Top 4 Challenges in IoT
Data Collection and Management

5 min read

In 2021, the Internet of Things (IoT) has grown to 12.3 billion devices, of which over 16% connect to cellular networks. By collecting and processing large amounts of real-time data, these devices provide significant benefits to enterprises and consumers alike. However, this wide-scale data collection and processing also comes with its own set of unique challenges.

The importance of IoT data collection and management

IoT data collection is invaluable in many industries because it enables the monitoring and management of remote systems in real-time.  For example, IoT devices can monitor patients in the ward or at home, remotely control manufacturing systems, and track shipments and vehicles across distances. The data that IoT devices collect makes business more productive and effective.

With large-scale data collection comes the need to manage it effectively. Gathering a massive amount of data is useless if processes are not in place to sanitize, organize, and process it. IoT data management is essential because it enables organizations to take the data their IoT devices collect and extract the insights they need from them.

Four Key Challenges of IoT data collection and management

The IoT market has exploded in recent years; however, IoT device manufacturers and consumers face significant challenges related to IoT data collection and management. These include the following.

1. Data Security

Some IoT devices collect highly sensitive information. In the healthcare industry, the data collected by IoMT devices include protected health information (PHI). Internet-connected cameras, voice assistants, and similar tools can monitor peoples’ activities and conversations. IoT devices used in manufacturing have access to sensitive information about manufacturing processes and procedures.

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Securing this data is a common challenge for IoT devices. These devices are frequently designed to be accessible from the public Internet due to their need to send data to cloud-based servers for processing and are managed from mobile devices and web-based portals. As a result, they have notoriously poor security. Some common IoT security issues that can endanger the sensitive data that they contain include:

  • Poor Password (other unique identifier) Security: IoT devices are often deployed with default, weak, and hardcoded passwords, keys, or secrets. Cybercriminals exploit this poor password security to gain access to these devices, which provides access to the devices and the data they collect and process.
  • Unpatched Vulnerabilities: IoT manufacturers are largely unregulated and often have poor secure development practices, leading to vulnerable products being shipped. IoT devices are commonly deployed on a “set it and forget it” basis, without patches applied for newly discovered vulnerabilities. As a result, many IoT devices contain vulnerabilities that an attacker can exploit.

2. Data Privacy

Much of the information collected and processed by IoT devices may be protected under various data privacy laws. The EU’S General Data Protection Regulation (GDPR) protects any data that can be used to uniquely identify an EU citizen, including their name, address, phone number, medical data, and more. The US Health Insurance Portability and Accessibility Act (HIPAA) protects the types of PHI that an IoMT device would collect. Most IoT devices are likely to gather at least one type of protected information.

In addition to securing this protected data against attack, IoT device manufacturers and users must protect it per applicable laws. Some important considerations include:

  1. Consent to Collection: Under the GDPR and similar laws, data subjects must provide explicit consent to collect their personal, protected data. With IoT devices, this can be difficult as devices may inadvertently collect data without the appropriate permission. For example, voice assistants may overhear conversations that collect protected personally identifiable information (PII) or other sensitive data.
  2. Consent to Processing: In addition to consent to data collection, GDPR and other laws require explicit consent from data subjects for their data to be processed. With IoT devices, massive amounts of data are collected and processed, making it challenging to monitor how data will be processed and get consent for that processing.
  3. Encryption: Data protection laws require data to be encrypted at rest and in transit to protect against unauthorized access and misuse. IoT devices often have limited power and processing resources, making appropriate data encryption difficult. As a result, these devices may not always be designed to meet regulatory requirements for protecting the data that they collect.
  4. Access Management: Data protection laws like GDPR, HIPAA, and others mandate that access to sensitive information be limited to those who require it for their roles. IoT devices are designed to be distributed and have their data processed on cloud servers, making it more difficult to track and control access.
  5. Jurisdiction: The GDPR restricts the data from EU citizens from being transmitted to countries that do not have “adequate” data protection laws in place. With IoT devices and their cloud-based processing servers, tracking and limiting data flows can be complex.

3. Data Volume

The Internet of Things is snowballing, and IoT devices produce massive amounts of data. In 2019, IoT devices generated an estimated 18.3 zettabytes of data, which is expected to grow to 73.1 ZB by 2025.

The sheer volume of data IoT devices produces turns storing, transmitting, and processing it into significant challenges. IoT devices are commonly deployed in remote locations with limited Internet bandwidth, making it difficult and often expensive to transmit the collected data. In the cloud, servers must rapidly process and analyze growing volumes of data to extract essential insights and send any required alerts or commands to the IoT devices.

4. Data Complexity

Many IoT devices are designed to adopt a Big Data mentality. These devices collect as much information as possible and send it to cloud-based servers for processing. In addition to producing massive volumes of data, this approach also creates complex datasets.

The data produced by IoT devices is often unstructured and provides a limited perspective. This data must be carefully timestamped, indexed, and correlated with other data sources to make the context required for effective decision-making.

This data volume and complexity combination makes it difficult to effectively and efficiently process data from IoT devices. Many tools designed to manage complex datasets cannot cope with the volume of data that IoT devices produce. On the other hand, solutions that can handle massive volumes of data may not offer the required level of in-depth analysis and may not meet the latency requirements of IoT devices.

Overcoming the Challenges of IoT Data Collection and Management

IoT devices generate massive amounts of complex data, so they must be secured against breaches and protected under data privacy laws.

However, these challenges, while significant, are solvable. Next-generation 5G mobile networks provide the bandwidth and performance required to transmit massive amounts of data, and cloud infrastructure continues to scale to meet demand.

FirstPoint provides solutions for organizations concerned about the security and privacy of their IoT devices connected to mobile networks. FirstPoint Cybersecurity-as-a-Service enables centralized monitoring and security management for cellular IoT devices. Learn more about how you can overcome the security and privacy challenges of IoT devices with FirstPoint.

 

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