A cognitive WSN framework for highway safety based on weighted cognitive maps and Q-learning

Authors:
Amr El Mougy;Mohamed Ibnkahla
Affiliations:
Queen's University, Kingston, ON, Canada;Queen's University, Kingston, ON, Canada
Venue:
Proceedings of the second ACM international symposium on Design and analysis of intelligent vehicular networks and applications
Year:
2012

Citing 16
Cited 1

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Wireless sensor networks: a survey

Computer Networks: The International Journal of Computer and Telecommunications Networking
The Impact of Data Aggregation in Wireless Sensor Networks

ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
Reliable Energy Aware Routing In Wireless Sensor Networks

DSSNS '06 Proceedings of the Second IEEE Workshop on Dependability and Security in Sensor Networks and Systems
Wireless sensor network survey

Computer Networks: The International Journal of Computer and Telecommunications Networking
Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps

Information Sciences: an International Journal
On the Planning of Wireless Sensor Networks: Energy-Efficient Clustering under the Joint Routing and Coverage Constraint

IEEE Transactions on Mobile Computing
Transformation of cognitive maps

IEEE Transactions on Fuzzy Systems
Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications

Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications
Hybrid genetic algorithm using a forward encoding scheme for lifetime maximization of wireless sensor networks

IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Symphony: synchronous two-phase rate and power control in 802.11 WLANs

IEEE/ACM Transactions on Networking (TON)
Virtual worlds as fuzzy cognitive maps

VRAIS '93 Proceedings of the 1993 IEEE Virtual Reality Annual International Symposium
Cognitive wireless sensor networks for highway safety

Proceedings of the first ACM international symposium on Design and analysis of intelligent vehicular networks and applications
Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety

IEEE Communications Magazine
Wireless Sensor Networks: A Cognitive Perspective

Wireless Sensor Networks: A Cognitive Perspective
Achieving end-to-end goals of WSN using Weighted Cognitive Maps

LCN '12 Proceedings of the 2012 IEEE 37th Conference on Local Computer Networks (LCN 2012)

A cognitive framework for WSN based on weighted cognitive maps and Q-learning

Ad Hoc Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Wireless technology can provide new efficient techniques for improving highway safety. Wireless Sensor Networks (WSN) have been identified as a key enabling technology for monitoring road conditions and providing early warning messages to drivers about any dangers that may be present. In order to ensure that the end-to-end goals of such a WSN are achieved, this paper proposes a cognitive framework based on the mathematical tools known as Weighted Cognitive Maps (WCM) and Q-learning. WCM is used to design a reasoning machine that can consider multiple conflicting constraints with low complexity. On the other hand, the Q-learning algorithm is used to design a learning protocol that can build a knowledge base which enables the WCM system to make more informed decisions. Thus, a reward system is developed that directly addresses the end-to-end goals of the system. The performance of the cognitive framework is evaluated using extensive computer simulations and compared to state of the art systems. Simulation results show significant performance improvements with the proposed cognitive framework.