Data Storage in IoT: The Full Guide From Basics to Practical Implementations
Kevin Ashton, who coined the ‘Internet of Things,’ envisioned devices that could communicate with the web independently. For businesses, this underscored the urgent need for robust data storage solutions to process the vast volumes of data that would come.
The fundamental part of the Internet of Things is the data. Statista expected the IoT infrastructure to generate almost 80 zettabytes of data by 2025 — a staggering figure considering that 1 zettabyte equals 10⁹ terabytes.
This big data from IoT grants a truly unique opportunity for businesses to dive deep into processes, customer behavior, machinery health, and more, revealing trends and correlations that were previously invisible. Yet, it poses a daunting challenge:
In this article, I aim to tackle this question from a practical perspective by providing methods, techniques, tools, and theories about data management and storage. All info comes from a developer’s hands-on experience working with big data & IoT development services.
The last section is devoted to the real-life applications of the information from this article across several industries: manufacturing, healthcare, and smart cities.
Introduction to IoT and Data Generation
First, let’s clarify what is so special about IoT that it generates enormous volumes of data.
The Internet of Things (IoT) represents a vast network of millions of interconnected devices that communicate and exchange data with each other and the web. This network extends beyond standard computing devices to include a wide array of sensors, appliances, vehicles, and machinery — each capable of collecting and transmitting data autonomously.
Statista said the number of mobile devices operating worldwide stood at almost 15 billion. Considering every device’s capacity to generate data, the calculation to grasp the sheer volume of data becomes straightforward yet staggering.
Different organizations are looking for a solution to handle the growing amount of data and IoT devices. The US institute OSTI published a well-conceived technical report in 2022, offering its ways to do so.
To better understand this issue, I want to examine two topics:
- Data types IoT generates. This chapter is necessary to understand the diverse nature and shape of information the data storage in IoT must deal with.
- Challenges of handling IoT data. Here, I’ll designate the challenges that derive from the nature of data in IoT.
Data Is the Lifeblood of IoT
It is the data that the Internet of Things offers. There are 3 types of data generated by IoT devices:
Data TypeParametersGoalsSubcategoriesSensor DataTemperature, humidity, pressure, motion, light, air qualityMonitor conditions for decision-making, automation, optimizationEnvironmental conditions, Health/biometric information, Machine telemetryOperational DataDevice uptime, error logs, battery levels, network statusOptimize operations, improve maintenance, ensure reliabilityGeolocation, Machine performance, Network and securityUser DataUser settings, activity logs, interaction patternsEnhance user experience, personalize services, support marketingTransactional information, Preferences and behavior, Audio and visual data
Sensor Data
Sensor data is derived from the measurements collected by sensors integrated into IoT devices. These sensors capture various environmental and operational conditions in real-time.
- Example parameters: temperature, humidity, pressure, motion, light levels, and air quality.
- Goals: the primary goal of collecting sensor data is to monitor and analyze physical conditions for decision-making, automation, and optimization processes. It’s crucial for maintaining system integrity, ensuring safety, and enhancing operational efficiency.
Subcategories:
- Environmental conditions include data related to air quality, light, and weather conditions.
- Health and biometric information like heart rate, blood pressure, and sleep patterns are especially relevant in wearables and healthcare devices.
- Machine telemetry that captures vibrations, temperature, and energy consumption within industrial and manufacturing IoT applications.
Operational Data
Operational data encompasses information related to the functioning and performance of IoT devices and systems. This category includes data on device status, operational efficiency, and network health.
- Example parameters: Device uptime, error logs, battery levels, network status, and transactional records.
- Goals: The data is used to optimize operations, improve device maintenance, enhance system reliability, and streamline business processes. It supports predictive maintenance, operational decision-making, and resource management.
Subcategories:
- Geolocation data is actively used in logistics to track the movement and location of devices, goods, and vehicles.
- Machine performance includes detailed metrics on equipment efficiency and faults.
- Network and security data is used to track network activity and security incidents, as well as authentication logs to safeguard data integrity and network security.
User Data
User data is generated through interactions between the user and the IoT device or application. This category captures preferences, behavior, and engagement metrics, providing insights into how users interact with devices and services.
- Example parameters: User settings, activity logs, interaction patterns, and audio-visual inputs.
- Goals: The primary goal is to enhance user experience, personalize services, and improve product offerings. User data analysis supports targeted marketing, service customization, and user engagement strategies.
