Sensor Data Communication Research Articles

Battery configuration dependence to power line communication using high-order quadrature amplitude modulation

Power line communication (PLC) within future smart batteries facilitates the communication of high fidelity sensor data between smart cells and external systems, with application areas including intelligent vehicles and smart grids. This interconnected PLC system of smart cells will enhance cell utilisation and safety through cell-to-cell coordination at a system level, leveraging the existing bus bar within the battery and eliminating the need for additional wire harnesses. This paper studies the performance of a PLC system operating at carrier frequencies of 10MHz to 6GHz within four distinct configurations of lithium-ion batteries. This assessment focuses on changes in scattering parameters and data transmission error ratios. Furthermore, quadrature amplitude modulation (QAM) orders of up to 1024 are investigated for their viability within such environments. The results indicate that the addition of cells in parallel increases error ratios with high-order QAM, and that this effect varies substantially with carrier frequency. Using QAM increases data throughput, allowing for data transfers within large-scale battery systems without PLC bus bandwidth saturation. A prospective centre frequency of 3650MHz allows for a wide bandwidth of 300MHz and 1024-QAM with little signal attenuation and data error. At this frequency, the need for signal repeaters and higher signal output power is reduced. These results are used to determine the most suitable arrangement of cells within a smart battery with consideration of the PLC performance. The preliminary performance of a large-scale battery system with PLC in-situ could be derived from the findings in this research based upon the four battery configurations tested.

IoT 엣지디바이스를 이용한 경량화 클라우드 컴퓨팅환경 구축 및 검증

This study validated a cloud computing environment using edge devices for Internet of Things (IoT) sensor data collection and communication. Lightweight cloud infrastructure enhanced resource efficiency, minimized latency, and optimized data storage and processing. Technical achievements encompass standardized data exchange, improved device scalability and flexibility through software technologies, real-time data updates via synchronization, and the creation of an API-based user interface for an intuitive dashboard. The study substantiates the efficient establishment and validation of a cloud computing environment using edge devices, providing practical solutions for IoT data management and processing.

In-situ electronics and communications for intelligent energy storage

Lithium-ion batteries are increasingly common in high-power, safety–critical applications such as aerospace, spaceflight, automotive and grid storage. The voltage and power specifications of such applications usually require large numbers of individual cells combined in series and parallel to form a battery pack. It is then the role of the Battery Management System (BMS) to monitor these cells condition and ensure they remain within safe operating limits. To minimise cost and complexity, it is typical to monitor only a fraction of the cells in a battery pack. This creates potential safety and reliability issues and requires conservative limits imposed on the overall system to ensure safe operation. This is insufficient in high-power, safety–critical applications and thus alternative approaches to battery management are required. Here we demonstrate the development of novel miniature electronic devices for incorporation in-situ at a cell-level during manufacture. This approach enables local cell-to-cell and cell-to-BMS data communication of sensor data without the need for additional wiring infostructure within a battery module assembly. The electronics firmware and hardware integration within the cell’s electrode stack is demonstrated to function after triggering post cell formation and through cycling and electrochemical impedance analysis. This work shows that the proposed approach has a negligible impact on the cells’ performance and highlights a new technique for active monitoring of the cell’s in-situ conditions. This research will enable new methods of cells characterization and monitoring for optimum electrochemical and thermal performance while improving system safety.

LEDNet: Deep Learning-Based Ground Sensor Data Monitoring System

Wireless Sensor Networks (WSN) are generally used for precision agriculture. However, reliability and cost are the key limitations of such approaches. In recent times, the application of Unmanned Aerial Vehicles (UAVs) in the agricultural field has become popular due to scalability, cost efficient and user friendly adaptations with the help of improved navigation algorithms. A Novel and cost effective Light Emitting Diode (LED) based wireless communication of field sensor data to server has been recently explored. This paper proposes a LEDNet framework that utilizes the LED pattern based sensor data encoding in an image with computer vision and deep learning based data extraction/communication of data. The LED pattern image can be captured using any decent resolution cameras that can be mounted on UAVs. The proposed framework generates LED sequences with the help of embedded boards and utilizes image processing and deep learning for the decoding of the sensor data from the LED pattern image. The experiments are carried out with the images taken under various lighting conditions from different heights in the field. Promising performance in terms of accuracy and power consumption is observed for the collection of sensor data using the proposed LEDNet framework for the LED bit sequence extraction from the dataset of images collected under various environmental conditions.

