دوست‌یابی بر اساس ویژگی‌های اجتماعی در اینترنت اشیا اجتماعی
محورهای موضوعی : مهندسی برق و کامپیوترمحمد مهدیان 1 , سیدمجتبی متین‌خواه متین‌خواه 2
1 - دانشکده مهندسی کامپیوتر، دانشگاه یزد
2 - دانشکده مهندسی کامپیوتر، دانشگاه یزد
کلید واژه: اینترنت اشیای اجتماعی, اینترنت اشیا, شبکه اجتماعی, الگوریتم بهینه‌سازی فاخته باینری تطبیق‌داده‌شده, دوست‌یابی,
چکیده مقاله :
شبکه اینترنت اشیای اجتماعی (SIoT)، ناشی از اتحاد شبکه اجتماعی و شبکه اینترنت اشیاست که هر شیء در این شبکه سعی دارد با استفاده از اشیای اطراف خود از سرویس‌هایی بهره‌برداری کند که توسط اشیای دوست خود ارائه می‌شوند. پس در این شبکه، پیداکردن شیء دوست مناسب برای بهره‌بردن از سرویس مناسب مهم تلقی می‌شود. حال وقتی تعداد دوستان اشیا زیاد باشد، آنگاه استفاده از الگوریتم‌های کلاسیک برای پیداکردن سرویس مناسب با کمک اشیای دوست، ممکن است زمان و بار محاسباتی و پیمایش در شبکه را بالا ببرد. بنابراین در این مقاله برای کم‌کردن بار محاسباتی و پیمایش شبکه سعی شده است که برای انتخاب شیء دوست مناسب از رویکرد اکتشافی و با استفاده از الگوریتم بهینه‌سازی فاخته باینری تطبیق‌داده‌شده (AB-COA) و شاخص محلی آدامیک‌آدار (AA) که مبتنی بر معیار مرکزیت درجه است بهره برده شود و ویژگی‌های همسایه‌های مشترک اشیا را در انتخاب شیء دوست و اکتشاف سرویس مناسب در نظر گرفته شود. نهایتاً با اجرای الگوریتم AB-COA بر روی مجموعه داده وب استنفورد، میانگین گام مورد نیاز برای دستیابی به سرویس در شبکه، 8/4 به‌دست آمد که نشان‌دهنده برتری این الگوریتم نسبت به سایر الگوریتم‌هاست.
The Social Internet of Things (SIoT) network is the result of the union of the Social Network and the Internet of Things network; wherein, each object tries to use the services provided by its friends. In this network, to find the right friend in order to use the right service is demanding. Great number of objects' friends, in classical algorithms, causes increasing the computational time and load of network navigation to find the right service with the help of friendly objects. In this article, in order to reduce the computational load and network navigation, it is proposed, firstly, to select the appropriate object friend from a heuristic approach; secondly, to use an adapted binary cuckoo optimization algorithm (AB-COA) which tries to select the appropriate friendly object to receive the service according to the maximum response capacity of each friendly object, and finally, adopting the Adamic-Adar local index (AA) with the interest degree centrality criterion so that it represents the characteristics of the common neighbors of the objects are involved in the friend selection. Finally, by executing the proposed algorithm on the Web-Stanford dataset, an average of 4.8 steps was obtained for reaching a service in the network, indicating the superiority of this algorithm over other algorithms.
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