@ARTICLE{ElFellah26-IoTJ-IoTMS,
   AUTHOR       = {El Fellah, Yahia and Jean Baptiste Minani and Naouel Moha and 
      Julien Gascon-Samson and Yann-Ga�l Gu�h�neuc},
   JOURNAL      = {Internet of Things Journal (IoTJ)},
   TITLE        = {Analysis of Microservices-based IoT Systems: Deployment 
      Challenges, Industry Practices, and Performance Insights},
   YEAR         = {2026},
   MONTH        = {March},
   NOTE         = {21 pages.},
   OPTNUMBER    = {},
   PAGES        = {101867},
   VOLUME       = {36},
   EDITOR       = {Fatos Xhafa},
   KEYWORDS     = {Topic: <b>ESE for the IoT</b>, 
      Rubrique : <b>GL empirique pour l'IdO</b>, Journal: <b>IoTJ</b>},
   PUBLISHER    = {Elsevier},
   URL          = {http://www.ptidej.net/publications/documents/IoTJ26a.doc.pdf},
   ABSTRACT     = {As the adoption of microservices in Internet of Things 
      (IoT) systems grows, deploying them on the Edge remains a significant 
      challenge for practitioners. While Edge Computing offers improved 
      latency and resource efficiency by processing data near the source, 
      it also introduces complexity in managing microservices. Despite 
      increasing academic interest, few comprehensive studies have 
      investigated the specific challenges and effective software 
      engineering (SE) practices for deploying microservices-based IoT 
      systems on the Edge. Therefore, we conducted a multi-method study to 
      bridge this gap. We used three methods: (1) a systematic literature 
      review (SLR) to identify known challenges and approaches, (2) a gray 
      literature review (GLR) to extract SE practices used in the field, 
      and (3) an empirical evaluation using two versions of a case study, 
      one with and one without selected SE practices. The findings show 
      that (1) the most reported challenges relate to resource utilization 
      and performance optimization, (2) containerized microservices, API 
      gateways, and database-per-service are among the most commonly 
      recommended practices, and (3) implementing these practices led to a 
      132% throughput improvement, 49% reduction in latency, and memory 
      savings of up to 13% in Edge-based IoT systems. However, increased 
      architectural complexity also led to higher CPU usage. This study 
      offers a catalog of best practices and empirical evidence to support 
      IoT developers aiming to optimize microservices-based deployments on 
      the Edge, particularly in resource-constrained environments.}
}

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B.","Moha, N.","Gascon-Samson, J.","Gu�h�neuc, Y."],"bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["El","Fellah"],"firstnames":["Yahia"],"suffixes":[]},{"firstnames":["Jean","Baptiste"],"propositions":[],"lastnames":["Minani"],"suffixes":[]},{"firstnames":["Naouel"],"propositions":[],"lastnames":["Moha"],"suffixes":[]},{"firstnames":["Julien"],"propositions":[],"lastnames":["Gascon-Samson"],"suffixes":[]},{"firstnames":["Yann-Ga�l"],"propositions":[],"lastnames":["Gu�h�neuc"],"suffixes":[]}],"journal":"Internet of Things Journal (IoTJ)","title":"Analysis of Microservices-based IoT Systems: Deployment Challenges, Industry Practices, and Performance Insights","year":"2026","month":"March","note":"21 pages.","optnumber":"","pages":"101867","volume":"36","editor":[{"firstnames":["Fatos"],"propositions":[],"lastnames":["Xhafa"],"suffixes":[]}],"keywords":"Topic: <b>ESE for the IoT</b>, Rubrique : <b>GL empirique pour l'IdO</b>, Journal: <b>IoTJ</b>","publisher":"Elsevier","url":"http://www.ptidej.net/publications/documents/IoTJ26a.doc.pdf","abstract":"As the adoption of microservices in Internet of Things (IoT) systems grows, deploying them on the Edge remains a significant challenge for practitioners. While Edge Computing offers improved latency and resource efficiency by processing data near the source, it also introduces complexity in managing microservices. Despite increasing academic interest, few comprehensive studies have investigated the specific challenges and effective software engineering (SE) practices for deploying microservices-based IoT systems on the Edge. Therefore, we conducted a multi-method study to bridge this gap. We used three methods: (1) a systematic literature review (SLR) to identify known challenges and approaches, (2) a gray literature review (GLR) to extract SE practices used in the field, and (3) an empirical evaluation using two versions of a case study, one with and one without selected SE practices. The findings show that (1) the most reported challenges relate to resource utilization and performance optimization, (2) containerized microservices, API gateways, and database-per-service are among the most commonly recommended practices, and (3) implementing these practices led to a 132% throughput improvement, 49% reduction in latency, and memory savings of up to 13% in Edge-based IoT systems. However, increased architectural complexity also led to higher CPU usage. This study offers a catalog of best practices and empirical evidence to support IoT developers aiming to optimize microservices-based deployments on the Edge, particularly in resource-constrained environments.","bibtex":"@ARTICLE{ElFellah26-IoTJ-IoTMS,\r\n AUTHOR = {El Fellah, Yahia and Jean Baptiste Minani and Naouel Moha and \r\n Julien Gascon-Samson and Yann-Ga�l Gu�h�neuc},\r\n JOURNAL = {Internet of Things Journal (IoTJ)},\r\n TITLE = {Analysis of Microservices-based IoT Systems: Deployment \r\n Challenges, Industry Practices, and Performance Insights},\r\n YEAR = {2026},\r\n MONTH = {March},\r\n NOTE = {21 pages.},\r\n OPTNUMBER = {},\r\n PAGES = {101867},\r\n VOLUME = {36},\r\n EDITOR = {Fatos Xhafa},\r\n KEYWORDS = {Topic: <b>ESE for the IoT</b>, \r\n Rubrique : <b>GL empirique pour l'IdO</b>, Journal: <b>IoTJ</b>},\r\n PUBLISHER = {Elsevier},\r\n URL = {http://www.ptidej.net/publications/documents/IoTJ26a.doc.pdf},\r\n ABSTRACT = {As the adoption of microservices in Internet of Things \r\n (IoT) systems grows, deploying them on the Edge remains a significant \r\n challenge for practitioners. While Edge Computing offers improved \r\n latency and resource efficiency by processing data near the source, \r\n it also introduces complexity in managing microservices. Despite \r\n increasing academic interest, few comprehensive studies have \r\n investigated the specific challenges and effective software \r\n engineering (SE) practices for deploying microservices-based IoT \r\n systems on the Edge. Therefore, we conducted a multi-method study to \r\n bridge this gap. We used three methods: (1) a systematic literature \r\n review (SLR) to identify known challenges and approaches, (2) a gray \r\n literature review (GLR) to extract SE practices used in the field, \r\n and (3) an empirical evaluation using two versions of a case study, \r\n one with and one without selected SE practices. The findings show \r\n that (1) the most reported challenges relate to resource utilization \r\n and performance optimization, (2) containerized microservices, API \r\n gateways, and database-per-service are among the most commonly \r\n recommended practices, and (3) implementing these practices led to a \r\n 132% throughput improvement, 49% reduction in latency, and memory \r\n savings of up to 13% in Edge-based IoT systems. However, increased \r\n architectural complexity also led to higher CPU usage. This study \r\n offers a catalog of best practices and empirical evidence to support \r\n IoT developers aiming to optimize microservices-based deployments on \r\n the Edge, particularly in resource-constrained environments.}\r\n}\r\n\r\n","author_short":["El Fellah, Y.","Minani, J. 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