Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions

Security has become the primary concern in many telecommunications industries today as risks can have high consequences. Especially, as the core and enable technologies will be associated with 5G network, the confidential information will move at all layers in future wireless systems. Several incidents revealed that the hazard encountered by an infected wireless network, not only affects the security and privacy concerns, but also impedes the complex dynamics of the communications ecosystem. Consequently, the complexity and strength of security attacks have increased in the recent past making the detection or prevention of sabotage a global challenge. From the security and privacy perspectives, this paper presents a comprehensive detail on the core and enabling technologies, which are used to build the 5G security model; network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others. Additionally, the paper includes discussion on security monitoring and management of 5G networks. This paper also evaluates the related security measures and standards of core 5G technologies by resorting to different standardization bodies and provide a brief overview of 5G standardization security forces. Furthermore, the key projects of international significance, in line with the security concerns of 5G and beyond are also presented. Finally, a future directions and open challenges section has included to encourage future research.European CommissionNational Research Tomsk Polytechnic UniversityUpdate citation details during checkdate report - A

Development of Novel Big Data Analytics Framework for Smart Clothing

© 2013 IEEE. Recent advances in micro electro-mechanical systems (MEMS) have produced wide variety of wearable sensors. Owing to their low cost, small size and interfacability, those MEMS based devices have become increasingly commonplace and part of daily life for many people. Large amount of data from heart and breath rates to electrocardiograph (ECG) signals, which contain a wealth of health-related information, can be measured. Hence, there is a timely need for novel interrogation and analysis methods for extracting health related features from such a Big Data. In this paper, the prospects from smart clothing such as wearable devices in generating Big Data are critically analyzed with a focus on applications related to healthcare, sports and fashion. The work also covers state-of-the-art data analytics methods and frameworks for health monitoring purposes. Subsequently, a novel data analytics framework that can provide accurate decision in both normal and emergency health situations is proposed. The proposed novel framework identifies and discusses sources of Big Data from the human body, data collection, communication, data storage, data analytics and decision making using artificial intelligence (AI) algorithms. The paper concludes by identifying challenges facing the integration of Big Data analytics with smart clothing. Recommendation for further development opportunities and directions for future work are also suggested

Information-Theoretic Secure Outsourced Computation in Distributed Systems

Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir\u27s secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using SSS to protect privacy in distributed signal processing. There are three main contributions to this dissertation. First, the proposed computing framework is shown to be significantly more efficient than GC. Second, a novel game-theoretical framework is proposed to analyze different types of collusion attacks. Third, using the proposed game-theoretical framework, specific mechanism designs are developed to deter collusion attacks in a fully distributed manner. Specifically, for a collusion attack with known detectors, I analyze it as games between secret owners and show that the attack can be effectively deterred by an explicit retaliation mechanism. For a general attack without detectors, I expand the scope of the game to include the computing agents and provide deterrence through deceptive collusion requests. The correctness and privacy of the protocols are proved under a covert adversarial model. Our experimental results demonstrate the efficiency of SSS-based protocols and the validity of our mechanism design

A comprehensive survey of V2X cybersecurity mechanisms and future research paths

Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.This work is supported by the H2020-INSPIRE-5Gplus project (under Grant agreement No. 871808), the ”Ministerio de Asuntos Económicos y Transformacion Digital” and the European Union-NextGenerationEU in the frameworks of the ”Plan de Recuperación, Transformación y Resiliencia” and of the ”Mecanismo de Recuperación y Resiliencia” under references TSI-063000-2021-39/40/41, and the CHIST-ERA-17-BDSI-003 FIREMAN project funded by the Spanish National Foundation (Grant PCI2019-103780).Peer ReviewedPostprint (published version

Fuzzy Logic in Surveillance Big Video Data Analysis: Comprehensive Review, Challenges, and Research Directions

CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term “Big Video Data” (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognition methods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this paper, we draw researchers’ attention towards the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook towards future research directions derived from our critical assessment of the efforts invested so far in this exciting field

Deep learning-based signal processing approaches for improved tracking of human health and behaviour with wearable sensors

This thesis explores two lines of research in the context of sequential data and machine learning in the remote environment, i.e., outside the lab setting - using data acquired from wearable devices. Firstly, we explore Generative Adversarial Networks (GANs) as a reliable tool for time series generation, imputation and forecasting. Secondly, we investigate the applicability of novel deep learning frameworks to sequential data processing and their advantages over traditional methods. More specifically, we use our models to unlock additional insights and biomarkers in human-centric datasets. Our first research avenue concerns the generation of sequential physiological data. Access to physiological data, particularly medical data, has become heavily regulated in recent years, which has presented bottlenecks in developing computational models to assist in diagnosing and treating patients. Therefore, we explore GAN models to generate medical time series data that adhere to privacy-preserving regulations. We present our novel methods of generating and imputing synthetic, multichannel sequential medical data while complying with privacy regulations. Addressing these concerns allows for sharing and disseminating medical data and, in turn, developing clinical research in the relevant fields. Secondly, we explore novel deep learning technologies applied to human-centric sequential data to unlock further insights while addressing the idea of environmentally sustainable AI. We develop novel deep learning processing methods to estimate human activity and heart rate through convolutional networks. We also introduce our ‘time series-to-time series GAN’, which maps photoplethysmograph data to blood pressure measurements. Importantly, we denoise artefact-laden biosignal data to a competitive standard using a custom objective function and novel application of GANs. These deep learning methods help to produce nuanced biomarkers and state-of-the-art insights from human physiological data. The work laid out in this thesis provides a foundation for state-of-the-art deep learning methods for sequential data processing while keeping a keen eye on sustain- able AI