{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T10:57:20Z","timestamp":1780570640289,"version":"3.54.1"},"reference-count":37,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T00:00:00Z","timestamp":1657843200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IR"],"published-print":{"date-parts":[[2023,6,6]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Apart from, the smart edge computing (EC) robot (SECR) provides the tools to manage Internet of things (IoT) services in the edge landscape by means of real-world test-bed designed in ECR. Eventually, based on the results from two experiments held in little constrained condition, such as the maximum data size is 2GB, the performance of the proposed techniques demonstrate the effectiveness, scalability and performance efficiency of the proposed IoT model.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>Certainly, the proposed SECR is trying primarily to take over other traditional static robots in a centralized or distributed cloud environment. One aspect of representation of the proposed edge computing algorithms is due to challenge to slow down the consumption of time which happened in an artificial intelligence (AI) robot system. Thus, the developed SECR trained by tiny machine learning (TinyML) techniques to develop a decentralized and dynamic software environment.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Specifically, the waste time of SECR has actually slowed down when it is embedded with Edge Computing devices in the demonstration of data transmission within different paths. The TinyML is applied to train with image data sets for generating a framework running in the SECR for the recognition which has also proved with a second complete experiment.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The work presented in this paper is the first research effort, and which is focusing on resource allocation and dynamic path selection for edge computing. The developed platform using a decoupled resource management model that manages the allocation of micro node resources independent of the service provisioning performed at the cloud and manager nodes. Besides, the algorithm of the edge computing management is established with different path and pass large data to cloud and receive it. In this work which considered the SECR framework is able to perform the same function as that supports to the multi-dimensional scaling (MDS).<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ir-02-2022-0045","type":"journal-article","created":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T12:54:44Z","timestamp":1657716884000},"page":"581-594","source":"Crossref","is-referenced-by-count":5,"title":["The implementation and performance evaluation for a smart robot with edge computing algorithms"],"prefix":"10.1108","volume":"50","author":[{"given":"Joy Iong-Zong","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping-Feng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chung Sheng","family":"Pi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2022,7,15]]},"reference":[{"key":"key2023060606260428400_ref001","first-page":"687","article-title":"Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT","volume-title":"Advanced Information Networking and Applications (AINA), 2015 IEEE 29th International Conference","year":"2015"},{"key":"key2023060606260428400_ref002","unstructured":"Arduino Nano 33 BLE Sense (2021), available at: https:\/\/store.arduino.cc\/arduino-nano-33-ble-sense (accessed 31 January)."},{"key":"key2023060606260428400_ref003","first-page":"6","article-title":"Low latency peer to peer robot wireless communication with edge computing","volume-title":"2021 IEEE 11th International Conference on System Engineering and Technology (ICSET)","year":"2021"},{"key":"key2023060606260428400_ref004","first-page":"108821","article-title":"Intelligent search and find system for robotic platform based on smart edge computing service","volume":"8","year":"2020","journal-title":"Special Section on Edge Computing and Networking for Ubiquitous, IEEE Access"},{"key":"key2023060606260428400_ref005","unstructured":"Benchmann, K. 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