Context-Aware Trust Prediction for Optimal Routing in Opportunistic IoT Systems
The Social Opportunistic Internet of Things (SO-IoT) is a rapidly emerging paradigm that enables mobile, ad-hoc device communication based on both physical and social interactions. In such networks, routing decisions heavily depend on the selection of intermediate nodes to ensure secure and efficien...
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MDPI AG
2025-06-01
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| author | Abdulkadir Abdulahi Hasan Xianwen Fang Sohaib Latif Adeel Iqbal |
| author_facet | Abdulkadir Abdulahi Hasan Xianwen Fang Sohaib Latif Adeel Iqbal |
| author_sort | Abdulkadir Abdulahi Hasan |
| collection | DOAJ |
| description | The Social Opportunistic Internet of Things (SO-IoT) is a rapidly emerging paradigm that enables mobile, ad-hoc device communication based on both physical and social interactions. In such networks, routing decisions heavily depend on the selection of intermediate nodes to ensure secure and efficient data dissemination. Traditional approaches relying solely on reliability or social interest fail to capture the multifaceted trustworthiness of nodes in dynamic SO-IoT environments. This paper proposes a trust-based route optimization framework that integrates social interest and behavioral reliability using Bayesian inference and Jeffrey’s conditioning. A composite trust level is computed for each intermediate node to determine its suitability for data forwarding. To validate the framework, we conduct a two-phase simulation-based analysis: a scenario-driven evaluation that demonstrates the model’s behavior in controlled settings, and a large-scale NS-3-based simulation comparing our method with benchmark routing schemes, including random, greedy, and AI-based protocols. Results confirm that our proposed model achieves up to an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>88.9</mn><mo>%</mo></mrow></semantics></math></inline-formula> delivery ratio with minimal energy consumption and the highest trust accuracy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>86.5</mn><mo>%</mo></mrow></semantics></math></inline-formula>), demonstrating its robustness and scalability in real-world-inspired IoT environments. |
| format | Article |
| id | doaj-art-c8a454c63ac64ad5b2b0eb6f6c5f2b2a |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-c8a454c63ac64ad5b2b0eb6f6c5f2b2a2025-08-20T02:21:53ZengMDPI AGSensors1424-82202025-06-012512367210.3390/s25123672Context-Aware Trust Prediction for Optimal Routing in Opportunistic IoT SystemsAbdulkadir Abdulahi Hasan0Xianwen Fang1Sohaib Latif2Adeel Iqbal3School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232001, ChinaDepartment of Computer Science and Software Engineering, Grand Asian University, Sialkot 51310, PakistanSchool of Computer Science and Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of KoreaThe Social Opportunistic Internet of Things (SO-IoT) is a rapidly emerging paradigm that enables mobile, ad-hoc device communication based on both physical and social interactions. In such networks, routing decisions heavily depend on the selection of intermediate nodes to ensure secure and efficient data dissemination. Traditional approaches relying solely on reliability or social interest fail to capture the multifaceted trustworthiness of nodes in dynamic SO-IoT environments. This paper proposes a trust-based route optimization framework that integrates social interest and behavioral reliability using Bayesian inference and Jeffrey’s conditioning. A composite trust level is computed for each intermediate node to determine its suitability for data forwarding. To validate the framework, we conduct a two-phase simulation-based analysis: a scenario-driven evaluation that demonstrates the model’s behavior in controlled settings, and a large-scale NS-3-based simulation comparing our method with benchmark routing schemes, including random, greedy, and AI-based protocols. Results confirm that our proposed model achieves up to an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>88.9</mn><mo>%</mo></mrow></semantics></math></inline-formula> delivery ratio with minimal energy consumption and the highest trust accuracy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>86.5</mn><mo>%</mo></mrow></semantics></math></inline-formula>), demonstrating its robustness and scalability in real-world-inspired IoT environments.https://www.mdpi.com/1424-8220/25/12/3672Social Opportunistic Internet of Things (SO-IoT)trust-based routingintermediate node selectionsocial interestreliabilityBayesian inference |
| spellingShingle | Abdulkadir Abdulahi Hasan Xianwen Fang Sohaib Latif Adeel Iqbal Context-Aware Trust Prediction for Optimal Routing in Opportunistic IoT Systems Sensors Social Opportunistic Internet of Things (SO-IoT) trust-based routing intermediate node selection social interest reliability Bayesian inference |
| title | Context-Aware Trust Prediction for Optimal Routing in Opportunistic IoT Systems |
| title_full | Context-Aware Trust Prediction for Optimal Routing in Opportunistic IoT Systems |
| title_fullStr | Context-Aware Trust Prediction for Optimal Routing in Opportunistic IoT Systems |
| title_full_unstemmed | Context-Aware Trust Prediction for Optimal Routing in Opportunistic IoT Systems |
| title_short | Context-Aware Trust Prediction for Optimal Routing in Opportunistic IoT Systems |
| title_sort | context aware trust prediction for optimal routing in opportunistic iot systems |
| topic | Social Opportunistic Internet of Things (SO-IoT) trust-based routing intermediate node selection social interest reliability Bayesian inference |
| url | https://www.mdpi.com/1424-8220/25/12/3672 |
| work_keys_str_mv | AT abdulkadirabdulahihasan contextawaretrustpredictionforoptimalroutinginopportunisticiotsystems AT xianwenfang contextawaretrustpredictionforoptimalroutinginopportunisticiotsystems AT sohaiblatif contextawaretrustpredictionforoptimalroutinginopportunisticiotsystems AT adeeliqbal contextawaretrustpredictionforoptimalroutinginopportunisticiotsystems |