IoT Enabled Framework for Real-time Management of Power-Tools at Construction Projects

Saxena, Ashish Kumar; Reja, Varun Kumar; Varghese, Koshy

doi:10.22260/isarc2020/0137

Cited by 5 publications

(2 citation statements)

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“…This significant enhancement in processing the information at the edge brings a new level of efficiency, responsiveness, and adaptability to a broad range of applications, from smart homes [6] to industrial automation [7] and beyond, pointing to a promising future for IoT technology [8], [9]. Among other applications of IoT, one promising in the industrial domain is asset-and utilization-tracking, aiming to optimize the asset park, health, and usage of physical assets such as machinery, vehicles, or power tools [10].…”

Section: Introductionmentioning

confidence: 99%

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Optimizing IoT-Based Asset and Utilization Tracking: Efficient Activity Classification with MINIROCKET on Resource-Constrained Devices

Giordano,

Cortesi,

Crabolu

et al. 2023

2023 IEEE 9th World Forum on Internet of Things (WF-IoT)

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This paper introduces an effective solution for retrofitting construction power tools with low-power Internet of Things (IoT) to enable accurate activity classification. We address the challenge of distinguishing between when a power tool is being moved and when it is actually being used. To achieve classification accuracy and power consumption preservation a newly released algorithm called MINImally RandOm Convolutional KErnel Transform (MINIROCKET) was employed. Known for its accuracy, scalability, and fast training for timeseries classification, in this paper, it is proposed as a TinyML algorithm for inference on resource-constrained IoT devices. The paper demonstrates the portability and performance of MINIROCKET on a resource-constrained, ultra-low power sensor node for floating-point and fixed-point arithmetic, matching up to 1% of the floating-point accuracy. The hyperparameters of the algorithm have been optimized for the task at hand to find a Pareto point that balances memory usage, accuracy and energy consumption. For the classification problem, we rely on an accelerometer as the sole sensor source, and BLUETOOTH LOW ENERGY (BLE) for data transmission. Extensive real-world construction data, using 16 different power tools, were collected, labeled, and used to validate the algorithm's performance directly embedded in the IoT device. Experimental results demonstrate that the proposed solution achieves an accuracy of 96.9% in distinguishing between real usage status and other motion statuses while consuming only 7 kB of flash and 3 kB of RAM. The final application exhibits an average current consumption of less than 15 µW for the whole system, resulting in battery life performance ranging from 3 to 9 years depending on the battery capacity (250 − 500 mAh) and the number of power tool usage hours (100 − 1500 h).

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Section: Introductionmentioning

confidence: 99%

“…Optimizing assets, such as power tools in the construction sector, is crucial for achieving higher productivity and sustainability standards [10]. Retrieving information on their utilization and health becomes therefore essential.…”

Section: Introductionmentioning

confidence: 99%