The world of connected devices has led to the rise of the Internet of Things paradigm, where applications rely on multiple devices, gathering and sharing data across highly heterogeneous networks. The variety of possible mechanisms, protocols, and hardware has become a hindrance in the development of architectures capable of addressing the most common IoT use cases, while abstracting services from the underlying communication subsystem. Moreover, the world is moving toward new strict requirements in terms of timeliness and low latency in combination with ultra-high availability and reliability. Thus, future IoT architectures will also have to support the requirements of these cyber-physical applications. In this regard, edge computing has been presented as one of the most promising solutions, relying on the cooperation of nodes by moving services directly to end devices and caching information locally. Therefore, in this article, we propose a modular and scalable architecture based on lightweight virtualization. The provided modularity, combined with the orchestration supplied by Docker, simplifies management and enables distributed deployments, creating a highly dynamic system. Moreover, characteristics such as fault tolerance and system availability are achieved by distributing the application logic across different layers, where failures of devices and micro-services can be masked by this natively redundant architecture, with minimal impact on the overall system performance. Experimental results have validated the implementation of the proposed architecture for on-demand services deployment across different architecture layers.

Industrial Internet of things (IIoTs) relies on different devices working together, gathering and sharing data using multiple communication protocols. This heterogeneity becomes a hindrance in the development of architectures that can support applications operating independently of the underlying protocols. Therefore in this paper, we proposed a modular and scalable architecture based on lightweight virtualization. The modularity provided by the proposed architecture combined with lightweight virtualization orchestration supplied by Docker simplifies management and enables distributed deployments. Availability and fault-tolerance characteristics are ensured by distributing the application logic across different devices where a single microservice or even device failure can have no effect on system performance. The proposed architecture is instantiated and tested on a simple time-dependent use case. The obtained results validates that the proposed architecture can be used to deploy services on demand at different architecture layers.

Industrial Internet of Things applications are distributed Cyber-Physical systems with long life-cycles, with strict security requirements, that are being pervasively adopted with the proliferation of Industry 4.0. A fundamental aspect of security, in such applications, is a trustworthy identity to avoid impersonation attacks. Classical approaches for identity management usually rely on some variation of a centralized system, either based in a Public Key Infrastructure or in a central identity database. These solutions have some well-known drawbacks, notably the single point of failure in the central node and the limited scalability. Self-Sovereign Identity is a novel paradigm for digital identity allowing individuals and devices to fully own and manage their digital identities. According to this paradigm, identity artifacts can be stored by their owners and freely presented whenever a transaction requires some proof of identity, without relying on a central entity. This paper provides a review and discussion of the use-cases, technologies, and challenges that Self-Sovereign Identity faces in the context of Industrial Internet of Things’ applications. The potential advantages of using this new paradigm are discussed in detail, together with the key challenges that still need to be addressed.

Intelligent Transportation Systems (ITS) have evolved as a key research topic in recent years, revolutionizing the overall traffic and travel experience by providing a set of advanced services and applications. These data-driven services contribute to mitigate major problems arising from the ever growing need of transport in our daily lives. Despite the progress, there is still need for an enhanced and distributed solution that can exploit the data from the available systems and provide an appropriate and real-time reaction on transportation systems. Therefore, in this paper, we present a new architecture where the intelligence is distributed and the decisions are decentralized. The proposed architecture is scalable since the incremental addition of new peripheral subsystems is supported by the introduction of gateways which requires no reengineering of the communication infrastructure. The proposed architecture is deployed to tackle the problem of traffic management inefficiency in urban areas, where traffic load is substantially increased, by vehicles moving around unnecessarily, to find a free parking space. This can be significantly reduced through the availability and diffusion of local information regarding vacant parking slots to drivers in a given area. Two types of parking systems, magnetic and vision sensor based, have been introduced, deployed, and tested in different scenarios. The effectiveness of the proposed architecture, together with the proposed algorithms, is assessed in field trials.