Abstract
Recent days, research in wireless network becomes major area for the past few decades. In wireless rou ting many routing methods such as table driven, source d riven; many characteristics such as reactive routin g, proactive routing; many routing algorithms such as dijikstra’s shortest path, distributed bell-man for d algorithm are proposed in the literature. For effec tive wireless routing, the recent ant colony optimization proves better result than the existing methodologies. The ant colony optimization is a swarm intelligence technique which widely used for combinatorial optimization problems such as travelling salesman, network routing, clustering. T he ant colony optimization is a real time routing protocol which offers highly reliable and optimal r outing for both single path and multi path routing. As the ant is a small tiny mobile agent, providing security is critical issue. In this study, a secure d ant colony optimization using Chinese remainder theorem is proposed.
Highlights
Circulated in the network for searching the destination
The ants move in the network randomly at regular destination address, the intermediate node Identification intervals to scan the characteristics of large number of network nodes
The Forward Ants (FA) moves in the network searching for the destination using the probability routing table of intermediate nodes
Summary
Circulated in the network for searching the destination. Destinations are locally selected according to the data traffic. The ants move in the network randomly at regular destination address, the intermediate node Identification intervals to scan the characteristics of large number of network nodes While moving, they collect information about the network and deliver it to the network nodes. The FA moves in the network searching for the destination using the probability routing table of intermediate nodes. Every source node in the network, in a regular interval (∆t), generates Forward Ants (FA) and the FA is number of the FA have a probability to visit other nodes and other paths still have a probability to be visited. As the BA moves in the reverse path, the intermediate nodes modify their four data structures based on the path grade carried by the BA and update their probability routing tables. The ACo applied the simple probability rule and later it extended to the state transition rule for the decision model
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