Edge/Fog Computing Technologies for IoT Infrastructure

The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies

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

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Addressing training data sparsity and interpretability challenges in AI based cellular networks

To meet the diverse and stringent communication requirements for emerging networks use cases, zero-touch arti cial intelligence (AI) based deep automation in cellular networks is envisioned. However, the full potential of AI in cellular networks remains hindered by two key challenges: (i) training data is not as freely available in cellular networks as in other fields where AI has made a profound impact and (ii) current AI models tend to have black box behavior making operators reluctant to entrust the operation of multibillion mission critical networks to a black box AI engine, which allow little insights and discovery of relationships between the configuration and optimization parameters and key performance indicators. This dissertation systematically addresses and proposes solutions to these two key problems faced by emerging networks. A framework towards addressing the training data sparsity challenge in cellular networks is developed, that can assist network operators and researchers in choosing the optimal data enrichment technique for different network scenarios, based on the available information. The framework encompasses classical interpolation techniques, like inverse distance weighted and kriging to more advanced ML-based methods, like transfer learning and generative adversarial networks, several new techniques, such as matrix completion theory and leveraging different types of network geometries, and simulators and testbeds, among others. The proposed framework will lead to more accurate ML models, that rely on sufficient amount of representative training data. Moreover, solutions are proposed to address the data sparsity challenge specifically in Minimization of drive test (MDT) based automation approaches. MDT allows coverage to be estimated at the base station by exploiting measurement reports gathered by the user equipment without the need for drive tests. Thus, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in current and emerging cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. For the first time, this dissertation reveals the existence of an optimal bin width for coverage estimation in the presence of inaccurate user positioning, scarcity of user reports and quantization error. The presented framework can enable network operators to configure the bin size for given positioning accuracy and user density that results in the most accurate MDT based coverage estimation. The lack of interpretability in AI-enabled networks is addressed by proposing a first of its kind novel neural network architecture leveraging analytical modeling, domain knowledge, big data and machine learning to turn black box machine learning models into more interpretable models. The proposed approach combines analytical modeling and domain knowledge to custom design machine learning models with the aim of moving towards interpretable machine learning models, that not only require a lesser training time, but can also deal with issues such as sparsity of training data and determination of model hyperparameters. The approach is tested using both simulated data and real data and results show that the proposed approach outperforms existing mathematical models, while also remaining interpretable when compared with black-box ML models. Thus, the proposed approach can be used to derive better mathematical models of complex systems. The findings from this dissertation can help solve the challenges in emerging AI-based cellular networks and thus aid in their design, operation and optimization