Advanced topics in signal processing
Advanced topics in signal processing
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
A distance routing effect algorithm for mobility (DREAM)
MobiCom '98 Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networking
Smooth is better than sharp: a random mobility model for simulation of wireless networks
MSWIM '01 Proceedings of the 4th ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems
Mobility modelling and trajectory prediction for cellular networks with mobile base stations
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
GPS-Free Positioning in Mobile ad-hoc Networks
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 9 - Volume 9
A Mobility Based Metric for Clustering in Mobile Ad Hoc Networks
ICDCSW '01 Proceedings of the 21st International Conference on Distributed Computing Systems
Real-Time Mobility Tracking Algorithms for Cellular Networks Based on Kalman Filtering
IEEE Transactions on Mobile Computing
Self organization in mobile ad hoc networks: the approach of Terminodes
IEEE Communications Magazine
Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks
IEEE Journal on Selected Areas in Communications
Mobility tracking for mobile ad hoc networks
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
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In ad hoc networks, node mobility causes the network topology to change dynamically over time, which complicates important tasks such as routing and flow control. We propose a distributed scheme for accurately and efficiently tracking the mobility of nodes in ad hoc networks. A first-order autoregressive model is used to represent the evolution of the mobility state of each node, which consists of position, velocity, and acceleration. Each node uses an extended Kalman filter to estimate its mobility state by incorporating network-based signal measurements and the position estimates of the neighbor nodes. Neighbor nodes exchange their position estimates periodically by means of HELLO packets. Each node re-estimates its mobility model parameters, allowing the scheme to adapt to changing mobility characteristics. In practice, a small number of reference nodes with known coordinates is required for accurate mobility tracking. Simulation results validate the accuracy of the proposed tracking scheme.