In-body to Out-of-body Communication Channel Modeling for Ruminant Animals for Smart Animal Agriculture.

Continuous real-time health monitoring in animals is essential for ensuring animal welfare. In ruminants like cows, rumen health is closely intertwined with overall animal health. Therefore, in-situ monitoring of rumen health is critical. However, this demands in-body to out-of-body communication of sensor data. In this paper, we devise a method of channel modeling for a cow using experiments and FEM based simulations at 400 MHz. This technique can be further employed across all frequencies to characterize the communication channel for the development of a channel architecture that efficiently exploits its properties.

A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT

A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish–subscribe-based protocol for the communication of sensor or event data. The publish–subscribe strategy makes it more attractive for intruders and thus increases the number of possible attacks over MQTT. In this paper, we proposed a Deep Neural Network (DNN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using two different publicly available datasets, including (1) MQTT-IoT-IDS2020 and (2) a dataset with three different types of attacks, such as Man in the Middle (MitM), Intrusion in the network, and Denial of Services (DoS). The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the DNN-based model achieved 99.92%, 99.75%, and 94.94% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 97.08%, 98.12%, and 90.79%, respectively. On the other hand, the proposed DNN model attains the highest accuracy of 97.13% against LSTM and GRUs for the second dataset.

Communication of Sensor Data in Underground Mining Environments: An Evaluation of Wireless Signal Quality over Distance

The technologies of the fourth industrial revolution have the potential to make zero harm possible for the first time in the history of mining. In the journey toward zero harm, rock stress monitoring systems are important for the risk management process. Although communication systems for underground mining have improved significantly over the past two decades, it remains difficult to achieve reliable-all-the-time wireless communication in ultra-deep level underground mines. The aim of this study is to explore and test a smart phone network for communicating sensor data from the underground production environment to the surface. In this paper, the evaluation and performance over distance of a wireless communication system is performed in underground mining environments. The wireless system transmits the data collected from a sensor installed in a narrow reef stope, horizontal tunnel, and vertical shaft area of a mock underground mine. The evaluation was performed using the received signal strength of a mobile receiver over distance. The path loss coefficients of the underground mining environment were then derived for the measurement areas. The results show that a communication speed of 80 Mbps was achieved in a 60 m range, thus, indicating the potential for the support of applications requiring higher data rates.

Design of Toxic Environment Monitoring System for Industrial Sites Based on Wireless Sensor Networks

In view of the actual demand of toxic gas monitoring in the production, storage and use field environment of chemical enterprises, considering the large amount of monitoring data and the difficulty of real-time transmission of information, a toxic gas environment monitoring system is designed. The system based on low-power microcontroller chip CC2530 by using wireless sensor intelligent information processing and data communication technology, which evaluates and improves the ambient air conditions and provides theoretical basis and technical support. The sensor node collects the concentration data of CO, formaldehyde, ozone, SO2 and other toxic gases in real time, processes them and sends them to the receiving end. According to the stored data, the receiver monitors the air quality of the industrial production and storage environment in real time, and effectively solves the problems of complex wiring and maintenance difficulties of traditional cable sensors to reduce the risk of accidents or early warning and reduce the hazards of accidents. The test results show that the system has stable performance, can effectively detect industrial toxic environmental parameters, and can be widely used in various fields, especially in unattended or inaccessible environment for monitoring tasks.

Retracted] Optimization Strategy of IoT Sensor Configuration Based on Genetic Algorithm‐Neural Network

This article carries out the overall design framework of the IoT sensor data processing platform and analyzes the advantages of using the integrated construction platform. The platform is divided into two parts, a web management platform and a data communication system, and interacts with the database by integrating the business layers of the two into one. The web management platform provides configurable communication protocol customization services, equipment information, personal information, announcement information management services, and data collection information monitoring and analysis services. The collected data is analyzed by the sensor data communication service system and then provided to the web management platform for query and call. This paper discusses the theoretical basis of the combination of genetic algorithm and neural network and proposes the necessity of improving genetic algorithm. The improved level involves chromosome coding methods, fitness function selection, and genetic manipulation. We propose an improved genetic algorithm and use an improved genetic algorithm (IGA) to optimize the neural network structure. The finite element method is adopted, the finite element model is established, and the shock piezoelectric response is numerically simulated. The genetic neural network method is used to simulate the collision damage location detection problem. The piezoelectric sensor is optimized, and the optimal sensor configuration corresponding to its initial layout is obtained, which provides guidance for the optimal configuration of the actual piezoelectric sensor.

