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Smart Street Lighting System u sing LoRa For Smarter Kolkata City

With increasing population and the corresponding increase in road’s there has been a significant increase in the number of street light requirements, thereby, increasing the energy and investment costs [2]. However, with the advancement in technology, many new different energy efficient and smart street lighting solutions approaches proposed. The conventional systems prove to be without purpose on most days as they are manually operated which thus leads to huge amounts of meaningless power and human-power wastage [4]. In this project, I have designed an automated IOT based street lighting framework with integrated sensors and controllers communicating with each other using the LoRa technology which requires low power and cost thereby making the system more secure, cost and power effective solution. The LoRa end nodes(streetlights) send sensor data over the air to the LoRa gateway using LoRa WAN protocol, placed at a maximum distance of 5km. The gateway in turn forwards the data to the cloud server which processes it and communicates with the sensors for required actions.

Alcohol Sensor Calibration on the Edge Using Tiny Machine Learning (Tiny-ML) Hardware

Introduction Detecting precise concentrations of alcohol by means of a wearable sensor with varying relative humidity and temperature, has been a challenge in gas detection methods. By acquiring sensor data under these varying conditions and analyzing the data, we can better quantify the sensor behavior and extract precise measurements from the sensor. Improved analysis of the system behavior is required to identify decay or drift over a period of time Machine Learning (ML) can be used to improve the sensor calibration by using the collected data. Machine Learning can be leveraged for a reduction in costs and implementation effort for decorrelating the effect of changing humidity and temperature on sensor response. Microcontrollers offer benefits such as low energy consumption, small size and are suitable as a computing device for wearable applications. By running machine learning inference on microcontrollers, dependency on the network connectivity can be reduced, which is generally focused on bandwidth and power limitation and results in higher latency. Since data is processed at the device itself during testing, it can preserve the confidentiality of data under test. Previous studies have implemented decorrelation of temperature and humidity for the gas sensor, where model building and testing of sensor data were done at the remote computer. The goal of this study is to improve the precision of alcohol sensing by decorrelating the effect caused due to change in environmental conditions by applying machine learning at the edge. Methodology We have implemented an alcohol sensor for wearable applications to find the level of alcohol consumed by the subject. An alcohol sensor is interfaced with nRF52480 microcontroller with Bluetooth Low Energy (BLE) being a special feature to send sensor data to other computing devices as shown in Fig. 1. The response of the sensor was assessed under precise test environments to recognize the and compute their relation with the alcohol sensor output. In this study, data from a temperature sensor, humidity sensor and alcohol sensor are collected over a period of a week at the local computer and these are used as a training dataset for building the machine learning model. The received dataset is pre-processed and an ML model is trained to quantify the sensor output. Google Colab is used to build a Tiny Machine Learning (Tiny-ML) model with the help of a tensor-flow lite micro-library. The generated machine learning model is uploaded to the microcontroller for the prediction of data under test to compensates for the influence of ambient situations on an alcohol sensor. The novelty of this study is to quantify the output of an alcohol sensor by applying the ML model on test data at the embedded system itself and to calibrate the sensor response locally. The corrective procedure appears to be effective in refining the sensor response enactment under a variety of thermal and humidity situations. Keyword : Sensor Calibration, Tiny Machine Learning, Edge Computing Reference 1. Huerta, Ramon, et al. "Online decorrelation of humidity and temperature in chemical sensors for continuous monitoring." Chemometrics and Intelligent Laboratory Systems 157 (2016): 169-176. doi: 10.1016/j.chemolab.2016.07.0042. Jalal, A. H.; Umasankar, Y.; Gonzalez, P. J.; Alfonso, A.; Bhansali, S. 2017. Multimodal technique to eliminate humidity interference for specific detection of ethanol, Biosensors and Bioelectronics, 2017Jan 15;87:522-530. doi: 10.1016/j.bios.2016.08.1063. Peng Wei, Zhi Ning, Sheng Ye, Li Sun, Fenhuan Yang, Ka Chun Wong, Dane Westerdahl, and Peter K. K. Louie, Impact Analysis of Temperature and Humidity Conditions on Electrochemical Sensor Response in Ambient Air Quality Monitoring, Sensors (Basel). 2018 Feb; 18(2): 59, doi: 3390/s180200593. Tyagi, V Singh, Decorrelation of Temperature and Humidity Sensor’s by Comparing Classifier’s Performance on Metal Oxide Semiconductor Sensor’s Dataset,2018 International Conference on Bioinformatics and Systems Biology(BSB), doi: 10.1109/BSB.2018.8770589 Figure 1

