Performance-Energy Trade-offs in Smartphones

Chis, Tiberiu S.; Harrison, Peter G.

doi:10.1145/2988287.2989140

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2020

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Cited by 2 publications

(2 citation statements)

References 26 publications

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“…One of the well-known strategies to prolong the battery life of embedded systems is through power-saving modes, that focus on minimizing the device's power consumption at the cost of increasing the application's execution time. In this context, there is a concern regarding the balance between performance and power saving [26].…”

Section: Energy-delay Productmentioning

confidence: 99%

Detecting abnormal sensors via machine learning: An IoT farming WSN-based architecture case study

et al. 2020

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Section: Energy-delay Productmentioning

confidence: 99%

Detecting abnormal sensors via machine learning: An IoT farming WSN-based architecture case study

et al. 2020

View full text Add to dashboard Cite

“…One of the well-known strategies to prolong the battery life of embedded systems is through power-saving modes that focus on minimizing the device's power consumption at the cost of reducing the application's performance. In this context, there is a concern regarding the balance between performance and power saving [31]. Therefore, in our analysis, we also considered the Energy-Delay Product (EDP), which is given by P erf ormance ⇥ P ower Consumption, to evaluate the impact of the chosen algorithms to this trade-off.…”

Section: E Energy-delay Productmentioning

confidence: 99%

Evaluating Energy and Thermal Efficiency of Anomaly Detection Algorithms in Edge Devices

Rubin

Souza

Marques

et al. 2020

2020 International Conference on Information Networking (ICOIN)

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Although IoT delivers several benefits, it also raises concerns regarding privacy and security, from revenue disruption in industrial facilities to life-threatening situations caused by smart houses hacking. As a consequence, anomaly detection algorithms stand out to improve data reliability. However, little has been said about the implications of running these computationally expensive programs in hardware-constrained edge devices. Therefore, in this paper, we present an evaluation of six anomaly detection algorithms running in an edge device regarding performance, accuracy, temperature, and power consumption. The results showed that time complexity, resources demand, and detection approach directly impact on the feasibility of running anomaly detection algorithms in edge devices. Based on these results, we present a recommendation on which algorithms would best satisfy the requirements of several IoT environments.

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Performance-Energy Trade-offs in Smartphones

Cited by 2 publications

References 26 publications

Detecting abnormal sensors via machine learning: An IoT farming WSN-based architecture case study

Detecting abnormal sensors via machine learning: An IoT farming WSN-based architecture case study

Evaluating Energy and Thermal Efficiency of Anomaly Detection Algorithms in Edge Devices

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