With the appearance of wearable devices and the IoT, energy harvesting nodes are becoming more and more important. The design and evaluation of these small standalone sensors and actuators, which harvest limited amounts of energy, requires novel tools and methods. Fast and accurate measurement systems are required to capture the rapidly changing harvesting scenarios and characterize leakage currents and energy efficiencies. The need for real-world experiments creates a demand for compact and portable equipment to perform in-situ power measurements and environmental logging. This work presents the ROCKETLOGGER, a hand-held measurement device that combines both properties: portability and accuracy. The custom analog front-end allows logging at sampling rates up to 64 kSPS. The fast range switching within 1.4 µs guarantees continuous power measurements starting from 4 pW at 1 mV up to 2.75 W at 5.5 V. The software provides remote control and manages data acquisition of up to 13 Mb/ sec in real-time. We extensively characterize the ROCKETLOGGER's performance, demonstrate the need for its properties in three use-cases at different stages of the system design flow, and show its advantages in measuring and validating new harvesting-driven devices for the IoT.

EDGE-based EC-GSM-IoT is a promising candidate for the billion-device cellular IoT (cIoT), providing similar coverage and battery life as NB-IoT. The goal of 20 dB coverage extension compared to EDGE poses significant challenges for the initial network synchronization, which has to be performed well below the thermal noise floor, down to an SNR of −8.5 dB. We present a low-complexity synchronization algorithm supporting up to 50 kHz initial frequency offset, thus enabling the use of a low-cost ±25 ppm oscillator. The proposed algorithm does not only fulfill the 3GPP requirements, but surpasses them by 3 dB, enabling communication with an SNR of −11.5 dB or a maximum coupling loss of up to 170.5 dB.

Brain-inspired high-dimensional (HD) computing represents and manipulates data using very long, random vectors with dimensionality in the thousands. This representation provides great robustness for various classification tasks where classifiers operate at low signal-to-noise ratio (SNR) conditions. Similarly, hyperdimensional modulation (HDM) leverages the robustness of complex-valued HD representations to reliably transmit information over a wireless channel, achieving a similar SNR gain compared to state-of-the-art codes. Here, we first propose methods to improve HDM in two ways: (1) reducing the complexity of encoding and decoding operations by generating, manipulating, and transmitting bipolar or integer vectors instead of complex vectors; (2) increasing the SNR gain by 0.2 dB using a new soft-feedback decoder; it can also increase the additive superposition capacity of HD vectors up to 1.7$$\times$$ × in noise-free cases. Secondly, we propose to combine encoding/decoding aspects of communication with classification into a single framework by relying on multifaceted HD representations. This leads to a near-channel classification (NCC) approach that avoids transformations between different representations and the overhead of multiple layers of encoding/decoding, hence reducing latency and complexity of a wireless smart distributed system while providing robustness against noise and interference from other nodes. We provide a use-case for wearable hand gesture recognition with 5 classes from 64 EMG sensors, where the encoded vectors are transmitted to a remote node for either performing NCC, or reconstruction of the encoded data. In NCC mode, the original classification accuracy of 94% is maintained, even in the channel at SNR of 0 dB, by transmitting 10,000-bit vectors. We remove the redundancy by reducing the vector dimensionality to 2048-bit that still exhibits a graceful degradation: less than 6% accuracy loss is occurred in the channel at − 5 dB, and with the interference from 6 nodes that simultaneously transmit their encoded vectors. In the reconstruction mode, it improves the mean-squared error by up to 20 dB, compared to standard decoding, when transmitting 2048-dimensional vectors.

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