The Internet of Things (IoT) is a technological framework that is being adopted in myriad industries at a fierce rate. There has been continuous innovation in this field of technology as it converges with various technology stacks associated with Big Data and Artificial Intelligence. Simultaneously, the number of connected IoT devices is also increasing rapidly, posing the need to improve one of the most important aspects of IoT - scalability.
IoT is basically a network of electronic devices connected to the internet capable of transferring and analyzing data through embedded sensors. These capabilities of IoT have seen applications across industries such as healthcare, logistics, education, home entertainment, and banking.
A recent study by the research firm Verified Market Research reported that the IoT market is expected to be worth US$ 3.28 billion by 2027. To accommodate the massive volume of IoT devices and support systems flooding the markets, constant updates need to be rolled out for error-free scalability.
Let us begin by understanding the challenges associated with IoT scalability.
Challenges Associated with IoT Scalability
With the explosion in market share, IoT stakeholders must also deal with certain challenges. Aspects like network security, identity management, data volume, and privacy are sure to pose challenges. These challenges are discussed in detail below:
- Network Security: The expansion in the volume of IoT devices is accompanied by an urgent need to secure the network against malicious attacks. We must define new protocols and incorporate encryption algorithms to enable high throughput.
- Privacy: Ensuring the anonymity and individuality of IoT users must be critical for any IoT provider. And this will only get more challenging as more IoT devices enter an ever-expanding network.
- Governance: Without a proper governance system for trust management between the user and provider, a breach of confidence between entities is imminent. This is one of the leading research challenges in IoT scalability.
- Access Control: With the low bandwidth between the IoT device and the internet, low power usage, and distributed architecture, access control will be a challenge. So, conventional access control systems for admins and end-users must be refurbished as and when new IoT scalability challenges arrive.
- Big Data Generation: IoT systems make programmed judgments based on categorized data compiled from multitudinous sensors. As data volume increases disproportionately with an increasing number of devices, scaling will present the challenge of large silos of Big Data. Determining the relevance of this data will require unprecedented computing power.
Let us understand the types of scalability in IoT.
Techniques to Facilitate Seamless Scalability in IoT
IoT networks and applications need to be made capable to handle an increase in features and users and especially the number of devices. Most IoT projects start with the long-term goal of improving performance while scaling up. Some of the following techniques can help such projects achieve practical and effective long-term scalability:
All IoT devices on the same network can interface with each other which introduces a host of security issues. With an increase in the number of devices, it is no longer feasible to manually do tasks like bootstrapping, software configuration, device registration, and upgrades.
The feasibility of carrying out configuration tasks associated with scaling manually can be solved with automated bootstrapping. Adding required bootloaders to enable automation in IoT devices saves time and increases efficiency.
The security of device interfaces can be enhanced by bootstrapping remote security key infrastructures. This uses third-party services for authentication of the devices to the master and vice versa. In this case, the device would come embedded with a unique identifier for facilitating secure HTTPS connections between devices and interfaces.
2)Better Control Over IoT Data Pipeline
The inflated volume of data generated by IoT devices requires a high-throughput low-latency data pipeline that allows for ease of control. This would allow for making insights and model inferences that are easily consumable by artificial intelligence algorithms, even at scale.
Scaling up to include an increased number of devices requires data pipelines to handle sudden surges in data as well. The number of simultaneously connected devices and data streams would decide the capacity of the data pipeline.
Proper control over the data pipeline would allow for adjustments according to the above parameters. The right service endpoints, message queues, and stream compute functions must be applied to the pipeline.
3)Three-Axis Approach for Scaling
IoT applications can scale up through web service methodologies for increased information exchange, encryption, and access control. They do so across three fundamental directions or "axes" - scaling by cloning across X-axis, scaling by splitting different things across Y-axis, and scaling by splitting similar things across the Z-axis.
X-axis scaling defines the utilization of more resources to distribute demands as and when they are received across various servers. These demands are serviced through servers that are capable of preserving state information from one request to another. Scaling up becomes easier with such servers.
The Y-axis approach perpetually distributes the tasks at hand based on differences between the processes involved. Scaling in the Z-axis means allocating tasks as and when the request and response data arrives at the server. So, a scalability model across these three directions is ideal for IoT systems.
4)Reliable Microservices Architecture
In this type of architecture, applications consist of individualistic micro-processes communicating with each other through platform-independent APIs. Dividing each application with this architecture allows for easily manageable IoT scalability.
Each segment or functional unit of the divided IoT application, performs a separate function. For optimal scalability, each of these functional units must be compiled separately before they are executed. The functional units communicate with each other systematically allowing for simultaneous optimization of IoT applications.
5)Multiple Data Storage Technologies
An IoT system has various components and using different data storage technologies for them would help with scalability. By compartmentalizing the storage of data generated for each component, it would make scaling a more organized process.
Different data storage technologies would also have different data querying and retrieval methods. Low-cost high-volume data stores such as data lakes, data warehouses, Hadoop HDFS, or cloud blob storage are viable options for IoT scalability.
Machine learning algorithms would be applied for retrieving large amounts of IoT data effectively. Following proper discipline in defining the scheme of the data being collected and cataloging it would clean the data for these algorithms.
Boost the Efficiency of your IoT Scaling Plan
If you are looking to scale up your IoT applications you need well-defined roadmaps for the developers and your partners in the IoT ecosystem. You need to leverage critical edge expertise to simplify IoT scalability with proven success in specific use cases.
The prospects for your IoT ecosystem are limitless with the Daffodil team of IoT Software Developers. You can schedule a free consultation with Daffodil if you are on the lookout for smart, cost-effective IoT solutions.