An adaptive location management strategy for mobile IP
MobiCom '95 Proceedings of the 1st annual international conference on Mobile computing and networking
Mobile networking in the Internet
Mobile Networks and Applications - Special issue: mobile networking in the Internet
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Mobile IP; Design Principles and Practices
Mobile IP; Design Principles and Practices
On minimizing the cost of location management in mobile environments
Journal of Parallel and Distributed Computing - Special issue on wireless networks
The lookahead strategy for distance-based location tracking in wireless cellular networks
ACM SIGMOBILE Mobile Computing and Communications Review
Mobile Networking Through Mobile IP
IEEE Internet Computing
On the Optimal Selection of Proxy Agents in Mobile Network Backbones
ICPP '02 Proceedings of the 2001 International Conference on Parallel Processing
IP for 3G: Networking Technologies for Mobile Communications
IP for 3G: Networking Technologies for Mobile Communications
Supporting Reduced Location Management Overhead and Fault Tolerance in Mobile-IP Systems
ISCC '99 Proceedings of the The Fourth IEEE Symposium on Computers and Communications
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In a mobile environment, each mobile host should have a home agent on its home network that maintains a registry of the current location of the mobile host. This registry is normally updated every time a mobile host moves from one subnet to another. We study the tradeoff between the cost of updating the registry and the cost of searching for a mobile host while it is away from home. Using a set of special agents, called proxy agents, which implement a two-tier update process, the cost of updates could be reduced; however, the search cost might increase. We study different approaches to identify a set of proxy agents that minimizes the cost of search. In this paper, we use mathematical programming to obtain optimal solutions to the problem. We consider two situations: the cost of search measured by the sum of all search message costs, and the cost of search measured by the maximum cost of such messages. For these two respective cases we formulate the minimization of the cost of search as Min-Sum and Min-Max problems. For large networks in which the optimization problem may be intractable, we study three different approximate approaches: (1) clustering, (2) genetic algorithms, and (3) simulated annealing. Results of a large set of experiments are presented.