From sensor networks to behaviour profiling: a homecare perspective of intelligent buildings

iPower: an energy conservation system for intelligent buildings by wireless sensor networks

International Journal of Sensor Networks ◽

10.1504/ijsnet.2009.023311 ◽

2009 ◽

Vol 5 (1) ◽

pp. 1 ◽

Cited By ~ 37

Author(s):

Lun Wu Yeh ◽

You Chiun Wang ◽

Yu Chee Tseng

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Energy Conservation ◽

Wireless Sensor ◽

Intelligent Buildings

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A Study on Sensor Networks and its Research in Recent Application Developments

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.34.18707 ◽

2018 ◽

Vol 7 (3.34) ◽

pp. 15

Author(s):

K Immanuvel Arokia James ◽

R Prabu ◽

A Mary Judith ◽

L Gladis Flower

Keyword(s):

Sensor Networks ◽

Traffic Management ◽

Cluster Head ◽

Research Area ◽

Energy Utilization ◽

Quality Level ◽

Intelligent Buildings ◽

Storage Cluster ◽

Recent Developments ◽

Hospital Transport

In recent days Wireless Sensor Networks and Internet of Things have become a growing and challenging research area. Those are used in various hard and sophisticated real time environments. A lot of challenges have to be faced by the researchers in these areas to meet the features like the quality level of sensed data, nodes autonomy, less energy utilization, battery storage, cluster range with cluster head selection and size of nodes…etc. In this paper, We did an extensive analysis on their recent developments in various application areas such as intelligent buildings, smart homes, Smart city developments, healthcare and smart hospital, transport and traffic management, Horticulture, water resources and quality monitoring, smart grid, space research…etc. This analysis will be helpful for the fresh researchers for doing research in WSN and IoT. The researchers have to look in identifying better solutions to the above said challenges must meet.

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An integrated framework for anomaly detection in big data of medical wireless sensors

Modern Physics Letters B ◽

10.1142/s0217984918502834 ◽

2018 ◽

Vol 32 (24) ◽

pp. 1850283 ◽

Cited By ~ 3

Author(s):

Bharti Saneja ◽

Rinkle Rani

Keyword(s):

Big Data ◽

Sensor Networks ◽

Anomaly Detection ◽

Precision Agriculture ◽

Classification Accuracy ◽

Wireless Sensors ◽

Sensor Data ◽

Unified Framework ◽

Hadoop Mapreduce ◽

Intelligent Buildings

Wireless sensor networks (WSNs) are ubiquitous nowadays and have applications in variety of domains such as machine surveillance, precision agriculture, intelligent buildings, healthcare etc. Detection of anomalous activities in such domains has always been a subject undergoing intense study. As the sensor networks are generating tons of data every second, it becomes a challenging task to detect anomalous events accurately from this large amount of data. Most of the existing techniques for anomaly detection are not scalable to big data. Also, sometimes accuracy might get compromised while dealing with such a large amount of data. To address these issues in this paper, a unified framework for anomaly detection in big sensor data has been proposed. The proposed framework is based on data compression and Hadoop MapReduce-based parallel fuzzy clustering. The clusters are further refined for better classification accuracy. The modules of the proposed framework are compared with various existing state-of-art algorithms. For experimental analysis, real sensor data of ICU patients has been taken from the physionet library. It is revealed from the comparative analysis that the proposed framework is more time efficient and shows better classification accuracy.

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