Sensor Data Management Middleware

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Overview

Middleware are software packages and procedures that reside virtually between data collectors, such as automated sensors, and data ‘consumers’, such as data repositories, websites, or other software applications. Middleware can be used to perform tasks such as streaming data from data loggers to servers, archiving data, analyzing data, or generating visualizations.

Many middleware packages are available for developing a comprehensive, reliable, and cost-effective environmental information management system. Each middleware option can have a unique set of requirements or capabilities, and costs can vary widely. A single middleware package may be used if it includes all of the user requirements, or multiple middleware may be bundled into a data management system if they are compatible or interoperable with each other and the rest of the data collection and management system.

This section describes multiple middleware packages that are currently available, and provides examples of how different software and procedures are being used to collect, analyze, visualize, and disseminate sensor-supplied environmental data.

Introduction

There are multiple factors that may affect the choice, use, and performance of middleware. These factors may be classified according to a group’s research agenda, technological requirements, and personnel skill sets.

Research Agenda: The research agenda of a group is a major determinant of the type of middleware system needed. A group focused on only one or a few narrowly focused research questions may need fewer types of sensors and consequently, fewer software modules may be adequate to streamline data processing from collection to the end goal. A team that investigates multiple questions spanning multiple research domains is likely to use more diverse and/or larger sets of sensors. There may not be a single middleware package that can meet all of the needs of a research group. In this case, multiple packages will need to be linked into a workflow.

Technological Requirements: The technological requirements of a research program may vary from simple to complex. If the research can be done with sensors from a single, well-managed company, the proprietary software packaged with the purchased sensor network may be adequate for at least a major portion of the information management system. For example, for Campbell Scientific dataloggers, their “LoggerNet” software integrates communication, data download, display and graphics functions. However, some dataloggers and sensors (particularly innovative ones, custom-built), may need custom-written software. It is important to plan time and budgets for required software upgrades, licensing, additional packages, support, and maintenance. Systems that cost less in the outset may not always be cheaper over the long run. It is also important to consider how to best meet infrastructure and bandwidth requirements, while deploying middleware on a variety of servers or laptop computers in the field or lab setting. Depending on the data and hardware infrastructure characteristics, each middleware option can introduce benefits or drawbacks to the overall system functionality.

Personnel Skills: Another key factor to consider is the skill set of the personnel. A complex data management system may require multiple people, each with a unique skill set such as database design, system architecture, web programming, etc. It is important to correctly identify each person’s skill set and role in data management tasks. It may also be necessary to plan for additional hires or job-training to addresses various scenarios and solutions, to identify appropriate salaries, and to budget enough time for software development and system administration. More details about the personnel roles and skills can be found in the “Roles and required skill sets“ section.

Methods

Middleware can be classified with respect to the functionality they provide, such as:

Controlling instrumentation and data collection: Modules may be used to control sampling intervals, manage the event-triggered (burst) or continuous sampling regimes, communicate and transfer data between the instrumentation and other system components.

Data monitoring, processing, and analysis: Modules may provide alarm management, perform automated QA/QC on data streams, or run derivative calculations including averages, aggregation and accumulation, data shifting and transformation, filtering of time series records with respect to the dates, value range, location, station/variable type, or other criteria.

Export and publishing of data: Modules may provide functionality to export sensor data to different formats (e.g., ASCII, binary, or xml), different archives, make data discoverable through geospatial catalogues, or publish the data through web services.

Data visualization: Modules may provide visualization (e.g., tables, graphs, sonograms) of geospatial and/or time series data from sensor arrays or workflow structures.

Documentation: Modules may be used to document field events through paperless collection of field data, integrate sensor data and documentation (see sensor tracking & documentation section), or handle sensor calibration records.

Other supported functionality: Modules may be used to provide access to external data (e.g., ODBC, JDBC, OLE DB), to connect or chain other middleware components, or to implement mobile applications.

