The next wave of Internet of Things (IoT), artificial intelligence (AI) and edge computing opens the potential for intelligent autonomous applications to become significant factors in creating the Internet of Intelligent Things.
The emergence of intelligent autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, can power the transformation and optimisation of industrial sectors and change the innovation landscape.

IoT systems deliver disconnected and distributed capabilities into the embedded IoT world. Edge computing is part of the technical fabric across the industrial sector that provides these capabilities with advanced and specialised processing resources, data storage and analytics. IoT applications powered by edge computing help keep traffic and processing local, reduce latency, exploit edge capabilities and enable higher autonomy of IoT devices at the edge.

As IoT technologies have evolved and moved IoT architectures from centralised to decentralised and distributed, these architectures must implement functions that provide data analysis at the edge. Advances in edge cloud computational capabilities are applied to distributed node IoT edge devices and embedded into various edge computing implementations with specific capabilities (e.g. fog, mobile edge computing, dew computing etc.). These edge computing variants offer complementary and additional features to the ones provided by cloud computing.

The emerging ecosystems for an intelligent edge are centred around intelligence sensing (e.g. advanced perception systems for IoT devices), embedded AI technologies and seamless connectivity. The convergence of these technologies accelerates the development of new concepts such as Artificial Intelligence of Things (AIoT), Internet of Things Senses (IoTS), Tactile Internet of Things (TIoT) and Internet of Robotic Things (IoRT) to build seamless, automatic and context-driven applications and services integrated with Internet resources.

IoTS extends the sensing capabilities of IoT devices aiming to reproduce hearing, sight, taste, smell and touch over the Internet, enabled by AI, virtual reality, intelligent connectivity and automation. These IoTS advancements are imperative for IoT, considering that AI algorithms can execute cognitive decision-making capabilities for intelligent IoT devices (e.g. robotic things) at the edge.

Tactile IoT combines high availability, reliability and security with ultra-low latency and enables machines to interact in real time with their environment while on the move, employing haptic interaction with visual feedback and within a specific spatial communication range.

IoT sensing, actuating and computing processing at the edge provide IoT systems with the capability to deliver disconnected or distributed functions to the embedded physical environment and provide digital representation and modelling, simulation and augmented functions through ‘digital twins’ in digital, virtual and cyber domains.

Combining AI and distributed ledger technologies (DLTs), the IoT and edge computing continuum creates a distributed intelligent architecture consisting of a wide range of sensing/actuating intelligent things and services linked in a dynamic mesh and connected by a set of distributed and federated edge/cloud services.

IoT edge is unfolding and expanding increasingly intelligent and diversified. IoT edge intelligence drives the creation of SW/WH solutions that disrupt the existing edge to cloud architectures and stimulate the emergence of IoT edge granularity, e.g. micro, deep and meta edge.

Edge granularity evolution is necessary to address the challenges of the new distributed computing continuum for a more secure, safe, efficient and reliable processing of information at the edge. In this context, IoT expands beyond static monitoring to active device automation with new edge computing technologies based on real-time processing of data usage, edge processing, AI, mesh connectivity and end-to-end security.

Intelligent edge technologies enable real-time responsiveness, local processing and efficient movement of data, pushing the convergence of operational technology (OT) and information technology (IT) and servicing the digital transformation in sectors such as energy, agri-food, automotive, mobility and manufacturing using data resources efficiently while addressing sustainable development goals and accelerating the decarbonisation of the economy.

The integration of swarm intelligence abilities into physical and virtual IoT edge systems and platforms extends the Internet of mobile things. The collective intelligence of edge IoT applications improves the autonomous capabilities of IoT devices while generating new possibilities for humans to connect seamlessly, facilitating new ways to collaborate between machines and humans.

New development tools are required to develop computing capabilities further and provide new functions for data management, real-time applications and services, including new virtual verification, validation and testing techniques.

IoT edge intelligence applications have several challenges in scalability, efficiency, trustworthiness and transparency. Enhancing IoT edge processing capabilities, bandwidth, resources, management and orchestration requires intense effort from the IoT community to find optimal solutions for various applications. For instance, two challenges must be addressed in edge data sharing. The first is the increase of data interfaces that can lead to critical consequences (e.g. intrusion and destruction). The second is the limited performance of IoT edge devices (e.g. strong/robust security algorithms are difficult to run on resource-constrained IoT edge devices).

IoT, AI and edge computing technologies are rapidly developing and their convergence is enabling new industrial paradigm shifts. These technologies promise exciting new revenue opportunities and more significant economic benefits resulting from delivering new services to new kinds of applications in both consumer and industrial sectors.