Performance Evaluation of 6LoWPAN Protocol
|Research Area:||Networking and Communication Architecture||Year:||2011|
|Type of Publication:||Mastersthesis||Keywords:||6LoWPAN, Z-Monitor, RPL, Wireless Sensor Networks, LoWPANs|
The main objective of this master thesis is to evaluate the behavior of 6LoWPAN networks in real implementation. We aim mainly to evaluate the performance of the RPL routing protocol as it represents the main candidate for acting as the routing protocol for 6LoWPAN networks. The evaluation will be performed for different network settings to understand the impact of the protocol attributes on the network formation performance, namely in terms energy, storage overhead, communication overhead, network convergence time and maximum hop count. This work is motivated by the fact that simulation links doesn’t reflect an important aspect of reality. They can’t give insight of protocols operating characteristics which may confuse protocol’s developers analyzers. To understand the behavior and find out the limitations of the protocols, it is necessary to have a powerful network monitoring and protocol analysis at hand. There exist a few solutions for LoWPAN monitoring, but they are either very expensive or of proprietary nature. Therefore, we aim in a first step to design a monitoring tool. Our solution, Z-Monitor, is an open source software for monitoring IEEE 802.15.4-based networks, does not require special sniffer hardware and is easy to extend. It is designed to support many LoWPAN protocols such as 6LoWPAN and RPL.
Autonomous Navigation of a Wifibot Robot using Odometery for Obstacle Avoidance
This video shows a Wifibot Lab robot navigating in the corridors of the College of Computer and Information Sciences at Al-Imam Mohamed bin Saud University. It implements a simple odometry based algorithm to avoid obstacles while navigating.
The demo shows that the robot is able to navigate freely without hitting any obstacle and efficiently deviating from approaching obstacles. There is no apriori map embedded in the robot ((blind navigation). This work is done under the R-Track project.