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This paper presents the first systematic design of a robust sensing system suited for the challenges presented by soil environments. We describe three soil deployments we have undertaken: in Bangladesh, and in California at the James Reserve and in the San Joaquin River basin. We discuss our experiences and lessons learned in deploying soil sensors. We present data from each deployment and evaluate our techniques for improving the information yield from these systems. Our most notable results include the following: in-situ calibration techniques to postpone labor-intensive and soil disruptive calibration events developed at the James Reserve; achieving a 91% network yield from a Mica2 wireless sensing system without end-to-end reliability in Bangladesh; and the javelin, a new platform that facilitates the deployment, replacement and in-situ calibration of soil sensors, deployed in the San Joaquin River basin. Our techniques to increase information yield have already led to scientifically promising results, including previously unexpected diurnal cycles in various soil chemistry parameters across several deployments.

Rapidly deployable sensor networks are portable, reusable, and can take advantage of a human user in the field attending to the deployment. Unfortunately, even small disruptions or problems in collected data must be addressed quickly, as the overall quantity of data gathered is small relative to longterm deployments. In this paper we describe a procedure for calibration and a system for online fault remediation. Care in the calibration process for ion selective electrodes used for water quality assists interpretation of the data. Scientists will have more confidence in the data obtained from a rapid deployment if in-field users can detect and compensate for problems as they occur. We have designed and implemented a tool for use in the field to detect potential faults and provide actions to remedy or validate the faulty data. In January of 2006 we deployed 48 sensors over a period of 12 days in Bangladesh in order to aid in validating a hypothesis on the mass presence of arsenic in the groundwater. Our system is based on the the approximately 25,000 measurements we collected.

This paper presents the first systematic design of a robust sensing system suited for the challenges presented by soil environments. We describe three soil deployments we have undertaken: in Bangladesh, and in California at the James Reserve and in the San Joaquin River basin. We discuss our experiences and lessons learned in deploying soil sensors. We present data from each deployment and evaluate our techniques for improving the information yield from these systems. Our most notable results include the following: in-situ calibration techniques to postpone labor-intensive and soil disruptive calibration events developed at the James Reserve; achieving a 91% network yield from a Mica2 wireless sensing system without end-to-end reliability in Bangladesh; and the javelin, a new platform that facilitates the deployment, replacement and in-situ calibration of soil sensors, deployed in the San Joaquin River basin. Our techniques to increase information yield have already led to scientifically promising results, including previously unexpected diurnal cycles in various soil chemistry parameters across several deployments.

Rapidly deployable sensor networks are portable, reusable, and can take advantage of a human user in the field attending to the deployment. Unfortunately, even small disruptions or problems in collected data must be addressed quickly, as the overall quantity of data gathered is small relative to longterm deployments. In this paper we describe a procedure for calibration and a system for online fault remediation. Care in the calibration process for ion selective electrodes used for water quality assists interpretation of the data. Scientists will have more confidence in the data obtained from a rapid deployment if in-field users can detect and compensate for problems as they occur. We have designed and implemented a tool for use in the field to detect potential faults and provide actions to remedy or validate the faulty data. In January of 2006 we deployed 48 sensors over a period of 12 days in Bangladesh in order to aid in validating a hypothesis on the mass presence of arsenic in the groundwater. Our system is based on the the approximately 25,000 measurements we collected.

Understanding how a sensor network system works requires running the system, extracting log files, and manually interpreting system metrics. When interpreting system metrics, we often try to correlate behavior over multiple modalities. For example, if a node is exhibiting strange behaviors, the cause may be due to weak battery, geographically bad placement, collision, interference, sensor failure, algorithmic faults, or a combination of the above. This approach of interpreting metrics is adequate for closed systems such as the ones run in simulations, with limited duration. However, for complex sensor network systems that have already been deployed for weeks or even months in the fields, this approach is difficult, laborious, and error-prone. Thus, a suite of tools to help analyze complex sensor network system is desirable. We have implemented Deployment Analysis System (DAS), a centralized data mining suite designed to better understand sensor networks. It supports visualization and deployment-related queries that allow the user to inspect historical system metrics, environmental data, geographical placements, and system status.

EmStar is a software system for developing and deploying wireless sensor networks involving Linux-based platforms. As the wireless sensor network community has attempted to deploy more complex designs---large-scale, long-lived systems that need self-organization and adaptivity---a number of difficult software design issues have arisen. Advances in software design have not kept pace with the capabilities of hardware. This is because designing for an adaptive, efficient, and useful sensor network has turned out to be surprisingly complex and difficult. EmStar is a Linux-based software framework, whose goal is to dramatically reduce this complexity, enabling work to be shared and reused, and simplifying and speeding the design of new sensor network applications.

Understanding how a sensor network system works requires running the system, extracting log files, and manually interpreting system metrics. When interpreting system metrics, we often try to correlate behavior over multiple modalities. For example, if a node is exhibiting strange behaviors, the cause may be due to weak battery, geographically bad placement, collision, interference, sensor failure, algorithmic faults, or a combination of the above. This approach of interpreting metrics is adequate for closed systems such as the ones run in simulations, with limited duration. However, for complex sensor network systems that have already been deployed for weeks or even months in the fields, this approach is difficult, laborious, and error-prone. Thus, a suite of tools to help analyze complex sensor network system is desirable. We have implemented Deployment Analysis System (DAS), a centralized data mining suite designed to better understand sensor networks. It supports visualization and deployment-related queries that allow the user to inspect historical system metrics, environmental data, geographical placements, and system status.

EmStar is a software system for developing and deploying wireless sensor networks involving Linux-based platforms. As the wireless sensor network community has attempted to deploy more complex designs---large-scale, long-lived systems that need self-organization and adaptivity---a number of difficult software design issues have arisen. Advances in software design have not kept pace with the capabilities of hardware. This is because designing for an adaptive, efficient, and useful sensor network has turned out to be surprisingly complex and difficult. EmStar is a Linux-based software framework, whose goal is to dramatically reduce this complexity, enabling work to be shared and reused, and simplifying and speeding the design of new sensor network applications.

Sensor networks have emerged from research on low power, networked, and embedded systems and are now being applied to important scientific issues. Networked Infomechanical Systems, or NIMS, has been developed to exploit infrastructure assisted mobility. With the ability to move and precise knowledge of its own location, NIMS nodes will be able to tackle new areas such as sensing uncertainty, event aware fidelity driven sampling, and active fusion. A brief introduction to each of these fields will be presented. Also included will be a description of the second generation NIMS prototype node currently deployed at the James San Jacinto Mountain Reserve in Idyllwild California and some of the current field biology experiments being undertaken with this technology

Sensor networks have emerged from research on low power, networked, and embedded systems and are now being applied to important scientific issues. Networked Infomechanical Systems, or NIMS, has been developed to exploit infrastructure assisted mobility. With the ability to move and precise knowledge of its own location, NIMS nodes will be able to tackle new areas such as sensing uncertainty, event aware fidelity driven sampling, and active fusion. A brief introduction to each of these fields will be presented. Also included will be a description of the second generation NIMS prototype node currently deployed at the James San Jacinto Mountain Reserve in Idyllwild California and some of the current field biology experiments being undertaken with this technology