MQTT, once the cornerstone of IoT communication, is starting to show its age.

The Internet of Things (IoT) has reshaped entire industries, from smart homes to industrial automation, revolutionizing how we connect with the world. At the heart of this transformation has been MQTT, the trusted backbone of IoT communication for over a decade. But as AI pushes intelligence to the edge and devices grow smarter, MQTT is starting to buckle under the weight of these new demands. The time has come to rethink the technologies driving the next generation of IoT.

In this blog, we’ll explore the key factors driving IoT companies to rethink their dependence on MQTT and make the shift to InfinyOn’s data streaming platform. We’ll examine the triggers behind this transition, revealing why businesses are embracing more powerful, real-time solutions to meet the growing demands of AI-driven, edge-focused IoT systems. Finally, we’ll dive into how InfinyOn’s platform empowers businesses to stay ahead in the fast-evolving IoT landscape, ensuring they remain competitive and future-ready.

Challenges with MQTT for IoT

For the past few years, we have interviewed dozens of IoT companies and have seen the following challenges surface over and over again:

• Resolve Intermitent Connectivity Issues
• Deploy AI Features at the Edge
• Transition to Real-Time Data Streaming
• Address Scallability and Operational Challenges
• Ensure Platform Flexibility

Let’s dive into each of these challenges.

Resolve Intermitent Connectivity Issues

IoT systems used by cars, scooters, vessels, and various other devices connected via cellular networks often encounter intermittent connectivity, resulting in frequent connection losses. During these disruptions, critical data is at risk of being lost, particularly because MQTTlacks native caching capabilities. When a TCP/IP session is broken, messages sent with the Fire and Forget quality of service (QoS 0) are irretrievably lost. To mitigate these risks, developers typically employ lightweight databases like InfluxDB or TimescaleDB on edge devices, which can store data temporarily until the connection is restored. This approach requires additional development effort and introduces new challenges.

Other users have implemented custom compression and caching mechanisms to temporarily store data until connectivity is restored. However, as more services are added at the edge, these bespoke caching layers demand continuous updates, becoming increasingly complex and difficult to maintain. The limited resources on edge devices and the way MQTT processes events make it difficult to prevent data loss. This is the reason why vendors like The Thing Industries offer support for QoS 0 only. The growing complexity not only increases operational overhead but also diverts valuable resources from core business objectives, driving companies to explore more scalable and reliable solutions.

Deploy AI Features at the Edge

AI is increasingly shifting to the edge to meet the growing demands for faster, more reliable, and cost-efficient solutions. In critical applications like autonomous vehicles, industrial automation, and smart devices, real-time decision-making is essential, and relying solely on cloud processing leads to unacceptable latency. By processing data locally at the edge, organizations can ensure greater reliability, particularly in areas with intermittent connectivity, while also reducing bandwidth usage and cloud costs by transmitting only relevant data. This localized processing enhances privacy and security by minimizing the transfer of sensitive information. With the increase in 5G and low-power IoT networks using LoRaWAN and improved edge devices with compute, complex AI models can now run directly on edge devices, powering emerging technologies such as smart cities and connected vehicles. Additionally, edge AI enables the delivery of personalized services by learning from local data and ensuring compliance with data regulations.

MQTT does not provide users with the ability to run additional services on the edge, forcing companies to build custom processing layers to meet these demands, which adds operational complexity and overhead.

Transition to Real-Time Data Streaming

The demand for real-time streaming in IoT applications spans across various industries where immediate insights and rapid decision-making are crucial.

In smart cities, traffic management systems, public safety networks, and energy grids rely on continuous data to optimize operations and respond to emergencies.

• Autonomous vehicles and fleet management use real-time data for split-second decisions and route optimization, while healthcare devices stream patient vitals to ensure timely interventions.

• In industrial automation, factories monitor equipment and adjust workflows dynamically, and supply chains track shipments and inventory in real time to prevent delays and stockouts.

• Retailers leverage smart shelves and sensors to personalize promotions and manage inventory efficiently, while smart home devices adjust settings automatically based on user behavior or environmental changes.

• Agriculture relies on streaming data to optimize irrigation and crop health, and environmental sensors monitor air and water quality to respond swiftly to potential hazards.

• Telecom operators use real-time streaming to monitor performance, allocate resources, and mitigate security threats as they arise.

Across these sectors, real-time streaming empowers businesses to make faster decisions, improve operational efficiency, and deliver better user experiences, solidifying it as a cornerstone for the future of IoT.However,

MQTT struggles to handle high-throughput and vast amounts of data. This limitation becomes particularly apparent in applications where data must be processed and analyzed as it arrives, making MQTT troublesome for large-scale IoT systems that demand real-time streaming capabilities.

