Unlocking the deployment of spectrum sharing with a policy enforcement framework

Spectrum sharing has been proposed as a promising way to increase the efficiency of spectrum usage by allowing incumbent operators (IOs) to share their allocated radio resources with licensee operators (LOs), under a set of agreed rules. The goal is to maximize a common utility, such as the sum rate throughput, while maintaining the level of service required by the IOs. However, this is only guaranteed under the assumption that all “players”respect the agreed sharing rules. In this paper, we propose a comprehensive framework for licensed shared access (LSA) networks that discourages LO misbehavior. Our framework is built around three core functions: misbehavior detection via the employment of a dedicated sensing network; a penalization function; and, a behavior-driven resource allocation. To the best of our knowledge, this is the first time that these components are combined for the monitoring/policing of the spectrum under the LSA framework. Moreover, a novel simulator for LSA is provided as an open access tool, serving the purpose of testing and validating our proposed techniques via a set of extensive system-level simulations in the context of mobile network operators, where IOs and several competing LOs are considered. The results demonstrate that violation of the agreed sharing rules can lead to a great loss of resources for the misbehaving LOs, the amount of which is controlled by the system. Finally, we promote that including a policy enforcement function as part of the spectrum sharing system can be beneficial for the LSA system, since it can guarantee compliance with the spectrum sharing rules and limit the short-term benefits arising from misbehavior

The nature and detection of unauthorized waste dump sites using remote sensing

Now a day’s waste disposal has become a massive tricky for environmentalist, as it is responsible for a various issue such as sanitary hygienic, ecological security, illegal dumps, lack of paved or asphalt access roads to the landfill, inadequate treatment facilities and low efficiency of public services of waste management. In the present study, we have discussed the detection of unauthorized dumps of municipal solid waste being potential raw materials for biofuel obtaining. The aim of the study is to investigate the possibility of using wide access data of remote sensing of Earth and geographic information technologies for operative detection of unauthorized waste dumps for the further extraction of waste from the environment to turn them into biofuel. The topicality of the study is substantiated with the complexity of detection of unauthorized waste dumps due to their multiplicity adjoined with unknown geographic and temporary dislocation. The universal classification of the image does not allow detecting unauthorized waste dumps and determining whether these wastes may be the source of potential raw materials for biofuel obtaining. The research results show that the developed specialized model based on the exclusion of the low hazard dumpsites allows distinguishing dump areas enriched with carbon-containing materials

A software defined radio based anti-UAV mobile system with jamming and spoofing capabilities

The number of incidents between unmanned aerial vehicles (UAVs) and aircrafts at airports and airfields has been increasing over the last years. To address the problem, in this paper we describe a portable system capable of protecting areas against unauthorized UAVs, which is based on the use of low-cost SDR (software defined radio) platforms. The proposed anti-UAV system supports target localization and integrates effective jamming techniques with the generation of global positioning system (GPS) spoofing signals aimed at the drone. Real-life tests of the implemented prototype have shown that the proposed approach is capable of stopping the reliable reception of radionavigation signals and can also divert or even take control of unauthorized UAVs, whose flight path depends on the information obtained by the GPS system.info:eu-repo/semantics/publishedVersio

Improved Forensic Medical Device Security through Eating Detection

Patients are increasingly reliant on implantable medical device systems today. For patients with diabetes, an implantable insulin pump system or artificial pancreas can greatly improve quality of life. As with any device, these devices can and do suffer from software and hardware issues, often reported as a safety event. For a forensic investigator, a safety event is indistinguishable from a potential security event. In this thesis, we show a new sensor system that can be transparently integrated into existing and future electronic diabetes therapy systems while providing additional forensic data to help distinguish between safety and security events. We demonstrate three bowel sound detection methods, the best of which has an 84.26% bowel sound classification accuracy. We provide additional contextual information by using detected bowel sounds to detect when a patient begins to eat. We achieved 100% eating detection accuracy in a laboratory environment. From the eating data, an algorithm or forensic investigator can identify potential malfeasance in a test subject

GPS Anomaly Detection And Machine Learning Models For Precise Unmanned Aerial Systems

