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Signal Process."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper investigates the integration of relay-assisted Internet of Things (IoT) systems, focusing on the use of multiple relays to enhance the system performance. The central metric of interest in this study is system outage probability, evaluated in terms of latency. Our research provides a comprehensive analysis of system outage probability, considering different relay selection criteria to optimize the system\u2019s transmission performance. Three relay selection strategies are employed to enhance the system transmission performance. Specifically, the first strategy, optimal relay selection, aims to identify the relay that minimizes the latency and maximizes the data transmission reliability. The second approach, partial relay selection, focuses on selecting a subset of relays strategically to balance the system resources and achieve the latency reduction. The third strategy, random relay selection, explores the potential of opportunistic relay selection without prior knowledge. Through a rigorous investigation, our paper evaluates the impact of these relay selection criteria on the performance of relay-assisted edge computing systems. By assessing the system outage probability in relation to latency, we provide valuable insights into the trade-offs and advantages associated with each selection strategy. Our findings contribute to the design and optimization of reliable and efficient edge computing systems, with implications for various applications, including the IoT and intelligent data processing.<\/jats:p>","DOI":"10.1186\/s13634-024-01123-5","type":"journal-article","created":{"date-parts":[[2024,2,24]],"date-time":"2024-02-24T15:02:54Z","timestamp":1708786974000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Distributed transmission and optimization of relay-assisted space-air-ground IoT systems"],"prefix":"10.1186","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8636-9770","authenticated-orcid":false,"given":"Ying","family":"Sun","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,24]]},"reference":[{"key":"1123_CR1","doi-asserted-by":"crossref","unstructured":"Wu, Y., Tang, S., Zhang, L.: Resilient machine learning based semantic-aware MEC networks for sustainable next-G consumer electronics. 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