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Register a new userPerovskite PV-powered RFID: enabling low-cost self-powered IoT sensors
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Photovoltaic (PV) cells have the potential to serve as on-board power sources for low-power IoT devices. Here, we explore the use of perovskite solar cells to power Radio Frequency (RF) backscatter-based IoT devices with a few {mu}W power demand. Perovskites are suitable for low-cost, high-performance, low-temperature processing, and flexible light energy harvesting that hold the possibility to significantly extend the range and lifetime of current backscatter techniques such as Radio Frequency Identification (RFID). For these reasons, perovskite solar cells are prominent candidates for future low-power wireless applications. We report on realizing a functional perovskite-powered wireless temperature sensor with 4 m communication range. We use a 10.1% efficient perovskite PV module generating an output voltage of 4.3 V with an active area of 1.06 cm2 under 1 sun illumination, with AM 1.5G spectrum, to power a commercial off-the-shelf RFID IC, requiring 10 - 45 {mu}W of power. Having an on-board energy harvester provides extra-energy to boost the range of the sensor (5x) in addition to providing energy to carry out high-volume sensor measurements (hundreds of measurements per min). Our evaluation of the prototype suggests that perovskite photovoltaic cells are able to meet the energy needs to enable fully autonomous low-power RF backscatter applications of the future. We conclude with an outlook into a range of applications that we envision to leverage the synergies offered by combining perovskite photovoltaics and RFID.
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We present a new approach to ubiquitous sensing for indoor applications, using high-efficiency and low-cost indoor perovksite photovoltaic cells as external power sources for backscatter sensors. We demonstrate wide-bandgap perovskite photovoltaic cells for indoor light energy harvesting with the 1.63eV and 1.84 eV devices demonstrate efficiencies of 21% and 18.5% respectively under indoor compact fluorescent lighting, with a champion open-circuit voltage of 0.95 V in a 1.84 eV cell under a light intensity of 0.16 mW/cm2. Subsequently, we demonstrate a wireless temperature sensor self-powered by a perovskite indoor light-harvesting module. We connect three perovskite photovoltaic cells in series to create a module that produces 14.5 uW output power under 0.16 mW/cm2 of compact fluorescent illumination with an efficiency of 13.2%. We use this module as an external power source for a battery-assisted RFID temperature sensor and demonstrate a read range by of 5.1 meters while maintaining very high frequency measurements every 1.24 seconds. Our combined indoor perovskite photovoltaic modules and backscatter radio-frequency sensors are further discussed as a route to ubiquitous sensing in buildings given their potential to be manufactured in an integrated manner at very low-cost, their lack of a need for battery replacement and the high frequency data collection possible.
NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for a massive number of low-throughput low-cost devices in delay-tolerant applications with low power consumption. To provide reliable connections with extended coverage, a repetition transmission scheme is introduced to NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure. To avoid the difficulty in replacing the battery for IoT devices, the energy harvesting is considered as a promising solution to support energy sustainability in the NB-IoT network. In this work, we analyze RACH success probability in a self-powered NB-IoT network taking into account the repeated preamble transmissions and collisions, where each IoT device with data is active when its battery energy is sufficient to support the transmission. We model the temporal dynamics of the energy level as a birth-death process, derive the energy availability of each IoT device, and examine its dependence on the energy storage capacity and the repetition value. We show that in certain scenarios, the energy availability remains unchanged despite randomness in the energy harvesting. We also derive the exact expression for the RACH success probability of a {randomly chosen} IoT device under the derived energy availability, which is validated under different repetition values via simulations. We show that the repetition scheme can efficiently improve the RACH success probability in a light traffic scenario, but only slightly improves that performance with very inefficient channel resource utilization in a heavy traffic scenario.
Current air pollution monitoring systems are bulky and expensive resulting in a very sparse deployment. In addition, the data from these monitoring stations may not be easily accessible. This paper focuses on studying the dense deployment based air pollution monitoring using IoT enabled low-cost sensor nodes. For this, total nine low-cost IoT nodes monitoring particulate matter (PM), which is one of the most dominant pollutants, are deployed in a small educational campus in Indian city of Hyderabad. Out of these, eight IoT nodes were developed at IIIT-H while one was bought off the shelf. A web based dashboard website is developed to easily monitor the real-time PM values. The data is collected from these nodes for more than five months. Different analyses such as correlation and spatial interpolation are done on the data to understand efficacy of dense deployment in better understanding the spatial variability and time-dependent changes to the local pollution indicators.
The internet of things refers to the network of devices connected to the internet and can communicate with each other. The term things is to refer non-conventional devices that are usually not connected to the internet. The network of such devices or things is growing at an enormous rate. The security and privacy of the data flowing through these things is a major concern. The devices are low powered and the conventional encryption algorithms are not suitable to be employed on these devices. In this correspondence a survey of the contemporary lightweight encryption algorithms suitable for use in the IoT environment has been presented.
Over the past several years, the electrocardiogram (ECG) has been investigated for its uniqueness and potential to discriminate between individuals. This paper discusses how this discriminatory information can help in continuous user authentication by a wearable chest strap which uses dry electrodes to obtain a single lead ECG signal. To the best of the authors knowledge, this is the first such work which deals with continuous authentication using a genuine wearable device as most prior works have either used medical equipment employing gel electrodes to obtain an ECG signal or have obtained an ECG signal through electrode positions that would not be feasible using a wearable device. Prior works have also mainly dealt with using the ECG signal for identification rather than verification, or dealt with using the ECG signal for discrete authentication. This paper presents a novel algorithm which uses QRS detection, weighted averaging, Discrete Cosine Transform (DCT), and a Support Vector Machine (SVM) classifier to determine whether the wearer of the device should be positively verified or not. Zero intrusion attempts were successful when tested on a database consisting of 33 subjects.
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