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In recent years, distributed ledger technologies (DLTs) and blockchain have become disruptive technologies to support distributed and trusted sharing ecosystems in various domains. Among the potential scenarios that can leverage their benefits, cooperative intelligent transport systems (C-ITS) and autonomous vehicles (AV) represent a key trend of the next digital era to build a safer society. However, different aspects such as performance and practical issues, as well as conformance with current standards and legislation, may hinder the adoption of DLT in such scenarios. This article analyses the potential applications that could leverage DLTs features and the challenges to be overcome in the coming years to foster the adoption of DLTs in C-ITS and AV. Through this analysis, we additionally provide a set of potential research directions and ways forward to exploit the advantages of DLTs in C-ITS and AV in terms of decentralized trust and transparency.

With the continuous development of distributed ledger and blockchain technologies, new use cases apart from cryptocurrencies have come into the spotlight. In this article, we evaluate whether an e-government service could be a suitable candidate for a blockchain transformation. We selected as a reference test system an existing cross-border e-government service that is used for supporting goods exchanges across the European Union. We show how such an indicative paradigm can be transformed into a blockchain system. In order to do so, we deployed it in an emulated architecture for evaluating its performance under various realistic conditions. Our results show that the deployed system is able to meet the requirements, both in terms of throughput and transaction speed. Moreover, it shows clear advantages in terms of usability and synchronization between all entities.

A data-driven society requires a common regulatory umbrella to provide a harmonized vision of cybersecurity. We describe cybersecurity policies and joint initiatives in the European Union and give insights about the need to align ongoing technological advances with such regulatory efforts.

Radiometric Identification (RAI) is the identification of wireless devices through their Radio Frequency (RF) emissions. In recent years, the research community has investigated it applying different methods and sets of statistical features extracted from the digitized RF emissions. In this paper, the authors investigate the application of Variational Mode Decomposition (VMD), recently introduced as an improvement to Empirical Mode Decomposition (EMD). VMD is applied to two sets of RF emissions from: wireless devices supporting Dedicated Short Range Communications (DSRC) at 5.9 GHz and Internet of Things wireless devices transmitting in the Industrial, Scientific and Medical (ISM) band at 2.4 GHz. Various machine learning algorithms have been used for classification and results are compared. Performances of VMD are evaluated against other approaches used in literature in Line of Sight (LOS) conditions, with Additive White Gaussian Noise (AWGN) and fading effects. Results show that VMD significantly outperforms other approaches.

In recent years, several research studies have investigated the identification of electronic devices through their physical components and properties, both from a theoretical point of view and through extensive experimental studies. Results have shown that, in many cases, a very high identification accuracy can be obtained by exploiting imperfections and small differences in the electronic components, which are called fingerprints in this context. Part of these studies have focused on a specific category of electronic device, the mobile phone or smartphone, which is usually equipped with components, such as radio frequency front-ends, cameras, micro-electro-mechanical systems, microphones, and speakers that are likely to reveal fingerprints in their digital outputs and then allow the identification of the component and of the mobile phone itself. Keeping the focus on mobile phones, this paper provides a survey of the different techniques for mobile phone identification on the basis of their built-in components. This paper describes the methodology, the classification algorithms, and the types of features that are typically used in literature. Outstanding challenges and research issues are also identified and described, together with an overview of the potential applications of mobile phone fingerprinting. In addition, this paper analyzes the potential privacy risks associated to the tracking of the mobile phone on the basis of its fingerprints and the related mitigation techniques. Finally, it summarizes the main issues and identifies research opportunities and potential future trends for mobile phone fingerprinting.