Printed, flexible, compact UHF-RFID sensor tags enabled by hybrid electronics

Sensor data can be wirelessly transmitted from simple, battery-less tags using Radio Frequency Identification (RFID). RFID sensor tags consist of an antenna, a radio frequency integrated circuit chip (RFIC), and at least one sensor. An ideal tag can communicate over a long distance and be seamlessly integrated onto everyday objects. However, miniaturized antenna designs often have lower performance. Here we demonstrate compact, flexible sensor tags with read range comparable to that of conventional rigid tags. We compare fabrication techniques for flexible antennas and demonstrate that screen and stencil printing are both suitable for fabricating antennas; these different techniques are most useful at different points in the design cycle. We characterize two versions of flexible, screen printed folded dipoles and a meandered monopole operating in the 915 MHz band. Finally, we use these antennas to create passive sensor tags and demonstrate over the air communication of sensor data. These tags could be used to form a network of printed, flexible, passive, interactive sensor tags.

Insulation Condition Monitoring in Distribution Power Grid via IoT-Based Sensing Network

Insulation condition monitoring is essential for the safety of our distribution power grid. However, the traditional monitoring solution is hardly applied due to the concerns that arise from natures of distribution grid, such as complex feeder networks, massive operation site, and high economic benefit. In this paper, a wireless sensing network based on Internet of things (IoT) technology is demonstrated for insulation condition perception. First of all, the general framework of the wireless sensing network is designed with considerations on extendibility and accessibility. Next, it goes into detail about the technical requirements of the essential units of the sensing network including sensor node, power management, and data communication. With this concept, two practical cases of IoT-based partial discharge sensing networks realized on the switchgear cabinet and power cable are presented with details involving the design of transient earth voltage and high frequency current sensor nodes, the energy consumption, Lora/NB-IoT-based network structures, and data usage strategies. This IoT-based insulation condition monitoring is proven to be applicable for the distribution grid and expected to be referenced for more possible IoT applications.

The Heart Attack Detection by ESP8266 Data Communication at a Real Time to Avoid Sudden Death

The problem Heart attack is one of the causes of death at this time. A person may not realize that he suddenly experiences an improper heartbeat so that a sudden heart attack can result in sudden death. Usually a traditional heart rate calculation has been done using hardware such as oximeters or electrocardiogram devices. Although this device has a reliable method for normal users, the use of this device takes a long time to carry out the inspection process. In this paper, we propose a method that can detect heartbeats that are detect directly and can be shared to several telephone numbers of family users, hospitals, paramedics, or private doctors. We developed an internet of thing method that integrates a pulse sensor, microcontrollers and Wi-Fi series data communication device modules ESP8266. The detection of pulse sensors attached of the patient’s body (users) and the microcontroller will be sent in a real time by the ESP8266 circuit module to the several smartphone numbers that have been set it before. The result and benefit of this method is that the patient (user) can be helped immediately if there is a heart rate abnormality, because of the heart rate detection results will spread to the several other people so that the patient (user) can be helped immediately before the condition becomes more fatal.

Rapid Simulation of Optimally Responsive Façade during Schematic Design Phases: Use of a New Hybrid Metaheuristic Algorithm

Operation of environmentally responsive building components requires rapid prediction of the optimal adaptation of geometric shapes and positions, and such responsive configuration needs to be identified during the design process as early as possible. However, building simulation practices to characterize optimized shapes of various geometric design candidates are limited by complex simulation procedures, slow optimization, and lack of site information. This study suggests a practical approach to the design of responsive building façades by integrating on-site sensors, building performance simulation (BPS), machine-learning, and 3D geometry modeling on a unified design interface. To this end, a novel and efficient hybrid optimization algorithm, tabu-based adaptive pattern search simulated annealing (T-APSSA), was developed and integrated with wireless sensor data communication (using nRF24L01 and ESP8266 WiFi modules) on a parametric visual programming language (VPL) interface Rhino Grasshopper (0.9.0076, McNeel, Seattle, USA). The effectiveness of T-APSSA for early-stage BPS and optimal design is compared with other metaheuristic algorithms, and the proposed framework is validated by experimental optimal envelope (window shading) designs for single (daylight) and multiple (daylight and energy) objectives. Test results demonstrate the improved efficiency of T-APSSA in calculations (two to four times faster than other algorithms). This T-APSSA-integrated sensor-enabled design optimization practice supports rapid BPS and digital prototyping of responsive building façade design.

