Factors to Consider in IoT Implementation

Have you resolved to use IoT to power your organization?

The entire process to develop an IoT ecosystem is quite a big challenge. IoT implementation is not comparable to other IT deployments that are largely software-based because it includes multiple components like devices, gateways, and platforms. If you plan to adopt IoT, then you have the following factors to consider in IoT implementation.


The year 2016 turned out to be an unforgettable year for the telecommunication industry. At that time, the telecom infrastructure was badly hit by a DDoS attack. As a consequence, many users faced difficulties while establishing a connection with the internet. Earlier, it was speculated that there was a cyber warfare element in the attack. Nation-backed attacks are nothing new. Over the past few years, the battleground has changed from the land to the digital realm as cybercriminal groups find support from different countries.

However, in this case, the culprit was someone else. It was known as Mirai, a malware. Soon, it was revealed that the malware belonged to the “botnet” category. A botnet is an attack which compromises multiple systems at once and uses them as digital zombies to carry out malicious actions. Mirai was able to bypass several IoT devices. These devices included residential gateways, digital cameras, and even baby monitors! All of these devices were invaded through a brute-force strategy.

Unfortunately, this is just the tip of the iceberg. The botnet is not the only threat looming over IoT. There has been other malware like ransomware which makes matters worse in the Medical IoT. Similarly, last year’s cyber attacks in Bristol Airport and Atlanta Police paint a worrisome picture which illustrates IoT devices as highly insecure against modern cyber attacks.

If any part of the IoT ecosystem of business is hacked, then it offers the perpetrators remote access to trigger actions. Therefore, it is necessary that cybersecurity strategy of any organization dealing with IoT focuses on the complete system security from sensors and actuators to the IoT platforms for minimizing loopholes.


Authentication is one of the integral key points for IoT implementation. It is important to ensure that a system’s security is not completely reliant on authentication mechanisms which fall into the category of one-time authentication. An enterprise IoT infrastructure demands that connectivity for devices and endpoints is carefully assessed.

There should be an environment which can be “trusted.” Such an environment entails proper identification of all users, applications, and devices so they can be authenticated easily while eliminating unknown devices from the network. There have to be appropriate roles and access defined for all the linked devices. This ensures that the network can only permit authorized activities. Similarly, the incoming and outgoing data in the ecosystem can only be accessed by the user having the required clearance and authority.

Reference Datasets

The data production from IoT devices can only be useful if it is used in the proper context. This context can be utilized from third-party data which stores information of aggregated values, look-up tables, and historical trends. For instance, if IoT is used in home automation, then it can adjust the temperature of the home. The decision for using an air conditioner to increase cooling in the room or for using a heater to increase the inflow of warm air in the room depends upon the real-time data extraction from the weather data sources. Likewise, in the case of a connected car, the car has to send its location coordinateness to the closest service center. Therefore, it is necessary to ensure that adequate reference points are available for IoT devices.


During IoT implementation, one has to factor in all the activities related to managing, processing, and saving data in the sensors. This aggregation enriches the value of data by enhancing the frequency, scope, and scale. However, aggregation requires the correct use of different standards.

There are two standards which are associated with aggregation.

  • Technology Standards – They include data aggregation standards (Loading (ETL), Transformation), communication protocols (HTTP), and network protocols (Wi-Fi).
  • Regulatory Standards – They are specified and overseen by federal authorities like HIPAA and FIPP.

The use of standards springs several questions, for example, which standard will be used to manage unstructured data? The traditional relational database store structured data and are queried with SQL. On the other hand, modern databases like MongoDB use NoSQL (Not SQL) to store unstructured data.

Data Sensitivity

When organizations began providing services and products on the digital realm, they collected user information for processing. However, no one exactly knew what happened behind the scene. Was the information only being used to provide better user experience or was it exploited for hidden purposes? Studies revealed that a large chunk of organizations sold their private customer data to third parties.

In the past few years, a strong wave has emerged to improve data transparency for clients. For instance, in May 2018, the European Union implemented GDPR (General Data Protection Regulation). GDPR is a comprehensive list of data privacy regulations, which is created to make organizations transparent about their data processing. The objective behind GDPR is to protect the privacy of EU residents. This law is applied on any business which engages with EU residents, irrespective of the business’ geographical location.

These emerging trends are important for IoT since the sensors and devices are responsible to store and process large datasets. Hence, it is vital that any of this data does not breach privacy laws. There are four main tips for data sensitivity.

  • Understand the exact nature of data which is going to be stored by the IoT equipment.
  • Know the security measures used to encrypt or secure data.
  • Identify roles in the businesses which access the data.
  • Learn data processing of each component of the IoT ecosystem.



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