The widespread adoption of the Android operating system in a variety type of devices ranging from smart phones to smart TVs, makes it an interesting target for developers of malicious applications. One of the main flaws exploited by these developers is the permissions granting mechanism, which does not allow users to easily understand the privacy implications of the granted permissions. In this paper, we propose an approach to enforce fine-grained usage control privacy policies that enable users to control the access of applications to sensitive resources through application instrumentation. The purpose of this work is to enhance user control on privacy, confidentiality and security of their mobile devices, with regards to application intrusive behaviours. Our approach relies on instrumentation techniques and includes a refinement step where high-level resource-centric abstract policies defined by users are automatically refined to enforceable concrete policies. The abstract policies consider the resources being used and not the specific multiple concrete API methods that may allow an app to access the specific sensitive resources. For example, access to the user location may be done using multiple API methods that should be instrumented and controlled according to the user selected privacy policies. We show how our approach can be applied in Android applications and discuss performance implications under different scenarios.

Extensive research has been performed in recent years for the identification of wireless devices from their radio frequency (RF) emissions. The main idea of identifying a wireless device through its RF emissions is that the electronic circuits and the RF components have specific characteristics determined by the production and manufacturing processes. These characteristics, which result in unique differences, can be used to distinguish a wireless device from another because they appear as subtle modification of the RF signal in space even if the wireless device generates a signal conformant to the standard. This chapter describes the main techniques for the fingerprinting of wireless devices using their RF transmission. There are still some key challenges to overcome. This chapter tries to identify them in this context as well as providing possible approaches to solve them. Further research work is needed to investigate the portability issues between fingerprints taken using different receivers, as well as to identify and remove potential other sources of bias.

This paper addresses the problem of authentication and identification of wireless devices using their physical properties derived from their Radio Frequency (RF) emissions. This technique is based on the concept that small differences in the physical implementation of wireless devices are significant enough and they are carried over to the RF emissions to distinguish wireless devices with high accuracy. The technique can be used both to authenticate the claimed identity of a wireless device or to identify one wireless device among others. In the literature, this technique has been implemented by feature extraction in the 1D time domain, 1D frequency domain or also in the 2D time frequency domain. This paper describes the novel application of the synchrosqueezing transform to the problem of physical layer authentication. The idea is to exploit the capability of the synchrosqueezing transform to enhance the identification and authentication accuracy of RF devices from their actual wireless emissions. An experimental dataset of 12 cellular communication devices is used to validate the approach and to perform a comparison of the different techniques. The results described in this paper show that the accuracy obtained using 2D Synchrosqueezing Transform (SST) is superior to conventional techniques from the literature based in the 1D time domain, 1D frequency domain or 2D time frequency domain.

The control and protection of user data is a very important aspect in the design and deployment of the Internet of Things (IoT). The heterogeneity of IoT technologies, the large number of devices and systems, and the different types of users and roles create important challenges in this context. In particular, requirements of scalability, interoperability, trust and privacy are difficult to address even with the considerable amount of existing work both in the research and standardization community. In this paper we propose a Model-based Security Toolkit, which is integrated in a management framework for IoT devices, and supports specification and efficient evaluation of security policies to enable the protection of user data. Our framework is applied to a Smart City scenario in order to demonstrate its feasibility and performance.

The General Data Protection Regulation (GDPR) [1], which will be enforceable from May 2018, introduces significant changes on the obligations of data controllers and processors in the context of the data protection legistlation of the European Union (EU). These obligations are defined by a single set of rules that should be adopted by all EU Member States including, among others, the need for explicit consent with the possibility of withdrawal and the right to erasure. The GDPR applies to data controllers (organizations) that access data of a data subject (persons) and data processors (organizations) that process data on behalf of the controller.

The concept of Radio Frequency (RF) fingerprinting is that electronic devices can be identified and authenticated through their radio frequency emissions, which contain intrinsic features of the device itself. RF fingerprinting can be used to enhance the security of wireless networks because the fingerprints provide a form of authentication. In previous research papers, the RF fingerprints have typically been obtained by extracting statistical features from the time series generated by the analog-to-digital conversion of the RF emissions. In this paper, we investigate a novel approach to the RF fingerprinting of Internet of Things (IOT) devices, where the time series are converted into images, out of which image processing features are extracted. The performance of this approach is experimentally evaluated by applying different machine learning algorithms on different types of conversions of time series to images. Our analysis shows that the proposed approach provides a better identification accuracy as compared to the accuracy achieved by conventional sets of statistical features used in the literature. Even if relatively small (around 1%), this accuracy improvement is statistically significant when classification is repeated over different folds of the training and test data. Yet, this enhanced accuracy is obtained at the cost of the longer time taken to process the images.

