Motion monitoring forms a part of condition based maintenance. These include structural monitoring (monitoring bridges, buildings, manufacturing plant, etc.), medical maintenance and urban terrain mapping. The structures such as motor, buildings, and bridges are prone to wear and fatigue during their lifetime and have typical mode of vibrations, acoustic emissions especially during wear and tear.
WSN can continuously monitor these structural environments and report if there is any anomaly in the working of these structures. For instance, sensor networks can be used to monitor the structural changes of a bridge. The sensors are embedded beneath the road surface of a bridge and are powered by the piezoelectric crystals. When heavy vehicles move along the surface, the sensor nodes are powered.
Let us take another example. In a large semi-conductor manufacturing plants, there are thousands of routine machinery. For efficient plant operation, the machines need to operate smoothly. Any flaw in a particular machine may halt the production process. A team of electricians carry a computing device which attaches itself to the sensors in various machines and logs the data on to a central computer. The team would then analyze the data and check for signs for wear and tear. Months elapse between visits to a particular machine.
WSN offers a better approach- monitor the machinery through sensors, perform local data processing at each device and transmit the data to the operations staff. What distinguishes this operation from environment monitoring is the fact that in motion monitoring, the no of samples are extremely large-100 Hz for vibration analysis and several kilo Hz for acoustic analysis. Such a scenario poses several constraints on WSN. At this high sample rate, data processing needs to be done at faster rate demanding more power and buffer storage. Rather than transmitting the raw data, the sensor nodes can perform signal analysis and transmit only detected anomalies in the system. Reducing the cost of obtaining and processing data reduces the overall cost.
Also, the WSN for structural monitoring must operate within a time frame, i.e. they need to gather time correlated data. Nodes need to share the data or processed data at a correlated time. In structural analysis the output data from one sensor node can be used as input to the sensor nodes that are operating at critical points in the structure and thereby evaluating the overall performance of the structure as a whole.
