Ultimate IoT implementation guide for businesses – TechTarget

The internet of things provides organizations with real-time information and business insights that, when acted upon, can make processes and operations more efficient and safer. IT administrators, architects, developers and CIOs considering an internet of things deployment must have a thorough understanding of what the internet of things is; how it operates; its uses, requirements, and tradeoffs; and how to implement internet of things devices and infrastructures.
The internet of things (IoT) is a network of dedicated devices — called things — deployed and used to gather and exchange real-world data across the internet or other networks. Examples of this technology in operation include the following:
Key concepts of IoT are as follows:
A focus on real-world data. Where an enterprise routinely deals with documents, PowerPoints, images, videos, spreadsheets and many other forms of static digital information, IoT devices produce data that reflects one or more physical conditions in the real world. As an example, physical data can include actual manufacturing machine conditions derived from sensors and material workflows tracked by RFID tags across a factory floor. IoT devices help a business not only learn what’s happening, but also exercise control over what’s happening.
The vital importance of immediacy in real-time operation. Where routine data — such as a memo document — can exist for days or months without ever being used, IoT devices must deliver data for collection and processing without delay. This makes related factors, such as network bandwidth and connectivity, particularly important for IoT environments where the usefulness or relevance of data can be measured in seconds. For example, cardiac data from a heart patient or pressure data from a nuclear reactor must be delivered and processed immediately.
The resulting data itself. IoT projects are often defined by the larger project or business purpose driving IoT deployment. In many cases, IoT data is part of a control loop, with a straightforward cause-and-effect objective. For example, a sensor tells a homeowner that their front door is unlocked, and the homeowner can use an actuator — an IoT device designed to translate control signals received from the network into real-world actions — in the door to lock it remotely. Similarly, the data provided by sensors collecting telemetry from factory machines can be processed to find bottlenecks in maintenance and workflows, which can then be optimized to save time and money.
Scope of data and collection. The goal of IoT is to help businesses create a broad and detailed picture of an operational environment, such as a city highway system. Creating this picture takes many sensors of varying types and capabilities, including pressure, temperature, moisture and countless other parameters. IoT deployments can involve hundreds, thousands or tens of thousands of individual sensors used to collect and deliver data for processing and decision-making.
But IoT can support much larger and more far-reaching business goals. Millions of IoT sensors can produce vast quantities of raw data — far too much for humans to review and act upon. Increasingly, large IoT projects are the core of big data initiatives, such as machine learning (ML) and artificial intelligence (AI) projects. The data collected from vast IoT device deployments can be processed and analyzed to make vital business projections or train AI systems based on the real-world data collected from vast sensor arrays. Those back-end analyses can demand substantial storage and computing power. Computing can be managed in centralized data centers, in public clouds or distributed across several edge computing locations close to where data is collected.
IoT isn’t a single device, software or technology; it is an amalgam of devices, networks, computing resources and software tools and stacks. Understanding IoT terminology usually starts with the IoT devices themselves.
Things. Every IoT device — a thing or smart sensor — is a small, dedicated computer possessing an embedded processor, firmware and limited memory and network connectivity. The device collects specific physical data and sends that data out onto an IP network, such as the internet. Depending on the sensor’s work, it might also include amplifiers, filters and converters. IoT devices are battery-powered, rely on wireless network connectivity through individual IP addresses and can be configured individually or in groups.
Connections. The data gathered by IoT devices must be transmitted and collected. This second layer of IoT involves the broad network, along with an interface between the network and back-end processing. The network is typically a conventional IP-based network, such as an Ethernet LAN or public internet. Every IoT device receives a unique IP address and unique identifier. The thing passes its data to the network using a wireless network interface, such as Wi-Fi, or a cellular network, such as 4G or 5G. As with any network device, data packets are marked with a destination IP address where the data is to be routed and delivered. Such network data exchange is identical to the everyday exchange of network data between ordinary computers. The destination for this raw sensor data is typically an intermediary interface, such as a local IoT hub or IoT gateway. The IoT gateway usually serves to collect and collate the raw sensor data, often applying early preprocessing tasks such as normalization and filtering to IoT data.
