Sensor Data Management Middleware

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Fig. 1 The position of middleware in a generic sensor data management system.

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.

Middleware Classifications

Classification by functionality

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.

Classification by propriety and type

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.

Best Practices

Choosing the middleware components that will best fit the tasks and work environment can be challenging. In addition to the personnel roles and skills, budget, and infrastructure considerations already discussed in the Introduction and other chapters of this best practices guide, it is important to be aware of the whole sensor management process in order to identify the suitable middleware components. In some cases, a proprietary middleware software will required as part of the information management system if the instrumentation only outputs data in a proprietary format. In other cases, multiple open source software packages may be suitable for chaining into a comprehensive system that manages data from collection to final archiving and sharing.

Some steps in selecting middleware are:

Fig 2. Sensor management workflow. Simple sensor management configuration is presented in blue; optional system components are shown in grey.

1. Identify your objectives. What do you want the middleware to do?
2. Assemble a list of candidate software.
3. Rate the candidates based on capabilities, cost (keeping in mind that a simple-to-use but expensive package may cut costs in the long-term), stability, and ease of use with respect to the personnel skills available on your team.
4. If no single software product can meet all the objectives, test to see how well different candidate software integrate with one another to perform the needed functions.

During this planning stage, consider the following recommendations:

• Identify workflow components and describe their functional requirements from the instrumentation to the archive level of organization (see Figure x). Some components can be optional or part of the more complex solutions.
• Plan for robust execution and choose software and hardware components that can handle the loss of connectivity, power, or other failures related to harsh environmental or operational conditions.
• Choose reusable/sharable components.
• Keep field deployment of middleware as simple as possible (keep out of field if possible).
• Use as few middleware components as possible based on research group requirements.
• Document and diagram the entire workflow and update as needed.

Case Studies

We present several real world case studies in that vary widely in the types of ecosystems that sensors are deployed in and in complexity of the information management system. Some case studies include proprietary software only, some include free or open-source software, and some include both.

Marmot Creek Research Site, Rocky Mountains, Canada

Fig. 3. Marmot Creek research site's PacBus Network with mixed data loggers and Raven to RF401 Base

Introduction Marmot Creek research site is located on the eastern slopes of Rocky Mountains in Alberta, Canada. The site is dominated by the needle leaf vegetation and poorly developed mountain soils. Precipitation, snow depth, soil moisture, soil temperature, short and longwave radiation, air temperature, humidity, wind speed, and turbulent fluxes of heat and water vapour data sets are collected and used for the hydrological modelling of the Marmot Creek Basin. Time series records are obtained at Hay Meadow, Upper Clearing , Vista View, Fisera Ridge, and Centennial Ridge hydro-meteorological stations equipped with different sensor configurations and Campbell Scientific data loggers.

Communication equipment and methods The telemetry network consists of one Raven CDMA cellular modem and RF401 spread spectrum radio modem located at the Upper Clearing base station, four additional RF401 modems located at each of the Meteorological stations serviced by telemetry, and the desktop computer located at the University of Saskatchewan. The radios connected to the data loggers at each of the meteorological stations talk to the base station on an ongoing basis. All of the data loggers and RF401 radios have PacBus addresses and they operate as PacBus Nodes. Also, data loggers are set to operate as routers enabling routing inside this network through the various paths. The telemetry network configuration is presented in Figure 3.

Data collection and processing At the intervals prescribed within the LoggerNet application running on a desktop computer, data is collected from the meteorological stations. The Raven CDMA transfers data utilizing a dynamic IP address and its static alias associated through the Airlink IPmanager software. The unique PakBus address is assigned to each of the dataloggers in this telemetry network. In most cases, logger data files at the off-site location will be appended on a daily, four-hourly and hourly basis. In addition to the scheduled intervals, field data can be downloaded on demand through the LoggerNet application.

LoggerNet “Task Master” utility is used to execute custom programs after each successful collection of the field data. Also, the utility can be used to start scheduled executions of different programs and operations. For Marmot Creek records, Task Master is used to rename the collected data logger files and upload them to the FTP server.

Data publishing Field data downloaded to the off-site computer are accessed by the RTMCPRO LoggerNet utility. Last measured values are mapped to the specified locations on a web page. The web server hosts different RTMC files for daily summary information, station data tables, alarms, and other records. The main interface contains individual windows for the main screen web page as well as the screens for individual stations, weekly data graphs, and site information. RTMC files interface with the web page via the RTMC Web Server desktop utility.

Reference Centre for Hydrology, University of Saskatchewan. University of Saskatchewan Hydrology Field Data Retrieval and Management Manual. 2009. PDF file.

Middleware Package Descriptions

In this section, several middleware packages are described as a basic introduction to the packages. No specific endorsement or criticism is implied, and it is important to keep in mind that software packages are often revised.

Aquatic Informatics Aquarius: AQUARIUS is a software for water time series data management that provides functionality to correct and quality control time series data, build rating curves, transform and visualize the hydrological data, as well as to publish the field data in real-time. To accomplish these tasks, AQUARIUS uses three main components. Data Acquisition System enables accessing the real time data from the field instrumentation either as the extension to the EnviroSCADA or through the hot folders. Aquarius Server is a web controlled data management platform that enables centralized access to the database stored data sets. Also, the server is used to publish the data either through the public web portal or as a Representational State Transfer (REST) web service that supports the WaterML representation of the data. Finally, AQUARIUS Workstation provides a set of data processing tools to import and process the data, create rating curves, and apply QA/QC procedures to the time series records.

Aquarius system components

Aquarius modeling approaches

Campbell Scientific LoggerNet: LoggerNet is the main Campbell Scientific software application used to set up, operate, and manage a sensor network that uses Campbell Scientific equipment. LoggerNet uses serial ports, telephony drivers, and Ethernet hardware to communicate with data loggers via cellular and phone modems, RF devices, and other peripherals. LoggerNet also includes a suite of tools such as text editors for creating Campbell Scientific datalogger programs, and methods for real-time monitoring, automated data retrieval, data post-processing, data visualization and monitoring of retrieved information, and data publishing options. More advanced features, such as export to to MySQL or SQL Server databases, are also offered through additional LoggerNet applications not included in the standard version (LoggerNet Database, LoggerNet Admin, LoggerNet Remote etc.).

Table 1. Basic features

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	Hardware communication	QA/QC capacity	Capacity to stream to archive	Data transformation and analysis	Data visualization	Custom SQL queries/Scripting
Antelope Orb	Yes, customizable	Yes, customizable	Yes, customizable	Yes, customizable	Yes, customizable	Yes
Aquarius	Yes, not customizable		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

Program	Task automation	Multi-tier architecture	Website publishing	Streaming through web service	Support for modeling
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