The recent approval of the General Data Protection Regulation (GDPR) imposes new data protection requirements on data controllers and processors with respect to the processing of European Union (EU) residents' data. These requirements consist of a single set of rules that have binding legal status and should be enforced in all EU member states. In light of these requirements, we propose in this paper the use of a blockchain-based approach to support data accountability and provenance tracking. Our approach relies on the use of publicly auditable contracts deployed in a blockchain that increase the transparency with respect to the access and usage of data. We identify and discuss three different models for our approach with different granularity and scalability requirements where contracts can be used to encode data usage policies and provenance tracking information in a privacy-friendly way. From these three models we designed, implemented, and evaluated a model where contracts are deployed by data subjects for each data controller, and a model where subjects join contracts deployed by data controllers in case they accept the data handling conditions. Our implementations show in practice the feasibility and limitations of contracts for the purposes identified in this paper.

Micro-generation promises to greatly contribute to the energy balance of the energy grid; however, so far, its market penetration is going slow due to the few, or not-existing, direct economic benefits end-users would enjoy by deploying an in-house micro-generation system. In this paper, taking advantage of the potentialities of blockchain technologies, we propose a solar energy production and distribution architecture using smart contracts, a particular distributed ledger paradigm, to support automatic energy exchanges and auctions, potentially enabling a new, open and more fruitful, under an end-user perspective, energy micro-generation market. We present the conceptual design of the approach, as well the energy grid prototype and the control layer, running on the Ethereum platform. The proposed architecture has been implemented and validated through an in-house developed test-bed.

Internet of Things (IoT) can support numerous applications and services in various domains, such as smart cities and smart homes. IoT smart objects interact with other components e.g., proxies, mobile devices, and data collectors, for management, data sharing and other activities in the context of the provided service. Though such components contribute to address various societal challenges and provide new advanced services for users, their limited processing capabilities make them vulnerable to well-known security and privacy threats. Until now various research works have studied security and privacy in IoT, validating this claim. However, to the best of our knowledge literature lacks research focusing on security and privacy flaws introduced in IoT through interactions among different devices supporting a smart home architecture. In particular, we set up the scene for a security and privacy threat analysis for a typical smart home architecture using off the shelf components. To do so, we employ a smart home IoT architecture that enables users to interact with it through various devices that support smart house management, and we analyze different scenarios to identify possible security and privacy issues for users.

The Internet-of-Things (IoT) is promising to inter-connect physical objects and machines in an intelligent network. In this context, scalable and resilient Machine-to-Machine (M2M) communication protocols are required, and the new Device-to-Device (D2D) feature of the 4G system constitutes an appealing solution. Standardization activities are still in progress, but the rapid diffusion of both LTE and IoT devices and applications leads to development of experimental proposals aimed at merging these two technologies. We contribute in this work with a novel D2D communication protocol, which enables user equipments (UEs) to become the hubs of machine-type traffic by means of a suitable radio interface and upload data in the Web via the mobile network, thus opening to new cloud services. The protocol we propose is designed for multi-hop communications between D2D-enabled terminals and it is equipped with a light-weight security mechanism. It is meant to address the communication needs of UEs inside mobile network coverage, and those of UEs that suffer from scarce radio coverage. In terms of performance, we analyse connectivity and security in the multi-hop D2D network, taking into account the interference created by the transmission of beacon signals during the discovery phase of the wide network. We resort to stochastic geometry to model the end-to-end delay and communication resilience of the multi-hop D2D network.