Subcategories:
- Transactional information, like purchase data, inventory levels, and shipping status, is the key to retail and eCommerce.
- Preferences and behavior. It’s a broad category that encompasses various insights into user settings and usage patterns.
- Audio and visual data, which is unstructured data from devices like security cameras and voice assistants, is used for security and interaction analysis.
Challenges in Handling IoT Data
In the complex landscape of the Internet of Things (IoT), three critical questions emerge, each tied to a fundamental challenge in IoT data management:
1. How to Deal with a Vast Volume of Zettabytes of Data?
The exponential growth in IoT devices has led to an unprecedented surge in data production, with projections indicating the annual generation of zettabytes of data. This volume exceeds the capacity of traditional data storage and management systems, presenting a significant challenge for businesses.
Effective strategies must be developed to store, access, and analyze this vast data efficiently, ensuring businesses can leverage this information to drive decision-making and innovation.
2. How to Process and Analyze Data in Real-Time?
IoT devices operate in real-time, generating continuous data streams that offer valuable insights into operations, customer behavior, and environmental conditions.
The challenge lies in swiftly capturing, processing, and analyzing this data to inform timely decisions. Solutions must accommodate high-velocity data and provide actionable intelligence at the speed of business.
3. How to Manage the Diversity of Data Types?
The data generated by IoT devices encompasses a broad spectrum, from structured numerical data to unstructured text and images. This variety adds complexity to data management efforts, as each data type requires different processing, storage, and analysis techniques.
Data Storage in IoT As the Primary Data Management Tool
There are two broad categories of data storage in IoT:
- on-device storage;
- cloud storage.
Each serves distinct roles, from offering immediate, local data access to providing scalable, remotely accessible storage capacities. This section delves into these primary storage types, setting the stage for a deeper understanding of their respective subcategories and how they cater to the diverse needs of IoT data management.
On-Device Storage
On-device data storage in IoT refers to storing data directly on the device or a local network. This approach can include anything from using simple onboard flash memory to more sophisticated storage solutions like embedded SSDs or external hard drives connected to the device.
Advantages:
- Low latency. Direct access to data on the device reduces latency, making it ideal for real-time processing and decision-making.
- Operational without Internet. Functions independently of internet connectivity, ensuring that operations can continue even in disconnected environments.
- Data sovereignty. Data remains physically close to the device, which can be crucial for compliance with data residency and privacy regulations.
Disadvantages:
- Limited capacity. Storage capacity is inherently limited by the device’s physical size and cost considerations, which may not be suitable for applications generating large amounts of data.
- Maintenance and security. Requires regular maintenance and robust security measures at the device level to protect against data breaches and physical tampering.
Cloud Storage
Cloud data storage in IoT involves sending data from IoT devices to remote servers in data centers, where it is stored, managed, and processed. This can be facilitated through public, private, or hybrid cloud infrastructures provided by various service providers.
Advantages:
- Scalability. Easily scales to accommodate large volumes of data, allowing storage capacity to be adjusted based on current needs without significant upfront investment.
- Accessibility. Data can be accessed, analyzed, and managed from anywhere worldwide, provided there is internet connectivity, facilitating remote monitoring and management.
- Cost-effectiveness. Offers a pay-as-you-go model, which can be more cost-effective than maintaining physical storage infrastructure, especially for small to medium-sized enterprises.
Disadvantages:
- Latency. Depending on the network and the physical distance to the cloud servers, there can be higher latency than on-device storage, which might be problematic for real-time applications.
- Internet dependency. Requires a reliable internet connection to access the data, which could be a limitation in areas with poor connectivity.
- Security and privacy concerns. Storing data off-site introduces potential security and privacy risks, necessitating trust in the cloud provider’s ability to protect the data and ensure compliance with relevant regulations.
Their subcategories are as follows:
Finally, we are approaching the core question: how to use data storage in IoT to process, sort, and analyze the data effectively. This question is part of a more global question:
Let’s answer it.
Data Management in IoT: Lifecycle, Core Principle, Techniques
Data management might sound like a broad term, but it’s incredibly hands-on. It covers everything from collecting and validating data to storing, protecting, and processing it. Think of it as the backbone of how information flows and is handled within an organization.
Let’s break this down into three digestible parts to make it even clearer:
- First off, I’ll walk you through the concept of data lifecycle management. This will help us understand where data storage in IoT fits into the bigger picture.
- Next, we’ll dive into two fundamental data management principles and practical ways to bring them to life.