Back end. The enormous volume of real-time data produced by an IoT sensor fleet and collated at the IoT gateway must be analyzed to yield deeper insights, such as exposing business opportunities or driving ML. The IoT gateway sends its cleaned and secured sensor data across the internet to a back end for processing and analysis. Analyses are performed using extensive computing clusters, such as Hadoop clusters. This back end might be located at a corporate data center, a colocation facility or a computing infrastructure architected in the public cloud. There, the data is stored, processed, modeled and analyzed.
Interface. IoT plays a vital role in automation, providing data collection and computing power to help systems analyze, identify and react to issues far faster than humans ever could. However, every IoT system will also provide some form of human interface — whether an alert, dashboard, big data reporting or other form — intended to let human operators keep watch and check on the behaviors of the IoT infrastructure. As one simple example, a smart home needs an interface that lets the homeowner set the inside temperature and check the status of smart devices within the home.
The discussion of sensor, connection and back-end layers can help business and IT staff understand IoT technology, but such discussion also demands consideration of IoT architecture. Although the scope and detail of an IoT architectural plan can vary depending on the IoT initiative, it’s vital for leaders to consider how IoT will integrate into the current IT infrastructure.
There are four major layers in an IoT architecture. These layers possess a relationship like the Open Systems Interconnection networking model and can be discussed from bottom to top:
There are four major architectural issues to consider:
The vast array of small and capable IoT devices has found meaningful business applications in most major consumer, industrial, medical and government verticals. Consider some of the expanding use cases in five important industries:
When business leaders research and consider IoT adoption, it’s easy to find lists that cite the benefits of IoT, such as more efficient operations and long-term cost savings. Although this can be true, such conversations are tangential to the principal overarching benefits of IoT: knowledge and insight.
Accurate and timely business decisions demand knowledge and insight that can be difficult or even impossible to obtain. Businesses rely on knowledge and insight each time a sales manager forecasts the next quarter’s revenue or a production manager decides whether to shut down a key machine in a vital production line for routine maintenance. The stakes are far higher when state inspectors discover structural defects in long-neglected municipal infrastructure, or when physicians struggle to diagnose serious maladies and improve patient outcomes.
IoT provides better immediate knowledge through measuring and reporting specific real-world conditions. Through modern instrumentation, such as the thermostat in a home that measures current temperatures to control heating or cooling systems, the real-world condition can be examined and responded to in real time. If a heart rate monitor alerts an excessive heart rate, the patient can slow down and relax to lower the heart rate to an acceptable level, take appropriate medication, contact their physician for further guidance or even call for medical assistance. If a traffic monitoring system sees a backup on a major highway, it can update travel apps of the prevailing conditions and enable commuters to select alternate routes and avoid the congestion.
But the real power and benefits of IoT are the long-term insights it can provide to business leaders. Consider the vast number of IoT sensors that can be distributed throughout equipment, vehicles, buildings, factories, campuses and municipal areas that enable better long-term insight through advanced analytics — the back-end computing processes capable of evaluating and correlating a huge quantity of unrelated data to answer business questions and make accurate predictions about future circumstances. The data collected can also be used to train ML models, supporting the development of AI initiatives that achieve a deep understanding of the data and its relationships.
For example, the varied sensors distributed in a vehicle or industrial machine can be analyzed to detect variations in operation and condition, which might suggest the need for maintenance or even predict an impending failure. Such insights enable a business to order parts, schedule maintenance or make proactive repairs while minimizing the disruption to normal operations.
IoT projects can bring strong benefits to the business regardless of the deployment scope. But IoT can also pose serious challenges that a business must recognize and consider before undertaking any IoT project.
Project design. Although IoT devices readily implement a variety of standards, such as Wi-Fi or 5G, there are currently no significant international standards that guide the design and implementation of IoT architectures, and there’s no rulebook to explain how to approach an IoT project. This allows for a great deal of flexibility in design, but it also allows for major design flaws, vulnerabilities and oversights. IoT projects should be led by IT staff with IoT expertise, but such expertise is constantly evolving. There is no substitute for careful, well-considered design and demonstrated performance based on copious testing and proof-of-principle projects.
