How will IoT cloud platforms evolve in the next five years?

The IoT cloud platform market is experiencing significant growth, projected to increase from $6.4 billion in 2020 to $11.5 billion by 2025, driven by widespread IoT device adoption across industries. This growth demands scalable, reliable, and cost-effective cloud solutions. With increasing competition from major players like Amazon Web Services, Microsoft Azure, and Google Cloud Platform, the market is rapidly evolving, highlighting the need for continuous innovation.

The different cloud platforms in IoT, keeping the things changes every time will be crucial. Easy-to-use interfaces, clear dashboards, and straightforward setup are important. The goal is to make sure that businesses, no matter how tech-savvy, can easily use IoT solutions. This approach aims to make IoT technology simple and useful for a wide range of users.

There are many players in this market today. Large players like Microsoft Azure, Google Cloud, AWS as well as Software AG, PTC, Oracle, SAP, and many more. Then there are smaller and more focused vendors on specific industries and solution spaces. It is obvious that there will be a lot of change here - larger vendors will merge, smaller vendors will be gobbled up by larger vendors, smaller vendors might fail and close business, etc. It is going to be critical for any manufacturer looking to work with an IoT platform vendor to choose the right strategy, i.e. look at the longevity of the IoT Platform vendor, or making sure the solution can be easily converted into another platform if required in the future.

The growth of the IoT cloud platform market reflects the expanding influence of IoT technology. The competition among major players emphasizes the need for continuous innovation and the ability to address diverse customer needs across various industries. As the market evolves, companies that can balance scalability, reliability, and cost-effectiveness are likely to thrive in this dynamic landscape.

We have been in IoT AEP (Application Enablement Platform) for the past 10 years with MasterOfThings IoT AEP and I can assure you there has been a considerably increase in the demand but not as much as it was estimated & it is not booming yet. From a competition perspective, a lot of people do not know the fact that, large players have already shut down their claimed IoT platform business for a good reason. Google IoT cloud core announced shut down, IBM Watson IoT announced shutdown, Cisco Kinect announced shutdown, Ericsson has sold/gave away their IoT Accelerator. Nokia silently killed both Impact & Wing. SAP Leonardo IoT silently disappeared. Oracle has never been their. Still naive new commers appear making use of unaware customers.

IoT cloud platforms are set to undergo a transformative shift over the next five years. Beyond edge computing and hybrid cloud adoption, industries like healthcare and manufacturing will increasingly rely on IoT platforms to enhance operational efficiency. In healthcare, real-time patient monitoring and predictive maintenance will become the norm. Meanwhile, in manufacturing, IoT platforms will enable predictive quality control and supply chain optimization. Furthermore, privacy and data security will remain paramount, driving the development of robust encryption and compliance features to protect sensitive IoT data, ensuring a secure IoT future.

The Edge and the Cloud IoT are two pillars of the Internet of Things, Cloud-to-Edge hybrid strategy is the way forward for Industrial IoT deployments to scale 1)    It allows customers to run IoT enabled business processes at the edge and in the cloud, allowing for consistent execution of business processes across the cloud and multiple Edge nodes. 2)   Customers can train predictive or Machine Learning models in the Cloud and deploy them on the Edge. 3)   Customers can design interoperable rules, and actions across the cloud and the edge.   Industry 4.0 requires computation intensive use cases at the edge. Edge analytics, especially ML is widely used in the manufacturing plants for predictive maintenance and predictive quality use cases

Edge computing, a trend set to shape IoT cloud platforms, processes data near its source to reduce latency and security risks, supporting real-time analysis. This complements cloud computing, leading to hybrid models that balance local processing with cloud-based analytics, necessitating platforms that facilitate integration and management of both edge and cloud resources.

Secure and efficient device onboarding is mandatory to enable the widespread adaptation of Edge computing. Connecting devices to today's IoT Cloud Platforms is typically a manual process that is cumbersome for the end users and error-prone. Vendors must provide plug-and-connect capabilities for IoT devices to support customers and utilize the potential of Edge computing.

This is a definite trend. As the number of connected IoT devices grow, the requirements to process data and generate real-time insights is placing pressure on centralized data centers and the cloud resulting in bandwidth, energy, and latency issues. There are reports out there sharing ~35% of workloads in the US reside at the edge. Certain industry segments are very focused on streaming and analyzing data real-time at the edge. Supporting the growth of intelligent edge solutions across industry segments will enable automation and rapid machine-to-machine communication and revenue growth at the edge.

