Abstract
This study introduces a secure, adaptable, and decentralized learning framework empowered by blockchain technology to enhance smart grid security and efficiency. Security is achieved through blockchain's ledger, ensuring data integrity, privacy, and resilience. Adaptability refers to the framework's ability to adjust to changing conditions, supporting multiple learning paradigms. Decentralization enhances fault tolerance by distributing control across nodes. Our framework excels in scalability, data-exchange security, and rapid response times, aiming to establish an intelligent blockchain-based smart grid supporting centralized learning (CL), federated learning (FL), and active federated learning (AFL). We present an innovative blockchain-based architecture customized to optimize information sharing and security within the blockchain. Our solution addresses various learning paradigm requirements by: (i) Selecting reliable entities for participation based on high-quality training data models; (ii) Acquiring a reliable subset of data for CL and AFL, balancing learning performance, latency, and cost; (iii) Adjusting blockchain configuration to align with specific learning paradigm requirements. Results from real-world datasets demonstrate superior performance compared to existing solutions. Our framework achieves high learning performance while minimizing latency and blockchain costs.

Abstract
This article introduces a comprehensive frame-work for Post-Disaster Search and Rescue (PDSR), aiming to optimize search and rescue operations leveraging Unmanned Aerial Vehicles (UAVs). The primary goal is to improve the precision and availability of sensing capabilities, particularly in various catastrophic scenarios. Central to this concept is the rapid deployment of UAV swarms equipped with diverse sensing, communication, and intelligence capabilities, functioning as an integrated system that incorporates multiple technologies and approaches for efficient detection of individuals buried beneath rubble or debris following a disaster. Within this framework, we investigate an architectural solution and address the associated challenges to ensure superior performance in real-world disaster scenarios. The proposed framework is designed to provide comprehensive coverage of affected areas by utilizing a multi-tier swarm architecture with multi-modal sensing capabilities. By integrating data from var-ious sensors and applying machine learning for data fusion, the framework enhances detection accuracy and supports precise survivor identification, even in complex environments.

Abstract
The shift to AI-native 6G networks demands autonomous slicing strategies that can adapt to diverse and evolving edge and IoT service needs. Two paradigms have emerged: Learn to Slice (L2S), where AI optimizes network slicing for general services, and Slice to Learn (S2L), where slices support AI model training, often offloaded from Internet of Things (IoT) devices. Existing S2L approaches typically optimize communication or computation in isolation. This paper presents the first unified framework that jointly optimizes communication resources, computation capacity, and AI hyperparameters to maximize the average accuracy of multiple concurrent AI services. We address the complexity of this joint problem by applying L2S-inspired techniques to enhance S2L, introducing two autonomous agents: EXP3 from online convex optimization and DQN from deep reinforcement learning. Extensive experiments demonstrate and contrast the effectiveness of these agents in maximizing aggregated AI accuracy, supporting knowledge transfer, and sustaining robust performance under adversarial and long-term conditions, thereby enhancing the realization of zero-touch network management for AI services in 6G networks, supporting resource-constrained IoT.

Abstract
The forthcoming 6G networks will embrace a new realm of AI-driven services that requires innovative network slicing strategies, namely slicing for AI, which involves the creation of customized network slices to meet Quality of Service (QoS) requirements of diverse AI services. This poses challenges due to time-varying dynamics of users' behavior and mobile networks. Thus, this paper proposes an online learning framework to determine the allocation of computational and communication resources to AI services, to optimize their accuracy as one of their unique key performance indicators (KPIs), while abiding by resources, learning latency, and cost constraints. We define a problem of optimizing the total accuracy while balancing conflicting KPIs, prove its NP-hardness, and propose an online learning framework for solving it in dynamic environments. We present a basic online solution and two variations employing a pre-learning elimination method for reducing the decision space to expedite the learning. Furthermore, we propose a biased decision space subset selection by incorporating prior knowledge to enhance the learning speed without compromising performance and present two alternatives of handling the selected subset. Our results depict the efficiency of the proposed solutions in converging to the optimal decisions, while reducing decision space and improving time complexity. Additionally, our solution outperforms State-of-the-Art techniques in adapting to diverse environmental dynamics and excels under varying levels of resource availability.

Abstract
In the smart grid, data communication between smart meters and utility servers should be authentic, private, have integrity while being accessible. To mitigate the risks of potential attacks, securing these two-way communications is crucial. Equally important is maintaining near real-time communication and avoiding significant delays when extra security levels are involved. Existing research on smart grids has not simultaneously tackled the issues of security, communication speed, and network scalability. In this work, we propose a novel delay-optimized blockchain solution for securing cryptographic communication between consumers and the utility in a smart grid. Our solution, based on EOS smart contracts, Edge computing, asymmetric Cryptographic functions, and Group signatures (EECG), treats data communication as transactions that are asymmetrically encrypted and signed in groups before being stored on the EOS blockchain, ensuring confidentiality, privacy, availability, and low cost. The use of edge computing reduces the computational burden of smart meters, increases transaction speed, enhances data privacy, and improves scalability. Furthermore, an optimization problem for associating smart meters with edge nodes is formulated to minimize data exchange and processing delays over the blockchain, facilitating near real-time secure data access.

Abstract