Abstract: Nature is of course a great and immense source of inspiration for solving hard and complex problems in computer science since it exhibits extremely diverse, dynamic, robust, complex and fascinating phenomenon. It always finds the optimal solution to solve its problem maintaining perfect balance among its components. This is the thrust behind fauna-inspired computing. Fauna inspired computing technique called Monkey Tree Search (MTS) is a meta-heuristic search model. Its usage in solving complex problems in Wireless Mesh Networks (WMNs) embrace the broadcast benefits of a wireless medium in a more flexible manner. Exploiting the broadcast properties and the path diversity of wireless meshes to implement an efficient multipath routing is a challenging factor. With Multiple radios, nodes capacity can be improved by transmitting simultaneously using orthogonal channels. Capitalizing and taking advantage over these properties requires efficient channel estimation and assignment to radios technique with effective route discovery mechanism. To address these challenges in this study we have proposed a Fauna inspired computing model that uses a Probabilistic and Randomly Computed Channel Assignment Algorithm (PRC-CA) to achieve efficient multipath routing in a multi-radio multi-channel WMN in an interference constrained topology. This approach utilizes adaptive random network coding to analyze conflict and non-conflict channels. It does a meta-heuristic search on all possible non-conflict channels and segments it with respect to the channel capacity for efficient route discovery. All possible routing path information is then processed using PRC-CA with right scheduling and fairness index maintaining Quality of Service. Proposed PRC-CA enables nodes to organize their data transmissions in different time slots with no contention. It dynamically reconfigures the channel assignment as a consequence of a change in the traffic matrix. Simulation results show that PRC-CA converges to a stable state In finite time. Each node gets fair end-to-end throughput across multiple-channels allocating distinct non-overlapping channels to each set of communicating radios increasing network connectivity. Performance evaluation results of Fauna inspired PRC-CA shows promising gains compared to other traditional and existing methods.
B. Sathyasri, E.N. Ganesh and P. Senthil Kumar, 2016. Fauna Inspired Probabilistic and Randomly Computed Channel Assignment and Multipath Routing for Multi-Channel Multi-Radio Mesh Networks. Asian Journal of Information Technology, 15: 3883-3898.