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 10^9 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 Type	Parameters	Goals	Subcategories
Sensor Data	Temperature, humidity, pressure, motion, light, air quality	Monitor conditions for decision-making, automation, optimization	Environmental conditions, Health/biometric information, Machine telemetry
Operational Data	Device uptime, error logs, battery levels, network status	Optimize operations, improve maintenance, ensure reliability	Geolocation, Machine performance, Network and security
User Data	User settings, activity logs, interaction patterns	Enhance user experience, personalize services, support marketing	Transactional 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.
• Comprehensive IoT update management. 
• Error detection and correction techniques: algorithms and error-detection codes (e.g., checksums, CRCs) to detect data corruption.
• Access controls: robust authentication measures, role-based access control.
• Audit trails: detailed logs and audit trials of all data access and changes.  
• Data encryption.
• Regular backups.
• Version control: version control systems for critical data and documents. 

Part 3/3: Data Management Techniques

Effective data management means optimized storage efficiency, secure data handling and storage, and quick and reliable access to data. Technologies and strategies like data deduplication, compression, and encryption play critical roles in achieving these objectives. Here’s a brief overview of each:

Data Deduplication

Data deduplication is a technique used to eliminate redundant copies of data, storing only one unique instance of the data and referencing it whenever needed. This process can occur at either the file level (eliminating duplicate files) or the block level (eliminating duplicate data blocks within files).

Data Compression

Data compression reduces the size of data files without losing the original data’s integrity. It can be achieved through various algorithms and applied to different data types, including text, images, and videos. Compression can be categorized into two broad types: 

• Lossless compression is where the original data can be perfectly reconstructed. The algorithms used are Huffman coding, Lemple-Ziv-Welch (LZW), Deflate, and Run-Length Encoding (RLE). 
• Lossy compression is where some data is lost, but the size reduction can be more significant. Famous examples are JPEG, MPEG, MP3, AAC. 

Data Encryption

Encryption is the process of converting data into a coded format to prevent unauthorized access. Only users with the decryption key can access the original data. Encryption can be applied to data at rest (stored data) and in transit (data being transferred over networks).

Incorporating data deduplication, compression, and encryption into data management practices enables organizations to optimize storage use, enhance data security, and ensure efficient data accessibility and compliance with regulatory standards.

Finally, it’s time to combine all the knowledge from this article and examine IoT applications in several business domains. 

Case Studies and Real-world Applications

This section examines three distinct IoT infrastructure cases across healthcare, manufacturing, and smart cities, each demonstrating unique data management challenges and solutions. I’ll also highlight the positive impact of IoT on these businesses. 

In healthcare, remote patient monitoring underscores the necessity for efficient data handling and analysis. 

Manufacturing’s focus on predictive maintenance illustrates the critical role of real-time sensor data processing. 

Meanwhile, smart cities’ traffic management systems highlight integrating diverse data sources to optimize urban flow. 

You will see that every case requires a different type of data storage in IoT. Below is an overview of the cases with a detailed explanation following:

	Healthcare: Remote Patient Monitoring	Manufacturing: Predictive Maintenance	Smart Cities: Traffic Management
Data types	Sensor data; User data	Sensor data; Operational data	Sensor data; Operational data; User data
Main challenge	Volume	Velocity	Variety
Used data storage in IoT	Edge computing (on-device storage); Hybrid cloud storage; Object storage	Time-series databases (object storage, block storage)	Distributed databases (public cloud storage, object storage, file storage)
Used methods for data integrity	Data validation and sanitization; Encryption; Access control; Regular backups	Error detection and correction techniques; Data quality management	Audit trails; Access controls; Data encryption
Data management techniques	Data compression; Data encryption	Data deduplication; Data compression	Data compression; Data encryption; Data deduplication

Healthcare: Remote Patient Monitoring

Challenge: in healthcare, the challenge is to manage vast amounts of data generated by remote patient monitoring devices efficiently. This data includes vital signs, medication adherence, and patient activity levels, critical for personalized care and early detection of health issues.

Solution: a combination of edge computing and cloud storage solutions is often employed. Edge computing devices preprocess patient data locally, performing initial analyses and filtering to identify critical information that needs immediate attention. 

This reduces latency and ensures that doctors receive alerts in real-time. The processed data is then securely transmitted to cloud storage, where further analysis is conducted, and long-term health trends are monitored. This approach is widely applied by remote IoT device monitoring providers and it ensures scalability, data security, and compliance with healthcare regulations.

