Towards Automated and Optimal IIoT Design
In today’s world, the Internet of Things has become an integral part of our lives. The increasing number of intelligent devices and their pervasiveness has made it challenging for developers and system architects to plan and implement systems of Internet of Things and Industrial Internet of Things e...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Russian Academy of Sciences, St. Petersburg Federal Research Center
2024-03-01
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| Series: | Информатика и автоматизация |
| Subjects: | |
| Online Access: | http://ia.spcras.ru/index.php/sp/article/view/16031 |
| _version_ | 1827303965120790528 |
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| author | Ali Ebraheem Ilya Ivanov |
| author_facet | Ali Ebraheem Ilya Ivanov |
| author_sort | Ali Ebraheem |
| collection | DOAJ |
| description | In today’s world, the Internet of Things has become an integral part of our lives. The increasing number of intelligent devices and their pervasiveness has made it challenging for developers and system architects to plan and implement systems of Internet of Things and Industrial Internet of Things effectively. The primary objective of this work is to automate the design process of Industrial Internet of Things systems while optimizing the quality of service parameters, battery life, and cost. To achieve this goal, a general four-layer fog-computing model based on mathematical sets, constraints, and objective functions is introduced. This model takes into consideration the various parameters that affect the performance of the system, such as network latency, bandwidth, and power consumption. The Non-dominated Sorting Genetic Algorithm II is employed to find Pareto optimal solutions, while the Technique for Order of Preference by Similarity to Ideal Solution is used to identify compromise solutions on the Pareto front. The optimal solutions generated by this approach represent servers, communication links, and gateways whose information is stored in a database. These resources are chosen based on their ability to enhance the overall performance of the system. The proposed strategy follows a three-stage approach to minimize the dimensionality and reduce dependencies while exploring the search space. Additionally, the convergence of optimization algorithms is improved by using a biased initial population that exploits existing knowledge about how the solution should look. The algorithms used to generate this initial biased population are described in detail. To illustrate the effectiveness of this automated design strategy, an example of its application is presented. |
| first_indexed | 2024-04-24T17:11:06Z |
| format | Article |
| id | doaj.art-e888f6feddf64cfd973afa0d828a13f4 |
| institution | Directory Open Access Journal |
| issn | 2713-3192 2713-3206 |
| language | English |
| last_indexed | 2024-04-24T17:11:06Z |
| publishDate | 2024-03-01 |
| publisher | Russian Academy of Sciences, St. Petersburg Federal Research Center |
| record_format | Article |
| series | Информатика и автоматизация |
| spelling | doaj.art-e888f6feddf64cfd973afa0d828a13f42024-03-28T12:01:00ZengRussian Academy of Sciences, St. Petersburg Federal Research CenterИнформатика и автоматизация2713-31922713-32062024-03-0123237740610.15622/ia.23.2.316031Towards Automated and Optimal IIoT DesignAli Ebraheem0Ilya Ivanov1HSE UniversityHSE UniversityIn today’s world, the Internet of Things has become an integral part of our lives. The increasing number of intelligent devices and their pervasiveness has made it challenging for developers and system architects to plan and implement systems of Internet of Things and Industrial Internet of Things effectively. The primary objective of this work is to automate the design process of Industrial Internet of Things systems while optimizing the quality of service parameters, battery life, and cost. To achieve this goal, a general four-layer fog-computing model based on mathematical sets, constraints, and objective functions is introduced. This model takes into consideration the various parameters that affect the performance of the system, such as network latency, bandwidth, and power consumption. The Non-dominated Sorting Genetic Algorithm II is employed to find Pareto optimal solutions, while the Technique for Order of Preference by Similarity to Ideal Solution is used to identify compromise solutions on the Pareto front. The optimal solutions generated by this approach represent servers, communication links, and gateways whose information is stored in a database. These resources are chosen based on their ability to enhance the overall performance of the system. The proposed strategy follows a three-stage approach to minimize the dimensionality and reduce dependencies while exploring the search space. Additionally, the convergence of optimization algorithms is improved by using a biased initial population that exploits existing knowledge about how the solution should look. The algorithms used to generate this initial biased population are described in detail. To illustrate the effectiveness of this automated design strategy, an example of its application is presented.http://ia.spcras.ru/index.php/sp/article/view/16031iiotiotngsa-iitopsiscloudfog computingmultiobjective optimizationgatewayedge devices |
| spellingShingle | Ali Ebraheem Ilya Ivanov Towards Automated and Optimal IIoT Design Информатика и автоматизация iiot iot ngsa-ii topsis cloud fog computing multiobjective optimization gateway edge devices |
| title | Towards Automated and Optimal IIoT Design |
| title_full | Towards Automated and Optimal IIoT Design |
| title_fullStr | Towards Automated and Optimal IIoT Design |
| title_full_unstemmed | Towards Automated and Optimal IIoT Design |
| title_short | Towards Automated and Optimal IIoT Design |
| title_sort | towards automated and optimal iiot design |
| topic | iiot iot ngsa-ii topsis cloud fog computing multiobjective optimization gateway edge devices |
| url | http://ia.spcras.ru/index.php/sp/article/view/16031 |
| work_keys_str_mv | AT aliebraheem towardsautomatedandoptimaliiotdesign AT ilyaivanov towardsautomatedandoptimaliiotdesign |