Authors:
Tiziana Campana
1
and
Gregory M. P. O'Hare
2
Affiliations:
1
School of Computer Science and Informatics, Ireland
;
2
University College Dublin, Ireland
Keyword(s):
Distributed Monitoring and Debugging, Distributed Intelligent Sensor Node, Connectivity Monitor.
Related
Ontology
Subjects/Areas/Topics:
Aggregation, Classification and Tracking
;
Applications and Uses
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Communication Networking
;
Data Manipulation
;
Fault Detection and Management
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial and Structural Monitoring
;
Methodologies and Methods
;
Network Performance
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Obstacles
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Telecommunications
Abstract:
A diverse range of faults and errors can occur within a wireless sensor network (WSN), and it is difficult to predict and classify them, especially post-deployment within the environment. Current monitoring and debugging techniques prove deficient for systems which contain bugs characteristic of both distributed and embedded systems. The challenge that faces researchers is how to comprehensively address network, node and data level anomalies within WSNs through the creation, collection and aggregation of local state information while minimizing additional network traffic and node energy expenditure. This paper introduces Intellectus which seeks to develop sensor motes that are both self and environment aware. The sensor node relies on local information in order to monitor itself and that of its neighborhood, by adding a learning approach based upon perceived events and their associated frequency.