Sensor-based occupancy detection using neutrosophic features fusion

Abstract

Occupancy detection using ambient sensors has many benefits such as saving energy and money, enhancing security monitoring systems, and maintaining the privacy. However, sensors data suffers from uncertainty and unreliability due to acquisition errors or incomplete knowledge. This paper presents a new heterogeneous sensors data fusion method for binary occupancy detection which detects whether the place is occupied or not. This method is based on using neutrosophic sets and sensors data correlations. By using neutrosophic sets, uncertain data can be handled. Using sensors data fusion, on the other hand, increases the reliability by depending on more than one sensor data. Accordingly, the results of experiments applied using Random Forest (RF), Linear Discriminant Analysis (LDA), and FUzzy GEnetic (FUGE) algorithms prove the new method to enhance detection accuracy.

Efficient Occupancy Detection System Based on Neutrosophic Weighted Sensors Data Fusion

“…• NS_all [33]: a fused data vector generated using the dynamic fusion equations mentioned in [33]. The features fused are the truth of each sensor data affected by other sensors' data (F2F fusion).…”

“…To evaluate the proposed technique, various classification models are used. LDA and RF were used in [29], [33]. So, they were used for comparing the proposed technique results…”

“…with the results in [29], [33]. Also, K-NN, NB, and SVM are used to compare with other state-of-the-art techniques.…”

“…The third one is for the Multi-Sensor-based Weighted (MSW) dataset, Fig.4. The fourth case is for the NS dataset[33], Fig.5. Finally, the fifth one is for the NS_all dataset[33], Fig.6.…”

“…The fourth case is for the NS dataset[33], Fig.5. Finally, the fifth one is for the NS_all dataset[33], Fig.6. Two points were observed from these correlation…”