Data storage and retention. IoT devices produce enormous amounts of data, which is readily multiplied by the number of devices involved. That data is a valuable business asset that must be stored and secured in accordance with proper compliance and retention requirements. And unlike traditional business data, such as emails and contracts, IoT data is highly time-sensitive. For example, a vehicle’s speed or road data conditions reported yesterday or last month might have no timeliness today or next year. This means IoT data might possess a radically different lifecycle than traditional business data. This requires a significant investment in storage capacity, data security and data retention/lifecycle management.
Network support. IoT data must traverse an IP network, such as a LAN or the public internet. Consider the effect of IoT device data on network bandwidth and ensure that adequate, reliable bandwidth is available. Congested networks with dropped packets and high latency can delay and jeopardize the timeliness of IoT data. This might involve some architectural changes to the network and the addition of dedicated networks. For example, rather than pass all IoT data across the internet, a business might opt to deploy an edge computing architecture that stores and preprocesses the raw data locally before passing only curated data to a central location for analysis.
Device and data security. IoT devices are small computers connected to a common network, making them vulnerable to hacking and data theft. IoT projects must implement secure configurations to protect devices, data in flight and data at rest. A proper and well-planned IoT security posture might have direct implications for regulatory compliance.
Device management. One often overlooked problem is the proliferation of IoT devices. Every single IoT device must be procured, prepared, installed, connected, configured, managed, maintained and replaced or retired. It’s one thing to deal with this for a few servers, but it’s another problem entirely for hundreds, thousands or even tens of thousands of IoT devices. Consider the logistical nightmare involved in battery procurement and replacement for thousands of remote IoT devices. IoT leaders must employ tools and people to manage IoT devices from initial setup and configuration through monitoring, routine maintenance and disposition.
IT and business leaders must embrace the considerations of security and compliance in any IoT deployment. IoT devices present the same basic security vulnerabilities found in any networked computer or device. The problem with IoT is volume, as there can be tens or even hundreds of thousands of IoT devices involved in an IoT deployment, each posing the same potential weaknesses that must be managed. Other security challenges include the following:
IoT security can pose problems for businesses because weak default security is multiplied by countless devices that all rely on human monitoring and management efforts. The attack surface can be enormous. Thus, IoT security comes down to three principal issues:
Still, IoT devices are plagued by a range of potentially devastating security risks that include botnet attacks, as well as weak DNS systems that can allow the introduction of malware, ransomware, potential attack vectors caused by unauthorized and unsecured devices on the network, and even physical threats where malicious individuals on site introduce or exploit a vulnerable IoT device or network.
Security risks carry corresponding risks to an organization’s compliance posture. Imagine what happens when patient data is stolen from a medical IoT infrastructure, or a business can’t manufacture products because hackers have infected the IoT infrastructure with ransomware. Such events create potential compliance headaches for business leaders and regulators. Any discussion of IoT security must include a careful evaluation of compliance.
IoT continues to evolve. There are still no common, universally adopted standards for designing, configuring, operating and securing an IoT infrastructure. In most cases, all a business can do is document design and process decisions and attempt to correlate them to other IT best practices. One example is to choose IoT devices that adhere to existing technological standards such as IPv6 and connectivity standards including Bluetooth Low Energy, Wi-Fi, Thread, Zigbee and Z-Wave. It’s a good start, but it’s often not enough.
Fortunately, additional compliance standards are gaining traction from industry-leading organizations, such as the IEEE. For example, the IEEE 2413-2019 standard offers a common architectural framework for IoT across transportation, healthcare, utility and other domains. It conforms to the international standard ISO/IEC/IEEE 42010:2011. Although such standards don’t guarantee compliance by themselves, organizations that follow the established frameworks and practices can strengthen existing compliance postures in IoT implementation.
