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.
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:
Data Management and Processing
Volume of Data: The sheer volume of data generated by IoT devices can be overwhelming. Managing and processing this massive amount of data in real-time poses challenges in terms of storage, bandwidth, and computational resources.
Data Quality and Consistency: Ensuring data quality and consistency across a distributed and scalable IoT system can be challenging. Inconsistent data can lead to errors and unreliable insights.
Interoperability
Diverse Ecosystem: IoT devices come from different manufacturers and use various communication protocols. Achieving seamless interoperability among diverse devices and protocols is a significant challenge, as it requires standardization and compatibility efforts.
Consider a smart home with devices from different manufacturers—smart thermostats, lights, and security cameras. In this scenario, allowing them to work together, can be difficult due to varying communication protocols and standards.
Security and Privacy
Increased Attack Surface: As the number of connected devices grows, the attack surface for potential security breaches also expands. Ensuring the security of IoT devices, networks, and data becomes increasingly challenging.
Data Privacy Concerns: With a vast amount of sensitive data being generated by IoT devices, maintaining privacy becomes a critical concern. Ensuring secure data transmission, storage, and access control is challenging.
Resource Constraints
Limited Processing Power: Many IoT devices operate with limited processing power, memory, and energy resources. Scaling IoT systems while considering these constraints requires efficient resource management and optimization.
Network Bandwidth: Limited network bandwidth in IoT deployments can lead to bottlenecks and increased latency. Balancing the communication needs of devices with constrained bandwidth is a persistent challenge.
Example: Wearable health devices often operate on battery power and have limited processing capabilities. Scaling up the number of connected wearable devices while optimizing for energy efficiency becomes a balancing act to ensure prolonged battery life and continuous functionality.
Scalable Architecture
Complexity of Scaling: Scaling involves more than just adding devices; it requires adapting the architecture to handle increased data flow and processing demands. Designing an adaptable architecture is essential for sustainable scalability.
Orchestration and Coordination: Coordinating a multitude of devices efficiently, ensuring fault tolerance, and dynamically scaling the system present ongoing challenges in IoT architecture.
Regulatory and Standards Compliance
Lack of Standards: The absence of universal standards for IoT devices and communication protocols can hinder scalability efforts. Adhering to diverse regional and industry-specific regulations further complicates IoT scalability.
Cost Management
Infrastructure Costs: Scaling an IoT deployment often involves significant infrastructure costs. Managing these costs efficiently while ensuring the required level of performance is a challenge for organizations.
Device Lifecycle Management: Handling the lifecycle of a large number of devices, including maintenance, upgrades, and replacements, can be financially and operationally challenging.
Let us understand the types of 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:
1)Automated Bootstrapping
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.
6) Distributed Event Bus
A distributed event bus is akin to a sophisticated communication infrastructure for your IoT system. Instead of individual components directly interacting, they exchange messages or "events" on this shared platform. This promotes independence and flexibility, allowing each component to operate without being tightly coupled to others.
This approach enhances the adaptability and resilience of your system. In the event of a glitch in one area, it minimally impacts others. It's a strategic architecture that promotes a more coordinated and efficient operation of your IoT devices.
Selecting the appropriate technology for this communication infrastructure is critical. Reliability and simplicity should be prioritized to ensure seamless and dependable message exchange.
7) Intelligent Caching Mechanisms
Intelligent caching is like having a smart memory system. Instead of fetching data from a central storage point every time, frequently used information is strategically stored closer to where it's needed. It's akin to having a well-organized filing system that keeps frequently accessed files within easy reach.
This leads to quicker data retrieval, a more responsive system, and less strain on the central server during busy periods. It's like having a personal assistant who anticipates your needs by keeping the most-used information readily available.
Imagine setting up this assistant—deciding how long information should be kept readily available, ensuring it stays up-to-date, and making sure it doesn't take up too much space.
8) Elastic Cloud Infrastructure
Elastic cloud infrastructure provides the ability to dynamically adjust the resources allocated to your system based on demand. It's akin to magically expanding or contracting your marketplace space in response to fluctuations in user activity.
This flexibility ensures optimal resource utilization, providing precisely the computational capacity needed. It's a cost-effective approach, akin to paying only for the resources consumed during peak times, minimizing unnecessary expenses.
Careful planning is required for the seamless scaling of resources. Striking the right balance in the speed of scaling and optimizing costs is analogous to having a strategic real estate approach, ensuring resources align precisely with the demands of your IoT applications.
ALSO READ: Breaking The Top 10 Myths About The Internet of Things
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.