Kiran Ijaz

A Fault Tolerant Infrastructure for Mobile Agen

Mobile agent technology is a promising paradigm for a myriad of real world applications. Owing to their tremendous capabilities, multiagent systems have been scoped in a large number of applications. However issues related to fault tolerance can hamper the suitability of mobile agents in these real world systems. In this paper we have proposed an infrastructure which provides agent fault tolerance. An algorithm similar to the sliding window model ensures a fault tolerant behavior. Different types of agents, work in collaboration to provide the desired system behavior by tolerating faults. The proposed infrastructure will be applicable in a variety of systems including, ecommerce, online banking etc. With the increasing market of electronic commerce it becomes an interesting aspect to use autonomous mobile agents for electronic business transactions.

Distributed Dependable Enterprise Business System – DDEBS

The inherent qualities of agent technology make it a perfect choice for many real world applications. However this usefulness can be undermined due to problems incurred by the lack of fault tolerance and security in Multi Agent Based Applications. In this paper we have proposed an infrastructure which provides fault tolerance (at both agent and system level) and security. An algorithm similar to the sliding window model ensures the agent level fault tolerance, while the system level fault tolerance has been provided by dynamic discovery of alternate paths. The proposed framework is envisaged to be used in critical financial applications like Banking applications, E-commerce, Stock exchange etc. Therefore security is provided by means of Triple DES Algorithm for encryption / decryption and MD5 Algorithm for the integrity of the message.

Applying Color Code Coordinated LRTA* (C3LRTA*) Algorithm on Multiple Targets

In this paper, we have modified original LRTA* and C3LRTA* Algorithms, instead of single target we have use multiple targets in randomly generated mazes. Both modified algorithms have been applied to solve randomly generated mazes with multiple targets. We have evaluated both modified algorithms on a large number of test cases with random obstacles and varying obstacle ratio. Through simulation experiments, we have shown that C3LRTA* is effective in both search time and solution quality. In addition, the strategy used in C3LRTA* can be made more efficient if the number of agents and/or obstacle ratio is increased