Vehicular ad-hoc networks (VANETs) form when vehicles are equipped with devices capable of short-range wireless communication. Vehicular Ad hoc networks involve motion of nodes depending on the mobility model chosen. Three important considerations in simulation of VANETs are mobility models, network simulator and the routing protocols. Selection of appropriate mobility model for evaluating routing protocol leads to efficient simulation results. Performance of routing protocols in VANETs can be measured using four metrics; bandwidth, packet loss, throughput and scalability. This research work is based on the simulation based analysis of Vehicular Ad- hoc networks using NS-2 as the network simulator. Performance evaluation of the protocols is conducted on the basis of four defined metrics and as conclusion is made according to the simulation results.

In recent past, IT industry has seen massive growth. However it is not much mature in developing countries, facing many challenges particularly in project management. As a result performance of IT industry is not up to the mark and unable to provide professional support as compared to internationally recognized standards to public and private sector organizations. There are many challenges such as shortage of skilled and experienced human resources and high employee turnover. Tough and competitive market condition requires the product to be extremely good in performance, timeline and accuracy. Effective project management and collaborative efforts of the Government and IT professionals can make highly successful market for IT projects. Success rate of private sector organizations is high as compared to public sector organizations in developing countries like Pakistan. In this research we have explored the performance of IT project management in public and private sector organizations of developing countries focusing Pakistan, to find out the reasons of project failure and success in both public and private sector organizations.

Satellite imagery can produce maps including roads, railway tracks, buildings, bridges, oceans, lakes, rivers, etc. In developed countries like USA, Canada, Australia, Europe, images produced by Google map are of high resolution and good quality. On the other hand, mostly images of the third world countries like Pakistan, Asian and African countries are of poor quality and not clearly visible. Similarly railway tracks of these countries are hardly visible in Google map. We have developed an efficient algorithm for railway track detection from a low quality image of Google map. This would lead to detect damaged railway track, railway crossings and help to schedule/divert locomotive movements in order to avoid catastrophe.