TOAD: Profiling and Evaluating 3D Printed IoT Rapid Prototype Designs

3D printing has revolutionized DIY (Do-It-Yourself) IoT prototyping, enabling cost-effective, creative custom device creation. However, this freedom also presents challenges due to the interplay between components within an IoT design, which can influence the overall utility and performance of the prototype. Optimizing these designs is difficult due to limited means of estimating their efficacy. To address this, we introduce TOAD, a novel tool for profiling IoT prototypes and gauging their performance impact. TOAD uses thermal imaging and video analysis to extract and compare design performance characteristics. Unlike existing solutions that only profile overall performance, our tool assesses component interactions and overall design effects. It offers an affordable, non-intrusive method without needing device access or code instrumentation. Extensive benchmarks show TOAD accurately extracts performance data, aiding in selecting the best design for IoT applications. Additionally, it provides insights into how casing factors like thickness and material influence thermal behavior and performance. We demonstrate practical applications by optimizing offloading decisions based on thermal behavior, highlighting casing impacts on design performance. TOAD paves the way for efficient IoT prototype designs, offering a better understanding of component interactions and significantly enhancing the utility of custom IoT designs and their effectiveness.

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Institutions

Publication Details

Journal
ACM Transactions on Internet of Things
Published
2025-03-16
DOI
https://doi.org/10.1145/3724128
Citations
3
Primary Topic
Additive Manufacturing and 3D Printing Technologies
Type
article
Field-Weighted Citation Impact
2.05
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article

TOAD: Profiling and Evaluating 3D Printed IoT Rapid Prototype Designs

Marko Radeta, Francisco Airton Silva, Zhigang Yin, Mohan Liyanage et al.
3 citations
ACM Transactions on Internet of Things
Additive Manufacturing and 3D Printing Technologies
2.05
article

TOAD: Profiling and Evaluating 3D Printed IoT Rapid Prototype Designs

Marko Radeta, Francisco Airton Silva, Zhigang Yin, Mohan Liyanage, Janick Edinger, Mayowa Olapade, Xiang Su, Abdul-Rasheed Ottun, Petteri Nurmi, Ulrich Norbisrath, Huber Flores, Farooq Dar
article en
3 citations

Abstract

3D printing has revolutionized DIY (Do-It-Yourself) IoT prototyping, enabling cost-effective, creative custom device creation. However, this freedom also presents challenges due to the interplay between components within an IoT design, which can influence the overall utility and performance of the prototype. Optimizing these designs is difficult due to limited means of estimating their efficacy. To address this, we introduce TOAD, a novel tool for profiling IoT prototypes and gauging their performance impact. TOAD uses thermal imaging and video analysis to extract and compare design performance characteristics. Unlike existing solutions that only profile overall performance, our tool assesses component interactions and overall design effects. It offers an affordable, non-intrusive method without needing device access or code instrumentation. Extensive benchmarks show TOAD accurately extracts performance data, aiding in selecting the best design for IoT applications. Additionally, it provides insights into how casing factors like thickness and material influence thermal behavior and performance. We demonstrate practical applications by optimizing offloading decisions based on thermal behavior, highlighting casing impacts on design performance. TOAD paves the way for efficient IoT prototype designs, offering a better understanding of component interactions and significantly enhancing the utility of custom IoT designs and their effectiveness.

ACM Transactions on Internet of ThingsVol. 6(3)
University of Helsinki (FI), Universität Hamburg (DE), Dortmund University of Applied Sciences and Arts (DE), Norwegian University of Science and Technology (NO), Universidade Federal do Piauí (BR), University of Belgrade (RS), University of Tartu (EE), Universidade da Madeira (PT)
Openalex Percentile: Top 15%
Additive Manufacturing and 3D Printing Technologies
2.05
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