The Internet of Things is a framework for technology that is rapidly becoming popular in many industries. As this technology converges with other technology stacks such as Big Data and Artificial Intelligence, there has been constant innovation. Simultaneously, IoT devices are becoming more connected, which means that IoT scaling is essential.
IoT can be described as a network made up of electronic devices that are connected to the internet and capable of transmitting and analysing data via embedded sensors. IoT has many applications in industries like healthcare, logistics and education.
Verified Market Research has just released a recent study. It found that the IoT industry is estimated to reach US$ 3.28 trillion by 2027. Continuous updates are required to ensure that the IoT market can handle the huge volume of devices and support systems flooding it.
Let us start by understanding the difficulties associated with IoT scaleability.
IoT Scalability – Challenges
IoT stakeholders have to address certain issues due to the rapid growth in market share. There are many challenges that IoT stakeholders will need to address, including privacy, network security, identity management and data volume. Below are details on these challenges:
- Network Security The growth in IoT devices has brought about the need to secure the network from malicious attacks. For high throughput we need to create new protocols, and implement encryption algorithms.
- Privacy – IoT providers need to ensure the anonymity and individuality IoT users. As more IoT devices join the ever-expanding network, this will be more challenging.
- Governance A lack of a trust management system between providers and users can lead to a breach in confidence. This is one of the biggest research challenges in IoT Scalability.
- Access control: Due to low bandwidth, low power usage and a distributed architecture, access control is a challenge. As new IoT scaling challenges arise, traditional access control systems for end-users and admins will need to be redesigned.
- Big Data Generation IoT Systems make programmed judgements based upon categorized data compiled using multiple sensors. Data volume will increase in proportion to devices. Scaling will become a problem due to large data silos. This data will need unprecedented computing power to determine its relevance.
Let’s examine the various types of IoT scalability.
Techniques to Facilitate Seamless Scalability of IoT
IoT networks and apps must be capable of handling an increase in users and features, as well as the increased number of devices. IoT projects tend to have the long-term goal, which is to increase performance while scaling up. The following techniques are useful for projects that seek to scale up in the long-term.
1Automated Bootstrapping
The interoperability of all IoT devices within a network presents security problems. The number of devices has increased, making it impossible to manually complete tasks such as device registrations, software configuration, bootstrapping, and upgrade.
Automated bootstrapping can make it feasible to perform configuration tasks for scaling manually. To enable automation in IoT devices, it is possible to add the necessary bootloaders. This saves time as well as increases efficiency.
Bootstrapping remote security keys infrastructures can improve the security of device interfaces. Third-party services are used to authenticate devices to masters and vice versa. The device would be embedded with a unique identifier to facilitate secure HTTPS connections among devices and interfaces.
2)Better Control Over IoT Data Pipeline
IoT devices generate a lot of data, so a high-throughput, low latency data pipeline is necessary to allow for easy control. This would enable insights and model inferences to be made that can easily be accessed by artificial intelligence algorithms at scale.
Data pipelines must be able to handle large data volumes. The capacity of the data pipeline would depend on the number of devices connected simultaneously and the data streams.
Adjustments based on the above parameters can be made if the data pipeline is properly managed. The pipeline must have the correct service endpoints, message queues, and stream computation functions.
3 – Three-Axis Approach for Scaling
IoT applications are able to scale up using web service protocols for greater information exchange, encryption, access control, and security. They can do this in three basic directions or “axes”: scaling by cloning across the X-axis and scaling through splitting different things across the Y-axis. Scaling by scaling similar things across Z is another way.
X-axis scale refers to the allocation of more resources in order to distribute demand as they are received on different servers. These requests can be met by servers that can keep state information intact from one request to the next. Such servers make scaling up easier.
The Y axis approach distributes the tasks according to the differences between the processes. Scaling in Z-axis is a way to allocate tasks according to when request and response data reach the server. IoT systems need a scalability model that can scale in all three directions.
4)Reliable Microservices Architecture
These architectures allow applications to communicate with one another through micro-processes. This architecture allows you to easily manage IoT scaleability by splitting each application.
Each section or functional unit in the IoT divided application performs a distinct function. Each functional unit must be built separately in order to maximize scalability. Each functional unit communicates with the other in a way that optimizes IoT applications.
5)Multiple Data Storage Technologies
There are many components to an IoT network. Using different storage technologies for each component would improve scalability. This would allow for easier scaling by separating the data that is generated for each component.
Different data storage technology would have different data querying or retrieval methods. IoT scalability is possible using low-cost storage solutions such as Hadoop HDFS, data warehouses and cloud blobs.
Machine learning algorithms are used to efficiently retrieve large amounts of IoT information. It is important to be clear about the purpose of data collection and how it should be categorized.