Sistem pemantau iklim mikro pada kandang ayam pedaging tertutup berbasis internet of things

Smart agriculture has an emerged concept by using IoT sensors capable of providing various information about their field condition and conducting environmental monitoring to improve the yield of efficient crops. This research aims to develop a microclimate monitoring system in a closed house. The microclimate being monitored is the effective temperature, which is the temperature felt by broilers at that time in a fast area. In this research, IoT has been implemented using WeMos D1 R32 by sending sensor data to observe the effective temperature parameters as actual temperature, humidity, and wind speed into an MQTT cloud server. Microclimate control in the cage is based on effective temperature. The data can be displayed on a 16x4 LCD screen and accessed via an Android smartphone from anywhere and at any time.

Integration of ZigBee based GPS receiver to CAN network for precision farming applications

Precision farming applications are often data centric and aim collecting data from a set of sensor modules to be delivered to the central computer. For this aim, the ISO 11783 protocol which uses the Controller Area Network (CAN) as a data link protocol to perform the data communication are used to standardize and provide the serial data communication as wired between the various sensor modules and the central computer a plug/play approach. Many different types of sensors may use to collect temporal and spatial variability in precision farming applications. Especially GPS receivers are the most important sensor in a precision farming application. And also, different data bus protocols can be used for collected data transmission to the central computer. In this context, wireless sensor protocols, especially ZigBee, is gaining popularity for managing precision farming through real-time monitoring of agricultural variability. Various parameters in the precision farming can be monitored and controlled using ZigBee communication integrated with the CAN bus. In this paper, integration of the wired CAN Bus and ZigBee communication was designed, developed and implemented. In this system, the data regarding the geographical coordinate is extracted from the GPS receiver with the help of the ZigBee communication and send it to a central computer with the help of wired CAN Bus. This method has been implemented in order to adapt the ZigBee messages to the CAN Bus and reduce wire using. Finally, the data flow within designed system between CAN and ZigBee data frames was described. In this study, multiple CAN frames usage and handshaking mechanism are explained for sending sensor data longer than 64 bits. This system’s advantage is not only reduce cabling cost and increase the communication speed but also provide dynamic, flexible and applicable communication in precision farming applications.

A Study of LoRa Performance in Monitoring of Patient’s SPO2 and Heart Rate based IoT

In this research, a sensor that will be equipped with blood oxygen saturation function (SPO2) blood and Heart Rate is MH-ET Live max30102 Sensor with Library Max30105. The advantage of this sensor is compatible with ATmega 328P, which is the Arduino board, the first experiment using Arduino Uno. Therefore, MH-ET Sensor data is integrated with Wireless Sensor Network (WSN) devices, e.g, LoRa (Long Range) 915 MHz and calculate WSN path loss when sending sensor data in mountainous areas, the model used to represent signal analysis and measurements in this study is the Ground Reflection (2-ray) model. therefore, the conditions that can be explained are patients who will send their data over hilly areas and hospitals or medical treatments called receiving nodes or coordinator nodes in much lower areas, in the same situation adding routers is expected to be a comparison of whether the data sent faster or even no impact. Furthermore, in this study, it is expected to provide clear results on the function of the router as the sender of pulse sensor data. The point is patients who are in a higher area with the level of impossibility in bringing the patient due to the condition of the patient so that the SPO2 data transmission and heart rate of the patient are expected to be known quickly by the medical authorities through the sensor node device attached to the patient's body. The use of the Adaptive Data Rate (ADR) Algorithm is used to optimize data rate, time on air (ToA) or airtime and energy consumption in the network. Therefore, the End Device (ED) in the ADR algorithm must be static (non-mobile). In the process of measuring the ADR algorithm in the position of sending data (uplink) n-bits to n-gateway. Next, the application server used is ThingsSpeak or The Things Network (TTN).

