Adaptive Mediation for Data Exchange in IoT Systems

International audienceMessaging and communication is a critical aspect of next generation Internet-of-Things (IoT) systems where interactions among devices, software systems/services and end-users is the expected mode of operation. Given the diverse and changing communication needs of entities, the data exchange interactions may assume different protocols (MQTT, CoAP, HTTP) and interaction paradigms (point to point, multicast, unicast). In this paper, we address the issue of supporting adaptive communications in IoT systems through a mediation-based architecture for data exchange. Here, components called mediators support protocol translation to bridge the heterogeneity gap. Aiming to provide a placement of mediators to nodes, we introduce an integer linear programming solution that takes as input: a set of Edge nodes, IoT devices, and networking semantics. Our proposed solution achieves adaptive placement resulting in timely interactions between IoT devices for larger topologies of IoT spaces

A Service Discovery Solution for Edge Choreography-Based Distributed Embedded Systems

[EN] This paper presents a solution to support service discovery for edge choreography based distributed embedded systems. The Internet of Things (IoT) edge architectural layer is composed of Raspberry Pi machines. Each machine hosts different services organized based on the choreography collaborative paradigm. The solution adds to the choreography middleware three messages passing models to be coherent and compatible with current IoT messaging protocols. It is aimed to support blind hot plugging of new machines and help with service load balance. The discovery mechanism is implemented as a broker service and supports regular expressions (Regex) in message scope to discern both publishing patterns offered by data providers and client services necessities. Results compare Control Process Unit (CPU) usage in a request¿response and datacentric configuration and analyze both regex interpreter latency times compared with a traditional message structure as well as its impact on CPU and memory consumption.The choreography engine was developed and supported by the SABIEN research group of the Universitat Politecnica de Valencia (http://www.sabien.upv.es/en/).Blanc Clavero, S.; Bayo-Monton, JL.; Palanca-Barrio, S.; Arreaga-Alvarado, NX. (2021). A Service Discovery Solution for Edge Choreography-Based Distributed Embedded Systems. Sensors. 21(2):1-19. https://doi.org/10.3390/s21020672S11921

Timed protocol analysis of interconnected mobile IoT devices

International audienceWith the emergence of the Internet of Things (IoT), application developers can rely on a variety of protocols and Application Programming Interfaces (APIs) to support data exchange between IoT devices. However, this may result in highly heterogeneous IoT interactions in terms of both functional and non-functional semantics. To map between heterogeneous functional semantics, middleware connectors can be utilized to interconnect IoT devices via bridging mechanisms. In this paper, we make use of the Data eXchange (DeX) connector model that enables interoperability among heterogeneous IoT devices. DeX interactions, including synchronous, asynchronous and streaming, rely on generic post and get primitives to represent IoT device behaviors with varying space/time coupling. Nevertheless, non-functional time semantics of IoT interactions such as data availability/validity, intermittent connectivity and application processing time, can severely affect response times and success rates of DeX interactions. We introduce timing parameters for time semantics to enhance the DeX API. The new DeX API enables the mapping of both functional and time semantics of DeX interactions. By precisely studying these timing parameters using timed automata models, we verify conditions for successful interactions with DeX connectors. Furthermore, we statistically analyze through simulations the effect of varying timing parameters to ensure higher probabilities of successful interactions. Simulation experiments are compared with experiments run on the DeX Mediators (DeXM) framework to evaluate the accuracy of the results. This work can provide application developers with precise design time information when setting these timing parameters in order to ensure accurate runtime behavior

Protocol for a Systematic Literature Review on Adaptative Middleware Support for IoT and CPS

This protocol defines the procedure to conduct a systematic literature review on adaptive middleware support for the Internet of Things (IoT) and Cyber-physical Systems (CPS). The mentioned concepts deal with smart interactive objects which provide a set of services, but they look into the problem from various perspectives. We especially look into middleware design decisions for reactive/proactive adaptations. Following a systematic literature review (SLR) in the selection procedure, we selected 62 papers among 4,274 candidate studies. To this end, we applied the classification and extraction framework to select and analyze the most influential domain-related information. In addition to the academic database, we took advantage of the use-cases provided by our industrial partners within the CPS4EU 2 project. This document clarifies the primary studies' selection process. The analysis of the studies, discussion, and solution proposals will be presented separately in a journal article

Emerging research themes in services-oriented systems

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Dynamic service orchestration in heterogeneous internet of things environments

Internet of Things (IoT) presents a dynamic global revolution in the Internet where physical and virtual “things” will communicate and share information. As the number of devices increases, there is a need for a plug-and–interoperate approach of deploying “things” to the existing network with less or no human need for configuration. The plug-and-interoperate approach allows heterogeneous “things” to seamlessly interoperate, interact and exchange information and subsequently share services. Services are represented as functionalities that are offered by the “things”. Service orchestration provides an approach to integration and interoperability that decouples applications from each other, enhancing capabilities to centrally manage and monitor components. This work investigated requirements for semantic interoperability and exposed current challenges in IoT interoperability as a means of facilitating services orchestration in IoT. The research proposes a platform that allows heterogeneous devices to collaborate thereby enabling dynamic service orchestration. The platform provides a common framework for representing semantics allowing for a consistent information exchange format. The information is stored and presented in an ontology thereby preserving semantics and making the information comprehensible to machines allowing for automated addressing, tracking and discovery as well as information representation, storage, and exchange. Process mining techniques were used to discover service orchestrations. Process mining techniques enabled the analysis of runtime behavior of service orchestrations and the semantic breakdown of the service request and creation in real time. This enabled the research to draw observations that led to conclusions presented in this work. The research noted that the use of semantic technologies facilitates interoperability in heterogeneous devices and can be implemented as a means to bypass challenges presented by differences in IoT “things”

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

An interoperability framework for pervasive computing systems

Communication and interaction between smart devices is the foundation for pervasive computing and the Internet of Things. Pervasive platforms, that support developers in building new services and applications, have been extensively researched in the past. Nowadays, a multitude of heterogeneous pervasive platforms exist. In real-world deployments, this leads to the formation of platform-specific silos. Therefore, the need for interoperability between such platforms arises. This thesis presents a framework which addresses all elaborated issues preventing co-operation between different platforms and allows for extension and customisation of different aspects, including platforms and transformation mechanisms. The framework bases on uniform abstractions that support translations of different features. The transformation model provides an automatic as well as a manual transformation mechanism. For evaluation, a prototype is implemented and assessed, providing support for six distinct platforms. Particularly, the framework’s feasibility is demonstrated with three realistic scenario implementations, an effort evaluation, and a cost evaluation