A class of mobile motion prediction algorithms for wireless mobile computing and communication
Mobile Networks and Applications - Special issue: routing in mobile communications networks
Mobility modeling in wireless networks: categorization, smooth movement, and border effects
ACM SIGMOBILE Mobile Computing and Communications Review
The Spatial Node Distribution of the Random Waypoint Mobility Model
Mobile Ad-Hoc Netzwerke, 1. deutscher Workshop über Mobile Ad-Hoc Netzwerke WMAN 2002
ICON '00 Proceedings of the 8th IEEE International Conference on Networks
The changing usage of a mature campus-wide wireless network
Proceedings of the 10th annual international conference on Mobile computing and networking
Stationary Distributions for the Random Waypoint Mobility Model
IEEE Transactions on Mobile Computing
Effects of mobility in hierarchical mobile ad hoc networks
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
AP and MN-centric mobility prediction: a comparative study based on wireless traces
NETWORKING'07 Proceedings of the 6th international IFIP-TC6 conference on Ad Hoc and sensor networks, wireless networks, next generation internet
Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks
IEEE Journal on Selected Areas in Communications
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
A generic framework for mobility prediction and resource utilization in wireless networks
COMSNETS'10 Proceedings of the 2nd international conference on COMmunication systems and NETworks
MANET location prediction using machine learning algorithms
WWIC'12 Proceedings of the 10th international conference on Wired/Wireless Internet Communication
| Hi-index | 0.00 |
Wireless user-mobility prediction has been investigated from various angles to improve network performance. Student populations in campuses, pedestrian and vehicular movement in urban areas, etc have been studied by cell phone and mobility management researchers to address issues in Quality of Service (QoS), seamless session handoffs, etc. Access to information such as user movement times, direction, speed, etc provides an opportunity for networks to efficiently manage resources to satisfy user needs. Towards this goal, we propose a generic framework to approach the problem of mobility prediction using Hidden Markov Models (HMM). This method can be used to modd hidden parameters in the models. We propose a way to extract user movement information from a real dataset, train a HMM using this data and make predictions using the HMM. This model can successfully predict long sequences of a mobile user's path from observed sequences and also uses successive sequences of observed data to train its learning parameters to enhance prediction accuracy. Furthermore, we show that this model is very generic and can be suited to make predictions using the same information from the perspective of the access point or the mobile node.