Search

Industrial assistive systems result from a multidisciplinary effort that integrates IoT (and Industrial IoT), Cognetics, and Artificial Intelligence. This paper evaluates the Prediction by Partial Matching algorithm as a component of an assembly assistance system that supports factory workers, by providing choices for the next manufacturing step. The evaluation of the proposed method was performed on datasets collected within an experiment involving trainees and experienced workers. The goal is to find out which method best suits the datasets in order to be integrated afterwards into our context-aware assistance system. The obtained results show that the Prediction by Partial Matching method presents a significant improvement with respect to the existing Markov predictors. [ABSTRACT FROM AUTHOR]

• We discuss and analyze the main security issues affecting the CAN bus in the automotive. • We study the feasibility of using encryption algorithms for securing CAN-bus messages. • We propose a security solution based on lightweight block ciphers. • The proposed solution is efficient and reasonably secure against targeted attacks. • We use Automotive Grade Linux to evaluate the performance of our solution. In recent years, the automotive industry has made significant investments in the Internet of Things (IoT) paradigm, aiming to develop technologies and services for connected vehicles that focus on user-oriented solutions, providing unique and tailored driving experiences. Nowadays, the vehicles are connected to the car manufacturer network, the road operator networks, and the Internet. Such interconnections are used for infotainment applications and driving assistance services, such as real-time HD-maps, and road safety applications. For these reasons, the concept of the Internet of Vehicles (IoVs) is increasingly emerging and, vehicles become Internet terminals exposed to cyber-attacks. In this context, the main weakness is given by the Controller Area Network (CAN) protocol. This protocol, which governs the in-vehicle network, was designed to reduce transmission error problems and minimize latency times. However, it does not employ any security facility to protect the communication. In particular, one of the main issues of this protocol is that it uses unencrypted communication. Improving the security of this protocol is necessary while preserving its performance in terms of communication efficiency. This paper investigates the possibility of using lightweight block ciphers to secure in-vehicle devices such as microcontrollers, which have constrained hardware and software capabilities. The results obtained have shown that this proposal while guaranteeing a high degree of safety, has a negligible impact on the vehicle's performance. [ABSTRACT FROM AUTHOR]

The complexity of current cyber infrastructures requires specialized security-oriented skills to be acquired by a variety of different actors, in order to defend critical systems and sensitive data against emerging security threats and attacks. In order to facilitate this process, advanced cybersecurity education and training programs should be considered, as well as platforms and tools for simulating realistic training scenarios should be exploited. This paper presents an open-source solution for simulating cyber infrastructures, and reproducing realistic Internet of Things (IoT) scenarios, with specific focus on Edge applications. It can be used for the implementation of new cooperative and competitive cybersecurity training exercises and skills for different application domains, as well as validate solutions that can be used to prevent, detect, mitigate, recovery, and evaluate the attack impact. [ABSTRACT FROM AUTHOR]

Currently, our society is undergoing a radical change due to the increasing pervasive use of ICT within all of its processes. Such an evolution has been triggered by the advent of the Internet of Things vision, where smart sensing devices can be integrated in the daily objects surrounding any human being. The increasing demand of dealing with the big data generated, managed, and stored by the applications built on top of such novel sensory networks has called for innovative architectures, such as edge and fog computing, which have not yet been fully standardized. Considering the large scale and complexity of these architectures, testing a software artifact to be deployed in the nodes composing an edge-fog ecosystem could be extremely challenging. Pure simulated environments and real testbeds could be not representative enough of realistic scenarios or unacceptably expensive. We explore such issues and present a pseudo-dynamic testing approach, where a portion of the experimental scenario is simulated, while the edge and fog nodes under test are emulated or executed in a real environment. [ABSTRACT FROM PUBLISHER]

We designed and developed such a disaster resilient network based on software defined network (SDN) technology in order to automatically select the best network link and route among the possible access resources to Internet by periodically monitoring their operating status. Furthermore, the contribution presented by M. Song et al. proposes several cost-optimized mobility management schemes based on pointer forwarding for PMIPv6 networks with the aim of reducing the overall network traffic due to mobility-related signaling and packet delivery. In the new service environments empowered by mobile Internet connectivity Proxy Mobile IPv6 (PMIPv6) emerged as a promising network-based mobility management protocol, which does not need any participation of the involved Mobile Nodes. [Extracted from the article]

This Special Issue aims at collecting several original state-of-the-art research experiences in the area of intelligent applications in the IoT and Sensor networks environment, by analyzing several open issues and perspectives associated with such scenarios, in order to explore novel potentialities and solutions and face with the emerging challenges. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports published within the issue on the Internet of Things (IoT) devices and platforms with respect to security and privacy preserving technologies.

Internet of Things (IoT) is a promising technology for improving our lives and society by integrating smart devices in our environment and paving the way for novel ICT application, spanning from smart cities to energy efficiency and home automation. However, such a vision encompasses the availability of thousands of smart devices, or even more, that continuously exchange a huge volume of data among each other and with cloud-based services, raising a big data problem. Such a problem can be approached by properly applying data fusion practices within an IoT infrastructure. Due to the characteristics and peculiarities of the communications among smart devices within the IoT, an event-based data fusion is needed, where devices exchange notifications of events among each others. Such data fusion should be focused on special devices where notification heterogeneity, and data source trust issues have to be faced with. Accordingly, the contribution of this work is proposing (i) a novel broker-less event-based communication protocol specifically tailored to sensors with constrained resources, (ii) a solution for flexible event-based communications among heterogeneous data sources, and (iii) an approach based on the theory of evidence for data fusion processes that depend on the matching and trust degree of the data to be fused. [ABSTRACT FROM AUTHOR]

The ever-increasing number of IoT applications and cyber–physical services is introducing significant challenges associated to their cyber-security. Due to the constrained nature of the involved devices, some heavier computational tasks, such as deep traffic inspection and classification, essential for implementing automatic attack detection systems, are moved on specialized “edge” devices, in order to distribute the processing intelligence near to the data sources. These edge devices are mainly capable of effectively running pre-built classification models but have not enough storage and processing capabilities to build and upgrade such models from huge volumes of field training data, imposing a serious barrier to the deployment of such solutions. This work leverages the flexibility of cloud-based architectures, together with the recent advancements in the area of large-scale machine learning for shifting the more computationally-expensive and storage-demanding operations to the cloud in order to benefit of edge computing capabilities only for effectively performing traffic classification based on sophisticated Extreme Learning Machines models that are pre-built over the cloud. [ABSTRACT FROM AUTHOR]