Energy Efficient Sampling Policies for Edge Computing Feedback Systems

Moothedath, Vishnu Narayanan; Champati, Jaya Prakash; Gross, James

doi:10.1109/tmc.2022.3165852

Cited by 2 publications

(3 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…in [3]. Note that, these are the same characterisation that we used in our previous work [25] and we reuse them here for consistency.…”

Section: Performance Comparisonmentioning

confidence: 93%

“…Note that, T is a system property whereas S and W -are derived from T through the selection of { }. We can compute the energy E required at the terminal to detect an event as [25] E = (S + 1)…”

Section: System Model and Problem Statementmentioning

confidence: 99%

“…In our previous works, we have extensively studied the efficient capture of essential events in a video analytics system (VAS) and a general cyber-physical system (CPS) using an optimal periodic sampling [24], [25]. The VAS in our research is motivated by the WCA from [2], [3] where a human task progress is monitored continuously by a video stream processed at a remote server for the detection of a predefined task completion.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy-Optimal Sampling for Edge Computing Feedback Systems: Aperiodic Case

Moothedath

2022

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)

View full text Add to dashboard Cite

We study the problem of optimal sampling in an edge-based video analytics system (VAS), where sensor samples collected at a terminal device are offloaded to a back-end server that processes them and generates feedback for a user. Sampling the system with the maximum allowed frequency results in the timely detection of relevant events with minimum delay. However, it incurs high energy costs and causes unnecessary usage of network and compute resources via communication and processing of redundant samples. On the other hand, an infrequent sampling result in a higher delay in detecting the relevant event, thus increasing the idle energy usage and degrading the quality of experience in terms of responsiveness of the system. We quantify this sampling frequency trade-off as a weighted function between the number of samples and the responsiveness. We propose an energy-optimal aperiodic sampling policy that improves over the state-of-the-art optimal periodic sampling policy. Numerically, we show the proposed policy provides a consistent improvement of more than 10% over the state-of-the-art.

show abstract

“…in [3]. Note that, these are the same characterisation that we used in our previous work [25] and we reuse them here for consistency.…”

Section: Performance Comparisonmentioning

confidence: 93%

Section: System Model and Problem Statementmentioning

confidence: 99%