TY - JOUR
T1 - Features Selection Model for Internet of E-Health Things Using Big Data
AU - Din, Sadia
AU - Paul, Anand
AU - Guizani, Nadra
AU - Ahmed, Syed Hassan
AU - Khan, Murad
AU - Rathore, M. Mazhar
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017
Y1 - 2017
N2 - Internet of Things (IoT) plays a key role in connecting the e-health system with the cyber world through new services and seamless interconnection between heterogeneous devices. Therefore, it becomes computationally inefficient to analyze and select features from such massive volume of data. Therefore, keeping in view the needs above, this paper presents a system architecture that selects features by using Artificial Bee Colony (ABC). Moreover, a Kalman filter is used in Hadoop ecosystem that is used for removal of noise. Furthermore, traditional MapReduce with ABC is used that enhance the processing efficiency. Moreover, a complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed Hadoop-based ABC algorithm. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce with the ABC algorithm. ABC algorithm is used to select features, whereas, MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes with near real-time. Moreover, the proposed system is compared with Swarm approaches and is evaluated regarding efficiency, accuracy, and throughput by using ten different data sets. The results show that the proposed system is more scalable and efficient in selecting features.
AB - Internet of Things (IoT) plays a key role in connecting the e-health system with the cyber world through new services and seamless interconnection between heterogeneous devices. Therefore, it becomes computationally inefficient to analyze and select features from such massive volume of data. Therefore, keeping in view the needs above, this paper presents a system architecture that selects features by using Artificial Bee Colony (ABC). Moreover, a Kalman filter is used in Hadoop ecosystem that is used for removal of noise. Furthermore, traditional MapReduce with ABC is used that enhance the processing efficiency. Moreover, a complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed Hadoop-based ABC algorithm. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce with the ABC algorithm. ABC algorithm is used to select features, whereas, MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes with near real-time. Moreover, the proposed system is compared with Swarm approaches and is evaluated regarding efficiency, accuracy, and throughput by using ten different data sets. The results show that the proposed system is more scalable and efficient in selecting features.
KW - ABC algorithm
KW - Big Data
KW - IoT
KW - feature selection
UR - http://www.scopus.com/inward/record.url?scp=85046370794&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2017.8254418
DO - 10.1109/GLOCOM.2017.8254418
M3 - Conference article
AN - SCOPUS:85046370794
SN - 2334-0983
VL - 2018-January
SP - 1
EP - 7
JO - Proceedings - IEEE Global Communications Conference, GLOBECOM
JF - Proceedings - IEEE Global Communications Conference, GLOBECOM
M1 - 8254418
T2 - 2017 IEEE Global Communications Conference, GLOBECOM 2017
Y2 - 4 December 2017 through 8 December 2017
ER -