Low-Cost Road Traffic State Estimation System Using Time-Spatial Image Processing

Road traffic mobility can be described by its Level of Services (LoS). A major challenge in traffic state and LoS estimation is the limitation of observed traffic data. To derive the traffic state of a road network, a sensor network needs to be installed. Most stationary sensing techniques involve high investment in terms of the sensor installation, data communication and computational resources. This paper proposes a low-cost image processing system for road traffic state estimation using time-spatial image (TSI) processing. The TSI is an image processing technique for transforming a series of video images into a single image. Therefore, the TSI can reduce memory resources compared with the traditional methods. A camera can be exploited for traffic-state estimation through integration with TSI generating and processing modules. In addition, traffic state variables such as space-mean-speed, flow and density can be estimated. Empirical results are provided based on several experiments to show that TSI processing is a viable lowcost approach to traffic state estimation.

Wireless Sensor Network for Monitoring & Control of Environmental Factors using Arduino

<strong><em>Today there is demand to monitor environmental factors almost in all research institutes and industries and even for domestic uses. The analog data measurement requires manual effort to note readings and there may be a possibility of human error. Such type of systems fail to provide and store precise values of parameters with high accuracy. Analog systems are having drawback of storage/memory. Therefore, there is a requirement of a smart system which is fully automated, accurate and capable enough to monitor all the environmental parameters with utmost possible accuracy. Besides, it should be cost-effective as well as portable too. This paper represents the Wireless Sensor(WS) data communication using DHT11, Arduino, SIM900A GSM module, a mobile device and Liquid Crystal Display(LCD). Experimental setup includes the heating arrangement of DHT11 and transmission of its data using Arduino and SIM900A GSM shield. The mobile device receives the data using Arduino, GSM shield and displays it on LCD too. Heating arrangement is used to heat and cool the temperature sensor to study its characteristics.</em></strong>

A wirelessly powered RFID-based smart contact lens system

This paper presents a wireless smart contact lens system, comprising a capacitive sensor interface circuitry and a wirelessly powered radio frequency identification (RFID)-compatible system, for sensor control and data communication. To improve compliance and reduce user discomfort, the capacitive sensor was embedded on a 200-μm-thick soft contact lens using commercially available biocompatible lens material and a standard sandwich manufacturing process. The proposed lens can communicate with RFID equipment at an ultra-high frequency (UHF) RFID frequency of 920 MHz. A successful communication distance of 2 cm was achieved under 18 dBm incident radio frequency (RF) power, and a maximum distance of 4 cm was achieved under 28 dBm incident power. The uniform magnetic field distribution of the transmitting antenna enhances energy transmission and provides robust operation against shifts in relative location and misalignment of the contact lens and the glass caused by eye movement and blinking.

PUF Sensor: Exploiting PUF Unreliability for Secure Wireless Sensing

Wireless sensors are increasingly penetrating every domain of our lives through integration with Internet of Things, e.g., such devices are widely incorporated into smart buildings and for monitoring critical industrial infrastructure. Sensing, collecting and communication of sensor data, however, are under threat from various attacks due to the difficulty in implementing proper protection mechanisms and limited computational resources available in these cost-sensitive devices. This paper expands on recent research on physical unclonable function (PUF) sensors to secure sensing by taking the advantage of inherent physical randomness. In particular, PUF unreliability originates from its sensitivity to ambient parameter variations that is usually undesirable for elementary PUF applications—such as authentication and key generation—is exploited to guarantee the veracity of the sensed value. In this paper, a PUF naturally acting as a sensor or a PUF explicitly integrated with a sensor is called a PUF sensor. Security of sensing in a PUF sensor is attributed to the natural merging of cryptography and sensing to eschew the need for a standalone crypto module. Thus, the PUF sensor is appealing for low-cost applications. To obtain the sensed value, we develop an authenticated sensing protocol that is robust against eavesdropping, also capable of detecting man-in-the-middle manipulation of the sensed value. Compared to initial investigations of PUF sensors, we avoid the stringent requirements of a strong PUF. We validate the feasibility of the proposed authenticated sensing protocol based on an experimental implementation of a ring oscillator PUF sensor. To improve the sensing capability, we present an efficient approach to select sensitive responses and only employ them for sensing. Significantly improved efficacy is validated through comprehensive experimental results.

