IoT Cloud Computing in IoT: Benefits and Challenges Explained

Across diverse industries, spanning from manufacturing to healthcare, an abundance of sensors and other IoT devices diligently gather information and produce insightful data every day. Oftentimes, this data then needs to be passed down to some storage, processed accordingly, and acted upon if needed. In recent years, with the rise of cloud computing in the software industry, cloud platforms are becoming the central hub for such purposes, and the terms cloud computing and the Internet of Things are being mentioned together more and more frequently.

In this article, you'll gain insights into the advantages, challenges, and pivotal role of cloud computing in the IoT ecosystem.

Explanation of IoT and Cloud Computing

Let’s start with the basics.

Cloud computing delivers computing services, such as servers, storage, databases, networking, software, and more, over the Internet (‘the cloud’). 

The Internet of Things describes the network of physical objects embedded with sensors, software, and other technologies to connect and exchange information with other devices and systems over the Internet.

The Internet of Things technology tends to build an ecosystem of devices that generate data and can communicate with one another. On the other hand, cloud computing allows to store, process, and accessing the collected data from anywhere, at any time. 

Combining these two technologies has brought a new term to our lives — Cloud Internet of Things. In simple terms, the Cloud Internet of Things is an IoT infrastructure connected to cloud services. It uses cloud computing services to collect and process information from IoT devices and manage these devices remotely. Since the cloud systems are scalable, it’s possible to process large amounts of data simultaneously. 

How Does IoT Cloud Computing Generally Work?

Cloud IoT is an architecture that connects all the necessary IoT devices to cloud-based servers, resulting in real-time data analytics, data-driven decision-making, optimization, and risk mitigation. 

In practice, IoT cloud computing basically means there is a network of IoT devices that gather data and transmit it to the cloud for further processing, analysis, and storage. This can be broken down into the following steps:

1. Data gathering (via IoT devices)
2. Transmission to the cloud
3. Data processing
4. Data analysis
5. Decision making
6. Possible feedback loop back to the IoT devices if certain action needs to be taken based on the previous step.
7. Data storage

Note that “decision making,” “data analysis,” and “data storage” are not strictly ordered. In fact, even “transmission to the cloud” and “data processing” can switch places if one utilizes edge computing, whereby some data is processed and aggregated on the level of IoT devices or within the IoT network before sending the already processed (or partially processed) and aggregated data to the cloud, which ultimately saves the network bandwidth and decreases latency since the volume of the outgoing data is being decreased.

On the cloud, there is some application/service that collects the gathered data, processes it (if needed), performs on-the-fly analysis, and/or stores the data in the database. This data can later be accessed for further analytics or monitoring purposes.

If we, for instance, have sensors that gather some metrics about devices in some factory, we could later monitor this data on the cloud to check for potential malfunctioning of any devices or anomalies of any kind. This process can, of course, be fully automated.

Another simplified example can be a data center facility with some temperature sensors that send data to the cloud. Then, if the temperature rises, the cloud application could notice it, make a decision, and send a command back to the facility to spin up more fans for better cooling, as well as a notification to the staff.

Cloud vs. On-Premises Solutions

When talking about the cloud, we typically mean solutions provided by cloud providers. Another option is to operate the entire infrastructure similar to that of a cloud yourself on the premises of the company (typically referred to as on-prem solutions). The difference between the two is obvious: in the former case, the data is sent to external cloud providers, and in the latter, we have to manage everything ourselves internally.

The above-mentioned fact might raise some concerns and questions: whether it makes sense to transfer the data to a third party, especially in terms of security, data privacy, and peace of mind.

The fact is that on-premises solutions can, in principle, have the same level of functionality and should, in theory, have superior security than third-party cloud services since the data never leaves the company. However, an important thing is to understand that on-premises solutions can achieve a higher security level only if security is done right. We have to take into account that security is hard, and implementing everything yourself can result in many hidden vulnerabilities. On the other hand, well-established cloud providers service thousands of companies and invest heavily in their security, trying to incorporate many industry standards and best practices. They also typically provide readily available, highly customizable, and easily scalable web services that can be configured to the particular needs of your business and would be very challenging for a smaller company to implement on their own. This is to underline the idea that a lot of smaller companies can benefit from using the existing cloud platforms that provide vast functionality and rigorous security. In comparison, bigger companies with a lot of resources might consider investing in implementing an on-premise solution specifically tailored to all the requirements and policies they might have.

