IoT Data Management



IoT Data Management

Every communication deployment of IoT is unique. However, there are four basic stages that are common to just about every IoT application. Those components are: data collection, data transmission, data assessment, and response to the available information. Successful data management is therefore very important to the success of IoT.

Just as IoT has unique network requirements, it also has unique data management requirements.

Data Management for IoT can be viewed as a two-part system Online/Real-time Front-end (e.g. distributed nodes) and Off-line Back-end (centralized Cloud storage). The Online/real-time portion of the system is concerned with data management associated with distributed objects/assets/devices and their associated sensors. As discussed in Mind Commerce reports, there are issues pertaining to the need for “fast data” and distributed intelligence to deal with this data.

The Front-end also passes data (in the form of proactive push and responses to queries) results from the objects/devices/sensors to the Back-end. The frequent communication between Frontend and Backend is termed as Online. The Back-end is storage-intensive; storing select data produced from disparate sources and also supports in-depth queries and analysis over the long-term as well as data archival needs.

Data Integration represents another challenge. Data from different sources (sensors, contextual data, social media feeds, etc.) must be put into context. Unless the semantics for data are in the data itself (as opposed to being a part of the application), data integration can pose a substantial problem. This is an opportunity for Big Data technologies and next generation Analytics to solve.

More Information

Mobile cellular operators are making plans for Mobile Edge Computing (MEC), which enables cloud computing capabilities and an IT service environment at the edge of the cellular network. As part of the Mind Commerce “Computing at the Edge” series, Fog Computing and Data Management issues and challenges are introduced in this document.

Fog Computing and Data Management

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