Data bundling for energy efficient communication of wearable devices

Sung, Jung-Woong; Han, Seung-Jae

doi:10.1016/j.comnet.2017.04.025

Cited by 3 publications

(3 citation statements)

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“…This design matches a common approach in low-power sensing: batched communication. Batching data into a single communication event saves energy by both maximizing the time spent in sleep mode and amortizing the radio start-up costs over multiple measurements [73,74,104]. Periodic communication increases the latency for offloading readings, but this overhead is offset by the over 4× lower energy when batching [74].…”

Section: Low-power Sensors and System Modelmentioning

confidence: 99%

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Protecting adaptive sampling from information leakage on low-power sensors

Kannan

Hoffmann

2022

Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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Adaptive sampling is a powerful family of algorithms for managing energy consumption on low-power sensors. These algorithms use captured measurements to control the sensor's collection rate, leading to near-optimal error under energy constraints. Adaptive sampling's data-driven nature, however, comes at a cost in privacy. In this work, we demonstrate how the collection rates of general adaptive policies leak information about captured measurements. Further, individual adaptive policies display this leakage on multiple tasks. This result presents a challenge in maintaining privacy for sensors using energy-efficient batched communication. In this context, the size of measurement batches exposes the sampling policy's collection rate. Thus, an attacker who monitors the encrypted link between sensor and server can use message lengths to uncover information about the captured values. We address this side-channel by introducing a framework called Adaptive Group Encoding (AGE) that protects any periodic adaptive sampler. AGE uses quantization to encode all batches as fixed-length messages, making message sizes independent of the collection rate. AGE reduces the quantization error through a series of transformations. The proposed framework preserves the low error of adaptive sampling while preventing information leakage and incurring negligible energy overhead. CCS CONCEPTS• Computer systems organization → Embedded systems.

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Section: Low-power Sensors and System Modelmentioning

confidence: 99%

“…This defense increases device latency by delaying transmissions and sending values in batches. This latency is manageable as low-power sensors already employ batching to conserve energy [104,119]; batched communication minimizes the time that radio modules spend in active mode [73,74].…”

Section: Introductionmentioning

confidence: 99%

Protecting adaptive sampling from information leakage on low-power sensors

Kannan

Hoffmann

2022

Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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“…To assess performance of the various partitioning scenarios illustrated in Section III-C, we utilized a PowerMonitor similarly to previous research [28], [29]. Fig.…”

Section: Performance Evaluationmentioning

confidence: 99%

Profiling Performance of Application Partitioning for Wearable Devices in Mobile Cloud and Fog Computing

et al. 2019

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Wearable devices have become essential in our daily activities. Due to battery constraints, the use of computing, communication, and storage resources is limited. Mobile cloud computing (MCC) and the recently emerged fog computing (FC) paradigms unleash unprecedented opportunities to augment the capabilities of wearable devices. Partitioning mobile applications and offloading computationally heavy tasks for execution to the cloud or edge of the network is the key. Offloading prolongs the lifetime of the batteries and allows wearable devices to gain access to the rich and powerful set of computing and storage resources of the cloud/edge. In this paper, we experimentally evaluate and discuss the rationale of application partitioning for MCC and FC. To experiment, we develop an Android-based application and benchmark energy and execution time performance of multiple partitioning scenarios. The results unveil architectural tradeoffs that exist between the paradigms and devise guidelines for proper power management of the servicecentric Internet-of-Things applications. INDEX TERMS Mobile cloud computing, fog computing, energy efficiency, IoT, wearable devices.

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