Authors:
Meimanat Soleimanifar
;
Ming Lu
;
Ioanis Nikolaidis
and
Xuesong Shen
Affiliation:
University of Alberta, Canada
Keyword(s):
Construction Management, Location Aware Computing, Wireless Sensor Networks, Received Signal Strength, Real-time Positioning, Indoor Environments.
Related
Ontology
Subjects/Areas/Topics:
Aggregation, Classification and Tracking
;
Applications and Uses
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Data Mining
;
Databases and Information Systems Integration
;
Decision Support Systems
;
Enterprise Information Systems
;
Environmental Impact Reduction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial and Structural Monitoring
;
Infrastructure Reliability
;
Methodologies and Methods
;
Multi-Sensor Data Processing
;
Network Architecture
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Obstacles
;
Pattern Recognition
;
Physiological Computing Systems
;
Real-Time Constraints
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Software and Architectures
;
Ubiquitous Computing
;
Wireless Information Networks
Abstract:
Timely information of construction resource is always a concern and an essential task for construction engineers and managers. In the recent past, Wireless Sensor Networks (WSNs) have emerged as a promising means to improve the current construction localization applications due to the ease of deployment and expandability to large scale construction projects, low cost, and capacity to function efficiently under dynamic and rough environments. Received Signal Strength Indicator (RSSI) based localization is a popular technique especially for indoor environments, where satellite based positioning is infeasible. This study evaluates multilateration localization, a popular localization technique, in construction environments as well as a second, profiling-based, localization technique. Both techniques RSSI values collected in a WSN. Indoor experiments were conducted and their results reveal that acceptable position accuracy can be obtained with the profiling-based architecture.