Performance Study of Threshold Variations in Temporal Decision Systems for Routing in Vehicular Ad hoc Networks

Abstract

Problem statement: Route maintenance and re-discovery are expensive in signaling and computation for routing in Vehicular Ad hoc Networks (VANETs). Hence it was desirable to choose the optimal route during the route selection phase. Approach: In this study, the threshold-based routing protocol β-wt uses the notion of threshold from variable precision rough sets. This protocol was used to evaluate routing performance on freeway scenarios in VANETs. A Traffic Generator tool IMPORTANT was used to obtain vehicular movement traces, that are then given as input to the Network Simulator NS2. Results: Results of four performance metrics were got for different values of the thresholds. The performance of the new protocol was compared with that of the original Dynamic Source Routing (DSR). Conclusion: The new protocol performs better than DSR in Packet Delivery Ratio (PDR) and Normalized Routing Load (NRL). The study showed that variations in thresholds do not affect PDR and NRL, while for End to End Delay (EED) and Average Hop Count (AHC), certain values of these thresholds perform much better than other values in this particular VANET application.

DSDV, DYMO, OLSR: Link Duration and Path Stability

“…In these networks, nodes are mobile and act like routers to communicate with each other. Vehicular Ad-hoc Networks (VANETs) are the special case of MANETs, in which mobile nodes are vehicles with radio communication range of 250 to 300 meters [1]. In VANETs, nodes have high mobility that causes fast change of the topology, therefore, its link stability is less than MANETs.…”

An Independent Rough Set Approach Hybrid with Artificial Bee Colony Algorithm for Dimensionality Reduction

“…It is essential to reduce the dimensionality by selecting most relevant features which results in decreasing the measuring cost, transmission and storage cost and compact classification models. There are several techniques that have been proposed in the literature: Filter, wrapper and embedded (Selamat et al, 2010), unsupervised (Shylaja et al, 2010) and supervised (Ngo and Nguyen, 2009;Jensen and Shen, 2007) Rough set theory provides a mathematical tool that can be used for both feature selection and knowledge discovery (Jensen and Shen, 2007). It helps us to find out the minimal attribute sets called 'reducts' to classify objects without deterioration of classification quality and induce minimal length decision rules inherent in a given information system.…”

“…Dimensionality Reduction or Feature subset selection is one of the important steps in data mining (Ahmed et al, 2009;Ngo and Nguyen, 2009;Selamat et al, 2010;Shylaja et al, 2010). Numerous features have been acquired and stored in databases due to the growth and development in real-time applications.…”