Address Scalability and Operational Challenges

As IoT applications expand, they encounter several scalability challenges that impact performance, operational efficiency, and cost management. Managing the vast volumes of data generated by millions of devices becomes increasingly complex, often overwhelming cloud infrastructure and driving up storage and bandwidth costs. Device management, including firmware updates and health monitoring, grows more difficult as networks scale. Intermittent connectivity and network latency further hinder real-time performance, while edge computing introduces limits on local processing and storage capacity. Additionally, businesses must comply with data governance and privacy regulations, such as GDPR and CCPA, adding operational complexity. Addressing these challenges requires scalable platforms with seamless device orchestration between the edge and cloud to enable sustainable growth.

MQTT lacks built-in capabilities for managing device updates, health monitoring, and seamless orchestration between edge and cloud, as well as ensuring GDPR and CCPA compliance. These features are essential for maintaining operational efficiency in rapidly scaling networks.

Ensure Platform Flexibility

Platform flexibility is crucial for IoT as it empowers businesses to adapt to evolving technologies, streamline operations, and seize new opportunities. A flexible IoT platform ensures seamless integration with legacy systems while supporting multiple protocols and devices, fostering interoperability across varied ecosystems. It also provides the scalability needed to manage both small-scale deployments and expansive networks without major overhauls. As edge computing and AI continue to reshape the IoT landscape, flexible platforms can quickly adapt, ensuring future-proof operations. This adaptability also allows for customized solutions tailored to specific business needs, optimizing costs by avoiding and ensuring efficient resource management. Flexibility further enhances reliability by distributing workloads across edge and cloud environments, maintaining operational continuity during disruptions. Moreover, as regulations and security threats evolve, flexible platforms enable businesses to stay compliant and secure through timely updates. Ultimately, flexibility gives organizations the agility to innovate, respond swiftly to market demands, and maintain a competitive edge in a rapidly changing IoT environment.

MQTT lacks the adaptability needed to meet evolving IoT requirements. It does not offer native support for advanced edge computing, real-time data streaming, large-scale device management, or built-in AI capabilities. Additionally, it falls short in providing end-to-end orchestration with cloud services, which is essential for seamless integration and scalability in modern IoT environments.

InfinyOn Solution

InfinyOn is a full-featured edge-to-cloud IoT platform that provides a comprehensive solution for deploying intelligent, AI-driven IoT applications. The platform handles data transformation, transmission, and orchestration, allowing businesses to seamlessly integrate and manage their IoT systems.

How Can One Platform Solve All These Challenges?

1. Lightweight and Efficient Architecture

   InfinyOn is powered by Fluvio, an open-source data streaming platform built in Rust. Its low footprint and memory requirements, combined with high throughput, make it suitable for resource-constrained edge devices. The InfinyOn cluster can be effortlessly deployed on hardware like Raspberry Pi, enabling cost-effective and scalable solutions.

2. Seamless Scalability

   The InfinyOn cluster effortlessly scales for cloud environments, allowing users to orchestrate edge and cloud services from a single interface. This unified approach simplifies management and enhances operational efficiency as businesses grow.

3. Built-in Caching Mechanism

   InfinyOn features built-in caching backed by storage to preserve information during network outages. This means that the edge cluster remains operational for edge producers, regardless of cloud connection status, making it easier for businesses to build and maintain their systems.

4. Reliable Data Mirroring

   InfinyOn includes a mirroring function that leverages a robust replication engine to efficiently replicate data to the cloud. This function is designed to operate reliably in low-bandwidth environments, ensuring minimal impact on network performance while maintaining data integrity.

5. Built-in Runtime for AI Models

   With InfinyOn’s Stateful Data Flows (SDF) engine, powered by WebAssembly, system operators can upload AI models and execute them either on the edge or in the cloud. This flexibility enables organizations to deploy intelligent applications tailored to their specific needs, optimizing performance and enhancing decision-making capabilities.

Conclusion

The InfinyOn architecture simplifies the development and maintenance of end-to-end IoT applications. By addressing critical challenges with a robust and versatile platform, InfinyOn empowers businesses to harness the full potential of their IoT investments, ensuring they stay competitive in an ever-evolving landscape.

To learn more about how InfinyOn can transform your edge data processing power digital twins, predictive maintenance, anomaly detection type use cases contact us for a demo today.

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