The integration of AI and ML into IoT cloud platforms presents immense opportunities for innovation and growth. By integrating AI and ML capabilities into IoT cloud platforms and embracing these strategies, providers can unlock the full potential of IoT data, enhance customer experiences, and drive innovation within the rapidly evolving IoT landscape.

AI and ML will significantly shape IoT cloud platforms, enhancing data analysis to support tasks like predictive maintenance and anomaly detection. Platforms must offer AI and ML tools and services to help customers develop scalable IoT solutions, while also using these technologies to optimize platform performance and security.

In the coming five years, the synergy of IoT and AI/ML in IoT cloud platforms will redefine industries. Agriculture will see precision farming with AI-driven crop management. Retail will benefit from AI-powered inventory optimization and personalized shopping experiences. Additionally, AI-driven predictive analytics will revolutionize logistics and transportation, leading to efficient route planning and reduced emissions. The challenge lies in data integration and model accuracy. As these platforms evolve, partnerships between IoT providers and AI specialists will be key to unleashing the full potential of AI and ML in IoT applications.

Tenho visto nos ultimos 8 anos anos algumas iniciativas muito interessantes dos fabricantes de semicondutores para conseguir colocar o aprendizado de máquina e a inteligencia artificial dentro de microcontroladores de baixo custo e de processadores simples. Fazer o uso destes dispositivos nos nós de IoT permite que muito dado seja processado no próprio nó e na borda do sistema, reduzindo a carga de dados que se necessita transmitir para a nuvem, facilitando o uso de canais de transmissão mais simples e baratos e reduzindo os custos envolvidos no processamento em nuvem.

In den nächsten fünf Jahren werden IoT-Cloud-Plattformen zunehmend durch KI-Integration revolutioniert. KI wird die Analyse großer Datenmengen in Echtzeit ermöglichen, wodurch vorausschauende Wartung und optimierte Betriebsabläufe gefördert werden. Machine Learning wird Anomalien erkennen und autonome Entscheidungen unterstützen. Edge Computing wird verstärkt eingesetzt, um Latenzzeiten zu reduzieren und die Datensicherheit zu erhöhen. Zudem wird es stärkere Standards und Interoperabilität zwischen verschiedenen IoT-Plattformen geben, was die Integration und Skalierbarkeit erleichtert.

In the next five years, IoT platforms will enhance security and privacy by strengthening encryption, adopting zero-trust architecture, introducing specialized security services, and ensuring compliance with regulations. This includes improved device authentication, privacy-preserving techniques, certifications, edge security measures, secure development practices, and ongoing innovation to stay ahead of emerging threats.

Over the next five years, IoT cloud platforms will increasingly incorporate advanced security frameworks featuring AI-driven threat detection and automated response mechanisms. They'll prioritize compliance with international standards to safeguard user data, using blockchain technologies to enhance data integrity and transparency, addressing privacy concerns and regulatory demands effectively.

Security and privacy will continue to be a focus area. With maturing machine learning models and Generative AI applications like 'AI Coding Companions', IoT engineering teams will be in a position to shift security testing to the next level of automation, with self-detection, self-testing and self-healing techniques

Security and privacy are core functionalities in a real IoT platform. This includes (and not limited to) authontication of users, users access rights management, multiple levels of access rights, authontication of devices, devices access rights, data encryption, data protection, etc. In the next 5 years, lots of claimed platform providers will disappear due to lack of such core functionalities.

Segurança é um dos tópicos mais quentes quando se trata de desenvolvimento de sistemas de IoT. Porém poucos times e equipes conhecem realmente as ferramentas de software e hardware que estão disponíveis no mercado para aumentar o nível de segurança em uma solução IoT. Muitas vezes o que vemos são as equipes e empresas passarem a estudar e se preocupar com soluções de segurança apenas após alguma invasão ou roubo de dados de grandes proporções acontecer. É necessário treinar as equipes de desenvolvimento com o que há de mais moderno em termos de ferramental (software e hardware) de segurança e mostrar aos gestores e CEOs que esse investimento retorna facilmente para a empresa quando implementado corretamente.

