Fuzzy logic based traffic model considering cross-layer for future cognitive network

Traffic models play an important role in nowadays´ network, such as engineering, designing and evaluating the network and its performance. But performance predictions from wireless networking laboratory experiments seem to dismatch what is really deployed in future cognitive heterogeneous networks. To generate an appropriate traffic model is one of the major factors hampering researchers´ ability to make reliable forecasts. This paper proposes a fuzzy logic based traffic model considering cross-layer for cognitive heterogeneous networks. The analyzed traffic is characterized by link congestion parameter which is the output of the fuzzy logic control system with the MAC counters: packet error rate to derive link reliability, and received network layer parameter: latency to determine routing. The advantage of the traffic model using a fuzzy logic approach is that it does not make any statistical assumptions and does not require a huge data sets. It only uses a singleton fuzzifier, min inference and a defuzzifier, all of which are relatively simple and quick to compute. Hence it is fast and secure.