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RTCSA '05 Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Deploying a Wireless Sensor Network on an Active Volcano
IEEE Internet Computing
Wireless sensor networks for structural health monitoring
Proceedings of the 4th international conference on Embedded networked sensor systems
PIPENETa wireless sensor network for pipeline monitoring
Proceedings of the 6th international conference on Information processing in sensor networks
Event-based applications and enabling technologies
Proceedings of the Third ACM International Conference on Distributed Event-Based Systems
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Within the freight railroad industry, there is currently an industrywide effort to enable intelligent telemetry for freight trains. The conventional wisdom is that greater visibility of the status and proper functioning of trains would enable business transformation in numerous areas, including predictive maintenance, schedule optimization, and asset utilization. However, this effort to derive business value through new technology may also have significant environmental benefits. For example, failure prediction can drastically reduce derailments, thereby protecting the environment from the harmful effects of hazardous material spills. Equally important, better visibility of trains can lead to more efficient scheduling of trains, allowing more trains to move freight. Studies show that trains are three times more fuel efficient than trucks, and transferring just 1% of truck freight to railroads can reduce greenhouse gas emissions by 1.2 million tons annually. In this paper, we describe the potential benefits of employing a new, intelligent telemetry infrastructure for freight railroads. One proposed approach called SEAIT, or Sensor Enabled Ambient Intelligent Telemetry, is a wireless sensor network approach to supporting sensing and communication for advanced freight transportation scenarios. As part of a proof-of-technology exploration, we describe some preliminary performance results of SEAIT.