Measurement of Power Line Sagging Using Sensor Data of a Power Line Inspection Robot

The operation of energy transmission lines with high efficiency without failure has great importance in today's electricity-dependent world. Problems that may occur in electricity transmission lines are failure cause of many operations not only industrial but also daily life. One of the most important causes of the problems encountered in power lines is the change in the amount of sagging. The change of sagging amount causes line breaks and losing energy efficiency. This problem, which is frequently encountered due to seasonal and climatic changes, is one of the major problems of continuity in the power line. The calculation of sag contains uncertain and variable parameters that can change seasonally, climatically and/or structurally such as weight per unit length of the conductor, the horizontal component of tension, total tension, etc. In this case, it is difficult to calculate a precise and reliable sag amount. The sagging of power lines is generally calculated theoretically or measured on-site by the personnel in charge. In this study, a new approach is presented to measure the sag amount by using sensor data of a power line inspection robot, precisely and reliably. The inspection robot moving on the power line can be remotely controlled and send sensor data. The sagging is measured with an error of less than 2 percent in the laboratory test field by using this technique.

(Keynote) Machine Learning and High-Speed Circuitry in Thin Film Transistors for Sensor Interfacing in Hybrid Large-Area Electronic Systems

Large-Area (and flexible) Electronics (LAE) on substrates such as plastic is a technologically attractive platform for deploying large numbers (potentially millions) of sensors over surfaces, from strain sensors on bridges and airplanes to flexible neural sensing caps worn on the head. Eventually the data has to end up in conventional IC’s for external communication and system integration, resulting in a “hybrid electronic” system. With large numbers, each sensor clearly cannot be directly connected to an IC, and a classic approach is active-matrix addressing enabled by low-performance thin film transistors (TFTs) fabricated directly on the plastic, architecturally similar to many conventional imaging arrays. Our work at Princeton has focused on using novel circuit, algorithmic, and machine-learning approaches implemented in the TFT/LAE domain, to greatly further reduce the number of hard physical connections between the TFT/LAE domain and conventional IC’s. The work has been demonstrated by prototype hybrid electronic sensor systems ranging from structural health monitoring, remote gesture sensing, brain wave (EEG) sensing, handwriting recognition from images, pattern recognition of physical objects by weight distribution, to isolation of individual speakers (when many are speaking) via microphone arrays. A first set of work is based on the analog properties of TFT’s, especially via high-frequency TFT oscillators. While large feature sizes and low process temperatures typically lead to “low performance” TFT’s, with simple self-alignment methods we have extended ZnO TFT performance [2,3] to currently well over 2 GHz. Along with HF thin film diodes, this enables near field coupling of signals and power between adjacent large area sheets [4] and sending sensor data “off-sheet” by wireless methods [5]. With sufficient bandwidth, frequency-hopping (spread spectrum) methods can be used to send far more data for a fixed number of connections than with a conventional active matrix approach [6]. Second, in many real-world problems, the goal of sensor arrays is often some kind of pattern recognition, either the in time domain (e.g. seizure detection via neural sensors) or in the spatial domain (e.g. initial detection of a crack on a wing). Machine-learning (ML) is a powerful tool for this, conventionally done in software. This requires sending all of the raw data from the sensors to the CMOS ICs and perhaps then to the cloud. Instead, we have implemented various levels of machine learning on the sensor data directly in TFTs in the large-area domain, so that only the results are sent to the IC domain portion of the system. ML conceptually consists of two steps: (i) feature extraction/data compression, and (ii) classification/inference. Data compression can be implemented in several ways using digital TFT circuits. Classification requires “memory” to be integrated into TFTs to save the “learning.” We have directly implemented both steps in TFT circuits for problems such as handwriting recognition from images in amorphous silicon sensors arrays, seizure detection, and tactile sensing [6-10]. Finally, we address the number of TFT’s required for different approaches and yield dependence. N Verma, Y Hu; L Huang, W Rieutort-Louis; J. Sanz Robinson; T Moy; B. Glisic; S. Wagner; J. Sturm, Proc. IEEE (2015)Y. Mehlman, N. Verma, and J.C. Sturm, Dig. Device Research Conf. (2017)Y. Mehlman, C. Wu, S. Wagner, N. Verma, and J.C. Sturm, Dig. Device Research Conf. (2019)Y. Hu; W. Rieutort-Louis; J. Sanz-Robinson; K. Song; J.C. Sturm; S. Wagner; N. Verma, 2012 Symp. VLSI CircuitsY. Hu, T Moy, L. Huang, W Rieutort-Louis, J Robinson, S Wagner, J Sturm, N Verma, Proc. 2014 IEEE Custom IC Conf.Y. Afsar, T. Moy, N. Brady, S. Wagner; J. Sturm, N. Verma, IEEE J. Solid-State Circuits (2018)T. Moy, W. Rieutort-Louis, S. Wagner, J.C. Sturm, N. Verma, IEEE Trans. Circuits and Systems I (2016)W. Rieutort-Louis; T.Moy, Z. Wang; S. Wagner; J.C. Sturm; N. Verma, IEEE J. Solid State Circuits (2016)T. Moy, L. Huang; W. Rieutort-Louis; C. Wu; P. Cuff; S. Wagner; J Sturm; N Verma, IEEE J. Solid-State Circuits (2017)L.E. Aygun, P. Kumar, Z. Zheng, T-S. Chen, S. Wagner, J.C. Sturm, and N. Verma, Dig. IEEE Inter. Solid State Circ. Conf. (2019)

