order to verify the correct behavior of the brake system. Experimental results collected during a five-month field test were provided to validate the approach.

A Self-Powered and Battery-Free Vibrational Energy to Time Converter for Wireless Vibration Monitoring.

Wireless sensor nodes (WSNs) are the fundamental part of an Internet of Things (IoT) system for detecting and transmitting data to a master node for processing. Several research studies reveal that one of the disadvantages of conventional, battery-powered WSNs, however, is that they typically require periodic maintenance. This paper aims to contribute to existing research studies on this issue by exploring a new energy-autonomous and battery-free WSN concept for monitor vibrations. The node is self-powered from the conversion of ambient mechanical vibration energy into electrical energy through a piezoelectric transducer implemented with lead-free lithium niobate piezoelectric material to also explore solutions that go towards a greener and more sustainable IoT. Instead of implementing any particular sensors, the vibration measurement system exploits the proportionality between the mechanical power generated by a piezoelectric transducer and the time taken to store it as electrical energy in a capacitor. This helps reduce the component count with respect to conventional WSNs, as well as energy consumption and production costs, while optimizing the overall node size and weight. The readout is therefore a function of the time it takes for the energy storage capacitor to charge between two constant voltage levels. The result of this work is a system that includes a specially designed lead-free piezoelectric vibrational transducer and a battery-less sensor platform with Bluetooth low energy (BLE) connectivity. The system can harvest energy in the acceleration range [0.5 g-1.2 g] and measure vibrations with a limit of detection (LoD) of 0.6 g.

Application of NSGA-II to Obtain the Charging Current-Time Tradeoff Curve in Battery Based Underwater Wireless Sensor Nodes.

In this paper, a novel application of the Nondominated Sorting Genetic Algorithm II (NSGA II) is presented for obtaining the charging current-time tradeoff curve in battery based underwater wireless sensor nodes. The selection of the optimal charging current and times is a common optimization problem. A high charging current ensures a fast charging time. However, it increases the maximum power consumption and also the cost and complexity of the power supply sources. This research studies the tradeoff curve between charging currents and times in detail. The design exploration methodology is based on a two nested loop search strategy. The external loop determines the optimal design solutions which fulfill the designers' requirements using parameters like the sensor node measurement period, power consumption, and battery voltages. The inner loop executes a local search within working ranges using an evolutionary multi-objective strategy. The experiments proposed are used to obtain the charging current-time tradeoff curve and to exhibit the accuracy of the optimal design solutions. The exploration methodology presented is compared with a bisection search strategy. From the results, it can be concluded that our approach is at least four times better in terms of computational effort than a bisection search strategy. In terms of power consumption, the presented methodology reduced the required power at least 3.3 dB in worst case scenarios tested.

Design and Field Validation of a Low Power Wireless Sensor Node for Structural Health Monitoring.

Smart monitoring systems are currently gaining more attention and are being employed in several technological areas. These devices are particularly appreciated in the structural field, where the collected data are used with purposes of real time alarm generation and remaining fatigue life estimation. Furthermore, monitoring systems allow one to take advantage of predictive maintenance logics that are nowadays essential tools for mechanical and civil structures. In this context, a smart wireless node has been designed and developed. The sensor node main tasks are to carry out accelerometric measurements, to process data on-board, and to send wirelessly synthetic information. A deep analysis of the design stage is carried out, both in terms of hardware and software development. A key role is played by energy harvesting integrated in the device, which represents a peculiar feature and it is thanks to this solution and to the adoption of low power components that the node is essentially autonomous from an energy point of view. Some prototypes have been assembled and tested in a laboratory in order to check the design features. Finally, a field test on a real structure under extreme weather conditions has been performed in order to assess the accuracy and reliability of the sensors.

A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network.

In mobile wireless sensor network (MWSN), the lifetime of the network largely depends on energy efficient routing protocol. In the literature, cluster leader (CL) is selected based on remaining energy of mobile sensor nodes to enhance sensor network lifetime. In this study, a novel connectivity-based Low-Energy Adaptive Clustering Hierarchy-Mobile Energy Efficient and Connected (LEACH-MEEC) routing protocol was proposed, where CL is selected based on connectivity among neighboring nodes and the remaining energy of mobile sensor nodes. Consequently, it improves data delivery, network lifetime and balances the energy consumption. We studied various performance metrics including the number of alive nodes (NAN), remaining energy (RE) and packet delivery ratio (PDR). Our proposed LEACH-MEEC outperforms all other algorithms due to the connectivity metric. Moreover, the performance of mobility models was investigated through graphical and statistically tabulated results. The results show that Reference Point Group Mobility model (RPGM) is better than other mobility models.

A Simple Wireless Sensor Node System for Electricity Monitoring Applications: Design, Integration, and Testing with Different Piezoelectric Energy Harvesters.

