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1.
Sensors (Basel) ; 22(13)2022 Jun 24.
Artículo en Inglés | MEDLINE | ID: mdl-35808279

RESUMEN

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.

2.
Philos Trans A Math Phys Eng Sci ; 378(2164): 20190158, 2020 Feb 07.
Artículo en Inglés | MEDLINE | ID: mdl-31865884

RESUMEN

For decades, the design of untethered devices has been focused on delivering a fixed quality of service with minimum power consumption, to enable battery-powered devices with reasonably long deployment lifetime. However, to realize the promised tens of billions of connected devices in the Internet of Things, computers must operate autonomously and harvest ambient energy to avoid the cost and maintenance requirements imposed by mains- or battery-powered operation. But harvested power typically fluctuates, often unpredictably, and with large temporal and spatial variability. Energy-driven computers are designed to treat energy-availability as a first-class citizen, in order to gracefully adapt to the dynamics of energy harvesting. They may sleep through periods of no energy, endure periods of scarce energy, and capitalize on periods of ample energy. In this paper, we describe the promise and limitations of energy-driven computing, with an emphasis on intermittent operation. This article is part of the theme issue 'Harmonizing energy-autonomous computing and intelligence'.

3.
Sensors (Basel) ; 18(1)2018 Jan 10.
Artículo en Inglés | MEDLINE | ID: mdl-29320441

RESUMEN

Energy harvesting sensor systems typically incorporate energy buffers (e.g., rechargeable batteries and supercapacitors) to accommodate fluctuations in supply. However, the presence of these elements limits the miniaturization of devices. In recent years, researchers have proposed a new paradigm, transient computing, where systems operate directly from the energy harvesting source and allow computation to span across power cycles, without adding energy buffers. Various transient computing approaches have addressed the challenge of power intermittency by retaining the processor's state using non-volatile memory. However, no generic approach has yet been proposed to retain the state of peripherals external to the processing element. This paper proposes RESTOP, flexible middleware which retains the state of multiple external peripherals that are connected to a computing element (i.e., a microcontroller) through protocols such as SPI or I 2 C. RESTOP acts as an interface between the main application and the peripheral, which keeps a record, at run-time, of the transmitted data in order to restore peripheral configuration after a power interruption. RESTOP is practically implemented and validated using three digitally interfaced peripherals, successfully restoring their configuration after power interruptions, imposing a maximum time overhead of 15% when configuring a peripheral. However, this represents an overhead of only 0.82% during complete execution of our typical sensing application, which is substantially lower than existing approaches.

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