No abstract

Wireless smart sensor networks (WSSNs) have been proposed by a number of researchers to evaluate the current condition of civil infrastructure, offering improved understanding of dynamic response through dense instrumentation. As focus moves from laboratory testing to full-scale implementation, the need for multi-hop communication to address issues associated with the large size of civil infrastructure and their limited radio power has become apparent. Multi-hop communication protocols allow sensors to cooperate to reliably deliver data between nodes outside of direct communication range. However, application specific requirements, such as high sampling rates, vast amounts of data to be collected, precise internodal synchronization, and reliable communication, are quite challenging to achieve with generic multi-hop communication protocols. This paper proposes two complementary reliable multi-hop communication solutions for monitoring of civil infrastructure. In the first approach, termed herein General Purpose Multi-hop (GPMH), the wide variety of communication patterns involved in structural health monitoring, particularly in decentralized implementations, are acknowledged to develop a flexible and adaptable any-to-any communication protocol. In the second approach, termed herein Single-Sink Multi-hop (SSMH), an efficient many-to-one protocol utilizing all available RF channels is designed to minimize the time required to collect the large amounts of data generated by dense arrays of sensor nodes. Both protocols adopt the Ad-hoc On-demand Distance Vector (AODV) routing protocol, which provides any-to-any routing and multi-cast capability, and supports a broad range of communication patterns. The proposed implementations refine the routing metric by considering the stability of links, exclude functionality unnecessary in mostly-static WSSNs, and integrate a reliable communication layer with the AODV protocol. These customizations have resulted in robust realizations of multi-hop reliable communication that meet the demands of structural health monitoring.

No abstract

Quantitative monitoring of water conditions in a field is a critical ability for environmental science studies. We report the design, fabrication and testing of a low cost, miniaturized and sensitive electrochemical based nitrate sensor for quantitative determination of nitrate concentrations in water samples. We have presented detailed analysis for the nitrate detection results using the miniaturized sensor. We have also demonstrated the integration of the sensor to a wireless network and carried out field water testing using the sensor. We envision that the field implementation of the wireless water sensor network will enable "smart farming" and "smart environmental monitoring".

Troubleshooting unresponsive sensor nodes is a significant challenge in remote sensor network deployments. This paper introduces the tele-diagnostic powertracer, an in-situ troubleshooting tool that uses external power measurements to determine the internal health condition of an unresponsive host and the most likely cause of its failure. We developed our own low-cost power meter with low-bandwidth radio to report power measurements and findings, hence allowing remote (i.e., tele-) diagnosis. The tool was deployed and tested in a remote solar-powered sensing network for acoustic and visual environmental monitoring. It was shown to successfully distinguish between several categories of failures that cause unresponsive behavior including energy depletion, antenna damage, radio disconnection, system crashes, and anomalous reboots. It was also able to determine the internal health conditions of an unresponsive node, such as the presence or absence of sensing and data storage activities (for each of multiple sensors). The paper explores the feasibility of building such a remote diagnostic tool from the standpoint of economy, scale and diagnostic accuracy. To the authors' knowledge, this is the first paper that presents a remote diagnostic tool that uses power measurements to diagnose sensor system failures.