A novel NMR instrument for real time drilling fluid analysis

We present here a novel instrument for real time drilling fluid NMR analysis at well site. The quantitative detection of oil and gas in drilling fluid while drilling helps to characterize reservoir quality and evaluate reservoir productivity. There is a big demand to detect oil content in drilling fluid in real time at well site in the petroleum industry. To meet the requirement for continuous and quantitative analysis, the new designed integrate NMR system comprises several subsystems: drilling fluid auto-sampling device, small-sized NMR sensor, spectrometer, main controller and wireless data communication. Auto-sampling device takes drilling fluids samples continuously and quantitatively from the flow line returning from the well and removes debris with diameter greater than 1 mm to reduce interference in measurement process. The small-sized NMR sensor is implemented based on Halbach magnets with 20 kg weight and about 20 MHz operating frequency. The integrated system is capable of performing remote control and data acquisition at harsh environment. The instrument has been fully tested at a number of wells, and successful discovered oil- and gas-bearing formations.

Radar Sensor Signal Acquisition and Multidimensional FFT Processing for Surveillance Applications in Transport Systems

The design and test of a radio detection and ranging (Radar) sensor signal acquisition and processing platform is presented in this paper. The Radar sensor operates in real time and is suited for surveillance applications in transport systems. It includes a front-end with a continuous-wave frequency-modulated transceiver operating in X-band, with a single transmitter and multiple receivers, and a multichannel high-speed A/D converter. Sensor signal processing and data communication tasks with external hosts are managed by a field-programmable gate array. The signal processing chain includes region of interest selection, multidimensional fast Fourier transform, peak detection, alarm decision logic, data calibration, and diagnostic. By configuring the Radar sensing platform in low-power mode (7-dBm transmitted power), it is possible to detect still and moving targets with a covered range up to 300-m and 30-cm resolution. The measuring range can be increased up to 2 km by adding an extra 34.5-dBm power amplifier. The Radar sensing platform can be configured for a maximum detected speed of 200 km/h, with a resolution of 1.56 km/h, or a speed up to 50 km/h with a resolution of 0.4 km/h. The cross-range resolution depends on the number of receiving channels; a tradeoff can be found between cross-range resolution of the Radar sensor and its complexity and power consumption. With respect to the state of the art of surveillance Radar sensors and light detection and ranging, the proposed solution stands for its high configurability and for the better tradeoff that can be found in terms of covered distance and power consumption.

Analysis of short-term ozone and PM2.5 measurements: Characteristics and relationships for air sensor messaging

ABSTRACTAir quality sensors are becoming increasingly available to the general public, providing individuals and communities with information on fine-scale, local air quality in increments as short as 1 min. Current health studies do not support linking 1-min exposures to adverse health effects; therefore, the potential health implications of such ambient exposures are unclear. The U.S. Environmental Protection Agency (EPA) establishes the National Ambient Air Quality Standards (NAAQS) and Air Quality Index (AQI) on the best science available, which typically uses longer averaging periods (e.g., 8 hr; 24 hr). Another consideration for interpreting sensor data is the variable relationship between pollutant concentrations measured by sensors, which are short-term (1 min to 1 hr), and the longer term averages used in the NAAQS and AQI. In addition, sensors often do not meet federal performance or quality assurance requirements, which introduces uncertainty in the accuracy and interpretation of these readings. This article describes a statistical analysis of data from regulatory monitors and new real-time technology from Village Green benches to inform the interpretation and communication of short-term air sensor data. We investigate the characteristics of this novel data set and the temporal relationships of short-term concentrations to 8-hr average (ozone) and 24-hr average (PM2.5) concentrations to examine how sensor readings may relate to the NAAQS and AQI categories, and ultimately to inform breakpoints for sensor messages. We consider the empirical distributions of the maximum 8-hr averages (ozone) and 24-hr averages (PM2.5) given the corresponding short-term concentrations, and provide a probabilistic assessment. The result is a robust, empirical comparison that includes events of interest for air quality exceedances and public health communication. Concentration breakpoints are developed for short-term sensor readings such that, to the extent possible, the related air quality messages that are conveyed to the public are consistent with messages related to the NAAQS and AQI.Implications: Real-time sensors have the potential to provide important information about fine-scale current air quality and local air quality events. The statistical analysis of short-term regulatory and sensor data, coupled with policy considerations and known health effects experienced over longer averaging times, supports interpretation of such short-term data and efforts to communicate local air quality.