Cloud Based WiFi Multi-Sensor Network

A WiFi technology was the basis of the Internet of Things (IoT) and many popularity of the wireless communication in the social network. A smart device used any kinds of the detectors all of the analog and digital sensor. This study simulates on the scope of the home security system (HSS). There used three types of sensor; a temperature sensor, smoke-CO, and PIR sensor. This study aims to design a multi-sensor node. All of the sensors are connected on a microcontroller unit (MCU) with the general purpose input output (GPIO). After the connection, there got invalid multi-sensor data. This experiment tried to run over ten times. There appeared some invalid when the processor startup. First, the temperature sensor did not work. Second, the smoke-CO sensors read an invalid value there were higher than the actual. This problem can solve the situation by the sensor calibration methodology—to set the calibration time with the dynamic time follow up on the GPIO function of each sensor and self-calibrate by the finite impulse response (FIR) filter in the part of setup portion. When the system was running for a long time this should take the invalid data. There were high and low from the actual and there got the difference value suddenly a swinging value. During the system was running there had some noise and the heat collected on the device. There got the invalid value. This error is solved by the Full Scale Kalman Filter (FSKF) to fill and estimate the right value. Next, there used the OFF-Mode to save the power consumption and do not send sensor data to the Cloud all time. This method helps the device will be run as long time and work in long life. Finally, there got a high-performance WiFi multi-sensor network.

Detektor Dini Kebakaran Multisensor Terintegrasi Android Menggunakan Komunikasi Bluetooth

This research aims to design fire early warning system using Arduino and Android with Bluetooth communication. The system is constructed using three sensors which are KY-026 flame detector, DS18B20 temperature sensor, and MQ-07 gas sensor. The communication used HC-05 Bluetooth module to send sensor data from Arduino to Android. Testing results show that Arduino through Bluetooth communication able to send sensor data to Android within range of 20 cm with no obstacle condition and range of 10 cm if there exists the obstacle. The system will send ‘normal’ status to Android when fulfilled this conditions: flame sensor value less than 200, temperatures less than 30°C, and gas levels less than 300 ppm. There are seven other conditions that contain ‘warning’ and ‘danger’ data if those condition above not fulfilled.

MediKit : IoT Based Smart Healthcare System for Effective Supervision of Patient

Medikit - An IoT healthcare system is developed for patients, hospitals and care centers that regularly supervise the health condition and checks whether the patient has taken the prescribed medicine. The proposed work monitors patient’s vitals such as pulse rate and body temperature on real time basis. Such information will be used by the doctor to diagnose the patient remotely and prescribe optimum medication dose. Further, the Medikit system apart from collecting the data ensures that patient has taken the medicine on time. For the proposed system the authors have used Bluetooth Technology to send sensor data to a Smartphone and also to Cloud Service Platform for archiving and analysis. The information available on the Cloud can be supervised by the doctor by using a Mobile application. The mobile application also supports scheduling of medicines as per doctor’s prescription in the medical box. The comprehensive system developed, to the best of author’s knowledge, is low cost, efficient and service oriented as compared to the traditional healthcare practices and other eHealth solutions discussed.

