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The vision of this research paper is that the mobile phone is aware of its user's motion state and surroundings and modifies its behaviour especially the characteristics of Location-Based-Services based on this information. In the research it is implemented, and evaluated a methodology which can identify individual user, states. This learning is expected to occur online and not expected the requirement of any external supervision. The proposed system relies on hidden markov modelling and Log Likelihood Method. The implementation of the methodology is performed by first training the hidden markov model for the required number of states by an intended network trace. The log likelihood value of the data for each hidden markov model in the set is computed and the motion state is identified by choosing the hidden markov model that produced the highest value. The results of simulations carried out in WCDMA environment indicate that the proposed method is able to assist to create a meaningful user context model at various propagation conditions defined by both 3rd Generation Partnership Project (3GPP) and Wireless World Initiative New Radio (WINNER) propagation scenarios while only requiring a network trace without having an integrated sensor onboard cellular phone or any other wearable sensor device.

An Economical Approach for User Behaviour Determination for the Selection of Type of Location Based Services