Top Challenges in IoT Data Collection and Management

By Haseeb Awan

Over 16% of the 12.3 billion gadgets that make up the Internet of Things (IoT) in 2021 are connected to mobile networks. The trend of data expansion as a byproduct of the plethora of large and small gadgets that collect or generate information is beyond incredible. Currently, there is more information than yesterday, and tomorrow there will be far more content than now. 

The IoT most recently increased the enormous amounts of data generated each day. The market has been forced by this uptick to assess data management tactics in terms of scalability, data integration, gravity, and overall security. The diversity, volume, and interconnection that define IoT make it impossible to handle data using methods from the past. Centralized management and robust infrastructure are needed.

IoT Data Collection and Management Benefits

IoT data collection allows for the real-time management and monitoring of remote networks, which is crucial in many sectors. IoT devices, for instance, may follow deliveries and automobiles across long ranges, remotely access manufacturing systems, and monitor the patient in the hospital or at home. IoT devices' data collection increases corporate efficiency and productivity.

Efficient information management is required in conjunction with extensive data collecting. Colossal data collection is useless without systems to clean, manage, and interpret it. IoT data management is crucial because it allows businesses to analyze their IoT devices' data and derive the required information.

Top IoT Data Collection and Management Challenges

IoT device makers and users confront substantial issues connected to IoT data gathering and administration despite the recent explosion of the IoT sector. The following are some of them.

Privacy

Many data that IoT devices gather and process may be covered by various data privacy rules. Any information that can be used to identify an EU person, such as their name, phone number, address, or health records, is protected under the EU's GDPR (General Data Protection Regulation). The PHI that an IoMT device might gather is protected by the US HIPAA (Health Insurance Portability and Accessibility Act). At least one sort of privileged information will likely be collected by the majority of IoT devices.

IoT device producers and consumers must defend this secure information following applicable regulations and protect it from attack. Among the most crucial factors are:

  1. Data Collection Consent: By the GDPR and similar regulations, data users must consent to gather their private, classified information. It can be challenging with IoT devices since they may unintentionally gather data without authorization. For instance, virtual assistants might listen in on discussions that reveal sensitive data or restricted PII (personally identifiable information).
  2. Data Processing Consent: The GDPR and other legislation need specific consent from data users before their information may be analyzed. IoT devices capture and analyze enormous volumes of data, which makes it challenging to oversee data analysis and obtain consent for use.
  3. Data Access Control: According to data protection standards like GDPR, HIPAA, and others, only those who need access to classified information should have it. Because IoT devices are scattered, and their information is analyzed on cloud storage, monitoring and regulating access is more challenging.
  4. Data Encryption: Data security rules mandate that data be encrypted at rest and while in transmission to prevent unwanted access and exploitation. IoT devices frequently have restricted power and computational capability, which makes effective data encryption challenging. As a result, these gadgets are not made to comply with legal requirements for guarding the information they gather.
  5. Jurisdiction: The GDPR limits the information from being sent to governments that may have questionable data security laws. IoT devices and cloud-based processing servers can complicate monitoring and restrict data flow. 

Security

Some IoT gadgets gather highly private data. Sensitive healthcare data is among the data that IoMT devices capture in the healthcare sector (PHI). Virtual assistants, internet-connected camera systems, and similar devices can monitor private conversations and movements. IoT equipment used in production has access to private data regarding activities and procedures.

For IoT devices, protecting this data is a common problem. Given that they must send information to cloud-based servers for analysis and are controlled by portable devices and web-based interfaces, these devices are commonly made to be accessed from the public Internet. They are known for having lax security as a result. Typical IoT security concerns that could jeopardize the private information they hold include:

  1. Weak Passwords: IoT devices frequently deliver essential, insecure, defined keys, passwords, or secrets. Malicious hackers can acquire these systems and the data they gather and analyze using the weak password security.
  2. Unpatched Flaws: IoT vendors are highly uncontrolled and frequently use inefficient secure development processes, which results in the shipment of vulnerable goods with known vulnerabilities. IoT applications are frequently set up and left alone, with no fixes for recently revealed flaws. Consequently, many IoT devices have flaws that hackers can use against them.
Read Everything You Need to Know About IoT Security

Volume

The number of IoT devices is proliferating, and IoT devices generate enormous volumes of data. IoT devices produced approximately 18.3 zettabytes of information in 2019, which is predicted to increase to 73.1 zettabytes by 2025.

IoT devices' huge data volume makes collecting, transferring, and analyzing it difficult. IoT devices are frequently installed in remote regions with meagre Internet connectivity, which makes it challenging and frequently expensive to transfer the data gathered. Servers in the cloud must quickly process and analyze escalating data quantities to draw crucial conclusions and communicate any necessary alerts or directives to the IoT devices.

Complexity

Many IoT devices are built with a Big Data mindset in mind. These gadgets gather as much data as possible before sending it to cloud-based servers for processing. This method not only generates enormous amounts of data but also complex datasets.

IoT device data is frequently unorganized and offers a constrained viewpoint. This information must be properly captured, stored, categorized, and associated with other data sources to create the perspective necessary for efficient decision-making.

It is challenging to efficiently and effectively analyze data from IoT devices due to the volume and size of the problem involved. Numerous technologies created to manage complicated datasets cannot handle the amount of data that IoT devices generate. Similarly, solutions that can manage big data may or may not have the advanced analysis capabilities to meet the requirement of IoT devices.

How to Overcome IoT Data Collection and Management Challenges?

IoT devices produce enormous volumes of complex data. Thus, they need to be protected from hacks and data privacy rules.

Despite their importance, these difficulties can be overcome. Massive amounts of data may be transmitted using next-generation 5G mobile connections, and cloud architecture is constantly expanding to keep up with demand.

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