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ABSTRACT: Introduction of flexibility and intelligence in the wireless devicesand applications have introduced the concept of Cognitive Radio.This research objective has inspired various research activities ongoing which included the decision making aspects. In this paper, adecision making process in cognitive radio is analyzed usingfuzzy logic system, in which secondary user can use the spectrumeffectively. We have selected three descriptive factors forchoosing the proper secondary unlicensed user – velocity of thesecondary user, spectrum to be utilized by secondary user anddistance of the secondary user from primary user. The efficiencyof the decision making process in cognitive radios is analyzed.Based on linguistic knowledge 27 rules are set up. The output ofthe fuzzy logic system gives the probability of the decision basedon the three descriptive factors. We show how fuzzy logic systemcan be used for decision making operation in cognitive radio.
International Journal of Computer Applications. 01/2010;
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ABSTRACT: This paper focuses on the comparative investigation andperformance evaluation of the ML_TMLA algorithm thatgenerates multiple transaction tables for all levels in one databasescan with that of ML_T2L1 and ML_T1LA algorithms. Theperformance study has been carried out on different kinds of datadistributions (three synthetic and one real dataset) and thresholdsthat identify the conditions for algorithm selection. The AR Toolhas been used for the experimental and comparative evaluation ofthe proposed algorithm with other algorithms.
International Journal of Computer Applications. 01/2010;
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ABSTRACT: In this paper, an attempt has been made to develop a statistical model for the sensor data stream, estimating density for distribution of data and flagging a particular value as an outlier in the best possible manner without compromising with the performance. A statistical modeling technique transforms the raw sensor readings into meaningful information which will yield effective output, hence offering a more reliable way to gain insight into the physical phenomena under observation. We have proposed a model that is based on the approximation of the sensor data distribution. Our approach takes into consideration various characteristics and features of streaming sensor data. We processed and evaluated our proposed scheme with a set of experiments with datasets which is taken from Intel Berkeley research lab. The experimental evaluation shows that our algorithm can achieve very high precision and recall rates for identifying outliers and demonstrate the effectiveness of the proposed approach.
International Journal of Computer Applications. 01/2010;
