Abstract—In 802.11 wireless infrastructure networks, as the mobile node moves from one access point to another, the active connections will not be badly dropped if the handoff is smooth and if there are sufficient resources reserved in the target access point. In a 5x5 grid of access points, within a 6x6 grid of regions, by location tracking and data mining, we predict the mobility pattern of mobile node with good accuracy. The pre-scanning of mobile nodes, along with pre-authenticating neighbouring access points and pre-reassociation is used to reduce the scan delay, authentication delay and re-association delay respectively. The model implements first stage reservation by using prediction results and does second stage reservation based on the packet content type, so that sufficient resources can be reserved when the mobile node does the handoff to the next access point. The overall mobility management scheme thus reduces the handoff delay. The performance simulations are done to verify the proposed model. Index Terms—delay management, mobility prediction, mobility management, resource reservation management. I.

Abstract—In 802.11 wireless infrastructure networks, as the mobile node moves from the current access point to another, the active connections will not be badly dropped if the hand off is smooth and if there are sufficient resources reserved in the target access point. In a 5x5 grid of access points, within a 6x6 grid of regions, by location tracking and data mining, we predict the mobility pattern of mobile node with good accuracy. The mobility prediction, active scanning of mobile nodes, along with pre-authenticating neighbouring access points is used to reduce the re-association delay, probe delay and authentication delay respectively. The model implements first stage reservation by examining packet content types and second stage reservation by using information from mobility prediction results, so that sufficient resources can be reserved when the mobile node does the handoff to the next access point. The overall mobility management scheme thus improves the quality of service and enables smooth hand off. The performance simulation is done to verify the proposed model. Index Terms—delay management, mobility prediction, mobility management, resource reservation management. I.

Header compression (HC) is a useful technique for reducing the load on bandwidth-scarce wireless links. HC normally depends on the establishment and synchronisation of context at the compressor and de-compressor. In Mobile IP networks, it is desirable to transfer this context between access routers to avoid the expensive process of context reestablishment. In this paper we propose a method for avoiding the need for context re-establishment when packets are lost during handovers, as well as means for efficiently transferring the context to new access routers (by piggybacking the context on Mobile IP signalling messages). The analysis of our scheme shows that it can reduce signalling load during handovers, which is beneficial in mobile networks with frequent handovers (e.g. wireless LAN hot spots).

Prediction of mobile path of a mobile node is of great importance since it reduces the handoff delays incurred during the handoff procedure. The proposed system is called Predictive Mobility Management scheme. We track the movement of mobile nodes by location tracking and data mining. In location tracking, we continuously monitors the movement of mobile node. The movement of mobile nodes can be predicted by the direction of movement of mobile nodes and previous path history which can be obtained by data mining techniques. The data mining technique is used to search the path history of mobile node and uses this information to predict the future movement of mobile node. NG pruning scheme can be used for prediction along with location tracking. In NG pruning, we will exclude scanning of access points which are not reachable. Thus the scanning delay can be minimized to a large extent. Using the prediction scheme, we can minimize the delays during handoff, track a mobile node etc. When a node reaches out of range area, data sending to the node are directed to the AP which was selected by prediction where the node is going to get connected.