WideVision: A Low-Power, Multi-Protocol Wireless Vision Platform for Distributed Surveillance

Abstract

The trend in Internet of Things research points toward performing increasingly compute-intensive data analysis tasks on embedded sensor nodes, rather than server centers. Exploiting the technological advances in both energy efficiency, and Tiny Machine Learning algorithms and methods, an increasing number of recognition and classification tasks can be performed by small, low-power, wireless sensor nodes. This paper presents WideVision, a wireless, wide-area sensing platform capable of performing on-board person detection with power requirements in the mW range. The WideVision platform integrates seamlessly into the Internet of Things, by coupling a dedicated multiradio platform, including a LoRa interface, enabling mediumand long-range communication, with a novel parallel RISC-V microcontroller. We evaluate the proposed platform with the GAP8 microcontroller, which includes an 8-core RISC-V cluster, and greyscale camera to perform person detection by training and deploying an advanced, quantized neural network, achieving a statistical accuracy 84.5% for a 5-person detection task with a latency of only 182 ms. Experimental results demonstrate that the WideVision sensor node platform while performing inference at a rate of one image per minute on-board, is capable of lasting 300 days on a 2400 mAh Li-ion battery, and 65 days when evaluating one image per 10 seconds while providing effective surveillance of its perimeter.

MLAE2: Metareasoning for Latency-Aware Energy-Efficient Autonomous Nano-Drones

“…We used two pre-trained models in [5] as tiny machine learning and cloud models. Figure 4 (b) and (c) depict two different setups for the power measurement including a drone [5], [6] used in this paper and GAPuino [24], [25], [26] which has the GAP8 processor [27]. After extracting offline power consumption and latency, we used them in the simulation while implementing the proposed algorithm in this paper.…”