Intelligent Sensing in Multiagent-Based Wireless Sensor Network for Bridge Condition Monitoring System

This paper proposes the development of an autonomous system for dynamic response-based bridge condition assessment using wireless sensor network (WSN). The assessment identifies the bridge's fundamental frequency and uses the information to determine the bridge rating. Due to the computational capability in wireless sensor nodes, it is of practical interest to implement in-network processing in bridge condition monitoring system, in which data processing is conducted within the sensor networks to prevent data flooding in WSN. One of the promising in-network processing approaches is the agent-based processing that leverages the concept of system autonomy. However, uncontrolled in-network processing consumes a lot of energy. Thus, setting all sensors to wake up or sleep deterministically is often not a feasible solution. What is needed is for the system to perform in-network processing only in the event when the bridge is traversed by a single heavy truck, whereas this event occurs randomly. Thus, the two-player game and reinforcement learning algorithm are utilized to control the process. Simulation results show that the proposed control algorithm is able to effectively determine when the process should be executed. A case study, testing the algorithm using real measurements taken from a bridge, and then comparing the test results with the results generated from finite element analysis is provided for validation purpose. Comparison of the proposed approach with earlier works, in terms of processing time and energy consumption, is also presented.