Data Fusion for WSN Using Probabilistic Method and Particle Filter : A technique to reduce your sensor data

Nilay Patel

ISBN 10: 6138238052 / ISBN 13: 9786138238058

Published by LAP LAMBERT Academic Publishing, 2019

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9786138238058

Bibliographic Details

• Title: Data Fusion for WSN Using Probabilistic Method and Particle Filter : A technique to reduce your sensor data
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 6138238052
• ISBN 13: 9786138238058
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.