Interoperability and standardization are crucial for the evolution of IoT cloud platforms, ensuring seamless communication across diverse devices and systems. Achieving these, given the IoT ecosystem's complexity, requires platforms to offer open APIs, SDKs, and protocols to facilitate integration and data exchange, enabling broader collaboration and innovation in IoT applications.

FIWARE standard and Digital Twins applications are enabling the next generation of platforms to enable a higher level of impact and Return of Investment. Data is becoming the key value of any company and generating a data economy is a reality, Europe is leading Data Spaces initiating to promote data sharing and data sovereignty with the just signed AI Act. #IoT is also being evolved with Edge Computing and hyperscalar clouds are becoming more neutral and distributed than ever.

Perdi a conta de quantos são os sistemas de transmissão sem fio diferentes e disponíveis para se trabalhar com sistemas de IoT. Isso para ficar apenas em um dos múltiplos exemplos possíveis. Tentar fazer com que esses sistemas diferentes conversem entre si, garantindo uma interoperabilidade e garantindo uma normalização do uso, independente de quem é o fabricante do dispositivo IoT, é uma tarefa árdua. Tenho visto muitos times e empresas falharem nestes requisitos por insistirem em ter seu sistema proprietário, que não conversar com mais ninguém de mercado.

Even though the term IoT has become a buzzword recently, the IoT has been there for many decades with different terminologies such as M2M. With the presence of Legacy equipment and protocols, interoperability has been a big challenge for IoT. Having common approved standards by the Industry consortia is the solution to overcome this challenge. One such example we see nowadays is Matter. Matter protocol is formed by different vendors, manufacturers, software and hardware providers after forming a consortium. In the coming 5 years, Cloud platforms will see a gradual shift towards standardization and improved interoperability to foster innovation and productivity.

In the next five years, IoT cloud platforms will likely prioritize enhanced interoperability and standardization to tackle ecosystem fragmentation. We can expect increased adoption of universal communication protocols and consensus-driven standards, spearheaded by global technology consortia. This move will not only streamline device integration but also enable more robust cross-industry applications, facilitating seamless data flow and system interactions. The focus will be on creating a more cohesive IoT environment that supports innovative, scalable solutions while reducing development costs and accelerating go-to-market for new technologies.

We don’t need more “IoT platforms”, but rather more focus on delivering business outcome for customers across industries that leverages “IoT” (technology, platforms, constructs). The benefits of IoT apply to almost every industry, but not every customer can or wants to be a builder. Instead, Line of Business buyers are looking for use case specific solutions that are production-ready, scalable, and easy to purchase/deploy. Improving efficiency, productivity, and driving growth for customers. Without the traditional complexity of selecting, integrating, and maintaining multiple technologies, components, and vendor relationships themselves.

Over the next five years, IoT cloud platforms will evolve with edge computing integration for reduced latency and real-time processing, enhanced AI and ML capabilities for deeper insights and predictive analytics, and improved interoperability and standardization for seamless device and platform collaboration. These advancements will enable more efficient, intelligent, and scalable IoT solutions across various industries.

Beyond these trends, other considerations may include the impact of 5G technology on IoT, the role of open-source platforms in driving innovation, the increasing use of IoT in sustainable and green technologies, and the evolving regulatory landscape that could shape how IoT is implemented and used

The future of Enterprise IoT hinges on articulating use cases leveraging * AI: Enhancing the value of IoT-generated data through AI's data processing capabilities. * The advent of 5G networks, offering higher bandwidth and lower latency, is a catalyst for advanced industrial IoT use cases * Cloud-to-Edge interoperability: enables customers to run IoT enabled business processes on Edge or Cloud, enabling distributed computing across cloud and multiple edge nodes * IoT Cloud Interoperability: enables seamless exchange, processing, and synchronisation of device information across different IoT cloud stacks, fostering bidirectional communication for end-to-end IoT solutions integrated with business applications to achieve business outcomes.

I couldn’t agree more with the previous contributions — "use case and actionable insights in an easy-to-use scalable solution are key". But the “table stakes” of a carrier-grade “core” are another important element that many decision-makers take for granted. LoRaWAN and the architectures that use this protocol have enabled solution providers to package building blocks and we like to define “Iot cloud platform” as the network core component that enables scale, flexibility and maintenance to reduce TCO. Don't get caught thinking iot platform is only about the user visualization dashboard.