- Lastly, I’ll outline some strategies to supercharge data management.
Part 1/3: Data Lifecycle Management
Data lifecycle management (DLM) refers to the processes involved in managing an organization’s data flow throughout its lifecycle, from initial creation and collection to the eventual deletion or archival. Effective DLM ensures that data is managed securely, efficiently, and in compliance with relevant regulations and policies.
Here’s an overview of the stages of data lifecycle management, from collection to deletion:
1. Data Collection
The process of gathering data from various sources. The data could derive from:
- IoT devices;
- user interactions with websites and applications;
- business transactions records;
- social media and online content;
- external databases and APIs.
Considerations: implementing validation checks and data cleansing processes at the point of collection ensures the reliability of the data. Poor quality data can lead to incorrect conclusions and decisions.
2. Data Processing and Storage
During this step, the IoT system transforms raw data into actionable insights and stores it. It happens systematically: cleaning errors and inconsistencies for accuracy, integrating diverse datasets for a unified view, transforming data for analysis readiness, and applying statistical or machine learning techniques to uncover patterns and trends.
Considerations: implementing secure storage solutions, whether on-premises or in the cloud, and organizing data for optimal access and analysis. Ensuring the optimal combination of storage types for a particular application.
3. Data Usage
The data usage step in the data lifecycle involves leveraging the processed and analyzed data to serve a variety of objectives, including but not limited to informing business strategies, enhancing decision-making processes, generating reports, and powering applications. This step is where the value of data is actualized, influencing actions and outcomes across different facets of an organization.
Considerations: ensuring data is used and stored ethically, responsibly, and in accordance with user consent and regulatory requirements. It includes privacy laws and regulations like GDPR in the European Union, industry-specific regulations like HIPAA in the United States, or ethical considerations such as the Menio report, the Fair Information Practice Principles, etc.
4. Data Sharing and Distribution
Involves sharing data with internal teams or external partners while maintaining data security and privacy. The most common ways of sharing data:
- through Application Program Interfaces (APIs);
- through cloud-based platforms that connect many people across different departments;
- through Secure File Transfer Protocols (SFTP);
- using blockchain networks that allow sharing blocks within the network;
- using data anonymization when special tools remove the personal identifier from datasets.
Considerations: managing access controls, encryption, and secure transmission methods is necessary here.
5. Data Archiving:
The process of moving data that is no longer actively used to separate storage for long-term retention and can be accessed if needed. The archived data is valuable for future reference, compliance, or historical analysis. For example, financial records may need to be kept for at least seven years for audit purposes.
Considerations: not all data is worth archiving. Decisions on which data to archive should be based on regulatory requirements, data’s future utility, historical value, and business needs.
6. Data Deletion
The final stage involves securely deleting data that is no longer required or has reached the end of its retention period, ensuring it cannot be recovered.
Considerations: Implementing data deletion policies that comply with legal and regulatory requirements, ensuring the permanent removal of data to protect privacy and reduce storage costs.
Effectively established data lifecycle management helps organizations manage their data assets responsibly, optimize data usage, and mitigate risks associated with data breaches, legal non-compliance, and inefficient data management.
Part 2/3: A Core Principle of Data Management
There is a core underlying principles that every IoT ecosystem must adhere to:
Data integrity refers to data’s accuracy, consistency, and reliability throughout its lifecycle. It ensures that data remains unaltered, authentic, and complete from the moment it is created, during its storage and use, to its eventual archiving or deletion.
- Accuracy means that the data correctly reflects the real-world values or events it is supposed to represent. Accurate data is error-free and precisely matches the intended input or source data.
- Consistency refers to uniform and coherent data across different datasets, databases, or applications over time. It means that the data remains unaltered across its lifecycle unless by authorized and intended modification, ensuring that it does not contradict itself or present discrepancies when accessed from different points.
- Reliability in the context of data integrity implies that data is dependable and can be trusted to serve its purpose in decision-making, operations, and planning. Reliable data is available when needed and maintains its integrity over time, providing a stable foundation for analysis and actions.
Data integrity is crucial for maintaining the trustworthiness of data in decision-making processes, regulatory compliance, and safeguarding against corruption, unauthorized access, and operational errors. It encompasses measures and practices to prevent accidental or malicious modifications, ensuring that information is correct and accessible only to authorized users.
Methods allowing data integrity in IoT systems include:
- Data validation and sanitization: input validation, data sanitization.
- Data quality management: ongoing data cleansing and regular quality checks.
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