Novel Low Delay Marking Algorithm for Self-organizing Cellular Neural Networks Based on Topology Space Analysis

This paper analyzed two topologies of self-organizing cellular neural networks based on a hybrid mode that combines IEEE 802.11g and 802.11a in different dimensions, and investigated the relationship between the attributes of the networks and the topology spaces. Then a novel scheme for the low delay marking (LDM) algorithm, called modified-low delay marking (M-LDM), is proposed. Performance analysis of the connectivity and other properties was conducted and shown that the proposed modification to the LDM algorithm can optimize the round trip time (RTT) and make the network more effective and more stable in the case of the special scenarios that were investigated, by modifying the patterns for neurons, optimizing the window size and adjusting the marking probability