Wireless sensor networks (WSNs) have received significant attention in recent years and have found a wide range of applications, including structural and environmental monitoring, mobile health, home automation, Internet of Things, and others. As these systems are generally battery operated, major research efforts focus on reducing power consumption, especially for communication, as the radio transceiver is one of the most power-hungry components of a WSN. Moreover, with the advent of energy-neutral systems, the emphasis has shifted toward research in microwatt (or even nanowatt) communication protocols or systems. A significant number of wake-up radio receiver (WUR) architectures have been proposed to reduce the communication power of WSN nodes. In this work, we present an optimized ultra-low power (nanowatt) wake-up receiver for use in WSNs, designed with low-cost off-the-shelf components. The wake-up receiver achieves power consumption of 152 nW (with −32 dBm sensitivity), sensitivity up to −55 dBm (with maximum power of 1,2 µW), latency from 8 µs, tunable frequency, and short commands communication. In addition, a low power solution, which includes addressing capability directly in the wake-up receiver, is proposed. Experimental results and simulations demonstrate low power consumption, functionality, and benefits of the design optimization compared with other solutions, as well as the benefits of addressing false positive (FP) outcomes reduction.Index Terms-Nanowatt wake-up radio receiver (WUR), power optimization, ultra-low power, wireless sensor networks (WSNs).

Since in most wireless sensor network (WSN) scenarios nodes must operate autonomously for months or years, power management of the radio (usually consuming the largest amount of node's energy) is crucial. In particular, reducing the power consumption during listening plays a fundamental role in the whole energy balance of a sensor node, since shutting down the receiver when no messages are expected can remarkably increase the autonomy. Idle listening is a hard challenge because incoming messages are often unpredictable and developers have to trade off low power consumption and high quality of service. This paper is focusing on benefits of introducing a wake-up receiver over simple duty-cycling (wake-on radio). We analyze and compare the existing wake-up receiver prototypes and explore their benefits using simulations of two typical scenarios: with and without addressing requirements. A particular approach outperforms other solutions in terms of lifetime extension because of its very low power consumption (1µW). We also evaluate the overhead of the addressing capability, which sometimes has a non-negligible impact on the performance.

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Current medical practice of long-term chronic respiratory diseases treatment lacks a convenient method of empowering the patients and caregivers to continuously quantitatively track the intensity of respiratory symptoms. Such is "asthmatic wheezing", occurring in respiratory sounds. We envision a mobile, personalized asthma monitoring system comprising of a wearable, energy-constrained acoustic sensor and smartphone. In this article we address the energy-burden of acquisition and streaming of acoustic respiratory signal, and lessen it by applying the concept of compressed sensing (CS). First we analyse the adherence of normal and pathologic respiratory sounds frequency representations (DFT, DCT) to the sparse signal model. Given the pseudo-random non-uniform subsampling encoder implemented on MSP430 microcontroller, we review tradeoffs of accuracy and execution time of different CS algorithms, suitable for realtime respiratory spectrum recovery on smartphone. Working CS respiratory spectrum acquisition prototype is demonstrated, and evaluated. Prototype enables for real-time reconstruction of spectra dominated by approximately 8 frequency components with more than 80% accuracy, on Android smartphone using OMP algorithm, from only 25% signal samples (w.r.t. Nyquist rate) acquired and streamed by sensor at 8 kbit/s. Index Terms-M-health, asthmatic wheezing, compressive sensing, non-uniform sampling, orthogonal matching pursuit.1530-437X (c)

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