The Internet of Things bandwagon is expected to gain momentum in the coming years, substantiated by popular stats including Mckinsey’s that predicts 12.86 billion IoT sensors and devices to be in use in the consumer segment by 2020, and IDC’s that forecasts the combined markets of IoT to elevate to $520B by 2021. Though IoT is certainly a key driver for digital transformation among enterprises, the changing IoT landscape gives rise to significant implementation challenges. Can’t agree more right? Below are the key IoT challenges that were faced by the experts at ThinkPalm while working on consumer and industrial IoT solutions, and how they effectively overcame the stumbling blocks in their journey towards rolling out cutting-edge solutions!
One of the biggest IoT implementation challenges we face today is keeping the IoT system compatible and scalable with the changing standards and technologies. Employing microservices-driven architecture to build enterprise IoT apps can make sure the system does not become obsolete soon. This technique allows each service in the app to scale individually for better resource utilization and helps in the addition of new components independently for faster release cycles.
The solution should also be made cloud agnostic as far as possible so that it can deployed into multiple cloud environments. Open standards need to leveraged as far as possible to ensure compatibility.
While the server-client model used now is sufficient for the current IoT ecosystem, it may not be able to connect the 20 billion IoT devices that is expected to emerge by 2020 as per Gartner. To scale up the IoT system to accommodate the rising devices and to affirm users do not experience bottlenecks, the communication between the devices and the cloud infrastructure should be switched to asynchronous messaging model. In this architecture, the edge devices send the data as messages to the cloud using lightweight messaging protocols. This improves the connectivity, efficiency and performance of the entire system. Cloud messaging servers should be made highly scalable by setting them up in such a manner that they have multiple end points to parallely serve a large number of IoT devices.
In the wake of the rising ransomware attacks, security should be incorporated during the design phase itself and multi-factor authentication should be utilized wherever possible. For every component of an IIoT solution, we should ensure security of the data at rest and in transit using cryptographic algorithms. Encryption is critical for any machine-to-machine or device-to-device communication. Data exchanges over the networks should employ Public Key Infrastructure (PKI) and digital certificates.
The real purpose of IoT – arriving at actionable insights from the collected plethora of data is one of the key IoT challenges faced by IT stakeholders. Top hiccups quoted as per Hubspot survey are too much data to analyse, difficulties in capturing useful data, analysis capabilities are not flexible to ask the right questions and more. To mitigate such challenges and unlock the true potential of IoT, Big Data Analytics should be leveraged.
IoT data should be subjected to various analytics techniques like real-time analytics and batch processing to uncover patterns, predict outcomes and even come up with recommendations to improve products and services. Suitable data-processing algorithms, statistical techniques and AI models will enable enterprises to derive value from data sets to gain a competitive edge and improve revenue.
End-to-end testing of IoT systems in a live environment is shooting up the test infrastructure costs in the form of test labs. This can be overcome by employing virtual labs with edge device simulators. Simulators help in end-to-end testing of your IoT design including hardware setup, operator dashboards, analytics pipelines and more. Other advantages include testing the scalability of your app by simulating load conditions, realizing your ideas seamlessly through prototypes before investing in expensive hardware along with provisions to customize code as per your needs and test the security of the system.
Embark on automated testing stages as part of CI/CD pipelines for the design and development of IoT systems. This aids in eliminating resource constraints by virtualizing devices, broadens test coverage, expedites release cycles and results in fewer defects getting released to production.
It goes without saying that Internet of Things has the potential to disrupt industries and generate real economic value. Quoting stats once again, McKinsey’s predicts IoT to bring about a total potential economic impact of $3.9 trillion to $11.1 trillion a year by 2025. These massive numbers will become a reality only if the tech teams overcome the organizational and technical IoT challenges faced today through the right systems and practices to maximize the value delivered. Good luck!