IoT Data Management

GUPTA, Gagan       Posted by GUPTA, Gagan
      Published: December 5, 2021

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The Rise of IoT...

  Of all the many and varied changes in technology that have emerged over the past decade, the Internet of Things (IoT) is potentially one having biggest impact on society. The IoT devices shall rise to a whopping 75.44 billion by 2025. That's roughly 10 IoT devices per living human on planet ! Not only are these devices all connected to the internet, but many of them will also be talking to each other. The sheer volume of data that will come from all these billions of devices is one factor that needs addressing for organizations hoping to make sense of it, but also the actual relevance of that data.

  For consumers this offers a number of benefits - ever more personalized products, a deeper insight into health and fitness, greater convenience and ultimately a much better user experience. For businesses, it means yet more data - much more data, much much more data, estimated to be around 175 ZB by 2025.

  Managing all of this IoT data is set to be a major task for many businesses and the rise and growth of IoT data comes with a number of associated challenges. Many organizations will lack the architectures, policies and technologies that address the full data life cycle. Current approaches and infrastructures will need to be overhauled and / or scaled in order to get the most from IoT data.

  There is also the question of immediacy with IoT. Data is generated so quickly and has such a short shelf-life, storage becomes a problem. IoT is dependent on fast data and immediate insight, and connecting a wide range of devices can make real-time processing and analysis that much harder.

  With GDPR and similar data privacy laws from many leading countries; organizations need to demonstrate that they take appropriate care with every single piece of data coming into the business pipeline. Non-compliance penalties are severe. Unfortunately, the solutions to manage and utilize the massive volume of data produced by these things are yet to mature.

  The vision that the IoT should strive to achieve is to provide a standard platform for developing cooperative services and applications that harness the collective power of resources available through the individual Things and any subsystems designed to manage the aforementioned Things. A comprehensive management framework of data that is generated and stored by the objects within IoT is thus needed to achieve this goal.

In the context of IoT, data management should act as a layer between the objects and devices generating the data and the applications accessing the data for analysis purposes and services.

IoT data has distinctive characteristics that make traditional relational-based database management an obsolete solution. A massive volume of heterogeneous, streaming and geographically-dispersed real-time data will be created by billions of diverse devices periodically sending observations about certain monitored phenomena or reporting the occurrence of certain or abnormal events of interest. Communication, storage and process will be defining factors in the design of data management solutions for IoT.

5 must-have capabilities for IoT data management

Managing data from IoT devices is an important aspect of a real-time analytics journey. To be sure your data management solution can handle IoT data demands, look for these five key capabilities:
1. Versatile connectivity and ability to handle data variety: IoT systems have a variety of standards and IoT data adheres to a wide range of protocols (MQTT, OPC, AMQP, and so on). Also, most IoT data exists in semi-structured or unstructured formats. Therefore, your data management system must be able to connect to all of those systems and adhere to the various protocols so you can ingest data from those systems. It is equally important that the solution support both structured and unstructured data.
2.Edge processing and enrichments: A good data management solution will be able to filter out erroneous records coming from the IoT systems such as negative temperature readings - before ingesting it into the data lake. It should also be able to enrich the data with metadata (such as timestamp or static text) to support better analytics.
3.Big data processing and machine learning: Because IoT data comes in very large volumes, performing real-time analytics requires the ability to run enrichments and ingestion in sub-second latency so that the data is ready to be consumed in real time. Also, many customers want to operationalize ML models such as anomaly detection in real time so that they can take preventive steps before it is too late.
4.Address data drift: Data coming from IoT systems can change over time due to events such as firmware upgrades. This is called data drift or schema drift. It is important that your data management solution can automatically address data drift without interrupting the data management process.
5.Real-time monitoring and alerting: IoT data ingestion and processing never stops. Therefore, your data management solution should provide real-time monitoring with flow visualizations to show the status of the process at any time with respect to performance and throughput. The data management solution should also provide alerts in case any issues arise during the process.

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IoT Data Management
IoT Data Management

IoT Data Lifecycle

The lifecycle of data within an IoT system - illustrated in Figure, proceeds from data production to aggregation, transfer, optional filtering and preprocessing, and finally to storage and archiving. Querying and analysis are the end points that initiate (request) and consume data production, but data production can be set to be "pushed" to the IoT consuming services. Production, collection, aggregation, filtering, and some basic querying and preliminary processing functionalities are considered online, communication-intensive operations. Intensive preprocessing, long-term storage and archival and in-depth processing/analysis are considered offline storage-intensive operations.

Storage operations aim at making data available on the long term for constant access / updates, while archival is concerned with read-only data. Since some IoT systems may generate, process, and store data in-network for real-time and localized services, with no need to propagate this data further up to concentration points in the system, "edges" that combine both processing and storage elements may exist as autonomous units in the cycle. In the following paragraphs, each of the elements in the IoT data lifecycle is explained.

Querying: Data-intensive systems rely on querying as the core process to access and retrieve data. In the context of IoT, a query can be issued either to request real-time data to be collected for temporal monitoring purposes or to retrieve a certain view of the data stored within the system. The first case is typical when a (mostly localized) real-time request for data is needed. The second case represents more globalized views of data and in-depth analysis of trends and patterns.

Production: Data production involves sensing and transfer of data by the "Things" within the IoT framework and reporting this data to interested parties periodically (as in a subscribe/notify model), pushing it up the network to aggregation points and subsequently to database servers, or sending it as a response triggered by queries that request the data from sensors and smart objects. Data is usually time-stamped and possibly geo-stamped, and can be in the form of simple key-value pairs, or it may contain rich audio/image/video content, with varying degrees of complexity in-between.

