Comparison between On-line Supervised Learning and Unsupervised Learning of Link Quality estimates in wireless networks Performance

Document Type : Original Article

Authors

Dept. of Computers and Systems Eng., Faculty of Eng., Al-azhar University

Abstract

In this paper, Optimized Link State routing protocol (OLSR) and Link Quality Learning protocol (LQL) are employed to show how to control the rerouting time process of a local area network. At every node and for every wireless link, OLSR passively gathers measurements to quantify the network features current value and to assess the related link quality value according to many metrics. The measurements, of These metrics, are used as training samples for the incremental, regression mapping, and supervised learning model which can be used by network protocol LQL to derive link qualities in real-time and robust manner. According to OLSR and LQL, Nodes can also cooperate by exchanging training samples, speeding up the overall learning process. This cooperative feature is useful when the local network configurations are continually changing because of mobility and/or varying traffic pattern. We propose a distributed online protocol based on organized maps learning technique called Self Organized Mapping (SOM). Three performance methods are applied to the wireless network used within this paper: OLSR_SOM that cares making modification, OLSR protocol code, OLSR_LQL that depends on the introducing LQL as a supervised learning protocol and OLSR_SOM that depends on the introducing SOM as an unsupervised learning protocol.

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