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Advances in Internet of Things (IoT) systems are increasingly facilitating data collection and information transfer over the IoT network to provide a range of automated services to the users. Be it health care, traveling, learning, or smart home, through IoT systems, networks, and connected devices, users in the real world are enjoying personalized and intelligent services but at the cost of revealing sensitive personal data to a highly volatile environment, where the security of information exchange is one of the biggest concerns. 

As you go about building IoT solutions for your business and consumers, you understand that you will be onboarding your consumers into a user-centric network, where security will always be a key issue. Listed below are five points related to tracking or monitoring of user data movement that you must take into consideration as you design and develop an IoT system or solution, either in-house or through a third party. 

Understanding the Security Concerns

Information transfer over IoT networks causes two types of security concerns: application security and user security.

Application security is primarily about securing the user information when a user is using a particular IoT application or device. User security is broader and concerns the security of the user-sensitive information as the information moves across an IoT ecosystem. 

When building IoT solutions or systems, IoT developers need to ensure three security-relevant features: 

• The information exchange or data movement over the IoT network should be guarded with protocols that can stop unauthorized access to the information 
• The service accessibility of the authorized user shoulds should be seamless irrespective of location and context
• The system should have sufficient user privacy layers and those should not be violated under any circumstances

Real-Time Data Monitoring

IoT should be seen as a breathing ecosystem, where things are moving or changing every moment, thus it should not be treated as a typical, static network. To monitor the information exchange or data movement, an IoT system should be capable of providing a real-time view of what a network’s monitoring tool is doing at any given moment, like the data flow, changes in network traffic, which devices are interconnecting, configuration changes, uptime, and downtime, etc. 

Automated Network Discovery Scan

Detailed tracking of data movement over an IoT network is possible only when the network can track all of the physical assets that the network controls. An automated network discovery scan can quickly and automatically generate an inventory of everything attached to a network. Thus, asset management software or a network monitoring tool is a much-needed tool.

At the minimum, it should map network assets, and should make automatic updates if a device status changes or is reconfigured. An advanced version of the asset management software can collect more detailed data about the device’s hardware, like the manufacturer and model ID, hardware or software and firmware versions, the serial number, chassis ID, etc. A full-featured network monitoring tool is capable of even discovering new devices as getting added to a network and instantly gathering all the relevant information.

Centralized Data Management for Data Filtering

IoT networks are filled with high volumes of wide varieties of data streamed by a range of devices connected to the network. But the reality is that it is a highly volatile network, and not all kinds of data are relevant. In fact, there exists plenty of garbage data that doesn’t assist machine learning in any way. It is a risk to allow raw data to flow directly into enterprise systems because these data are incapable of self-correction and can cause most of the inconsistencies in the data. 

To achieve IoT value in the true sense, a clean source of trusted data is required; then only, the analytical tools or data assessing tools will be able to derive actionable insights. For this, it is important to architect an IoT system with a central Data Management plan in the prototype itself. Such a Data Management plan can flag data as trusted or untrusted, and even record the reason for that and also the source of the data. 

Managing Data Flow

The seriousness towards data movement tracking in your network must start from the development phase of the IoT system. As projects go from concepts to reality, it is important to incorporate the system with the right set of tools, necessary for regulating the data flow. How many devices will be creating information? Will the data capturing happen in real time, or in batches? What will be the role of data analytics? These are some of the important questions that need to be asked in the design phase itself. 

MQTT, HTTP, and CoAP are the most common standard protocols used to send information from devices over the network back to the central application. Another important point is to ensure the alignment in the order of data. Weather data is sent in real time or sent in batches as collated data, but the fundamental requirement is that on each device, the data transaction is put in at the right time-stamp to ensure accuracy in sorting and alignment. 

Final Words

Over a cloud network, as connected physical devices communicate with the cloud, send data, and receive actuation information, it is important that an IoT system has an adaptive mechanism or technique to control the flow of the data from the edge device to the cloud. The source of the user data (i.e., edge devices) should be made capable of dynamically controlling the data rate by reconfiguring the IoT devices based on the current network condition and load on the edge device.