Industries generate and collect a huge amount of data about their processes. Usually such data contain relevant information, which can be used to monitor and analyze the processes, but also improve them, applying optimization techniques that allow to enhance different aspects, such as machine maintenance scheduling, product quality, use of resources and so on and so forth. The Digital Twin (DT) concept has been recently applied in the context of Industry 4.0, to exploit this data, leveraging advanced physical modelling, data analysis, Artificial Intelligence (AI) algorithms for optimization and prediction, which are two key concepts in the Industry 4.0 paradigm. Currently, the major challenge in this field is to make these techniques available for the industries and ensure their ease of use for the final users. This paper presents an holistic solution that combining two open-source software - i.e., REclaim oPtimization and simuLatIon Cooperation in digitAl twin (REPLICA) and Optimization Platform for Refurbishment and Re-manufacturing (OPR2) - provides a flexible, open and easy-to-use AI environment that allows data scientists to create, test, connect and deploy their algorithms and to optimize them. This work presents a first prototype of this solution, then describes how it has been tested and validated with real industry data and finally provides the results obtained with these tests.

AI is one of the biggest megatrends towards the 4th industrial revolution. Although these technologies promise business sustainability as well as product and process quality, it seems that the ever-changing market demands, the complexity of technologies and fair concerns about privacy, impede broad application and reuse of Artificial Intelligence (AI) models across the industry. To break the entry barriers for these technologies and unleash its full potential, the knowlEdge project will develop a new generation of AI methods, systems, and data management infrastructure. Subsequently, as part of the knowlEdge project we propose several major innovations in the areas of data management, data analytics and knowledge management including (i) a set of AI services that allows the usage of edge deployments as computational and live data infrastructure as well as a continuous learning execution pipeline on the edge, (ii) a digital twin of the shop-floor able to test AI models, (iii) a data management framework deployed along the edge-to-cloud continuum ensuring data quality, privacy and confidentiality, (iv) Human-AI Collaboration and Domain Knowledge Fusion tools for domain experts to inject their experience into the system, (v) a set of standardisation mechanisms for the exchange of trained AI models from one context to another, and (vi) a knowledge marketplace platform to distribute and interchange trained AI models. In this paper, we present a short overview of the EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop], which is funded by the Horizon 2020 (H2020) Framework Programme of the European Commission under Grant Agreement 957331. Our overview includes a description of the project’s main concept and methodology as well as the envisioned innovations.

Nowadays the number of on-board sensors increases continuously due to their benefits in many different areas, such us driving efficiency, maintenance, autonomous driving, etc. Usually the vehicle itself and its users are those which take direct advantage from these benefits. By leveraging Internet-of-Things (IoT) technologies, it is possible to abstract data and functionalities provided by on-board sensors and actuators exposing relevant services outside the vehicle to external cloud-based applications and other vehicles. With these technologies the vehicle is thus transformed in an IoT object which can be part of external IoT platforms. This work focuses on the design and implementation of an in-vehicle IoT platform which exposes internal functionalities as IoT services enabling also the concept of “Virtual Sensor”, which leverages sensor fusion techniques to provide enhanced services combining raw data coming from on-board devices. This IoT platform solution is validated through a use case in which virtual real-time pothole detection sensor is implemented to evaluate the road surface conditions. In such use-case, multi-source sensing information - coming from 6LoWPAN sensors as well as Smartphones and Inertial Measurement Units - is fused, enabling IoT applications such as cooperative safety and early road maintenance.

Hosting multiple radio technologies in computing devices is becoming increasingly feasible, common, and widely-adopted by stakeholders to offer better services. Packet-loss analysis is essential for characterizing and evaluating the behavior of wireless systems, and thereby assists the procedures of delivering the required of quality of service level. This paper presents a packet transmission model, based on a finite-state discrete-time Markov chain. The model is used to calculate the packet loss rates of wireless communications; it is applied to multi-radio devices, and considers different transitions probability distributions. Moreover, this paper presents simulation results of the developed model as well as experimental packet loss rate evaluation of multi-radio devices equipped with 6LoWPAN and Wi-Fi radio interfaces.

The Internet of Things (IoT) is an emerging paradigm where smart objects are seamlessly connected to the overall Internet and can potentially cooperate to achieve common objectives such as supporting innovative home automation services. With reference to such a scenario, this paper presents an Intrusion Detection System (IDS) framework for IoT empowered by IPv6 over low-power personal area network (6LoWPAN) devices. In fact, 6LoWPAN is an interesting protocol supporting the realization of IoT in a resource constrained environment. 6LoWPAN devices are vulnerable to attacks inherited from both the wireless sensor networks and the Internet protocols. The proposed IDS framework which includes a monitoring system and a detection engine has been integrated into the network framework developed within the EU FP7 project `ebbits'. A penetration testing (PenTest) system had been used to evaluate the performance of the implemented IDS framework. Preliminary tests revealed that the proposed framework represents a promising solution for ensuring better security in 6LoWPANs.

The Internet of Things (IoT) concept attracts considerable interest from the academia and industry. This paper provides a set of proof-of-concept prototype descriptions, based on such IoT exploitation, which aim at gathering real-time data along the manufacturing processes that enables a responsive production management and maintenance, including energy consumption and water usage monitoring at each stage of a production cycle. The presented work takes advantage of the ebbits platform, which provides a middleware infrastructure for integrating industrial sensors, devices and emerging wireless technology in the physical world, transforming them into web services that enable seamless integration into mainstream business system such as MES and ERP.

