Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms
With the increasing growth of IoT applications in various sectors (e.g., manufacturing, healthcare, etc.), we are witnessing a rising demand of IoT middleware platform that host such IoT applications. Hence, there arises a need for new methods to assess the performance of IoT middleware platforms ho...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/1/7 |
| _version_ | 1827617237318500352 |
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| author | Shalmoly Mondal Prem Prakash Jayaraman Pari Delir Haghighi Alireza Hassani Dimitrios Georgakopoulos |
| author_facet | Shalmoly Mondal Prem Prakash Jayaraman Pari Delir Haghighi Alireza Hassani Dimitrios Georgakopoulos |
| author_sort | Shalmoly Mondal |
| collection | DOAJ |
| description | With the increasing growth of IoT applications in various sectors (e.g., manufacturing, healthcare, etc.), we are witnessing a rising demand of IoT middleware platform that host such IoT applications. Hence, there arises a need for new methods to assess the performance of IoT middleware platforms hosting IoT applications. While there are well established methods for performance analysis and testing of databases, and some for the Big data domain, such methods are still lacking support for IoT due to the complexity, heterogeneity of IoT application and their data. To overcome these limitations, in this paper, we present a novel situation-aware IoT data generation framework, namely, SA-IoTDG. Given a majority of IoT applications are event or situation driven, we leverage a situation-based approach in SA-IoTDG for generating situation-specific data relevant to the requirements of the IoT applications. SA-IoTDG includes a situation description system, a SySML model to capture IoT application requirements and a novel Markov chain-based approach that supports transition of IoT data generation based on the corresponding situations. The proposed framework will be beneficial for both researchers and IoT application developers to generate IoT data for their application and enable them to perform initial testing before the actual deployment. We demonstrate the proposed framework using a real-world example from IoT traffic monitoring. We conduct experimental evaluations to validate the ability of SA-IoTDG to generate IoT data similar to real-world data as well as enable conducting performance evaluations of IoT applications deployed on different IoT middleware platforms using the generated data. Experimental results present some promising outcomes that validate the efficacy of SA-IoTDG. Learning and lessons learnt from the results of experiments conclude the paper. |
| first_indexed | 2024-03-09T09:42:00Z |
| format | Article |
| id | doaj.art-bca27cbe2f314f8baf66d42d5fcfc4cf |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:42:00Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bca27cbe2f314f8baf66d42d5fcfc4cf2023-12-02T00:52:28ZengMDPI AGSensors1424-82202022-12-01231710.3390/s23010007Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware PlatformsShalmoly Mondal0Prem Prakash Jayaraman1Pari Delir Haghighi2Alireza Hassani3Dimitrios Georgakopoulos4School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn 3122, AustraliaSchool of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn 3122, AustraliaDepartment of Human-Centred Computing, Monash University, Clayton 3800, AustraliaSchool of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn 3122, AustraliaSchool of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn 3122, AustraliaWith the increasing growth of IoT applications in various sectors (e.g., manufacturing, healthcare, etc.), we are witnessing a rising demand of IoT middleware platform that host such IoT applications. Hence, there arises a need for new methods to assess the performance of IoT middleware platforms hosting IoT applications. While there are well established methods for performance analysis and testing of databases, and some for the Big data domain, such methods are still lacking support for IoT due to the complexity, heterogeneity of IoT application and their data. To overcome these limitations, in this paper, we present a novel situation-aware IoT data generation framework, namely, SA-IoTDG. Given a majority of IoT applications are event or situation driven, we leverage a situation-based approach in SA-IoTDG for generating situation-specific data relevant to the requirements of the IoT applications. SA-IoTDG includes a situation description system, a SySML model to capture IoT application requirements and a novel Markov chain-based approach that supports transition of IoT data generation based on the corresponding situations. The proposed framework will be beneficial for both researchers and IoT application developers to generate IoT data for their application and enable them to perform initial testing before the actual deployment. We demonstrate the proposed framework using a real-world example from IoT traffic monitoring. We conduct experimental evaluations to validate the ability of SA-IoTDG to generate IoT data similar to real-world data as well as enable conducting performance evaluations of IoT applications deployed on different IoT middleware platforms using the generated data. Experimental results present some promising outcomes that validate the efficacy of SA-IoTDG. Learning and lessons learnt from the results of experiments conclude the paper.https://www.mdpi.com/1424-8220/23/1/7IoTIoT middleware platformsbenchmarkingFuzzy Situation InferenceIoT data generationsituation transition |
| spellingShingle | Shalmoly Mondal Prem Prakash Jayaraman Pari Delir Haghighi Alireza Hassani Dimitrios Georgakopoulos Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms Sensors IoT IoT middleware platforms benchmarking Fuzzy Situation Inference IoT data generation situation transition |
| title | Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms |
| title_full | Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms |
| title_fullStr | Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms |
| title_full_unstemmed | Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms |
| title_short | Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms |
| title_sort | situation aware iot data generation towards performance evaluation of iot middleware platforms |
| topic | IoT IoT middleware platforms benchmarking Fuzzy Situation Inference IoT data generation situation transition |
| url | https://www.mdpi.com/1424-8220/23/1/7 |
| work_keys_str_mv | AT shalmolymondal situationawareiotdatagenerationtowardsperformanceevaluationofiotmiddlewareplatforms AT premprakashjayaraman situationawareiotdatagenerationtowardsperformanceevaluationofiotmiddlewareplatforms AT paridelirhaghighi situationawareiotdatagenerationtowardsperformanceevaluationofiotmiddlewareplatforms AT alirezahassani situationawareiotdatagenerationtowardsperformanceevaluationofiotmiddlewareplatforms AT dimitriosgeorgakopoulos situationawareiotdatagenerationtowardsperformanceevaluationofiotmiddlewareplatforms |