Benefits of Cloud Computing for IoT

Now that we have established a distinction between cloud and on-premise computing for IoT let’s consider some important benefits of the cloud IoT platforms:

1. Ease of Implementation

As mentioned in the previous section, the main benefit of cloud platforms is not having to spend additional time and resources on developing the data collection and processing infrastructure. Cloud offerings provide many out-of-the-box features suitable for a wide range of use cases.

2. Data Accessibility and Mobility

Data on the cloud can be easily accessed from anywhere, anytime, without the need for a VPN to be configured, which also allows you to hook in additional services to your cloud that will consume this data for their purposes. In addition, clouds can be accessed from any device, increasing the convenience of use and providing the ability to control and monitor your IoT fleet from mobile devices. What’s also important is that the collected data can usually be distributed, transferred to a different server (which might be geographically closer for example), or be easily moved to some other service within the cloud.

3. Integration With Other Services

Apart from being able to integrate your own or external services with the cloud, cloud platforms themselves offer a vast array of additional solutions for processing real-time data, predictive analytics, automated responses, and AI-driven decision-making.

4. Scalability

Many cloud services feature extensive scaling capabilities that can be configured and enabled for your systems, ensuring higher performance in cases of traffic volume growth.

5. Data Security and Reliability

Well-reputed cloud providers take security seriously and provide a lot of relevant features and safety measures. Many of these features have to, of course, be used and correctly configured by the customers, but the general set is extensive enough to provide comprehensive security for your systems without many pitfalls associated with implementing security on your own from scratch.

6. Cost-Effectiveness

When using cloud services, you usually pay for what you utilize, which might be much more cost-effective than the high investments into developing and maintaining your own computing, analytics, and storage infrastructures.

7. Resilience

Many clouds feature distributed solutions with high availability that make sure there is no single point of failure, enhancing resilience. On top of that, it is usually possible to configure automated backups of your data to ensure its safety.

8. Maintenance and Updates

Cloud providers regularly update and maintain their systems and hardware, which is something that can be easily overlooked or neglected by individual companies. If you have your own software running on the cloud, you would, of course, have to update it yourself, but the underlying systems and infrastructure are typically kept up to date for you.

9. Deployment Speed

Once the cloud system has been configured, it’s typically much faster and easier to deploy the system again, for example, to a different location or to set up a similar system by exporting the cloud configuration and applying it in subsequent deployment. The ease of this process, of course, depends on a particular cloud provider and service and can not always apply to cases of migrating from one provider to another, but a general deployment flexibility is still enhanced in many cases when using the cloud, and many providers advertise and pride themselves on such features.

Existing Solutions for Cloud Internet of Things

In order to be more practical, here are some examples of well-known services provided by popular cloud providers that can accommodate and work with IoT data:

Amazon Web Services (AWS)

• AWS IoT Analytics: This AWS service is able to process, enrich (with metadata), store, and analyze large volumes of IoT data with the ability to query and visualize the results.
• Amazon Kinesis: Amazon Kinesis was developed to collect, process, and analyze real-time streaming data, enabling IoT applications to work with data as it arrives.
• Amazon Redshift: Redshift is a highly scalable data warehouse service that can be utilized for running sophisticated analytical queries on large volumes of IoT data.

Google Cloud

• BigQuery: BigQuery is Google's serverless data warehouse that enables scalable analysis over massive amounts of data using SQL-like syntax.
• Dataflow: A stream and batch data processing service suitable for processing and analyzing IoT data in real time.

Microsoft Azure

• Azure IoT Hub: Azure IoT Hub is a central message hub that facilitates communication between IoT applications and devices and allows for monitoring and control of your IoT infrastructure.
• Azure Stream Analytics: A real-time analytics service that can process vast amounts of streaming data rapidly flowing in from the IoT devices and more.
• Azure Data Lake Storage and Analytics: This service offers a scalable data store for handling large data volumes and running extensive analytics.

