Smart Sensors

Fire detection and Extinguisher Robot is a Hardware based model used to extinguish the fire during fire accidents. It works in two modes autonomous mode and manual mode. In manual mode, the robot can be operated by use of remote control and live streaming camera. In autonomous mode, Robot has been developed which features to move in the direction with respect to the fire intensity. The flame sensing capability of the robot is varied by the flame sensor, above which the robot starts responding to the fire. The temperature sensor provides a backup to the flame sensor if needed in vast circumstances. The most added advantage of this Robot is that it turns ON automatically as it detects the fire around its surroundings, using flame sensor and tries to extinguish by moving in the direction with respect to the fire. The Robot shield is coated with aluminium boards that are capable of withstanding very high temperatures. The Robot finds its applications in Rescue operations during fire accidents, where the possibility for servicemen to enter the fire-prone areas is very less and also during wars to perform rescue functions.

El documento presenta una revisión de literatura en el contexto de redes de sensores y fusión de datos, el cual expone una breve descripción de algunas implicaciones importantes de las redes de sensores y las técnicas que frecuentemente se han usado como alternativa de solución ante los desafíos habituales en las principales topologías de las redes. De igual manera se efectúa una correspondencia entre los modelos que se han propuesto hasta la fecha y que comúnmente se han tomado como referencia para implementar un sistema de fusión de datos a fin de identificar el enfoque primordial de cada uno de estos. Finalmente se realiza una descripción de aportaciones dirigidas a sensores inteligentes, dejando claro que se requiere más investigación en este contexto que permitan formular soluciones viables a las problemáticas actuales en dicha área de aplicación.

The Internet of Things (IoT) involves a wide variety of heterogeneous technologies and resource-constrained devices that interact with each other. Due to such constraints, IoT devices usually require lightweight protocols that optimize the use of resources and energy consumption. Among the different commercial IoT devices, Bluetooth and Bluetooth Low Energy (BLE)-based beacons, which broadcast periodically certain data packets to notify their presence, have experienced a remarkable growth, specially due to their application in indoor positioning systems. This article proposes a family of protocols named Lightweight Protocol for Sensors (LP4S) that provides fast responses and enables plug-and-play mechanisms that allow IoT telemetry systems to discover new nodes and to describe and auto-register the sensors and actuators connected to a beacon. Thus, three protocols are defined depending on the beacon hardware characteristics: LP4S-6 (for resource-constraint beacons), LP4S-X (for more powerful beacons) and LP4S-J (for beacons able to run complex firmware). In order to demonstrate the capabilities of the designed protocols, the most restrictive (LP4S-6) is tested after implementing it for a telemetry application in a beacon based on Eddystone (Google's open beacon format). Thus, the beacon specification is extended in order to increase its ability to manage unlimited sensors in a telemetry system without interfering in its normal operation with Eddystone frames. The performed experiments show the feasibility of the proposed solution and its superiority, in terms of latency and energy consumption, with respect to approaches based on Generic Attribute Profile (GATT) when multiple users connect to a mote or in scenarios where latency is not a restriction, but where low-energy consumption is essential.

This work was supported by the project "Biosensoristica innovativa per i test sierologici e molecolari e nuovi dispositivi PoCT per la diagnosi di infezione da SARS-CoV-2" funded in 2020 by "Bando Straordinario di Ateneo per Progetti di Ricerca Biomedica in Ambito SARS-COV-2 e COVID-19"-University of Parma ABSTRACT Recent advances in Internet-of-Things technology have opened the doors to new scenarios for biosensor applications. Flexibility, portability, and remote control and access are of utmost importance to move these devices to people's homes or in a Point-of-Care context and rapidly share the results with users and their physicians. In this paper, an innovative portable device for both quantitative and semi-quantitative electrochemical analysis is presented. This device can operate autonomously without the need of relying on other devices (e.g., PC, tablets, or smartphones) thanks to built-in Wi-Fi connectivity. The developed hardware is integrated into a cloud-based platform, exploiting the cloud computational power to perform innovative algorithms for calibration (e.g., Machine Learning tools). Results and configurations can be accessed through a web page without the installation of dedicated APPs or software. The electrical input/output characteristic was measured with a dummy cell as a load, achieving excellent linearity. Furthermore, the device response to five different concentrations of potassium ferri/ferrocyanide redox probe was compared with a bench-top laboratory instrument. No difference in analytical sensitivity was found. Also, some examples of application-specific tests were set up to demonstrate the use in real-case scenarios. In addition, Support Vector Machine algorithm was applied to semi-quantitative analyses to classify the input samples into four classes, achieving an average accuracy of 98.23%. Finally, COVID-19 related tests are presented and discussed.

—Smart sensors are often designed to operate under harsh industrial conditions with incomplete information about noise and missing data. Therefore, signal processing algorithms are required to be unbiased, robust, predictive, and desirably blind. In this paper, we propose a novel blind iterative unbiased finite impulse response (UFIR) filtering algorithm, which fits these requirements as a more robust alternative to the Kalman filter (KF). The trade-off in robustness between the UFIR filter and KF is learned analytically. The predictive UFIR algorithm is developed to operate in control loops under temporary missing data. Experimental verification is given for carbon monoxide concentration and temperature measurements required to monitor urban and industrial environments. High accuracy and precision of the predictive UFIR estimator are demonstrated in a short time and on a long baseline.

This paper presents the use of smart sensors for monitoring both the stormwater and wastewater networks of the Scientific campus of the University of Lille in the North of France. This campus stands for a town of around 25 000 users. For each network, the paper presents the monitoring system, analysis of the recorded data and how this analysis resulted in enhancing our understanding of the network functioning as well as its improvement.

The Internet of Things paradigm has expanded the possibility of using sensors ubiquitously, particularly if connected to a cloud service for data sharing. There are several ways to connect sensors to the cloud: wearable or portable devices often lean on a smartphone that acts as a gateway, while other sensors, such as smart sensors for continuous monitoring (e.g. fall detectors) are connected through wireless networks covering a limited area (e.g. ZigBee or Wi-Fi). Their functionality can be improved using them in both outdoor and indoor environments without other devices. NB-IoT is a recently introduced wide-range protocol with a good compromise between low power, low deployment costs, payload length, and data rate. Traditionally, sensor nodes rely on only one type of radio: an innovative solution could be a sensor node exploiting a combination of different transmission technologies with the aim of achieving higher portability. In this paper, a hybrid solution based on NB-IoT/Wi-Fi is presented. The Wi-Fi connection is primarily selected due to its lower power consumption (compared to NB-IoT), while NB-IoT is activated only when a Wi-Fi network is not available. This study aims to evaluate the power consumption of the proposed solution with respect to single radio NB-IoT technology. Test boards have been implemented, and several data transmission tests have been carried out with both NB-IoT and Wi-Fi radios. Different received powers and payload lengths have been considered to analyze the impact on the energy profile of smart sensors. It has been demonstrated that using NB-IoT for both indoor and outdoor leads to an acceptable battery discharge time, with a strong dependence on the payload length. Under certain conditions, the proposed hybrid solution results in a battery duration up to two times higher than single-radio NB-IoT. INDEX TERMS Sensor systems and applications, smart devices, power measurements, low-power electronics, Internet of Things (IoT).