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

Ayten Ozge Akmandor;Hongxu YIN;Niraj K. Jha
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引用次数: 40

Abstract

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 $57.1\times$ for electrocardiogram (ECG) sensor based arrhythmia detection, $379.8\times$ for freezing of gait detection in the context of Parkinson's disease, $139.7\times$ for electroencephalogram (EEG) sensor based seizure detection, $216.6\times$ for human activity classification, $162.8\times$ for neural prosthesis spike sorting, and $912.6\times$ 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.
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智能、安全、节能的物联网传感器
物联网(IoT)的激增导致每年都会产生敏感数据。生成的数据通常是原始的,需要云资源进行处理和决策操作,以提取有价值的信息(即提取智能)。云资源的使用引发了严重的设计问题:带宽有限、能源不足和安全问题。已经提出了边缘计算和加密技术来解决这些问题。然而,由于计算负载和能耗的增加,很难同时实现智能性、安全性和能源效率。我们提出了一种通过在物联网传感器节点上使用信号压缩和机器学习推理来摆脱这种困境的新方法。一个重要的传感器操作场景是,当感兴趣的事件发生时,传感器立即向基站发送数据,例如,通过智能心电图传感器检测到心律失常或通过智能脑电图传感器检测到癫痫发作,否则在不太紧急的基础上发送数据。由于传感器上的压缩和推理大大减少了需要传输的数据量,与传统的传感和传输物联网传感器相比,我们实际上最终获得了巨大的能量奖励。我们使用部分能量奖金进行加密和哈希,以确保数据的机密性和完整性。我们分析了这种方法在两种数据传输场景下的六种不同物联网应用程序上的有效性:警报通知和连续通知。实验结果表明,与传统的感测和传输传感器相比,基于心电图(ECG)传感器的心律失常检测的物联网传感器能量降低了57.1\times$,帕金森病背景下步态检测的冻结降低了379.8\times$;基于脑电图(EEG)传感器的癫痫检测降低了139.7\times$,人类活动分类为216.6\times$,神经假体棘突分类为162.8\times$$,化学气体分类为912.6\times$。我们的方法不仅使物联网系统能够将信号处理和决策推向边缘侧(即传感器节点)的极致,还同时解决了数据安全和能效问题。
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