Resource-efficient cyber-physical systems design: A survey

Zhao, Li; Huang, Chengcheng; Dong, Xingxing; Ren, Chongguang

doi:10.1016/j.micpro.2020.103183

Cited by 5 publications

(2 citation statements)

References 49 publications

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“…However, that work focuses on a high-level interface model and does not address the non-functional require-ments of the systems. The work [22] provides a comprehensive survey for computation, communication, and memory resource efficiency in CPSs. In work [23], a rule-based simulation of biochemical processes is addressed.…”

Section: Related Workmentioning

confidence: 99%

Design of Cyber Bio-analytical Physical Systems: Formal methods, architectures, and multi-system interaction strategies

Ashraf

Moullec

Pardy

et al. 2023

Microprocessors and Microsystems

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Section: Related Workmentioning

confidence: 99%

Design of Cyber Bio-analytical Physical Systems: Formal methods, architectures, and multi-system interaction strategies

Ashraf

Moullec

Pardy

et al. 2023

Microprocessors and Microsystems

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“…T HE ubicomp and Pervasive computing vision of computing any time and everywhere through instrumented and connected environments and objects, has attracted significant attention over the past two decades [1]. Related research on the Internet of Things (IoT) [2], middleware [3], mobile computing [4], sensors [5], [6], microprocessors [7], [8], user interfaces [9], computer networks [10], new materials [11], localization [12] and activity recognition [13] have flourished and advanced this vision.…”

Section: Introductionmentioning

confidence: 99%

Camouflage Learning: Feature Value Obscuring Ambient Intelligence for Constrained Devices

Nguyen

Sigg

Lietzén

et al. 2023

IEEE Trans. on Mobile Comput.

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Ambient intelligence demands collaboration schemes for distributed constrained devices which are not only highly energy efficient with respect to distributed sensing, processing and communication, but which also respect data privacy. Traditional algorithms for distributed processing suffer in Ambient intelligence domains either from limited data privacy, or from their excessive processing demands for constrained distributed devices. In this paper, we present Camouflage learning, a distributed machine learning scheme that obscures the trained model via probabilistic collaboration using physical-layer computation offloading and demonstrate the feasibility of the approach on backscatter communication prototypes and in comparison with federated learning, a popular distributed learning scheme. We show that Camouflage learning is more energy efficient than traditional schemes and that it requires less communication overhead while reducing the computation load through physical-layer computation offloading. The scheme is synchronization-agnostic and thus appropriate for sharply constrained, synchronization-incapable devices. We demonstrate model training and inference on four distinct datasets and investigate the performance of the scheme with respect to communication range, impact of challenging communication environments, power consumption, and the backscatter hardware prototype.

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State-based switching multi-rate controller for improving resource utilization on predictable and composable platforms

Haghi

Goswami

et al. 2022

Microprocessors and Microsystems

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Resource-efficient cyber-physical systems design: A survey

Cited by 5 publications

References 49 publications

Design of Cyber Bio-analytical Physical Systems: Formal methods, architectures, and multi-system interaction strategies

Design of Cyber Bio-analytical Physical Systems: Formal methods, architectures, and multi-system interaction strategies

Camouflage Learning: Feature Value Obscuring Ambient Intelligence for Constrained Devices

State-based switching multi-rate controller for improving resource utilization on predictable and composable platforms

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