Impact: enhanced patient care through continuous monitoring, early detection of potential health issues, and personalized treatment plans based on long-term data analysis.

Manufacturing: Predictive Maintenance

Challenge: manufacturing companies need to predict equipment failures before they occur to minimize downtime and maintenance costs. This requires analyzing vast datasets generated by sensors on manufacturing equipment.

Solution: time-series databases are particularly useful for storing and analyzing sensor data over time. These databases can handle the high velocity and volume of data generated by manufacturing equipment, allowing for efficient storage and quick retrieval of historical data. 

Advanced analytics and machine learning models run on this data to predict when equipment might fail, scheduling maintenance only when needed. It’s a complex solution that requires great expertise in both IoT and big data services. 

Impact: reduced downtime and maintenance costs, increased equipment lifespan, and improved overall efficiency in manufacturing operations.

Smart Cities: Traffic Management

Challenge: smart cities aim to optimize traffic flow and reduce congestion. This involves processing data from various sources, including vehicle traffic cameras, sensors, and GPS data.

Solution: distributed databases play a key role in managing the diverse and voluminous data from different sources across the city. These databases can scale to accommodate the influx of real-time data and support the high availability required for critical traffic management applications. 

Data analytics and AI models utilize this data to adjust real-time traffic signals, predict congestion points, and recommend route changes to drivers. There are actually companies that specialize in IoT solutions for smart cities which are usually the only contractors capable of building such solutions 

Impact: improved traffic flow, reduced congestion, and enhanced road safety. Additionally, data collected over time can inform long-term urban planning decisions.

These case studies highlight the importance of selecting the right data storage in IoT based on the specific requirements of each IoT application, including data volume, velocity, and variety, as well as the need for real-time processing and long-term analysis. 

SumatoSoft’s Way of Implementing Data Storage In IoT

SumatoSoft has been delivering IoT software development services since 2012, delivering custom enterprise software and developing MVPs for startups to gain a competitive advantage and improve their efficiency, effectiveness, and profit through business digitalization. We build industry-focused IoT solutions in multiple domains.

Healthcare: A Platform for Farm Animals Signs Monitoring

We provide healthcare IoT development for a MedTech company that developed innovative technology for animal healthcare, focusing on a wearable device that accurately measures vital data in animals.  

We developed an IoT platform for data gathering, visualization, analytics, diagnosis, and calculation. 

Warehouse: Fridge Sensors For Monitoring of Industrial Refrigerators Online

The Client contacted us regarding our IoT in logistics development services. They needed to build software for a device that monitors refrigerators’ work in real time. 

We developed a web application with a convenient dashboard and roled-based administration system that gathers and systematizes device data, detects anomalies, notifies users, and much more. We implemented the cloud data storage in IoT solution. 

These are only a few of our services. We also cooperate with businesses in other domains: 

• IoT solutions for smart cities;
• the Internet of Things in climate change;
• IoT development for fleet management; 
• IoT development in banking; 
• IoT in retail; 
• manufacturing; 
• automotive.  

Here are some facts to help you know us: 

• We strive for quality and security, and ISO 27001 and ISO 9001 certificates can prove it. 
• Your project data stays safe. We guarantee the security of all data related to your project.
• 250 successful projects built in 27 countries for 11 business domains.
• 70% of our team is senior-level developers. 

SumatoSoft is great in every regard including costs, professionalism, transparency, and willingness to guide. I think they were great advisors early on when we weren’t ready with a fully fleshed idea that could go to market. They know the business and startup scene as well globally.

They did a great job hitting cost estimates and are a bargain for quality. They also helped our business concept greatly. We are confident in our plan and future in the hands of SumatoSoft.

David Logan

Founder, Umergence

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After more than 12 years on the market, the company became a reliable technical partner to its Clients, demonstrating a 98% Client satisfaction rate with the quality of services they provide.

Contact us to get a free quote for your project.

Takeaways

The exploration of IoT infrastructure across healthcare, manufacturing, and smart cities reveals the complexity and diversity of data management challenges and solutions deployed to address them. 

Each case study highlights the importance of choosing the right data storage options and implementing effective data management techniques. 

As IoT continues to evolve and expand, the insights gained from these scenarios offer valuable lessons for future deployments, emphasizing the need for strategic planning, robust security measures, and flexible data management to harness the full potential of IoT technology in improving operational efficiency, decision-making, and quality of life.