Real time electricity monitoring is critical to enable intelligent and customized energy management for users in residential, educational, and commercial buildings. This paper presents the design, integration, and testing of a simple, self-contained, low-power, non-invasive system at low cost applicable for such purpose. The system is powered by piezoelectric energy harvesters (EHs) based on PZT and includes a microcontroller unit (MCU) and a central hub. Real-time information regarding the electricity consumption is measured and communicated by the system, which ultimately offers a dependable and promising solution as a wireless sensor node. The dynamic power management ensures the system to work with different types of PZT EHs at a wide range of input power. Thus, the system is robust against fluctuation of the current in the electricity grid and requires minimum adjustment if EH unit requires exchange or upgrade. Experimental results demonstrate that this unit is in a position to read and transmit 60 Hz alternating current (AC) sensor signals with a high accuracy no less than 91.4%. The system is able to achieve an operation duty cycle from <1 min up to 18 min when the current in an electric wire varies from 7.6 A to 30 A, depending on the characteristics of different EHs and intensity of current being monitored.

Development of Implantable Wireless Sensor Nodes for Animal Husbandry and MedTech Innovation.

In this paper, we report the development, evaluation, and application of ultra-small low-power wireless sensor nodes for advancing animal husbandry, as well as for innovation of medical technologies. A radio frequency identification (RFID) chip with hybrid interface and neglectable power consumption was introduced to enable switching of ON/OFF and measurement mode after implantation. A wireless power transmission system with a maximum efficiency of 70% and an access distance of up to 5 cm was developed to allow the sensor node to survive for a duration of several weeks from a few minutes' remote charge. The results of field tests using laboratory mice and a cow indicated the high accuracy of the collected biological data and bio-compatibility of the package. As a result of extensive application of the above technologies, a fully solid wireless pH sensor and a surgical navigation system using artificial magnetic field and a 3D MEMS magnetic sensor are introduced in this paper, and the preliminary experimental results are presented and discussed.

Wake-Up Receiver with Equal-Gain Antenna Diversity †.

Small scale fading signals resulting from multipath propagation can cause signal strength variations in the range of several dB. Resulting from the fluctuating signal strengths, the wake-up packet reception rate can decrease significantly. Using antenna diversity can greatly mitigate these effects. This article presents a novel wireless sensor node with wake-up receiver that uses an equal-gain diversity method with two antennas in the wake-up path. Summation of the two diversity branch signals is done after the passive demodulation of the incoming signals. As a result, the wireless sensor node requires almost no additional active parts that would increase power consumption. Furthermore, we demonstrate experimentally the improved wake-up robustness and reliability achieved by this diversity technique in a multipath environment.

An Alternative Wearable Tracking System Based on a Low-Power Wide-Area Network.

This work presents an alternative wearable tracking system based on a low-power wide area network. A complete GPS receiver was integrated with a textile substrate, and the latitude and longitude coordinates were sent to the cloud by means of the SIM-less SIGFOX network. To send the coordinates over SIGFOX protocol, a specific codification algorithm was used and a customized UHF antenna on jeans fabric was designed, simulated and tested. Moreover, to guarantee the compliance to international regulations for human body exposure to electromagnetic radiation, the electromagnetic specific absorption rate of this antenna was analyzed. A specific remote server was developed to decode the latitude and longitude coordinates. Once the coordinates have been decoded, the remote server sends this information to the open source data viewer SENTILO to show the location of the sensor node in a map. The functionality of this system has been demonstrated experimentally. The results guarantee the utility and wearability of the proposed tracking system for the development of sensor nodes and point out that it can be a low cost alternative to other commercial products based on GSM networks.

Minimized Bolus-Type Wireless Sensor Node with a Built-In Three-Axis Acceleration Meter for Monitoring a Cow's Rumen Conditions.

Monitoring rumen conditions in cows is important because a dysfunctional rumen system may cause death. Sub-acute ruminal acidosis (SARA) is a typical disease in cows, and is characterized by repeated periods of low ruminal pH. SARA is regarded as a trigger for rumen atony, rumenitis, and abomasal displacement, which may cause death. In previous studies, rumen conditions were evaluated by wireless sensor nodes with pH measurement capability. The primary advantage of the pH sensor is its ability to continuously measure ruminal pH. However, these sensor nodes have short lifetimes since they are limited by the finite volume of the internal liquid of the reference electrode. Mimicking rumen atony, we attempt to evaluate the rumen condition using wireless sensor nodes with three-axis accelerometers. The theoretical life span of such sensor nodes depends mainly on the transmission frequency of acceleration data and the size of the battery, and the proposed sensor nodes are 30.0 mm in diameter and 70.0 mm in length and have a life span of over 600 days. Using the sensor nodes, we compare the rumen motility of the force transducer measurement with the three-axis accelerometer data. As a result, we can detect discriminative movement of rumen atony.