The ultimate IoT implementation guide: Challenges and rewards

The second article in the series discusses the requirements for IoT implementation, the common challenges of IoT projects, and the rewards for those who are determined to overcome those challenges. You can read the first part here.

Knowledge and insight

It's easy to find lists citing the advantages of IoT, such as more effective operations and long-term cost savings. Even though this might be the case, such discussions mainly have little to do with the knowledge and insight that are the IoT's two fundamental, overriding benefits.

Accurate and timely decisions require knowledge and insight, which can be challenging or even impossible. Businesses aspire to have this information and insight because they use it every time a sales manager projects revenue for the next quarter or a production manager chooses to shut down crucial equipment in an important manufacturing line for routine maintenance. When municipal infrastructure that has been neglected for a long time has structural flaws discovered by state inspectors, the stakes are much higher.

The Internet of Things (IoT) improves instant information by sensing and reporting particular real-world circumstances. The real-world state may be inspected and addressed in real time, thanks to the current instrumentation. The patient can slow down and relax to lower their heart rate to a tolerable level, take the right medication, get in touch with their doctor for more advice, or even call for medical help if a heart rate monitor alerts them to an excessive heart rate. Suppose a traffic monitoring system detects a backup on a busy road. In that case, it can notify travel apps about the situation, allowing commuters to choose an alternative route and bypass the gridlock.

However, the long-term insights that company leaders may gain from IoT are its true strength and advantage. Consider the enormous number of IoT sensors that can be installed in machinery, vehicles, buildings, campuses, and public spaces to enable better long-term insight through advanced analytics — the back-end computing procedures capable of analyzing and correlating a vast amount of apparently unrelated data to provide answers to business questions and provide precise forecasts of future events. The collected data can also be utilized to train ML models to promote AI efforts that acquire a thorough grasp of the data and its linkages.

For instance, it is possible to assess the various sensors placed throughout an industrial machine to find differences in operation and condition that may indicate the need for maintenance or even signal an impending breakdown. With the least disruption to regular operations, a company can use these insights to place component orders, plan maintenance, or carry out preventative repairs.

Challenges to consider

No matter the scope of the deployment, IoT initiatives can have a significant positive impact on the company. However, before beginning any IoT project, a corporation must be aware of and consider the major issues that IoT can provide.

There are currently no important worldwide standards that direct the design and execution of IoT architectures; there is no guidebook to describe how to approach an IoT project, even though IoT devices easily incorporate a range of standards, such as Wi-Fi or 5G. This gives designers much creative freedom but leaves room for serious mistakes and oversights. IoT project managers should typically come from the IT department. However, this knowledge is constantly changing. In the end, nothing can replace meticulous, well-thought-out design and performance that have been proven through extensive testing and proof-of-concept projects.

The number of connected devices easily compounds the massive volume of data generated by IoT devices. IoT data is an important company asset that needs to be protected. IoT data is much more time-sensitive than conventional corporate data like contracts and emails. A vehicle's speed or the circumstances of the road reported yesterday or last month, for instance, could not be timely today or next year. This suggests that the lifecycle of IoT data may be very different from regular commercial data. Data security, lifecycle management, and storage capacity must all be significantly increased to do this.

Data from IoT devices must travel across an IP network, like a LAN or the open internet. Make sure there is sufficient, dependable bandwidth available by considering the impact of IoT device data on network capacity. IoT data might be delayed by congested networks with missed packets and high latency. This can entail modifying the network's architecture and adding specialized networks. For instance, a company might implement an edge-computing architecture that stores and preprocesses the raw IoT data locally before sending the filtered data to a central location for analysis instead of sending all IoT data through the internet.

IoT devices are miniature computers linked to a single network and are susceptible to hacking and data theft. IoT projects must use secure setups to safeguard hardware, moving data, and data at rest. An appropriate and well-planned IoT security posture may directly impact regulatory compliance.

Every IoT device must be bought, set up, linked, configured, controlled, maintained, and replaced or retired. Dealing with this for a few servers is one thing, but dealing with hundreds of thousands or even tens of thousands of IoT devices presents different challenges. Think about the logistical nightmare of buying and replacing batteries for thousands of remote IoT devices. IoT executives must use tools to manage IoT devices from initial setup and configuration to monitoring, regular maintenance, and disposition.

Implementing IoT

IoT has many technical challenges, including the choice and deployment of devices, network connectivity, and the development of necessary analytical capability. All of those factors, however, pertain to the construction and maintenance of an IoT infrastructure. The fundamental questions are much simpler for many organizations: Why do it, and how should we get started?

An IoT endeavor must begin with a defined strategy that identifies the project's objectives and specifies its purpose, just like any other IT project. To support the necessary financial and intellectual investment, such an early plan can highlight the project's anticipated value proposition, such as greater productivity or decreased costs through predictive maintenance.

Businesses often enter into a research and experimentation phase after deciding on a plan to find IoT products, software, and other infrastructure components. Then, project managers put the technology into restricted proof-of-concept projects to show it off and improve its deployment and management strategies, such as security and configuration. Analysts assess potential uses for the resultant data and the tools and computer infrastructure required to extract business intelligence from the IoT data simultaneously. When the IoT project scales, this can entail employing limited data center resources for small-scale analytics while keeping a watch on public cloud resources and services.

There are three ways that a business can approach an IoT project. Regardless of the strategy, it's important never to lose sight of the benefits IoT offers the company.

A platform may be built as part of the initiative, enabling users to find value.

An organized project plan and deadline might be used in a more official initiative.

The initiative may reflect an organization-wide commitment to IoT, albeit such an effort typically necessitates greater experience and faith in IoT than others.

Implementation steps

There is no universal method for creating and putting an IoT infrastructure. But a common set of factors may assist firms in successfully architecting and deploying an IoT project. The following list of crucial implementation factors is provided.

Standardizing connectivity: IoT devices can offer multiple connection options, such as Wi-Fi, Bluetooth, 4G, and 5G. While not requiring all devices to use the same connectivity, sticking to a single method can make device setting and monitoring easier. Choose whether or not sensors and actuators should be connected to the same network.

The platform: It can be problematic and inefficient to transfer all IoT data from devices to an analytics platform. Before delivering data for analytics, an intermediary platform, such as an IoT hub, can assist in organizing, preprocessing, and encrypting data from devices spread throughout an area. A hub may gather and preprocess IoT data at the edge before transmitting it to a remote facility with IoT capabilities for further analysis.

Analytics: After being gathered, the data may be used to power reporting systems and actuators or be gathered for further in-depth analysis, querying, and other big data use. Select the software applications used to process, analyze, display, and operate ML. One illustration is the decision between static and streaming databases or SQL and NoSQL IoT databases. These resources may be made available through cloud or SaaS providers, local data centers, or both.

Management: Utilize a software tool that can dependably maintain every IoT device installed throughout the project's lifecycle. In order to simplify configuration and cut down on errors, look for high degrees of automation and group management capabilities. Organizations should pay particular attention to workflows for updating and upgrading IoT devices since IoT device patching and updating are becoming a concern.

Security: IoT implementations must carefully examine IoT setup and integration into already-existing security tools and platforms, such as intrusion detection and prevention systems and antimalware solutions, as every IoT device has the potential to be a security vulnerability.