IoT Storage: Approaches, Technologies, and 4 Key Challenges

What Is IoT Storage? 

Internet of Things (IoT) storage involves managing and processing the immense volumes of data generated by connected devices. These devices, ranging from simple sensors to complex industrial machinery, produce diverse, continuous streams of data that need to be stored for further analysis and real-time decision making. Efficient IoT storage solutions help maximize the value of IoT data.

IoT storage demands differ from traditional data storage, requiring highly scalable, durable, and accessible storage systems that can handle large volumes of high-velocity, unstructured data. This necessitates advanced storage architectures and technologies designed to meet the performance and scalability needs of IoT applications.

This is part of a series of articles about data lake.

In this article:

• Types of Data Generated by IoT
• IoT Data Storage Approaches
• Technologies Enabling IoT Storage
• IoT Storage Challenges and Solutions

Types of Data Generated by IoT 

IoT devices can generate different types of data.

Sensor Data

Sensor data, produced by devices like temperature sensors or motion detectors, is often raw and real-time. It requires prompt processing and analysis to be useful. The storage system must be able to handle high ingestion rates and provide instant access. Solutions often include time-series databases designed to handle this type of data, ensuring the data is actionable.

Operational Data

Data generated from the operation of devices themselves includes logs, system health data, and usage statistics. This data is crucial for monitoring, maintenance, and optimization of device performance. It typically requires storage that supports fast writing and reading speeds and can handle large volumes of data writes without degradation of performance. For example, NoSQL databases and in-memory data grids.

User Data

Information related to the users of IoT devices includes personal preferences, usage patterns, and interaction histories. This data aids in enhancing user experience and personalizing services. Privacy concerns are a major concern, requiring secure storage solutions that comply with data protection laws such as the GDPR. Encryption, both at rest and in transit, along with rigorous access controls, help protect user data.

IoT Data Storage Approaches 

There are several technical approaches to store IoT data:

Edge Storage

Edge storage involves storing data on local devices or near the data source, rather than transmitting it to a centralized data center. This mitigates latency issues by processing data close to where it is generated, reducing bandwidth usage on networks. Examples of use cases include manufacturing plants and autonomous vehicles. 

Cloud Storage

Cloud storage is scalable and flexible, leveraging the cloud’s resources to store data remotely. This allows IoT deployments to expand storage capacity as needed without investing in physical infrastructure. However, relying solely on cloud storage can introduce latency issues due to data having to travel from the IoT devices to the cloud. Data caching and choosing cloud data centers located nearer to the data sources can help mitigate these latency problems.

Hybrid Storage

Hybrid storage combines the advantages of edge and cloud storage, allowing data to be stored and processed both locally and in the cloud. This enables a balance between reducing latency and leveraging the scalable storage and advanced analytics capabilities of the cloud. It is useful for local decision-making, but where long-term data analysis can be offloaded to the cloud. 

Technologies Enabling IoT Storage 

Here are some of the technologies that support IoT storage:

• Database technologies: Databases support structured and semi-structured data storage for IoT systems:
  • Time-series databases, such as InfluxDB and TimescaleDB, are optimized for storing sequential data generated by IoT devices. They offer efficient data compression and specialized query capabilities to handle large volumes of timestamped data. 
  • NoSQL databases like Cassandra and MongoDB provide flexibility, scalability, and high performance, managing the varied data from IoT devices. They support a schema-less data model, allowing them to handle different data types.

• File systems: Suitable for IoT environments requiring high throughput and low-latency data access, file systems like ZFS or Btrfs provide features like data integrity checking and snapshot capabilities. This is useful for IoT applications that may need to restore historical data states.
• Block storage systems: These ensure high-performance data access for IoT applications, especially for real-time processing and analysis. They are useful for data, requiring immediate storage and retrieval. Examples include iSCSI and Fiber Channel. 
• Object storage solutions: These can handle unstructured data, such as video and images, from devices like surveillance cameras or drones. Solutions like Amazon S3 in the cloud and Cloudian for on-premises storage offer scalability and durability. Users can store, retrieve, and manage data non-sequentially.
• Data warehouses: These provide a structured format for querying and analyzing data, suitable for structured data in IoT scenarios where response times are important. They allow for complex queries and reporting on IoT data that has been processed and normalized.
• Data lakes: These offer a more flexible environment suitable for storing raw, unstructured data from IoT devices. Technologies like Hadoop or Azure Data Lake can handle large amounts of heterogeneous IoT data, enabling later refining and analysis.

Related content: Read our guide to data warehouse vs data lake

IoT Storage Challenges and Solutions 

Here are some of the main challenges associated with storing IoT data and how to address them.

1. Data Volume and Scalability

The massive growth of IoT devices leads to data volumes that traditional storage solutions struggle to manage. 
Scalable storage solutions like distributed file systems and cloud-based storage can accommodate this growth, enabling horizontal scaling to support changing data inflows.

2. Real-Time Processing and Latency

IoT applications often require real-time data processing to enable timely decision-making. High latency can severely impact the application’s effectiveness, making it crucial to implement storage solutions that ensure quick data access. 
Edge computing models help by processing data closer to where it is generated, reducing latency. Integrating fast caching layers and in-memory databases can speed up data retrieval times, supporting real-time processing in IoT environments.

3. Security and Privacy Concerns

IoT devices are often susceptible to security vulnerabilities due to their distributed nature and the sensitivity of the data they handle. Privacy is another critical concern, especially with devices that collect personal data.
Implementing encryption methods for data at rest and in transit is essential. Access controls, regular security audits, and real-time security threat analysis can mitigate breaches and ensure data integrity. Data anonymization techniques and compliance with data protection regulations like GDPR help protect sensitive information and maintain user trust.
Learn more in the detailed guide to IoT security challenges

4. Interoperability and Standardization

Interoperability between different IoT devices and platforms can be challenging due to diverse manufacturers and differing standards. 
Adopting universally accepted protocols and standards such as MQTT or CoAP can improve integration and communication between devices. Implementing APIs that enable different storage systems to interact seamlessly can help overcome data silos and enhance the efficiency of IoT systems. 

Data Protection and Privacy with Cloudian HyperStore

Data protection requires powerful storage technology. Cloudian’s storage appliances are easy to deploy and use, let you store Petabyte-scale data and access it instantly. Cloudian supports high-speed backup and restore with parallel data transfer (18TB per hour writes with 16 nodes).

Cloudian provides durability and availability for your data. HyperStore can backup and archive your data, providing you with highly available versions to restore in times of need.

In HyperStore, storage occurs behind the firewall, you can configure geo boundaries for data access, and define policies for data sync between user devices. HyperStore gives you the power of cloud-based file sharing in an on-premise device, and the control to protect your data in any cloud environment.

Learn more about data protection with Cloudian.