The prevalence of wireless networks is on the increase. Society is becoming increasingly reliant on ubiquitous computing, where mobile devices play a key role. The use of wireless networking is a natural solution to providing connectivity for such devices.However, the availability of infrastructure in wireless networks is often limited. Such networks become dependent on wireless ad hoc networking, where nodes communicate and form paths of communication themselves. Wireless ad hoc networks present novel challenges in contrast to fixed infrastructure networks. The unpredictability of node movement and route availability become issues of significant importance where reliability is desired.To improve reliability in wireless ad hoc networks, predicting future connectivity between mobile devices has been proposed. Predicting connectivity can be employed in a variety of routing protocols to improve route stability and reduce unexpected drop-offs of communication. Previous research in this field has been limited, with few proposals for generating future predictions for mobile nodes. Further work in this field is required to gain a better insight into the effectiveness of various solutions.This thesis proposes such a solution to increase reliability in wireless ad hoc routing. This research presents two novel concepts to achieve this: the Communication Map (CM),and the Future Neighbours Table (FNT). The CM is a signal loss mapping solution.Signal loss maps delineate wireless signal propagation capabilities over physical space.With such a map, connectivity predictions are based on signal capabilities in theenvironment in which mobile nodes are deployed. This significantly improves accuracyof predictions in this and in previous research. Without such a map available,connectivity predictions have no knowledge of realistic spatial transmission ranges.The FNT is a solution to provide routing algorithms with a predicted list of future periods of connectivity between all nodes in an established wireless ad hoc network. The availability of this information allows route selection in routing protocols to be greatly improved, benefiting connectivity. The FNT is generated from future node positional iv information combined with the CM to provide predicted signal loss estimations at future intervals. Given acceptable signal loss values, the FNT is constructed as a list of periods of time in which the signal loss between pairs of nodes will rise above or fall below this acceptable value (predicted connectivity). Future node position information is ideally found in automated networks. Robotic nodes commonly operate where future node task movement is developed and planned into the future, ideal for use in predicted connectivity. Non-automated prediction is also possible, as there exist some situations where travel paths can be predictable, such as mobile users on a train or driving on a highway. Where future node movement is available, predictions of connectivity between nodes are possible.Detailed analysis of the two proposed concepts are presented in this thesis. Comparisons with existing prediction algorithms illustrate that employing a signal loss map (the CM) vastly improves the accuracy of predictions. The fundamental concepts of the FNT are validated, though in the testing environment the FNT is not shown to be the ideal predicted connectivity architecture for wireless ad hoc networks in comparison to previous work.
|Date of Award||1 Nov 2005|
|Supervisor||Zheng Da Wu (Supervisor)|