IoT security and compliance isn’t a new or standalone issue; it demands complete and seamless alignment and integration with other IT-related compliance initiatives. Like other areas of modern IT infrastructure, IoT compliance requires addressing compliance in IoT equipment, configurations, processes and people. For example, business guidelines and best practices must be updated to include managing, using and securing IoT data. IT guidelines must be updated to reflect proper IoT configuration and monitoring of servers, storage and other IT gear.
Getting countless individual IoT devices set up can be a daunting task, but processing that data to divine useful business intelligence can bring its own problems, too. As the IoT industry evolves, the IoT ecosystem is expanding to bring new support for IoT implementation and facilitate new business models.
One of the biggest issues with IoT is simply getting it to work. Infrastructure demands can be extensive, security is often problematic, while network and processing loads can add new complexity for the business. IoT vendors are addressing these problems with a growing number of SaaS platforms designed to simplify IoT adoption and eliminate many of the deep investments needed for gateways, edge computing and other IoT-specific elements.
IoT SaaS operates between the IoT device field and the enterprise, managing many of the essential elements that an enterprise must otherwise provide. For example, the SaaS offering manages mundane infrastructure tasks, such as data security and reporting, but often includes high-level processing and computing, such as analytics, with additional support for ML. This relieves the enterprise data center from this IoT burden, and the business can focus on receiving and using the resulting analyses.
IoT SaaS offerings provide similar features, so carefully consider the pricing to select the provider best suited to the number of IoT devices, data volumes and analytical needs of your organization. Typical IoT SaaS providers include Altair SmartWorks, Emnify, Google Cloud IoT Core, IBM Watson IoT Platform, Microsoft Azure IoT Hub and Oracle Fusion Cloud IoT.
As the IoT industry expands, businesses will find that IoT tools and software are increasingly available at three principal places across the software stack:
IoT isn’t just changing the way businesses operate; it’s enabling a variety of new business models that let organizations derive revenue from IoT projects and products. Businesses are increasingly looking at ways to monetize and integrate IoT data as new growth and revenue streams. There are at least four types of business models that IoT can facilitate effectively:
There are many technical issues for IoT, including the selection and deployment of devices, network connectivity, building adequate analytical capabilities and capacity, and network and data security. But all those considerations relate to the actual building and operation of an IoT infrastructure. For many organizations, the initial questions are far simpler: Why undertake an IoT project, and how should we start?
There is no one way to approach an IoT project, and IoT initiatives can be incredibly diverse in purpose and scope. As with any IT project, an IoT initiative must start with a clear strategy that outlines the purpose of the project and clearly states its goals. Such an initial strategy might also underscore the project’s intended value proposition — such as increased productivity or decreased costs through predictive maintenance — to justify the financial and intellectual investment required.
With a strategy in mind, the business usually moves into a period of research and experimentation to identify IoT products, software and other infrastructure elements. Project managers then implement limited proof-of-principle projects to demonstrate the technology and refine its deployment and management tactics, such as configuration and security. At the same time, analysts evaluate ways to use the resulting data and understand the tools and computing infrastructure needed to derive business intelligence from the IoT data. This might involve using limited data center resources for small-scale analytics, with an eye to public cloud resources and services as the IoT project scales.
A business can approach an IoT project in three ways:
Regardless of the approach, the key is to remain focused on the value IoT and its data brings to the business.
Although the risks are well understood, the sheer volume and diversity of IoT devices requires a greater level of attention and control than a business might otherwise exercise. The most detrimental risks of IoT environments include the following:
There is no single, ubiquitous approach to designing and implementing an IoT infrastructure, but there is a common suite of considerations that can help organizations check all the boxes to successfully architect and deploy an IoT project. Below are some important implementation considerations.
Network connectivity. IoT devices can offer several alternatives for connectivity, including hardwired, Wi-Fi, Bluetooth, 4G and 5G. There’s no rule that requires all devices to use the same connectivity, but standardizing one approach can simplify device configuration and monitoring. Also decide whether sensors (inputs) and actuators (outputs) should use the same network or a different one that is segmented or isolated from other networks.
