Path Prediction Method for Effective Sensor Filtering in Sensor Registry System

Sukhoon Lee, Dongwon Jeong, Doo Kwon Baik, Dae Kyoo Kim

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The Internet of Things (IoT) has emerged and several issues have arisen in the area such as sensor registration and management, semantic interpretation and processing, and sensor searching and filtering in Wireless Sensor Networks (WSNs). Also, as the number of sensors in an IoT environment increases significantly, sensor filtering becomes more important. Many sensor filtering techniques have been researched. However most of them do not consider real-time searching and efficiency of mobile networks. In this paper, we suggest a path prediction approach for effective sensor filtering in Sensor Registry System (SRS). SRS is a sensor platform to register and manage sensor information for sensor filtering. We also propose a method for learning and predicting user paths based on the Collective Behavior Pattern. To improve prediction accuracy, we consider a time feature to measure weights and predict a path. We implement the method and the implementation and its evaluation confirm the improvement of time and accuracy for processing sensor information.

Original languageEnglish
Article number613473
JournalInternational Journal of Distributed Sensor Networks
Volume2015
DOIs
Publication statusPublished - 2015

Fingerprint

Sensors
Units of measurement
Processing
Wireless sensor networks
Wireless networks
Semantics

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Engineering(all)

Cite this

Path Prediction Method for Effective Sensor Filtering in Sensor Registry System. / Lee, Sukhoon; Jeong, Dongwon; Baik, Doo Kwon; Kim, Dae Kyoo.

In: International Journal of Distributed Sensor Networks, Vol. 2015, 613473, 2015.

Research output: Contribution to journalArticle

@article{558a4cc618fe4208bb8f06387e1fc4ba,
title = "Path Prediction Method for Effective Sensor Filtering in Sensor Registry System",
abstract = "The Internet of Things (IoT) has emerged and several issues have arisen in the area such as sensor registration and management, semantic interpretation and processing, and sensor searching and filtering in Wireless Sensor Networks (WSNs). Also, as the number of sensors in an IoT environment increases significantly, sensor filtering becomes more important. Many sensor filtering techniques have been researched. However most of them do not consider real-time searching and efficiency of mobile networks. In this paper, we suggest a path prediction approach for effective sensor filtering in Sensor Registry System (SRS). SRS is a sensor platform to register and manage sensor information for sensor filtering. We also propose a method for learning and predicting user paths based on the Collective Behavior Pattern. To improve prediction accuracy, we consider a time feature to measure weights and predict a path. We implement the method and the implementation and its evaluation confirm the improvement of time and accuracy for processing sensor information.",
author = "Sukhoon Lee and Dongwon Jeong and Baik, {Doo Kwon} and Kim, {Dae Kyoo}",
year = "2015",
doi = "10.1155/2015/613473",
language = "English",
volume = "2015",
journal = "International Journal of Distributed Sensor Networks",
issn = "1550-1329",
publisher = "SAGE Publications Inc.",

}

TY - JOUR

T1 - Path Prediction Method for Effective Sensor Filtering in Sensor Registry System

AU - Lee, Sukhoon

AU - Jeong, Dongwon

AU - Baik, Doo Kwon

AU - Kim, Dae Kyoo

PY - 2015

Y1 - 2015

N2 - The Internet of Things (IoT) has emerged and several issues have arisen in the area such as sensor registration and management, semantic interpretation and processing, and sensor searching and filtering in Wireless Sensor Networks (WSNs). Also, as the number of sensors in an IoT environment increases significantly, sensor filtering becomes more important. Many sensor filtering techniques have been researched. However most of them do not consider real-time searching and efficiency of mobile networks. In this paper, we suggest a path prediction approach for effective sensor filtering in Sensor Registry System (SRS). SRS is a sensor platform to register and manage sensor information for sensor filtering. We also propose a method for learning and predicting user paths based on the Collective Behavior Pattern. To improve prediction accuracy, we consider a time feature to measure weights and predict a path. We implement the method and the implementation and its evaluation confirm the improvement of time and accuracy for processing sensor information.

AB - The Internet of Things (IoT) has emerged and several issues have arisen in the area such as sensor registration and management, semantic interpretation and processing, and sensor searching and filtering in Wireless Sensor Networks (WSNs). Also, as the number of sensors in an IoT environment increases significantly, sensor filtering becomes more important. Many sensor filtering techniques have been researched. However most of them do not consider real-time searching and efficiency of mobile networks. In this paper, we suggest a path prediction approach for effective sensor filtering in Sensor Registry System (SRS). SRS is a sensor platform to register and manage sensor information for sensor filtering. We also propose a method for learning and predicting user paths based on the Collective Behavior Pattern. To improve prediction accuracy, we consider a time feature to measure weights and predict a path. We implement the method and the implementation and its evaluation confirm the improvement of time and accuracy for processing sensor information.

UR - http://www.scopus.com/inward/record.url?scp=84938151131&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84938151131&partnerID=8YFLogxK

U2 - 10.1155/2015/613473

DO - 10.1155/2015/613473

M3 - Article

VL - 2015

JO - International Journal of Distributed Sensor Networks

JF - International Journal of Distributed Sensor Networks

SN - 1550-1329

M1 - 613473

ER -