Highly Reliable Multi-Service Provisioning using Sequential Prediction of Zone and PL&T of Nodes in Mobile Networks

Abstract

In this paper, a dynamic Position Location and Tracking (PL&T) method is proposed which uses an integrated “zone finding by predictive algorithm” and “Triangulation with dynamic reference positioning” for target nodes. The nodes can be used as references or targets at different instances of time to maintain accurate PL&T of each node in a wireless mobile network. Each node is equipped with an omnidirectional antenna. It is shown that while “zone prediction” is fairly accurate for finding the PL&T of a target node based on previous good location data when the target is moving linearly in one direction, it is not adequate when the nodes change directions. However, once the zone is predicted, the triangulation will provide accurate position of the target node by placing the reference nodes to form a reasonably perfect geometry. The results show that triangulation alone does not provide accurate prediction of the target location without finding the zone of the target and zone finding alone does not support accuracy of PL&T when the target node is changing directions. Another aspect that should be of concern is the repeated PL&T operation on the same target node at different instance of time becomes erroneous and therefore, they need to be reinitialized to get accurate locations with known wait points. Also, the references need to be reinitialized to maintain the accuracy. This requires the nodes to be dynamically used as target nodes and with good PL&T, they are used as references at different time instances. There is an upper layer dynamic reference management is required for this technology. The proposed integrated predictive zone finding and triangulation is particularly suitable for clustered Mobile Ad hoc Networks (MANET). Finally, this paper shows the performance analysis of this system for a reasonable number of nodes within the cluster that demonstrates high accuracy of PL&T location data of the nodes. Of course the overhead in managing the dynamic references is increased. However, the frequency of changing the references is minimized in each cluster as it contains few nodes (40 or less).