Unmanned aerial vehicles (UAVs) have witnessed significant growth in various domains, such as agriculture, disaster management, and remote health management systems. However, the use of UAVs necessitates secure and efficient solutions that uphold privacy during task distribution. To address this challenge, this article introduces a novel architecture for privacy-aware task distribution in UAV communication systems. Our approach leverages the benefits of blockchain and smart token-based identification within the proposed architecture, ensuring decentralized, transparent, and tamper-proof operations. By adopting a crowdsourced task distribution model, our approach further optimizes task assignment among UAVs while prioritizing data privacy, user access control, and scalability. The architecture is designed to enhance fault tolerance, enabling seamless operation under dynamic and unpredictable conditions. We present a comprehensive implementation details of a proof-of-concept prototype of our proposed architecture, detailing its design and functionality. The experimental results demonstrate the feasibility, efficiency, and adaptability of our approach in diverse real-world scenarios, highlighting its potential for broader adoption across UAV applications.

Artificial intelligence and the Internet of Things (IoT) have brought great convenience to people’s everyday lives. With the emergence of edge computing, IoT devices such as unmanned aerial vehicles (UAVs) can process data instantly at the point of generation, which significantly decreases the requirement for on-board processing power and minimises the data transfer time to enable real-time applications. Meanwhile, with federated learning (FL), UAVs can enhance their intelligent decision-making capabilities by learning from other UAVs without directly accessing their data. This facilitates rapid model iteration and improvement while safeguarding data privacy. However, in many UAV applications such as UAV logistics, different UAVs may perform different tasks and cover different areas, which can result in heterogeneous data and add to the problem of non-independent and identically distributed (Non-IID) data for model training. To address such a problem, we introduce a novel cloud–edge–end collaborative FL framework, which organises and combines local clients through clustering and aggregation. By employing the cosine similarity, we identified and integrated the most appropriate local model into the global model, which can effectively address the issue of Non-IID data in UAV logistics. The experimental results showed that our approach outperformed traditional FL algorithms on two real-world datasets, CIFAR-10 and MNIST.

Gamification has the potential to significantly enhance student engagement and motivation in educational contexts. However, there is a lack of empirical research that compares different guiding strategies between AI-driven gamified and non-gamified modes in virtual learning environments to scaffold language learning. This paper presents an empirical study that examines the impact of AI-driven gamification and learning strategies on the learning experience and outcomes in virtual environments for English-language learners. A gamified English learning prototype was designed and developed. A between-group experiment was established to compare different gamified scaffolding groups: a traditional linear group (storytelling), an AI-driven gamified linear group (task-based learning), and a gamified exploration group (self-regulated learning). One hundred students learning English as a second language participated in this study, and their learning conditions were evaluated across three dimensions: engagement, performance, and experience. The results suggest that traditional learning methods may not be as effective as the other two approaches; there may be other factors beyond in-game interaction and engagement time that influence learning and engagement. Moreover, the results show that different gamified learning modes are not the key factor affecting language learning. The research presents guidelines that can be applied when gamification and AI are utilised in virtual learning environments.

In recent years, edge-based intelligent UAV delivery systems have attracted significant interest from both the academic and industrial sectors. One key obstacle faced by these smart UAV delivery systems is data privacy, as they rely on vast amounts of data from users and UAVs for training machine learning models for person re-identification (ReID) purposes. To tackle this issue, federated learning (FL) has been extensively adopted as a promising solution since it only involves sharing and updating model parameters with a central server, without transferring raw data. However, traditional FL still suffers from the problem of having a single point of failure. In this study, we present a performance optimization method for federated person re-identification using benchmark analysis in blockchain-powered edge-based smart UAV delivery systems. Our method integrates a decentralized FL mechanism enabled by blockchain, which eliminates the necessity for a central server and stores private data on a decentralized permissioned blockchain, thus preventing a single point of failure. We employ the person ReID application in intelligent UAV delivery systems as a representative example to drive our research and examine privacy concerns. Additionally, we introduce the Federated Re-identification Consensus (FRC) protocol to address the scalability issue of the blockchain in supporting UAV delivery systems. The efficiency of our proposed method is illustrated through experiments on energy efficiency, confirmation time, and throughput. We also explore the effects of the incentive mechanism and analyze the system’s resilience under various security attacks. This study offers valuable insights and potential solutions for addressing data privacy and security challenges in the fast-growing domain of smart UAV delivery systems.

Decentralized Autonomous Organizations (DAOs) signify a groundbreaking approach to Internet-based management, enabled by blockchain technology and cryptocurrencies, and are viewed as fundamental elements of the Web3 ecosystem. In this study, we delve into the concept of DAOs by thoroughly investigating their underlying structure, ideology, and operational principles. Furthermore, we present a novel DAO framework derived from a technical and organizational assessment and provide an overview of cutting-edge DAO tools currently available. This research enables the swift implementation of DAO creation or transformation customized to an organization’s specific stage. Additionally, we recognize current challenges and shortcomings in existing DAOs and propose areas for future exploration.