ارائه روشی نوین برای محاسبه اعتماد در کاربردهای اینترنت اشیاء

نوع مقاله : علمی-پژوهشی

نویسندگان

1 دانشکده مهندسی کامپیوتر - دانشگاه علم و صنعت ایران

2 گروه مهندسی کامپیوتر - دانشگاه یزد

چکیده

در سیستم‌های اینترنت اشیا کیفیت سرویس و اعتبار داده‌های مورد استفاده برای تصمیم‌گیری در کاربردهای مختلف اهمیت بالایی دارند. اشیاء مخرب می‌توانند با ارائه داده‌های نامعتبر موجب کاهش کیفیت و تجربه سرویس برای سایر گره‌ها در یک سیستم اینترنت اشیا شوند. یکی از راه‌کارهای ممکن برای حل این مشکل، مدیریت اعتماد است. اغلب پژوهش‌های موجود برای محاسبه اعتماد متمرکز بر محاسبه اعتماد موجودیت‌های یک سیستم است. در این مقاله روشی برای محاسبه اعتماد ارائه شده است که علاوه بر محاسبه اعتماد موجودیت‌ها در یک کاربرد اینترنت اشیا، به محاسبه اعتمادپذیری داده نیز بپردازد. به این منظور، روش نوینی برای محاسبه اعتماد با در نظر گرفتن ارتباط بین اعتمادپذیری داده و اعتماد موجودیت ارائه شده است که مبتنی بر یادگیری بیزی بوده و برای محاسبه اعتمادپذیری داده از قانون ترکیب نظریه دمپستر-شیفر استفاده می‌کند. برای آزمایش روش پیشنهادی، به شبیه‌سازی این روش در سناریوی پارکینگ هوشمند پرداخته شده و مقدار همگرایی و زمان همگرایی اعتماد در حضور رفتارهای مخرب و میزان تطبیق‌پذیری آن ارزیابی شده است. نتایج حاصل از ارزیابی روش پیشنهادی در قیاس با روش موجود نشان می‌دهد که روش ما حتی در حضور 70% گره مخرب در سیستم، تخمین صحیحی از اعتماد را به دست می‌دهد. 

کلیدواژه‌ها

عنوان مقاله [English]

A Novel Trust Computation Scheme for Internet of Things Applications

نویسندگان [English]

  • B. Shayesteh 1
  • V. Hakami 1
  • S.A. Mostafavi 2
  • A. Akbari Azirani 1
1 School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
2 Department of Computer Engineering, Yazd University, Yazd, Iran
چکیده [English]

Quality of service and trustworthiness of data is of high importance for decision making in Internet-of-Things (IoT) applications. Malicious nodes and devices may compromise the quality of service and experience for other nodes through providing invalid data and evaluations. Hence, a trust management system to assess the trust level of users and gathered data is deemed to be essential to every IoT system. The current approach in the literature for computing the trust level is entity-centric trust in which the trust level of end users are estimated. However, the trustworthiness of data is equally important in many applications. In this paper, we propose, Trusty, a hybrid trust computation approach, aiming at trust assessment for both entities as well as data. In our proposed approach, a Bayesian learning method is used for computing the entity trust, while Dempster-Shafer theory is exploited to data fusion and data trustworthiness assessment. We implement Trusty in a smart parking system scenario to investigate the performance of our model in the different settings for misbehavior nodes and faulty sensors. As shown by the extensive simulation experiments, Trusty outperforms the competing approaches in terms of convergence for both data trustworthiness and entity trust. 

کلیدواژه‌ها [English]

  • Internet of Things
  • Trust Computation
  • Data Trustworthiness
  • Smart Parking

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