Middleware can also be classified by software proprietary rights and whether they are considered applications or platforms. Accordingly, we can identify different groups of middleware:

• Proprietary data management applications and platforms
• Proprietary research applications
• Limited open source applications (free packages that can be used with proprietary solutions)
• Open source data management applications and platforms
• Open source research applications and programming languages

Some of the applications and platforms listed above are often identified as a software of choice for many different organizations. More details about each of these components are provided in the next section of this document.

Program	Licensing	Cost	Input data format	Export data format	Needed programming expertise
Antelope Orb	Proprietary	Pay	ASCII, Binary	ASCII, Binary	Advanced
Aquarius	Proprietary	Pay			Advanced
ArcGIS	Proprietary	Pay	ASCII, shapefiles	ASCII, shapefiles	Advanced
B3	Open source	Free	ASCII	ASCII	None to Basic
BigSense and LtSense	Open source	Free	Binary	CSV, JSON, TXT, XML	Advanced
Cosm
CUAHSI HIS	Open source	Free	ASCII	XML, WaterML	Intermediate
DataTurbine	Open source	Free	ASCII, Binary	ASCII, Binary	Advanced
EddyPro	Proprietary	Pay	Binary	ASCII, Binary	Intermediate
GCE Toolbox	Matlab is proprietary, Toolbox is open source	Matlab is pay, Toolbox is free	ASCII, Binary(?), database	ASCII, Binary(?), MAT, database	Intermediate to advanced
Hobolink (Onset)	Proprietary	Free	Proprietary	ASCII, Proprietary	None
Hoboware (Onset)	Proprietary	Pay	Proprietary	ASCII, Proprietary	None
Kepler	Open source	Free	ASCII, Binary	ASCII, Binary	Basic to Advanced
Lake Analyzer	Proprietary/Open source	Free	ASCII	ASCII	Basic
LoggerNet (Campbell)	Proprietary	Pay	Proprietary	ASCII, database	Intermediate
Nexsen's Technology	Proprietary	Pay
Pegasus
R	Open source	Free	ASCII, Binary, database	ASCII, Binary, database	Intermediate to Advanced
SAS	Proprietary	Pay	ASCII, Binary, database	ASCII, Binary, database	Intermediate to Advanced
Taverna	Open source	Free			Intermediate to Advanced
Vista Data Vision	Proprietary	Pay	ASCII	ASCII	Basic?
VizTrails	Open source	Free	ASCII	ASCII	Basic to Advanced
WaterML support
WISKI	Proprietary	Pay	ASCII	ASCII	Advanced
YSI EcoNet	Proprietary	Pay

Program	Licensing	Cost	Input data format	Export data format	Level of expertise
Antelope Orb	Example	Example	Example	Example	Example
Aquarius	Example	Example	Example	Example	Example
ArcGIS	Example	Example	Example	Example	Example
B3	Example	Example	Example	Example	Example
BigSense and LtSense	Example	Example	Example	Example	Example
Cosm	Example	Example	Example	Example	Example
CUAHSI HIS	Example	Example	Example	Example	Example
DataTurbine	Example	Example	Example	Example	Example
EddyPro	Example	Example	Example	Example	Example
GCE Matlab	Example	Example	Example	Example	Example
Onset Hobolink	Example	Example	Example	Example	Example
Onset Hoboware	Example	Example	Example	Example	Example
Kepler	Example	Example	Example	Example	Example
Lake Analyzer	Example	Example	Example	Example	Example
Campbell LoggerNet	Example	Example	Example	Example	Example
Nexsen's Technology	Example	Example	Example	Example	Example
Pegasus	Example	Example	Example	Example	Example
R	Example	Example	Example	Example	Example
SAS	Example	Example	Example	Example	Example
Taverna	Example	Example	Example	Example	Example
Vista Data Vision	Example	Example	Example	Example	Example
VizTrails	Example	Example	Example	Example	Example
WaterML support	Example	Example	Example	Example	Example
WISKI	Example	Example	Example	Example	Example
YSI EcoNet	Example	Example	Example	Example	Example