Informed Consent is an important element for data protection of Information and Communication Technology (ICT) systems as the consent of a data subject (e.g., the citizen) is often necessary for a third party to legitimately process personal data. To provide informed consent regarding the use of personal data, the citizen must have a clear understanding on how his/her personal data will be used by the system. This may not be an easy task in the upcoming paradigm of Internet of Things (IoT) where personal data can be collected without the full awareness of the user. A specific case study of IoT is Cooperative Intelligent Transport Systems (ITS), where vehicles will be capable of broadcasting or receiving data that allow them to communicate with each other and/or with the road infrastructure. C-ITS equipped vehicles constantly broadcasting data, including their speed and location may generate privacy risks, which must be addressed. While various privacy mitigation techniques have been proposed in literature, one key function is the provision of informed consent. This paper will describe a potential implementation for informed consent in C-ITS using a policy-based framework, where privacy settings and preferences can be defined by the user, thus empowering the user in the control of his/her private data.

The Informed Consent of a data subject (e.g., citizen) is often necessary to allow the legitimate processing of personal data by a third party application. The current implementation of Informed Consent based on End User License Agreements (EULA) has many limitations, which are likely to become more critical in future IoT applications, where the collection of personal data can happen in various ways and is not evident to the user. There is the need to define more sophisticated models of Informed Consent for IoT, which address the specific features of IoT, improve on the EULA approach, and protect the flow of personal data from the IoT sensors. In this paper, we propose an agent-based design for Informed Consent in IoT, where access to personal data is regulated through usage control policies, which can be tailored for the specific features of the user and the context. Policies are associated to users, service providers, and smart spaces containing IoT devices in a privacy-friendly way using pseudonyms. The main design concepts are described and applied to a smart city scenario, to evaluate the feasibility of the framework and the related deployment aspects.

The concept of Smart Home where appliances, sensors, actuators, displays and computing resources are connected and interact to support the life of the citizen is being increasingly researched. In this context, the Wi-Fi communication technology has grown to become the de-facto standard for data communications in Smart Home environments, with cordless telephony being dominated by the DECT protocol. Even though both technologies incorporate sets of security features aimed at securing the confidentiality and integrity of the communications, the nature and the design of both radio-frequency protocols make them vulnerable, up to a certain extent, to privacy leakages through traffic analysis attacks. In this paper we explore the information leakage vulnerabilities inherent to these technologies and their potential impact on citizens’ privacy in the context of the Smart Home. We demonstrate how the websites visited by a smart device can be inferred by applying machine learning and pattern matching techniques to eavesdropped encrypted traffic. Keywords—Privacy, Wireless communications, Machine learning

Assessing the progress made during post-traumatic rehabilitation does not always involve monitoring tools which allow to objectively record the rehabilitation activity and its comparison with a standard activity. IMUs (Inertial Measurement Units) are often used in this area for assessing a specific movement which is relevant to the diagnosis of a particular disease or trauma. The evaluation is predominantly based on the analysis of the sensors output waveform in which a pattern can be recognized or the initial and the final positions of the movement detected. Using IMUs composed of tri-axial digital accelerometers and gyroscopes, we have attempted to obtain the actual trajectory of the whole movement, in order to track basic exercises related to specific planes of movement and/or rotation. In this paper we will explain how the techniques used can be exploited in a system for assessing post-traumatic rehabilitation for obtaining an immediate visual result of the activity done by the patient, precise monitoring, a comparison and (automatic) evaluation according to a standard rehabilitation protocol.

One of the many applications of wireless sensors, wearable computing devices, is to collect data for analyzing body parameters useful for assessing athletic performance of an individual or group of individuals. Focusing on analysis methods using parameter extraction, in this paper we will see how the cooperation between a Body Sensor Network (BSN) and a larger system (e.g. an e-health system or an e-learning system) can be exploited for the assessment and/or distance teaching of physical education skills for a movement science course or in a training college.

Sensor networks are one of the first examples of pervasive computing, which is characterized by the massive use of increasingly smaller and powerful devices. A cloud of sensors arranged in a given environment is in itself a great source of data; accessing this source in order to extract useful information is not a trivial problem. It requires correct sensor deployment within the environment and a protocol for data exchange. We also have to bear in mind the problem of data and sensors security: sensors are often installed in areas difficult to protect and monitor. In this paper we describe SensorTree, a functioning model and a simulator for a network of wireless sensors installed on the sea surface to measure parameters useful for determining the weather situation.