Collection: The sensors and smart objects within the IoT may store the data for a certain time interval or report it to governing components. Data may be collected at concentration points or gateways within the network where it is further filtered and processed, and possibly fused into compact forms for efficient transmission. Wireless communication technologies such as Zigbee, Wi-Fi and cellular are used by objects to send data to collection points.

Aggregation/Fusion: Transmitting all the raw data out of the network in real-time is often prohibitively expensive given the increasing data streaming rates and the limited bandwidth. Aggregation and fusion techniques deploy summarization and merging operations in real-time to compress the volume of data to be stored and transmitted.

Delivery: As data is filtered, aggregated, and possibly processed either at the concentration points or at the autonomous virtual units within the IoT, the results of these processes may need to be sent further up the system, either as final responses, or for storage and in-depth analysis. Wired or wireless broadband communications may be used there to transfer data to permanent data stores.

Preprocessing: IoT data will come from different sources with varying formats and structures. Data may need to be preprocessed to handle missing data, remove redundancies and integrate data from different sources into a unified schema before being committed to storage. This preprocessing is a known procedure in data mining called data cleaning. Schema integration does not imply brute-force fitting of all the data into a fixed relational (tables) schema, but rather a more abstract definition of a consistent way to access the data without having to customize access for each source's data format(s). Probabilities at different levels in the schema may be added at this phase to IoT data items in order to handle uncertainty that may be present in data or to deal with the lack of trust that may exist in data sources.

Storage/Update-Archiving: This phase handles the efficient storage and organization of data as well as the continuous update of data with new information as it becomes available. Archiving refers to the offline long-term storage of data that is not immediately needed for the system's ongoing operations. The core of centralized storage is the deployment of storage structures that adapt to the various data types and the frequency of data capture. Relational database management systems are a popular choice that involves the organization of data into a table schema with predefined interrelationships and metadata for efficient retrieval at later stages. NoSQL key-value stores are gaining popularity as storage technologies for their support of big data storage with no reliance on relational schema or strong consistency requirements typical of relational database systems. Storage can also be decentralized for autonomous IoT systems, where data is kept at the objects that generate it and is not sent up the system. However, due to the limited capabilities of such objects, storage capacity remains limited in comparison to the centralized storage model.

Processing/Analysis: This phase involves the ongoing retrieval and analysis operations performed and stored and archived data in order to gain insights into historical data and predict future trends, or to detect abnormalities in the data that may trigger further investigation or action. Task-specific preprocessing may be needed to filter and clean data before meaningful operations take place. When an IoT subsystem is autonomous and does not require permanent storage of its data, but rather keeps the processing and storage in the network, then in-network processing may be performed in response to real-time or localized queries.

Looking back at Figure, the flow of data may take one of three paths:

1. a path for autonomous systems within the IoT that proceeds from query to production to in-network processing and then delivery,
2. a path that starts from production and proceeds to collection and filtering / aggregation / fusion and ends with data delivery to initiating (possibly global or near real-time) queries, and finally
3. a path that extends the production to aggregation further and includes preprocessing, permanent data storage and archival, and in-depth processing and analysis.

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Current challenges to IoT data management

Data volume. To face future challenges related to large-scale IoT, organizations need an optimized storage infrastructure for the constantly growing inflows of Big Data.
Time sensitivity real-time vs. batch processing. Incoming IoT device data has to be (re)organized at the storage facility in real time. The current alternative to this approach is batch processing, which brings its own challenges.
Heterogeneity (no data structures standards). You can harvest and stream data with the help of different protocols and standards.
Data flow controls. Keeping track of data transformations is essential if you want to achieve transparency and a clean data flow. You can deal with this task using dynamic SQL, metadata logging, or graphical pipe representations.
Metadata management. Network health and streaming optimization also have to be addressed. Keeping track of the properties of data sources such as machines, the factory environment, or device data is also important.
Data quality, transform for usability. Missing data at the storage facility continues to be an issue today. To remedy this, a maximally transparent process should be implemented, and quality management must be automated. This requires a combination of metadata management and data flow controls.
Creating large data histories. You need to keep track of time series and tags corresponding to data processes. Automating historization and versioning should be a standard.
Data auditability. In many cases, data has a business value or is collected to solve a problem. It is easier to deal with the storage mechanism if you already have a predictive model motivated by a business question.
But this is only one part of the problem. Organizations need to be compliant with national rules and regulations on securing data. One major regulation, the General Data Protection Regulation (GDPR), enforced since May of 2018, potentially leverages substantial fines for non-compliance.


While edge computing, Data Governance, and Metadata Management will help firms deal with scalability and agility, security, and usability, this provides only a start.
Often, companies experiment with their IoT strategy before they launch full-scale efforts. A proof of concept (POC) is a low-cost, low-risk approach that can help you refine your strategy. However, many enterprises that have implemented a successful IoT POC or pilot study are surprised as they shift into production model. Difficulties arise the project scales up. The data streaming from a few connected devices may be manageable, storage can become an issue as more devices come online.
It isn't feasible to simply hang on to all the data generated by your connected devices indefinitely. For one thing, storage costs would soon spiral out of control. For another, there's little point in capturing data just for the sake of having it.
If you really want your data to work for you - to help you identify patterns, trends and areas for improvement - you have to understand how to manage that data. There are no standard solutions here, definitely not any easy ones. You need a solution that enables you to optimize data storage, and I recommend finding a partner with a multi-tier data approach.

At Vyom Data Sciences, we can help you build and accomplish your IoT strategy or approach that suits your business requirements and your company's objectives. If you want to see how we can assist in your IoT dreams, schedule an appointment with one of our IoT experts today.

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