Keywords-cognitive networks, load balancing, reinforcement learning, wireless netwo. We present OPNET simulation results illustrating that, in comparison to original OSPF and AODV (2.18 Mbits/s with 46.46% packet loss rate), ANTS dramatically achieves a higher packet delivery (9.57 Mbits/s with 0.53% packet loss rate). Free Download HHD Software Network Monitor Ultimate 8.3 (圆4) 15. To enable cognitive intelligence for network-wide load balancing, we implement a cross-layer mechanism in which learning agents in middleware layer can monitor the queue sizes of MAC layer, thereby allowing for the discovery of optimal routes. An artist's depiction of a 2000s-era desktop-style personal computer, which includes a metal case with the computing components, a display monitor and a keyboard (mouse not shown) A personal computer ( PC) is a multi-purpose microcomputer whose size, capabilities, and price make it feasible for individual use. To maintain network-wide load balancing, we propose Autonomous Network management with Team learning based Self-configuration (ANTS) which attempts to manage a feasible route for traffic flow with QoS constraints in heterogeneous networks. Traditional hop-by-hop dynamic routing makes inefficient use of network resources as it forwards packets along already congested shortest paths while uncongested longer paths may be underutilized.
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