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We have seen a huge increase in the use and application of a tiny device called a sensor in a number of fields. A single network may consist of many tiny sensors that sense different or same types of data. All the data sensed may vary due to data inaccuracy, inconsistency, incorrectness and imperfect so a method should be developed to get the most accurate and correct result. Data fusion is the approach by which we can get the near correct results. In this work, I have simulated three methods and two algorithms to get the best posterior data. All the methods were simulated and the result shows…mehr

Produktbeschreibung
We have seen a huge increase in the use and application of a tiny device called a sensor in a number of fields. A single network may consist of many tiny sensors that sense different or same types of data. All the data sensed may vary due to data inaccuracy, inconsistency, incorrectness and imperfect so a method should be developed to get the most accurate and correct result. Data fusion is the approach by which we can get the near correct results. In this work, I have simulated three methods and two algorithms to get the best posterior data. All the methods were simulated and the result shows how one can reduce error from sensed data of sensors and predict future data based on previous data considering types of error possible in an environment.
Autorenporträt
Nilay Patel est maître de conférences à R.C.Technical Institute et a rejoint la profession académique en 2016. Il a obtenu un diplôme d'ingénieur en informatique de BVM Engg. College, Anand, Gujarat en 2015 et le diplôme de CITC, Changa en 2012. Son domaine de recherche porte sur un réseau de capteurs sans fil. Son domaine de recherche actuel porte sur la tolérance aux pannes dans le Cloud.