A performance comparison of delay-tolerant network routing protocols


Networks that lack continuous finish-to-finish connections among their nodes because of node mobility, constrained power sources, or limited information space for storing are referred to as DTNs. To beat the intermittent connectivity, DTN nodes store and carry the information packets they receive until they come into Communication range of each other. In addition, they unfold multiple copies of the identical packet on the network to increase the delivery chance. In recent years, many routing protocols have been developed specifically for DTNs. These protocols vary in the quantity of copies they spread and the data they use to guide the packets to their destinations. There are some reviews of these protocols, however no performance comparison has been conducted. In this article, we study four well-known DTN routing protocols: EPIDEMIC, Spray-and-Wait, PROPHET, and MAXPROP. We have a tendency to introduce a procedural kind to present the protocols. We have a tendency to measure the performance of the protocols in terms of packet delivery, delivery value, and average packet delay. We compare the protocols' performance along with the results of optimal routing using real-life eventualities of vehicles and pedestrians roaming in an exceedingly town. We tend to conduct many simulation experiments to show the impact of changing buffer capability, packet lifetime, packet generation rate, and variety of nodes on the performance metrics. The article is concluded by providing guidelines to develop an economical DTN routing protocol. To the best of our data, this work is the primary to produce a detailed performance comparison among the diverse collection of DTN routing protocols.

Did you like this research project?

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

PROJECT TITLE : Comparing Different Resampling Methods in Predicting Students Performance Using Machine Learning Techniques ABSTRACT: Predicting students' performance is one of the most valuable and important research areas in
PROJECT TITLE : Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification ABSTRACT: The problem of unbalanced data is common in network intrusion detection, spam filtering,
PROJECT TITLE : Deep Neural Networks Improve Radiologists Performance in Breast Cancer Screening ABSTRACT: To classify mammograms for breast cancer screening, we developed a deep convolutional neural network that was trained and
PROJECT TITLE : Predicting Detection Performance on Security X-Ray Images as a Function of Image Quality ABSTRACT: Research into how image quality impacts work performance is a hot topic in many industries. The security X-ray
PROJECT TITLE : A Novel Control Scheme for Enhancing the Transient Performance of an Islanded Hybrid AC-DC Microgrid ABSTRACT: In this research, we present an innovative supplementary feature for increasing the transient performance

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

Project Enquiry