Oracle Cloud

• Oracle IoT Cloud Service: This offering allows you to collect and analyze the data from your IoT devices in real time with the ability to integrate it with other Oracle services.
• Oracle Data Integration Platform Cloud: Assists in transforming, managing, and integrating data from various sources, including IoT devices, and provides analytics capabilities.

This is, of course, not a complete list of possible offerings, and there are more options on the market to suit anyone’s needs.

Challenges and Concerns When Integrating Cloud Computing With IoT

Even when using a good and easy-to-use cloud solution, integrating your IoT network can prove to be a challenging task. Here are some general points to take note of when considering such an integration.

Security Concerns

Data Exposure

When large amounts of data are being transferred from the end devices to the cloud, the risk of exposing sensitive data during transit increases.

Mitigation: authentication and secure data transfer protocols have to be employed. Remember that clouds usually provide security mechanisms, but those still need to be explicitly used by the end users. For example, certificate or username-password-based authentication, MQTTS, etc. The typical process of configuring transport security and MQTTS usually involves generating certificates for your broker and applying them in configuration files (though, in some cases, depending on the broker of choice and the cloud provider, the steps might differ significantly).

Vulnerable Devices

If data is securely transferred and stored on the cloud, then the end IoT devices themselves might become the weakest link in the security chain, being potentially susceptible to attacks (and side-channel attacks in particular).

Mitigation: Apart from the use of secure communication protocols, devices' firmware should be regularly updated and any unnecessary functions disabled. Additionally, regular security audits should be performed.

Device Issues and Standardization

Device Maintenance and Replacement

Old IoT devices need to be replaced and regularly maintained. Failure to do so might lead to service disruptions, introduction of vulnerabilities, etc.

Mitigation: use device management solutions that provide over-the-air updates and remote diagnostics to monitor the device's "health." Also, the logic of user applications and data processing solutions needs to account for potential device replacement downtimes.

Standardization of IoT Devices

Different IoT devices might use different protocols and data formats, which would make it hard to gather all the data into a single data store.

Mitigation: Use open protocols and standards or gateway devices (and/or applications) that will translate device-specific protocols into open protocols (a typical industry standard would be to conform to MQTT protocol).

Device Limitations

Some IoT devices are too limited in their resources to support data formats or encryption techniques required by data processing solutions from the cloud.

Mitigation: Again, make use of gateway/intermediary devices/applications that will aggregate and translate the data into appropriate format and/or use a required level of encryption.

Latency Issues

When using cloud solutions, data might arrive at the processing point a bit later than when compared to on-premise set-ups, especially if data centers for cloud computing are geographically very far from the IoT devices or if there are network disruptions. This might be a potential concern for time-critical IoT applications that require real-time data. However, in the real world, for most general applications, it is not such a common issue.

Mitigation: Choose closer data centers and/or apply edge computing to decrease the volume of the data being sent, which will save some bandwidth.

Other Concerns

Other concerns include many less prominent and more obvious considerations, like the need to enable and properly set up the service’s autoscaling if scalability is needed or configuring backups, monitoring, and managing the data. All these concerns are usually addressed by the cloud providers, but it is typically up to the user to configure the provided solutions to suit the needs of their application.

Hence, the most important thing to note is that even though clouds provide many features in regard to security, performance, and scalability, most of these have to be first properly and meaningfully configured to give the expected results. Apart from that, never forget that your physical devices also need to implement security and be regularly maintained.

Summary

In conclusion, combining IoT and cloud computing creates an unparalleled ecosystem where real-time data collection meets massive computational power, reshaping the fundamentals of how businesses operate and innovate.

By interlinking the vast network of IoT devices with the expansive capabilities of the cloud, organizations get a lot of benefits. This allows for more innovative, data-driven decision-making and fosters a proactive approach to business challenges, including predictive analytics, automated responses, and AI-driven strategies.