Snowflakes at the Edge

Abdelhafez, Hazem A.; Halawa, Hassan H.; Pattabiraman, Karthik; Ripeanu, Matei

doi:10.1145/3434770.3459729

Cited by 15 publications

(2 citation statements)

References 9 publications

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“…The Jetson Xavier also exhibited less variation in reconstruction times between repetitions in comparison to the reference system. The statistical differences between the different Jetson Xavier's in our disposal were not evaluated while it is known that slight performance differences may exist [24].…”

Section: Main Findingsmentioning

confidence: 99%

Local edge computing for radiological image reconstruction and computer-assisted detection: A feasibility study

et al. 2023

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Computational requirements for data processing at different stages of the radiology value chain are increasing. Cone beam computed tomography (CBCT) is a diagnostic imaging technique used in dental and extremity imaging, involving a highly demanding image reconstruction task. In turn, artificial intelligence (AI) assisted diagnostics are becoming increasingly popular, thus increasing the use of computation resources. Furthermore, the need for fully independent imaging units outside radiology departments and with remotely performed diagnostics emphasize the need for wireless connectivity between the imaging unit and hospital infrastructure. In this feasibility study, we propose an approach based on a distributed edge-cloud computing platform, consisting of small-scale local edge nodes, edge servers with traditional cloud resources to perform data processing tasks in radiology. We are interested in the use of local computing resources with Graphics Processing Units (GPUs), in our case Jetson Xavier NX, for hosting the algorithms for two use-cases, namely image reconstruction in cone beam computed tomography and AI-assisted cancer detection from mammographic images. Particularly, we wanted to determine the technical requirements for local edge computing platform for these two tasks and whether CBCT image reconstruction and breast cancer detection tasks are possible in a diagnostically acceptable time frame. We validated the use-cases and the proposed edge computing platform in two stages. First, the algorithms were validated use-case-wise by comparing the computing performance of the edge nodes against a reference setup (regular workstation). Second, we performed qualitative evaluation on the edge computing platform by running the algorithms as nanoservices. Our results, obtained through real-life prototyping, indicate that it is possible and technically feasible to run both reconstruction and AI-assisted image analysis functions in a diagnostically acceptable computing time. Furthermore, based on the qualitative evaluation, we confirmed that the local edge computing capacity can be scaled up and down during runtime by adding or removing edge devices without the need for manual reconfigurations. We also found all previously implemented software components to be transferable as such. Overall, the results are promising and help in developing future applications, e.g., in mobile imaging scenarios, where such a platform is beneficial.

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Section: Main Findingsmentioning

confidence: 99%

Local edge computing for radiological image reconstruction and computer-assisted detection: A feasibility study

et al. 2023

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“…Recent advances in high-performance embedded computation hardware and deep learning algorithms allowed tasks like object classification, detection, localization, and tracking to be performed in embedded platforms (e.g. NVIDIA Jetson [6], [7], Raspberry Pi [8]). However, their computation power can not support the burden of deep learning methods fusing inputs from multi-modal sensors.…”

Section: Introductionmentioning

confidence: 99%

Edge-Distributed Fusion of Camera-Lidar for Robust Moving Object Localization

Amendola,

Dayal,

Cenkeramaddi

et al. 2022

SSRN Journal

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Cyber-physical security for IoT networks: a comprehensive review on traditional, blockchain and artificial intelligence based key-security

Attkan

Ranga

2022

Complex Intell. Syst.

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The recent years have garnered huge attention towards the Internet of Things (IoT) because it enables its consumers to improve their lifestyles and professionally keep up with the technological advancements in the cyber-physical world. The IoT edge devices are heterogeneous in terms of the technology they are built on and the storage file formats used. These devices require highly secure modes of mutual authentication to authenticate each other before actually sending the data. Mutual authentication is a very important aspect of peer-to-peer communication. Secure session keys enable these resource-constrained devices to authenticate each other. After successful authentication, a device can be authorized and can be granted access to shared resources. The need for validating a device requesting data transfer to avoid data privacy breaches that may compromise confidentiality and integrity. Blockchain and artificial intelligence (AI) both are extensively being used as an integrated part of IoT networks for security enhancements. Blockchain provides a decentralized mechanism to store validated session keys that can be allotted to the network devices. Blockchain is also used to load balance the stressing edge devices during low battery levels. AI on the other hand provides better learning and adaptiveness towards IoT attacks. The integration of newer technologies in IoT key management yields enhanced security features. In this article, we systematically survey recent trending technologies from an IoT security point of view and discuss traditional key security mechanisms. This article delivers a comprehensive quality study for researchers on authentication and session keys, integrating IoT with blockchain and AI-based authentication in cybersecurity.

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Snowflakes at the Edge

Cited by 15 publications

References 9 publications

Local edge computing for radiological image reconstruction and computer-assisted detection: A feasibility study

Local edge computing for radiological image reconstruction and computer-assisted detection: A feasibility study

Edge-Distributed Fusion of Camera-Lidar for Robust Moving Object Localization

Cyber-physical security for IoT networks: a comprehensive review on traditional, blockchain and artificial intelligence based key-security

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