Design of Prototype System for Temperature and Humidity Control in Flour Storage

Storage of wheat flour requires good temperature and humidity to avoid mold growth that can damage the nutrients in wheat flour. The temperatures and humidity required for proper storage are above temperatures and humidity below 60% RH. The method used in flour storage is FIFO (First In First Out) using a Load cell sensor as a marker for flour input. The incoming flour will input the time, location and amount of flour, so that it can be easily determined which flour will come out first. To facilitate monitoring of temperature, humidity and stock amount in flour storage in this study, sending monitoring data wirelessly using an internet network that can be accessed anywhere. The test results of this study show that the DHT22 sensor can measure temperature and humidity well, which has a RE (Relative Error) value of 0.5% temperature and 3.9% humidity compared to the HTC-1 measuring instrument. The load cell test compared to digital scales has an accurate value using a linear formula. In the QoS test, sending sensor data to the web server has a good value according to the ITU-T standard, namely delay <150ms

Optimal Sensor Communications in Presence of Transmission Delays and Bandwidth Limitations

In a distributed sensor (and actuator) network, several sensors may be lumped to a single transmitter or communicate via a remote terminal unit. Due to bandwidth limitations in the network, the transmitter/remote terminal should decide on the communication sequence according to which it sends sensor data. This decision must be based on an appropriate performance criterion. In this paper, we formulize this problem and propose a convex optimization scheme to optimize the estimation variance via choosing the most appropriate communication sequence. The derived communication sequence is then used for designing a Kalman filter to estimate states of the underlying system. Both cases of Zero-Order Hold and Reset To Zero output policies are investigated and compared in the simulation.

COMPASS: an Interoperable Personal Health System to Monitor and Compress Signals in Chronic Obstructive Pulmonary Disease

In the past years the progress on the mobile market has made possible an advancement in terms of telemedicine systems and definition of systems for monitoring chronic illnesses. The distribution of mobile devices in developed countries is increasing. Many of these devices are equipped with wireless standards including Bluetooth and the amount of sold Smartphones is constantly increasing. Our approach is oriented towards this market, using existing devices to enable in-home patient monitoring and even further to ubiquitious monitoring. The idea is to increase the quality of care, reduce costs and gather medical grade data, especially vital signs, with a resolution of minutes or even less, which is nowadays only possible in an ICU (Intensive Care Units). In this paper we will present the COMPASS personal health system (PHS) platform, and how this platform enables Android devices to collect, analyze and send sensor data to an observation storage by means of interoperability standards. Furthermore, we will also present how this data can be compressed using advanced compressed sensing techniques and how to optimize these techniques with genetic algorithms to improve the RMSE of the reconstructed signal after compression. We also produce a preliminary evaluation of the algorithm against the state of the art algorithms for compressed sensing.

A Wireless Sensor Enabled by Wireless Power

Through harvesting energy by wireless charging and delivering data by wireless communication, this study proposes the concept of a wireless sensor enabled by wireless power (WPWS) and reports the fabrication of a prototype for functional tests. One WPWS node consists of wireless power module and sensor module with different chip-type sensors. Its main feature is the dual antenna structure. Following RFID system architecture, a power harvesting antenna was designed to gather power from a standard reader working in the 915 MHz band. Referring to the Modbus protocol, the other wireless communication antenna was integrated on a node to send sensor data in parallel. The dual antenna structure integrates both the advantages of an RFID system and a wireless sensor. Using a standard UHF RFID reader, WPWS can be enabled in a distributed area with a diameter up to 4 m. Working status is similar to that of a passive tag, except that a tag can only be queried statically, while the WPWS can send dynamic data from the sensors. The function is the same as a wireless sensor node. Different WPWSs equipped with temperature and humidity, optical and airflow velocity sensors are tested in this study. All sensors can send back detection data within 8 s. The accuracy is within 8% deviation compared with laboratory equipment. A wireless sensor network enabled by wireless power should be a totally wireless sensor network using WPWS. However, distributed WPWSs only can form a star topology, the simplest topology for constructing a sensor network. Because of shielding effects, it is difficult to apply other complex topologies. Despite this limitation, WPWS still can be used to extend sensor network applications in hazardous environments. Further research is needed to improve WPWS to realize a totally wireless sensor network.