Degree Name

Master of Engineering (Research)


School of Electricial, Computer and Telecommunications Engineering - Faculty of Informatics


To date, researchers have proposed many vehicular networks in which cars or buses act as a mechanical backhaul for transporting data. For example, a bus can be retrofitted with a computer and wireless card to automatically ferry data to/from rural villages without Internet connectivity. Alternatively, a person carrying a portable storage device can be used to link geographically disparate networks. These examples of challenged networks are characterized by frequent disruptions, long delays, and/or intermittent connectivity.

This thesis proposes TrainNet, a vehicular network that uses trains to transport latency insensitive data. TrainNet augments a railway network by equipping stations and trains with mass storage devices; e.g., a rack of portable hard disks. TrainNet has two applications. First, it provides a low cost, very high bandwidth link that can be used to deliver non real-time data. In particular, cable TV operators can use TrainNet to meet the high bandwidth requirement associated with Video on Demand (VoD) services. Moreover, TrainNet is able to meet this requirement easily because its links are scalable, meaning their capacity can be increased inexpensively due to the continual fall of hard disk price. Secondly, TrainNet provides an alternative, economically viable, broadband solution to rural regions that are reachable via a railway network. Therefore, using TrainNet, rural communities will be able to gain access to bandwidth intensive digital contents such as music, video, television programs, and movies cheaply.

A key problem in TrainNet is resource scheduling. This problem arises because stations compete for the fixed storage capacity on each train. To this end, this thesis is the first to propose three max-min scheduling algorithms, namely LMMF, WGMMF and GMMF, for use in challenged networks. These algorithms arbitrate the hard disk space among competing stations using local traffic information at each station, or those from other stations. To study these algorithms, the Unified Modeling Language (UML) is first used to construct a model of TrainNet, before a simulator is constructed using the DESMO-J framework. The resulting TrainNet simulator is then used to investigate the behavior of said max-min algorithms in scenarios with realistic traffic patterns. Results show that while LMMF is the fairest algorithm, it results in data loss and has the longest mean delay, the lowest average throughput, and the lowest hard disk utilization. Furthermore, Jain’s fairness index shows WGMMF to be the least fair algorithm. However, it avoids data loss as is the case with GMMF, and achieves the best performance in terms of mean delay, averaged throughput, and hard disk utilization.

02Whole.pdf (1025 kB)