The 4 main challenges of collecting and managing IoT data


In 2021, the Internet of Things (IoT) grew to 12.3 billion devices, of which more than 16% connect to cellular networks. By collecting and processing large amounts of data in real time, these devices provide significant benefits to businesses and consumers. However, this large-scale data collection and processing also comes with its own unique challenges.

The Importance of IoT Data Collection and Management

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

With large-scale data collection comes the need to manage it effectively. Collecting massive amounts of data is useless if processes are not in place to sanitize, organize and process it. IoT data management is essential because it allows organizations to take the data that their IoT devices collect and extract the information they need.

Four key challenges in collecting and managing IoT data

The IoT market has exploded in recent years; However, manufacturers and consumers of IoT devices face significant challenges in collecting and managing IoT data. These include the following.

1. Data Security

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


Securing this data is a common challenge for IoT devices. These devices are often 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 portals. As a result, they have notoriously poor security. Some common IoT security issues that can put the sensitive data they contain at risk include:

  • Poor password security (other unique identifier): IoT devices are often deployed with default, weak, hard-coded passwords, keys or secrets. Cybercriminals exploit this poor password security to gain access to these devices, allowing 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 shipment of vulnerable products. IoT devices are typically deployed on a “set it and forget it” basis, with no 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 by various data privacy laws. The EU General Data Protection Regulation (GDPR) protects all data that can be used to uniquely identify an EU citizen, including name, address, phone number, medical data , etc 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 collect at least one type of protected information.

In addition to securing this protected data against attacks, Manufacturers and users of IoT devices must protect it in accordance with applicable laws. Here are some important considerations:

  1. Consent to collection: Under the GDPR and similar laws, data subjects must give their explicit consent to collect their protected personal data. With IoT devices, this can be difficult because devices can inadvertently collect data without the proper 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, which makes it difficult to monitor how the data will be processed and to obtain 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 processing power and resources, making it difficult to properly encrypt data. Therefore, these devices may not always be designed to meet regulatory requirements to protect the data they collect.
  4. Access management: Data protection laws such as GDPR, HIPAA and others require that access to sensitive information be limited to those who need it for their job functions. IoT devices are designed to be distributed and their data is processed on cloud servers, making it difficult to track and control access.
  5. Jurisdiction: The GDPR restricts the transmission of data from EU citizens to countries that do not have “adequate” data protection laws in place. With IoT devices and their cloud-based processing servers, tracking and throttling data streams can be complex.

3. Data volume

The Internet of Things is snowballing and IoT devices are producing massive amounts of data. In 2019, IoT devices generated approximately 18.3 zettabytes of data, which is expected to reach 73.1 ZB by 2025.

The sheer volume of data that IoT devices produce makes storage, transmission, and processing significant challenges. IoT devices are commonly deployed in remote locations with limited internet bandwidth, making it difficult and often expensive to transmit collected data. In the cloud, servers must rapidly process and analyze increasing volumes of data to extract essential information and send required alerts or commands to 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 data sets.

The data produced by IoT devices is often unstructured and offers limited insight. These data must be carefully timestamped, indexed, and correlated with other data sources to create the context required for effective decision-making.

This combination of data volume and complexity makes it difficult to effectively and efficiently process data from IoT devices. Many tools designed to handle complex data sets cannot cope with the volume of data that IoT devices produce. On the other hand, solutions capable of handling huge 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 Collecting and Managing IoT Data

IoT devices generate huge amounts of complex data, so they must be protected against breaches and protected by data privacy laws.

However, these challenges, while significant, can be resolved. The next generation 5G mobile networks provide the bandwidth and performance 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 on how you can overcome IoT device security and privacy challenges with FirstPoint.

The post Top 4 Challenges in IoT Data Collection and Management appeared first on FirstPoint.

*** This is a syndicated blog from the Security Bloggers Network of Blog – FirstPoint written by Adam Weinberg. Read the original post at:


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