Detecting Node Failures in Mobile Wireless Networks A Probabilistic Approach - 2016


Detecting node failures in mobile wireless networks is very difficult because the network topology can be highly dynamic, the network may not be continuously connected, and therefore the resources are limited. In this paper, we tend to take a probabilistic approach and propose two node failure detection schemes that systematically combine localized monitoring, location estimation and node collaboration. Extensive simulation leads to each connected and disconnected networks demonstrate that our schemes achieve high failure detection rates (shut to an higher certain) and low false positive rates, and incur low Communication overhead. Compared to approaches that use centralized monitoring, our approach has up to 80 percent lower Communication overhead, and only slightly lower detection rates and slightly higher false positive rates. Furthermore, our approach has the advantage that it's applicable to both connected and disconnected networks while centralized monitoring is only applicable to connected networks. Compared to alternative approaches that use localized monitoring, our approach has similar failure detection rates, up to 57 % lower Communication overhead and much lower false positive rates (e.g., 0.01 versus 0.twenty seven in some settings).

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Traffic Anomaly Detection in Wireless Sensor Networks Based on Principal Component Analysis and Deep Convolution Neural Network ABSTRACT: Because of the proliferation of wireless networks, wireless sensor networks
PROJECT TITLE : The Devil Is in the Details An Efficient Convolutional Neural Network for Transport Mode Detection ABSTRACT: The objective of the classification problem known as transport mode detection is to devise an algorithm
PROJECT TITLE : Transferable Interactiveness Knowledge for Human-Object Interaction Detection ABSTRACT: In order to gain a better understanding of the ways in which people interact with things around them, it is necessary to
PROJECT TITLE : SWNet A Deep Learning Based Approach for Splashed Water Detection on Road ABSTRACT: Unfavorable weather conditions pose a significant risk to the public's safety on the roads, and this is especially true during
PROJECT TITLE : Deep Hough Transform for Semantic Line Detection ABSTRACT: We concentrate on a fundamental task known as semantic line detection in natural scenes, which involves identifying meaningful line structures. A great

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry