IoT Design Challenges and Solutions


The development of the internet of things architecture is riddled with various challenges. It is important to understand that there is a major difference between designing desktop systems and web applications in comparison to developing an IoT infrastructure as the latter has different hardware and software components. Hence, you cannot use the same traditional approach with your IoT applications which you have been using in the past with web and desktop software. What you can do is that consider the following IoT design challenges and solutions.

1.   Security

One of the key considerations in an IoT ecosystem is security. Users should have trust in their internet of things equipment which can help to share data easily. The lack of secure design means that IoT devices can encounter different types of security vulnerabilities in all of their entry points. As a result of these risks, private and business data can be exposed which can lead to compromise the complete infrastructure.

For example, in 2016, Mirai first arrived on the internet. Mirai is a type of botnet which went on to infect the IoT devices of a major telecommunications company in the US: Dyn. As a consequence, a large number of users were disconnected and they were without any internet connectivity. DDoS was one of the key hacking strategies which were used by hackers.

Solution

A considerable portion of the responsibility to secure IoT devices falls into the hands of the vendors. IoT vendors should incorporate security features in their devices and make sure to update them periodically. For this, they can use automation to perform regular patching. For instance, they can use Ubuntu in tandem with Snap which can help in a quick update of devices. These atomic styles assist developers in the writing and deployment of patches.

Another strategy is to ensure that DDoS attacks are prevented. For this, you have to configure routers so they can drop junk packets. Similarly, irrelevant external protocols like ICMP should be avoided. Lastly, a powerful firewall can do wonders and make sure to update the server rules.

2.   Scalability

By 2020, Cisco predicts that there will be around 50 billion functional IoT devices. Therefore, scalability is a major factor to handle such an enormous number of IoT devices.

Solution

For scalability, there are many IoT solutions which are not only scalable but are also reliable, flexible, and secure. For instance, one of the most common data management infrastructures is Oracle’s Internet of Things. This solution provides many efficient services that help with the connection of a large number of IoT devices. As Oracle is known to be a massive ecosystem with different services and products integrating into its architecture, thus they can help to fix a wide range of concerns.

For mobile development in your IoT ecosystem, you can use Oracle Database Mobile Service—a highly robust solution that helps to create a connection between embedded devices and mobile apps while fitting all the scalability requirements. There is also an option to use a scalable database like Oracle NoSQL Database which can offer you a chance to work on the modern “NoSQL” architecture.

3.   Latency

Latency is the time period which data packets take to move across the network. Usually, latency is measured through RTT: round-trip time. When a data packet goes back and forth from a source to its destination, the time it requires to do this is known as RTT. Milliseconds are needed to measure the latency of data centers where the range is fewer than 5 milliseconds.

IoT ecosystem usually employs several interconnected IoT devices at once. Thus, the latency increases as the network become heavier. The cloud is seen as the edge of the network by the IoT developers. It is necessary to understand that latency issues can affect even routine IoT applications. For instance, if you have an IoT-based automation system in your home and you turn on a fan then latency issues related to cloud processing, gateway processing, wireless transmission, sensing, and internet delivery can arise.

Solution

The latency issue is quite complex. It is imperative that businesses must learn the management of latencies if they plan to use cloud computing effectively. Distributed computing is one of the components which raises the cloud latency’s complexity. Application requirements have changed. Rather than using a local infrastructure for storage, services are distributed internationally. Additionally, the birth of big data and its tools like R and Hadoop are also boosting the distributed computing sector. Internet traffic has made latencies dependent on such scale that they find it hard to utilize similar bandwidth and infrastructure.

Another issue which plagues the developer is the lack of tools that can help with the measurement of the latest applications. Traditionally, connections over the internet were tested via the traditional ping and traceroute. However, this strategy does not bode well today as ICMP is not required for modern-day applications and networks in IoT. Instead, they need protocols like HTTP, FTP, etc.

By traffic prioritization and focusing on QoS (Quality of Services), you can address cloud latency. Before the birth of modern cloud computing, SLAs (Service Level Agreements) and Quality of Services were used to make sure that traffic prioritization was done well and ensure that latency-sensitive applications can use the suitable resources for networking.

Back-office reporting can force applications in accepting decreased uptime but the issue is that a lot of corporate processes cannot sustain the downtime because it causes major damage to the business. Therefore, SLAs should concentrate on specific services by using performance and availability as metrics.

Perhaps, the best option is to connect your IoT ecosystem with a cloud platform. For instance, you can use Windows Azure which is quite is powerful and robust, and can particularly serve those businesses which plan to develop hybrid IoT solutions—in such infrastructure on-premises resources are used for the storage of a considerable amount of data while cloud migration help with other components. Lastly, the collocation of IoT components to third-party data centers can also work out well.

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