مقاله انگلیسی رایگان در مورد رویکرد برآورد احتمالی اتصال گره ها بر اساس شبکه بیزی – IEEE 2018

IEEE

 

مشخصات مقاله
ترجمه عنوان مقاله رویکرد برآورد احتمالی اتصال گره ها بر اساس شبکه بیزی برای DTN
عنوان انگلیسی مقاله Nodes contact probability estimation approach based on Bayesian network for DTN
انتشار مقاله سال ۲۰۱۸
تعداد صفحات مقاله انگلیسی ۴ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه IEEE
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی کامپیوتر، فناوری اطلاعات
گرایش های مرتبط هوش مصنوعی، شبکه های کامپیوتری
مجله سمپوزیوم عملیات شبکه و مدیریت – IEEE/IFIP Network Operations and Management Symposium
دانشگاه School of Computer Science and Engineering – Beihang University – China
کلمات کلیدی شبکه Delay Tolerance، احتمال بیزی، ارزیابی احتمالی تماس
کلمات کلیدی انگلیسی Delay Tolerance Network, Bayesian Probability, Contact Probability Estimation
شناسه دیجیتال – doi
https://doi.org/10.1109/NOMS.2018.8406211
کد محصول E8935
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

بخشی از متن مقاله:
I. INTRODUCTION

A Delay-Tolerant Network (DTN) is a network that has high end-to-end path latency, limited resources, frequent network partitions and so on characteristics[1],[2], which has many potential applications, such as Inter Planetary Internet (IPN), mobile vehicle network [3]. DTN research and development will strongly provide scientific theory and technical support for messages interaction of military war, aerospace communications, disaster recovery, emergency rescue and other fields and greatly promote the development trends of intellectualization, generalization and integration of network communication in the future. The prediction of two nodes encounter probability is the core issue of DTN routing. For IPN and vehicular ad-hoc network (VANET) [5] with strong regularity and other network morphology, whose mobile law is comparative fixed and known, more precisely meet time of nodes encounter can be calculated by modeling the nodes motions, therefore, there is no need to predict encounter probability in such a network morphology. For portable switched network (PSN) [6], the mobile law of nodes can’t be modeled, so that the nodes encounter probability is predicted mainly by dividing communities or directly calculating, according to which the routing makes its choice. This article will focus on researching the method of direct prediction of the probability of node encounters in the latter case. In the encounter probability prediction method of PROPHET routing protocol [4], when one node has no contact with another, the probability of two nodes encountering will be gradually reduced over time; encounter probability prediction method based on the exponential distribution assumes that contact interval is exponentially distributed, and the predicted encounter probability is also gradually reduced over time. In order to better understand the problem, we analyzed two real DTN data sets, i.e. Haggle data set [7] and Reality data set [10], as is shown in F ig.1. As can be seen from F ig.1 (a), at the beginning, with the growth of the contact interval, the frequency of contact interval (i.e. the probability of two nodes encountering again) is gradually reduced. In this case, the two before routing algorithm meet the actual situation. However, with the growth of time, when the contact interval is more than ten hours, the contact interval frequency will gradually increase with interval growth and peak in about 18- 24 hours. Combined with the scene of the data set collected, the time interval starting from the end of the first day meeting to the beginning of next day meeting is about 18-24 hours. In F ig.1 (b), although the node contact time generally has a long-term regularity (i.e. contact probability reduced with the contact intervals growth), it is still can be seen that the distribution of the time interval is not a smooth curve, but there are many distinct peaks, and the peak arrange with a certain regularity: there will be a peak about every seven days or so. Combined with data set collected scene of view, there are strong week cycle regularity: courses, regular meetings, weekend social activities, campus activities, a lot of people will meet again in the interval of seven days or so. Combined with the earlier analysis, it shows the encounter probability prediction method in PROPHET routing protocol and the encounter probability prediction method based on exponential distribution both are unable to give a accurate forecast.

ارسال دیدگاه

نشانی ایمیل شما منتشر نخواهد شد. بخش‌های موردنیاز علامت‌گذاری شده‌اند *