5 Jul 2022| Marketing
IOT (Internet Of Things)
Effective Strategies To Overcome Scalability Issues In IoT
The Internet of Things (IoT) is a technological framework that is rapidly gaining traction in various industries. As it converges with various technology stacks associated with Big Data and Artificial Intelligence, this technology field has seen constant innovation. Simultaneously, the number of connected IoT devices is rapidly growing, necessitating the improvement of scalability, which is one of the most important aspects of IoT.
The Internet of Things (IoT) is a network of electronic devices that are connected to the internet and capable of transferring and analyzing data via embedded sensors. Healthcare, logistics, education, home entertainment, and banking have benefited from the Internet of Things capabilities.
Scalability is an important consideration in almost all technological projects, and the Internet of Things is no exception. Suppose you already know your IoT solution will be adding new devices regularly and will need to handle an increasing amount of data. In that case, IoT scalability should be top of mind.
Scalability refers to a system’s ability to handle an increasing amount of work by adding more resources; challenges unique to IoT technology remain a sticking point for many developers. Furthermore, if not addressed early on, such issues can develop into problems, resulting in longer maintenance times and latency issues.
IoT stakeholders must deal with many challenges as their market share grows. Network security, identity management, data volume, and privacy are likely problematic.
Some of the common IoT challenges are as follows:
As the number of IoT devices grows, there is a pressing need to protect the network from malicious attacks. To achieve high throughput, we must define new protocols and incorporate encryption algorithms.
Any IoT provider must prioritize ensuring the anonymity and individuality of IoT users. And as more IoT devices join an ever-growing network, this will only become more difficult.
Access control will be difficult due to the low bandwidth between the IoT device and the internet, low power consumption, and distributed architecture. As a result, traditional access control systems for administrators and end-users must be updated as new IoT scalability challenges arise.
A breach of trust between entities is imminent without a proper governance system for trust management between the user and the provider. One of the most important research challenges in IoT scalability is this.
Big Data Generation
IoT systems make pre-programmed decisions based on data categorized from various sensors. Scaling will present the challenge of large silos of Big Data as data volume grows disproportionately with the number of devices. The computing power required to determine the data’s relevance will be unprecedented.
Tips To Overcome IoT Scalability Issues
IoT networks and applications must be able to handle more features, users and a greater number of devices. The majority of Internet of Things projects begin with the long-term goal of increasing performance while scaling up.
Some of the tips or techniques listed below can aid in achieving practical and effective long-term scalability in such projects:
All IoT devices on the same network can communicate, posing a slew of security concerns. Due to the growing number of devices, manual tasks such as bootstrapping, software configuration, device registration, and upgrades are no longer feasible.
Automated bootstrapping solves the problem of manually performing configuration tasks associated with scaling. Adding required bootloaders to IoT devices to enable automation saves time and improves efficiency.
Bootstrapping remote security key infrastructures can improve the security of device interfaces. For authentication of the devices to the master and vice versa, third-party services are used. In this case, the device would have a unique identifier embedded in it to allow for secure HTTPS connections between devices and interfaces.
Choosing the right architecture for your project will result in fewer issues later on. Furthermore, it’s critical to select an option that considers the future, and the choice between MQTT and REST is one you’ll have to make.
With smaller projects, using the MQTT protocol’s one-to-many system for communication between IoT devices may seem like a good idea. However, it may not be as effective in the long run. This is due to the complexity of its programming, latency and security issues, and the need for ongoing maintenance. It would be best to use a more simple architecture in the long run.
A REST API, for example, provides developers with several advantages, including ease of use, improved security, and increased scalability.
Take Advantage Of A Decentralized AEP Platform
Using a decentralized IoT Application Enablement Platform is another way to scale up your IoT project (AEP) effectively.
For instance, A ‘traditional’ AEP solution, such as Amazon Web Services IoT or Microsoft Azure, uses the cloud to send data between IoT devices. As a result, data must be sent to an external centralized database and temporarily stored there before a client can interact with a device. This isn’t good for the security of the device. This is because, even if data is encrypted between the client and the database, the data in the database is still vulnerable to cyber-attacks.
Furthermore, because all data traffic must pass through the same central “relay” setup, a traditional AEP solution has more latency issues due to the extra step of going through the cloud. A decentralized AEP solution makes it easier to scale up by reducing security risks and having minimal impact on communication speed – regardless of how big you get.
While IoT projects have their own set of scalability issues, taking the necessary steps can help to alleviate these issues.
As a result, if you want an IoT project that can scale, be aware of the challenges, have a strong planning phase, invest the necessary time and money, and carefully choose your architecture.
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Tags: Technology Artificial Intelligence