A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Srividhya, V.
- Performance Evaluation of Scalable Ad-Hoc Network Using Dynamic Address Routing (DART)
Authors
1 CEG, Anna University, Chennai, Tamil Nadu, IN
2 Vellore Institute of Technology, School of Electronics Engineering, Vellore-632014, Tamil Nadu, IN
3 Vellore Institute of Technology, School of Electrical Sciences, Vellore-632014, Tamil Nadu, IN
Source
Networking and Communication Engineering, Vol 2, No 2 (2010), Pagination: 9-19Abstract
In this paper we develop a scalable network layer routing protocol for mobile ad hoc networks and also compared the Dynamic Address Routing (DART) performances like overhead and throughput with respect to network size and dataflow with the existing Protocol. Dynamic Address Routing (DART) addresses this scalability problem by separating the address of a node into two separate numbers: a) a unique and static node identifier, serving the same purpose as today's IP addresses and b) a dynamic routing address, which indicates the node's current position in the network topology. The use of dynamic routing addresses creates an opportunity for route aggregation which in the case of DART, greatly improves scalability. The paper describes the method of address allocation, which executes locally on each node, and relies only on routing updates from immediate neighbors to select an available and accurate routing address. Dynamic Address Routing (DART) does not require any geographical location information, nor does it make any assumption as to the underlying medium. Wireless Omni directional links, as well as directional and even wired links are supported equally well. In addition, nodes participating in a DART network do not require any manual network configuration, making Dynamic Address Routing (DART) a strong candidate for future mesh networking applications in additional to current ad hoc networking applications. The simulations for different scenarios are run and compared graphs are obtained. The analysis of these graphs shows the increase in number of Flows and network size the reduction in the overhead of the network. It is also shown by the graphs that the overall throughput of the network is increased.Keywords
Dynamic Address Routing (DART), Scalability, Wireless Ad Hoc Routing Protocol, Temporally Ordered Routing Algorithm (TORA).
- Resource Reservation Based on Mobility Prediction in Personal Communication Systems
Authors
1 VIT University, Vellore, Tamil Nadu, IN
Source
Networking and Communication Engineering, Vol 2, No 3 (2010), Pagination: 109-116Abstract
IEEE 802.11 Mobility of the users in Personal Communication systems gives rise to the problem of mobility management. Predictive reservation allows the reservation of resources for an ongoing call in the next cell, so that the call is sustained when the Mobile Station (MS) moves to the next cell. Mobility management covers the methods for storing and updating the location information. of the mobile users served by them. Mobility prediction thus becomes an inevitable process in mobility management. Mobility prediction is defined as the prediction of the mobile user’s next movement where the user is traveling between the cells of the network. By using the predicted movement, the system can effectively allocate resources to the most probable-to move cell instead of blindly allocating resources in the entire neighborhood of the cell. Mobility prediction based on data mining method to predict the mobile user’s next movement is implemented in this project. The method is based on mining the User Actual Paths to discover the regularities in the patterns, extracting mobility rules from these patterns and finally, the matching rule, having the highest confidence plus support value corresponding to the current trajectory of the user, is used to predict the mobile user’s next cell movement. Through accurate prediction, the system can reserve resources in an efficient manner, thus leading to improved resource utilization. The performance of the method is evaluated through simulation. The results obtained in each phase leading to more accurate prediction of the mobile user’s next cell movement have been presented.