Authors:
Evangelos D. Spyrou
1
and
Dimitris Mitrakos
2
Affiliations:
1
School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece
;
2
Aristotle University of Thessaloniki, Greece
Keyword(s):
Information Based Similarity, Euclidean distance, Wireless Traffic, Shannon Entropy, Signal-to-Interferenceand-Noise-Ratio.
Abstract:
Quality of service wireless traffic that often exhibits burstiness, occasionally occurring due to mobility, provides a critical networking issue. Traffic patterns in wireless networks are not of a traditional nature. Nodes transmit their information in batches over a short period of time, before they lose connection. Prediction of wireless incoming load plays an important role in the design of wireless local area networks. The issues of load balancing and Quality of Service constraints are a major problem, which is responsible for the increase of throughput of the network; thus, predicting traffic can be of a great assistance in the aforementioned research directions, leading to a significant optimisation of the wireless network operation. This paper addresses the problem of traffic prediction using Hidden Markov Models. The data is clustered using the Information Based Similarity index that classifies different types of traffic. We show the limitation of this approach and we finally
select Euclidean distance for data clustering. Together, they provide an efficient solution towards the solution of wireless traffic characterisation and prediction. We show the efficiency of our scheme in a series of simulations
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