In this paper, we describe a SOA-based middleware to integrate Internet-of-Things technologies in industrial setups. The middleware allows a seamless horizontal integration among heterogeneous technologies and vertical integration with applications and business systems. Using the middleware, we evaluated an approach to improve the reliability of 6LoWPAN-based sensor networks with self-configuration and self-healing capabilities to support an innovative monitoring and control framework in a manufacturing line. The sensor networks were evaluated in a test bed consisting of various physical devices that emulates a welding station.

This work proposes mechanisms enabling connectivity in multi-radio wireless sensor and actuator networks, namely: network discovery, interface/channel switching, and connection-loss recovery. The considered mechanisms are optimized with the aim to maximize the availability of system services (minimize network down-time) and achieve an increased overall robustness. The proposed approach offers dynamicity and adaptability to the constantly changing operating conditions of the different radio interfaces as well as to application-level requirements, and ensures self-organizing and autonomous operations.

The wide spread of smart embedded devices and the trend towards their inclusion in a unique Internet of Things are making urgent their full integration within the TCP/IP protocol stack. This paper considers ZigBee, a short-range radio communication standard for embedded devices, and compares two alternative protocol stacks, namely ZigBee Pro and ZigBee IP, developed for the integration of ZigBee in the so called Internet of Things. The performance evaluation is founded on experimental results achieved using two test-beds built to this purpose and is based on service reliability and latency as metrics.

Smart objects connected to the Internet, constituting the so called Internet of Things (IoT), are revolutionizing human beings' interaction with the world. As technology reaches everywhere, anyone can misuse it, and it is always essential to secure it. In this work we present a denial-of-service (DoS) detection architecture for 6LoWPAN, the standard protocol designed by IETF as an adaptation layer for low-power lossy networks enabling low-power devices to communicate with the Internet. The proposed architecture integrates an intrusion detection system (IDS) into the network framework developed within the EU FP7 project ebbits. The aim is to detect DoS attacks based on 6LoWPAN. In order to evaluate the performance of the proposed architecture, preliminary implementation was completed and tested against a real DoS attack using a penetration testing system. The paper concludes with the related results proving to be successful in detecting DoS attacks on 6LoWPAN. Further, extending the IDS could lead to detect more complex attacks on 6LoWPAN.

Ubiquitous systems and the Internet of Things (IoT) are on the rise. However, there are issues from businesses and users which hinder the wide-spread application of such systems. In the FP7 EU project ebbits, which deals with integrating IoT into business systems, we collected real-world requirements from participants. From these we designed a complete anonymous network solution including addressing, discovery, authentication and reputation management. Our authentication system relies on Non Interactive Zero Knowledge Proofs augmented for fine granulated access right decisions and accounting. Anonymous reputation is created using reputation tickets, which are obtained through public votings. A reputation ticket is an unforgeable evidence of trustworthiness held by the provider and presented to a consumer on request. Discovery also has to specially be designed for anonymous environments else they leak information about the identity. We provide an attribute based discovery mechanism built with Bloom-filters. Our concept protects privacy but still enables discovery based on partial matches.

Wireless Sensor Network (WSN) technology is being increasingly adopted in a wide variety of applications ranging from home/building and industrial automation to more safety critical applications including e-health or infrastructure monitoring. Considering mobility in the above application scenarios actually introduces additional technological challenges, especially with respect to security. The resource constrained devices should be robust to diverse security attacks and communicate securely while they are moving in the considered environment. To this aim, proper authentication and key management schemes supporting node mobility should be used. This paper presents an effective mutual authentication and key establishment scheme for heterogeneous sensor networks consisting of numerous mobile sensor nodes and only a few more powerful fixed sensor nodes. Moreover, OMNET++ simulations are used to provide a comprehensive performance evaluation of the proposed scheme. The obtained results show that the proposed solution assures better network connectivity, consumes less memory, has low communication overhead during the authentication and key establishment phase and has better network resilience against mobile nodes attacks compared with existing approaches for authentication and key establishment.

This paper addresses the problem of spectrum sensing for cognitive radio, in the case of a primary signal characterized by a discontinuous channel occupation within the considered sensing window. Under such conditions, the performance of two different energy detectors is investigated. The first one (energy average detector) decides whether the channel is free or busy on the basis of the energy sample average; the second one (energy threshold detector) performs a soft estimation of the channel occupation probability by comparing the energy samples with a proper threshold. For both the detectors, an analytical model is derived and validated through simulation, and the optimal decision threshold is found as a function of the primary signal parameters.

As a consequence of the diffusion of wireless systems operating in the 2.4 GHz ISM band, harmful interference among heterogeneous networks is becoming a serious issue for their performance. This paper is focused on IEEE 802.15.4 WSNs undergoing the interference of co-located IEEE 802.11b/g WLANs or Bluetooth piconets. On the basis of energy measurements carried out using a standard-compliant testbed, the statistics of the interfering energy are characterized for an extensive set of traffic conditions. Then the impact of each interference pattern onto a practical WSN application is analyzed in terms of packet loss rate at the application layer. The last part of the paper introduces a channel selection algorithm based on the estimation of the information-theoretic capacity of the considered channels.