Battery-Less Face Recognition at the Extreme Edge

Jokic, Petar; Emery, Stephane; Benini, Luca

doi:10.1109/newcas50681.2021.9462787

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…When the local inference task is executed, the final result is sent to a gateway using LoRa. A small battery-less computer vision platform has been presented by Jokic et al [10]. They used an ultra-low power image sensor and an ML system-on-chip to recognize faces on images, achieving selfsustainable operations by using solar energy harvesting with a small on-board solar cell.…”

Section: Tinyml On Battery-less Iot Devicesmentioning

confidence: 99%

“…Machine learning (ML) is successfully employed in many fields and applications (e.g., object detection, image classification, and audio recognition) [9], where it is used for data analysis, making systems intelligent in terms of decisionmaking. These ML algorithms are mostly based on neural networks, achieving high accuracy, but at the same time requiring large computational power and memory resources [10]. As battery-less IoT devices are resource-constrained with very limited power supply, and they are usually equipped with limited computing and storage capabilities [11], deploying ML on battery-less IoT devices is highly challenging.…”

Section: Introductionmentioning

confidence: 99%

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Towards energy-aware tinyML on battery-less IoT devices

et al. 2023

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Section: Tinyml On Battery-less Iot Devicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards energy-aware tinyML on battery-less IoT devices

et al. 2023

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“…In Reference 24, the same authors provide an alternative algorithm based on convolutional neural networks to accomplish the same goal. Finally, in Reference 25 the authors introduce a scheme that enables face recognition on a system that relies on solar panels for energy generation. With the advent of battery‐less devices, the use of TinyML applications has become key to run applications on such devices 18 .…”

Section: Motivation and Literature Reviewmentioning

confidence: 99%

Integrating cloud and mist computing to lower latency in IoT topologies

Herrero

2023

Trans Emerging Tel Tech

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Traditional IoT topologies involve access and core networks that share a common edge. On this edge, border routers and gateways are responsible for converting protocols at different layers of the stack. Devices like sensors and actuators sit on the access network while applications are located on the core network. The application performs predictions that trigger actuation based on received sensor readouts. Prediction, in turn, is the result of machine learning (ML) algorithms that are typically executed on the cloud. An alternative to this approach consists of performing the prediction on constrained devices on the IoT access network. This leads to Tiny ML (TinyML) and mist computing. In this context, there is a trade‐off between latency and computational power that becomes a deciding factor when choosing the application to carry on predictions. This paper introduces an algorithm that can be used to dynamically select the right application based on network layer parameters.

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“…In order to achieve this, sophisticated energy harvesting techniques must be implemented, but we must also focus on developing low power computation that may be aware of the dynamic power source level. Both power saving communication protocols, and data dissemination and analysis, such as lightweight machine learning, must be precisely combined with new alternative energy sources [96] to run complex systems in the Mist.…”

Section: Open Challengesmentioning

confidence: 99%

In-depth analysis and open challenges of Mist Computing

2022

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The advent and consolidation of the Massive Internet of Things (MIoT) comes with a need for new architectures to process the massive amount of generated information. A new approach, Mist Computing, entails a series of changes compared to previous computing paradigms, such as Cloud and Fog Computing, with regard to extremely low latency, local smart processing, high mobility, and massive deployment of heterogeneous devices. Hence, context awareness use cases will be enabled, which will vigorously promote the implementation of advantageous Internet of Things applications. Mist Computing is expected to reach existing fields, such as Industry 4.0, future 6G networks and Big Data problems, and it may be the answer for advanced applications where interaction with the environment is essential and lots of data are managed. Despite the low degree of maturity, it shows plenty of potential for IoT together with Cloud, Fog, and Edge Computing, but it is required to reach a general agreement about its foundations, scope, and fields of action according to the existing early works. In this paper, (i) an extensive review of proposals focused on Mist Computing is done to determine the application fields and network elements that must be developed for certain objectives, besides, (ii) a comparative assessment between Cloud, Fog, Edge, and Mist is completed and (iii) several research challenges are listed for future work. In addition, Mist Computing is the last piece to benefit from the resources of complete network infrastructures in the Fluid Computing paradigm.

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Battery-Less Face Recognition at the Extreme Edge

Cited by 3 publications

References 13 publications

Towards energy-aware tinyML on battery-less IoT devices

Towards energy-aware tinyML on battery-less IoT devices

Integrating cloud and mist computing to lower latency in IoT topologies

In-depth analysis and open challenges of Mist Computing

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