Smart, Secure, Yet Energy-Efficient, Internet-of-Things Sensors

The proliferation of Internet-of-Things (IoT) has led to the generation of zettabytes of sensitive data each year. The generated data are usually raw, requiring cloud resources for processing and decision-making operations to extract valuable information (i.e., distill smartness). Use of cloud resources raises serious design issues: limited bandwidth, insufficient energy, and security concerns. Edge-side computing and cryptographic techniques have been proposed to get around these problems. However, as a result of increased computational load and energy consumption, it is difficult to simultaneously achieve smartness, security, and energy efficiency. We propose a novel way out of this predicament by employing signal compression and machine learning inference on the IoT sensor node. An important sensor operation scenario is for the sensor to transmit data to the base station immediately when an event of interest occurs, e.g., arrhythmia is detected by a smart electrocardiogram sensor or seizure is detected by a smart electroencephalogram sensor, and transmit data on a less urgent basis otherwise. Since on-sensor compression and inference drastically reduce the amount of data that need to be transmitted, we actually end up with a dramatic energy bonus relative to the traditional sense-and-transmit IoT sensor. We use a part of this energy bonus to carry out encryption and hashing to ensure data confidentiality and integrity. We analyze the effectiveness of this approach on six different IoT applications with two data transmission scenarios: alert notification and continuous notification. The experimental results indicate that relative to the traditional sense-and-transmit sensor, IoT sensor energy is reduced by <inline-formula><tex-math notation="LaTeX">$57.1\times$</tex-math><alternatives><inline-graphic xlink:href="akmandor-ieq1-2864297.gif"/></alternatives></inline-formula> for electrocardiogram (ECG) sensor based arrhythmia detection, <inline-formula><tex-math notation="LaTeX">$379.8\times$</tex-math><alternatives><inline-graphic xlink:href="akmandor-ieq2-2864297.gif"/></alternatives></inline-formula> for freezing of gait detection in the context of Parkinson's disease, <inline-formula><tex-math notation="LaTeX">$139.7\times$</tex-math><alternatives><inline-graphic xlink:href="akmandor-ieq3-2864297.gif"/></alternatives></inline-formula> for electroencephalogram (EEG) sensor based seizure detection, <inline-formula><tex-math notation="LaTeX">$216.6\times$</tex-math><alternatives><inline-graphic xlink:href="akmandor-ieq4-2864297.gif"/></alternatives></inline-formula> for human activity classification, <inline-formula><tex-math notation="LaTeX">$162.8\times$</tex-math><alternatives><inline-graphic xlink:href="akmandor-ieq5-2864297.gif"/></alternatives></inline-formula> for neural prosthesis spike sorting, and <inline-formula><tex-math notation="LaTeX">$912.6\times$</tex-math><alternatives><inline-graphic xlink:href="akmandor-ieq6-2864297.gif"/></alternatives></inline-formula> for chemical gas classification. Our approach not only enables the IoT system to push signal processing and decision-making to the extreme of the edge-side (i.e., the sensor node), but also solves data security and energy efficiency problems simultaneously.