The rapid development and deployment of 5G/6G networks have brought numerous benefits such as faster speeds, enhanced capacity, improved reliability, lower latency, greater network efficiency, and enablement of new applications. Emerging applications of 5G impacting billions of devices and embedded electronics also pose cyber security vulnerabilities. This thesis focuses on the development of Global Positioning Systems (GPS) Based Anomaly Detection and corresponding algorithms for Unmanned Aerial Systems (UAS). Chapter 1 provides an overview of the thesis background and its objectives. Chapter 2 presents an overview of the 5G architectures, their advantages, and potential cyber threat types. Chapter 3 addresses the issue of GPS dropouts by taking the use case of the Dallas-Fort Worth (DFW) airport. By analyzing data from surveillance drones in the (DFW) area, its message frequency, and statistics on time differences between GPS messages were examined. Chapter 4 focuses on modeling and detecting false data injection (FDI) on GPS. Specifically, three scenarios, including Gaussian noise injection, data duplication, data manipulation are modeled. Further, multiple detection schemes that are Clustering-based and reinforcement learning techniques are deployed and detection accuracy were investigated. Chapter 5 shows the results of Chapters 3 and 4. Overall, this research provides a categorization and possible outlier detection to minimize the GPS interference for UAS enhancing the security and reliability of UAS operations

Multimedia Context Awareness for Smart Mobile Environments

openNowadays the development of the IoT framework and the resulting huge number of smart connected devices opens the door to exploit the presence of multiple smart nodes to accomplish a variety of tasks. Multimedia context awareness, together with the concept of ambient intelligence, is tightly related to the IoT framework, and it can be applied to a large number of smart scenarios. In this thesis, the aim is to study and analyze the role of context awareness in different applications related to smart mobile environments, such as future smart spaces and connected cities. Indeed, this research work focuses on different aspects of ambient intelligence, such as audio-awareness and wireless-awareness. In particular, this thesis tackles two main research topics: the first one, related to the framework of audio-awareness, concerns a multiple observations approach for smart speaker recognition in mobile environments; the second one, tied to the concept of wireless-awareness, regards Unmanned Aerial Vehicle (UAV) detection based on WiFi statistical fingerprint analysis.openXXXI CICLO - SC. E TECN. ING. ELETTR. E DELLE TEL. - Ambienti cognitivi interattiviGaribotto, Chiar

How neurophysiological measures can be used to enhance the evaluation of remote tower solutions

International audienceNew solutions in operational environments are often, among objective measurements, evaluated by using subjective assessment and judgement from experts. Anyhow, it has been demonstrated that subjective measures suffer from poor resolution due to a high intra and inter operator variability. Also, performance measures, if available, could provide just partial information, since an operator could achieve the same performance but experiencing a different workload. In this study we aimed to demonstrate i) the higher resolution of neurophysiological measures in comparison to subjective ones, and ii) how the simultaneous employment of neurophysiological measures and behavioural ones could allow a holistic assessment of operational tools. In this regard, we tested the effectiveness of an EEG-based neurophysiological index (WEEG index) in comparing two different solutions (i.e. Normal and Augmented) in terms of experienced workload. In this regard, 16 professional Air Traffic Controllers (ATCOs) have been asked to perform two operational scenarios. Galvanic Skin Response (GSR) has also been recorded to evaluate the level of arousal (i.e. operator involvement) during the two scenarios execution. NASA-TLX questionnaire has been used to evaluate the perceived workload, and an expert was asked to assess performance achieved by the ATCOs. Finally, reaction times on specific operational events relevant for the assessment of the two solutions, have also been collected. Results highlighted that the Augmented solution induced a local increase in subjects performance (Reaction times). At the same time, this solution induced an increase in the workload experienced by the participants (WEEG). Anyhow, this increase is still acceptable, since it did not negatively impact the performance and has to be intended only as a consequence of the higher engagement of the ATCOs. This behavioural effect is totally in line with physiological results obtained in terms of arousal (GSR), that increased during the scenario with augmentation. Subjective measures (NASA-TLX) did not highlight any significant variation in perceived workload. These results suggest that neurophysiological measure provide additional information than behavioural and subjective ones, even at a level of few seconds, and its employment during the pre-operational activities (e.g. design process) could allow a more holistic and accurate evaluation of new solutions

Spectra: Detecting Attacks on In-Vehicle Networks through Spectral Analysis of CAN-Message Payloads

Nowadays, vehicles have complex in-vehicle networks that have recently been shown to be increasingly vulnerable to cyber-attacks capable of taking control of the vehicles, thereby threatening the safety of the passengers. Several countermeasures have been proposed in the literature in response to the arising threats, however, hurdle requirements imposed by the industry is hindering their adoption in practice. In this paper, we propose SPECTRA, a data-driven anomaly-detection mechanism that is based on spectral analysis of CAN-message payloads. SPECTRA does not abide by the strict specifications predefined for every vehicle model and addresses key real-world deployability challenges