Jameel Shehu Yalli

As urban ecosystems evolve into data-intensive infrastructures, the transparency of supply chains has become pivotal to cybersecurity, governance, and trust. This chapter explores how blockchain, initially developed for decentralized finance, is now foundational for traceability in smart urban logistics. It examines its convergence with IoT and governance systems to enable tamper-resistant, auditable, and privacy-aware data flows. The chapter outlines blockchain's core features, immutability, decentralization, smart contracts, and permissioned architectures, and maps these to urban sectors like food safety, pharmaceuticals, procurement, and waste. Real-world deployments across Asia, Europe, and North America demonstrate its role in enhancing collaboration, preventing fraud, and supporting compliance with regulations such as the GDPR. The chapter also identifies adoption challenges and offers ethical governance models and a strategic roadmap to position blockchain as a socio-technical foundation for trust, sovereignty, and equity in the smart city era.

Asthma exacerbation prediction is critical for preventing severe respiratory complications and improving patient outcomes. Traditional predictive models rely on static machine learning approaches, which lack adaptability to evolving patient conditions and environmental changes. This study proposes a reinforcement learning (RL)-based model using Proximal Policy Optimization (PPO) to enhance personalized early prediction of asthma exacerbation attacks. Unlike conventional models, PPO dynamically refines its policy through iterative learning, allowing for real-time, adaptive risk assessment based on personalized patient data and environmental factors. The PPO model was trained on a comprehensive asthma dataset, integrating demographic, environmental, lifestyle, and Asthma Control Test (ACT) scores. The model achieved 96.60% accuracy, 95.79% precision, 96.65% recall, and 95.92% F1-score, outperforming baseline RL algorithms such as Deep Q-Learning (92.21% accuracy), Advantage Actor-Critic (94.34%), and Trust Region Policy Optimization (95.12%). Comparative evaluation with existing studies further demonstrated PPO’s superior recall and sensitivity to exacerbation cases, making it highly suitable for personalized asthma management. RL performance metrics confirmed the model’s stability and convergence, including cumulative rewards, policy, and value loss. These findings highlight PPO’s potential for real-time clinical integration, enabling continuous patient monitoring and proactive intervention strategies. Future work will focus on training the model on larger, multi-source datasets to improve generalization across diverse populations. Additionally, further research will explore deployment in telemedicine, integration with wearable health technologies, and adaptability to other chronic diseases, ensuring scalability and real-world applicability in personalized healthcare systems.

The idea of the Internet of Things (IoT) is to connect objects and smart devices with any digital element seamlessly at anytime, anywhere, with anything. These devices or objects are characterized by low power consumption, lower bandwidth usage, limited processing power, and limited memory capacity. This article presents a systematic literature review (SLR) of the state-of-the-art security challenges of IoT systems designed for authentication. It reports the strengths, weaknesses, threats, and attacks associated with the proposed authentication models. In addition, a comprehensive analysis of IoT-compatible protocols, enabling technologies, and countermeasures to mitigate risk in IoT is investigated. This study uses the PRISMA methodology to review peer-reviewed articles published from 2014 to 2023, studying 100+ papers by selecting approximately 10 papers each year. It provides a detailed overview that contrasts and compares the advances in securing IoT devices to date while identifying remaining open research questions for IoT security designs. This article highlights the need for a survey integrating authentication models with compatible protocols and other lightweight technologies. The examination and analysis in this work contribute to the body of knowledge and serve as a roadmap for researchers and practitioners to improve their work while designing IoT security models.

As 5G networks advance, integrating blockchain technology to enhance security and decentralization shows great potential but faces significant scalability challenges. This study focuses on addressing these scalability issues in 5G blockchain networks by analyzing key factors such as transaction throughput, consensus mechanisms, and network latency. We propose several models to improve scalability, including sharding, sidechains, optimized consensus algorithms, and off-chain solutions. These models were evaluated through simulations using real-time data, with accuracy levels of up to 95% in predicting performance improvements across various metrics. The paper investigates the challenges specific to 5G environments, such as high transaction volumes and diverse device requirements, and assesses the strengths and limitations of each scalability technique in this context. Our findings highlighted potential synergies between these solutions and the unique features of 5G network architecture, offering tailored strategies for enhancing blockchain scalability. Additionally, the study addresses energy efficiency concerns associated with blockchain technologies and suggests optimization strategies. Simulated results are compared with real-world data, achieving accuracy rates of over 90% in identifying bottlenecks and validating the effectiveness of the proposed solutions. The insights presented aim to optimize 5G blockchain performance in dynamic, resource-constrained environments, facilitating the adoption of secure and decentralized applications in next-generation networks.

The idea of the IOT started way back in 1982 when a bending machine is connected to the internet, then to the concept of Mark Weiser in 1992, then RFID, and so on. A detailed evolution of the IOT and how it was transforming is given. Due to the wide embracement of IOT by industries and home users, it has become the cornerstone of the Information and Communication Technology (ICT) market. The market value of the IOT was worth $1.90 billion in 2018; $25 billion in 2020; $925.2 billion as at 2023; while it is forecasted to hit $6 trillion in 2025 at 15.12% growing rate [1]. This work presented some major technologies integrated with the IOT to achieve a certain goal and improve an existing system, the capability of IOT to create smart applications is demonstrated and some major distinctions between IOT & the Internet were highlighted.

The integration of deep learning in healthcare has propelled advancements in diagnostics and decision support. However, the inherent opacity of deep neural networks (DNNs) poses challenges to their acceptance and trust in clinical settings. This survey paper delves into the landscape of explainable deep learning techniques within the healthcare domain, offering a thorough examination of deep learning explainability techniques. Recognizing the pressing need for nuanced interpretability, we extend our focus to include the integration of fuzzy logic as a novel and vital category. The survey begins by categorizing and critically analyzing existing intrinsic, visualization, and distillation techniques, shedding light on their strengths and limitations in healthcare applications. Building upon this foundation, we introduce fuzzy logic as a distinct category, emphasizing its capacity to address uncertainties inherent in medical data, thus contributing to the interpretability of DNNs. Fuzzy logic, traditionally applied in decision-making contexts, offers a unique perspective on unraveling the black box of DNNs, providing a structured framework for capturing and explaining complex decision processes. Through a comprehensive exploration of techniques, we showcase the effectiveness of fuzzy logic as an additional layer of interpretability, complementing intrinsic, visualization, and distillation methods. Our survey contributes to a holistic understanding of explainable deep learning in healthcare, facilitating the seamless integration of DNNs into clinical workflows. By combining traditional methods with the novel inclusion of fuzzy logic, we aim to provide a nuanced and comprehensive view of interpretability techniques, advancing the transparency and trustworthiness of deep learning models in the healthcare landscape.

IoT will be the main component of the next era of computing. IoT devices are increasing in number and will continue to increase due to automation. According to cisco, up to 50 billion devices and objects get connected with each other and to the internet in 2020 [1, 6]. IoT application domains includes smart environments, smart grid, smart cities, smart healthcare, smart industries, smart agriculture, smart home, smart transportation etc [2]. However, the IoT devices are characterized as resource constrained with low computational ability, lower power consumption and small memory. PUF is embedded on IoT device to uniquely identify the device. This characteristic feature of PUF is unique and unclonable, while fuzzy logic's ability to deal with uncertainty and hazy or vague information is used to categorize nodes by computing its record, status and reputation, so that nodes are authenticated according to the trust values it attained. The authentication is in three phases; first, second and third. With PUF's unique physical characteristics; authentication, reliability and lightweight are achieved.