IoT hub. Simply passing all IoT data directly from devices to an analytics platform can result in disparate connections and poor performance. An intermediary platform, such as an IoT hub, can help organize, preprocess and encrypt data from devices across an area before sending that data along for analytics. If a remote facility is IoT-enabled, a hub might gather and preprocess that IoT data at the edge before sending it along for further analysis.
Aggregation and analytics. After the data is collected, it might drive reporting systems and actuators or be gathered for deeper analysis, query and other big data purposes. Decide on software tools used to process, analyze, visualize and drive ML. One example includes the choice of IoT database and database architectures: SQL vs. NoSQL, or static vs. streaming. These tools might be deployed in the local data center or used through SaaS platforms or cloud providers.
Device management and control. Use a software tool capable of reliably servicing all the IoT devices deployed throughout the IoT project’s lifecycle. Look for high levels of automation and group/asset management capabilities to streamline configuration and reduce errors. IoT device patching and updating is emerging as a problem, and organizations should pay close attention to update and upgrade workflows.
Security. Every IoT device is a potential security vulnerability, so an IoT implementation must include a careful consideration of IoT configuration and integration into existing security tools and platforms, such as intrusion detection and prevention systems and antimalware tools. Similarly, the data produced by IoT devices will be subject to data protection, compliance and retention requirements that must be considered.
Storage and computing infrastructure. Although much of the focus is on IoT devices and support, it’s critical to consider what happens to the enormous volume of data produced by the IoT infrastructure. IoT data must be stored, and then the data must be analyzed using extensive computing resources. This can sometimes involve terabytes, petabytes and even exabytes of IoT data processed through dozens — even hundreds — of servers configured for big data computing. The storage and computing infrastructure can be local but is increasingly relegated to public cloud providers.
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The future of IoT can be difficult to predict because the technology and its applications are still relatively new and have enormous growth potential. Still, it’s possible to make some fundamental predictions.
IoT devices will continue to proliferate, and the next few years will see billions of additional IoT devices added to the internet. As of 2023, there are more than 15 billion connected IoT devices, and this is expected to double by 2030. This growth is fueled by a combination of technologies — including 5G connectivity — and countless new business use cases emerging across major industries, such as healthcare and manufacturing.
Coming years will likely also see a reevaluation and increase in IoT security, starting with initial device design through business selection and implementation. Future devices will incorporate stronger security features enabled by default. A combination of new legislation, regulatory pressures and device defaults will emphasize the use of end-to-end IoT data encryption. Existing security tools, such as intrusion detection and prevention, will include support for IoT architectures with comprehensive logging and active remediation. At the same time, IoT device management tools will emphasize security auditing and automatically address IoT device security weaknesses, such as replacing traditional IoT passwords with certificate-based authentication.
In addition, some aspects of AI and IoT are converging to form a hybrid artificial intelligence of things (AIoT) technology intended to blend the data-gathering capabilities of IoT with the computing and decision-making capabilities of AI. AIoT can create a platform more capable of human-machine interaction and advanced learning capabilities. AI use cases for IoT will continue to diversify. Basic AI is already used to predict potential events, such as system failures, using real-time IoT data. Mid-tier AI will grow to provide real-time aid, such as steering assistance during driving lane deviation. And advanced AI will emerge to bring autonomy, such as adjusting a patient’s insulin in response to dynamic blood glucose levels.
IoT data storage and processing at the edge will become more important as IoT device counts and data volumes place ever-greater pressure on network bandwidth and latency. This will continue to push more IoT computing work from centralized infrastructures to distributed/edge computing — closer to where the IoT data is generated and collected.
IoT data volumes will continue to swell and translate into new revenue opportunities for businesses. That data will increasingly drive ML and AI initiatives across many industries, from science, to transportation, to finance, to retail.
Finally, the IoT marketplace will continue to grow and mature as vendors seek to offer platforms and services across the IoT stack. Cloud providers such as AWS already offer comprehensive IoT services, while telecom service providers such as AT&T have focused on IoT marketplaces where users can find varied hardware and software services for different verticals. Market maturity will help speed the creation of new IoT deployments by removing complexity and making the design and deployment process more turnkey for businesses.
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