Internet of Things (IoT) and Network Softwarization are fast becoming core technologies of information systems and network management for the next-generation Internet. The deployment and applications of IoT range from smart cities to urban computing and from ubiquitous healthcare to tactile Internet. For this reason, the physical infrastructure of heterogeneous network systems has become more complicated and thus requires efficient and dynamic solutions for management, configuration, and flow scheduling. Network softwarization in the form of Software Defined Networks and Network Function Virtualization has been extensively researched for IoT in the recent past. In this article, we present a systematic and comprehensive review of virtualization techniques explicitly designed for IoT networks. We have classified the literature into software-defined networks designed for IoT, function virtualization for IoT networks, and software-defined IoT networks. These categories are further divided into works that present architectural, security, and management solutions. Besides, the article highlights several short-term and long-term research challenges and open issues related to the adoption of software-defined Internet of Things.

In recent years, the interplay between AI agents and blockchain has enabled secure and scalable collaboration among multi-agent systems, promoting unprecedented levels of autonomy and interoperability. AI agents play a vital role in facilitating complex decision making and improving operational efficiency in blockchain systems. This collaborative synergy is particularly evident in how multi-agent systems collectively tackle complex tasks to ensure seamless integration within these frameworks. While significant efforts have been made to integrate AI agents and blockchain, most studies overlook the broader potential of AI agents in addressing challenges such as interoperability, scalability, and privacy issues. In this paper, we bridge these gaps by illustrating the interplay between AI agents and blockchain. Specifically, we explore how AI agents enhance decentralized systems and examine blockchain’s role in enabling secure and scalable collaboration. Furthermore, we categorize practical applications across domains, such as Web3, decentralized finance (DeFi), asset management, and autonomous systems, providing practical insights and real-world use cases. Additionally, we identify key research challenges, including the complexities of multi-agent coordination, interoperability across diverse systems, and privacy maintenance in decentralized frameworks. Finally, we offer future directions in terms of governance, sovereignty, computation, and interpretability to promote a secure and responsible ecosystem.

Data annotation serves as a critical foundation for artificial intelligence (AI) and machine learning (ML). Recently, AI agents powered by large language models (LLMs) have emerged as effective solutions to longstanding challenges in data annotation, such as scalability, consistency, cost, and limitations in domain expertise. These agents facilitate intelligent automation and adaptive decision-making, thereby enhancing the efficiency and reliability of annotation workflows across various fields. Despite the growing interest in this area, a systematic understanding of the role and capabilities of AI agents in annotation is still underexplored. This paper seeks to fill that gap by providing a comprehensive review of how LLM-driven agents support advanced reasoning strategies, adaptive learning, and collaborative annotation efforts. We analyze agent architectures, integration patterns within workflows, and evaluation methods, along with real-world applications in sectors such as healthcare, finance, technology, and media. Furthermore, we evaluate current tools and platforms that support agent-based annotation, addressing key challenges such as quality assurance, bias mitigation, transparency, and scalability. Lastly, we outline future research directions, highlighting the importance of federated learning, cross-modal reasoning, and responsible system design to advance the development of next-generation annotation ecosystems.

Android malware poses a significant cybersecurity threat, enabling unauthorized data access, financial fraud, and device compromise. Although deep learning methods are widely used for malware detection, they often struggle with stability and adaptability in the face of evolving threats. While large language models (LLMs) have shown promise in this area, their application to Android malware detection remains underexplored, particularly with regard to optimizing the semantic relationships within Android application packages (APKs). To address this gap, we introduce LLM-MalDetect, a novel framework that improves LLM-based APK analysis by explicitly modeling semantic dependencies and leveraging structured prompt engineering for optimized detection. Our approach formalizes LLM adaptation through a robust string-based feature extraction method and a tailored fine-tuning strategy to enhance precision. Evaluations on benchmark datasets demonstrate that LLM-MalDetect achieves up to 98.97% accuracy, outperforms existing methods in terms of robustness, and enables real-time analysis.

Unmanned Aerial Vehicles (UAVs) have received significant research interest from academia due to their on-demand content distribution capabilities using mobile edge computation and the next-generation Flying Ad-hoc Network (FANET). With the addition of Software-Defined Networking (SDN) and network virtualization, these UAVs have transformed into three-dimensional distributed heterogeneous networks. However, the softwarized UAV-based communication is prone to high latency, energy consumption, resource constraints, and link failures. Hence, content orchestration has become a significant challenge. Information-Centric Networking (ICN) uses content-based rapid data dissemination in the dynamic wireless scenario. However, ICN-based content discovery and distribution have not been explored extensively for UAV-assisted networks. In this work, we propose a UAV-assisted multi-layer IC-SDN solution to tackle the content distribution challenges using distributed controllers placed hierarchically in the edge and cloud tiers. Besides, we formulate the traffic optimization problem into a joint forwarding and flow scheduling problem using M/M/1 queueing allocation model and propose a heuristic edge-cloud traffic flow assignment solution that allocates requests based on the service type and device location. We evaluate the proposed solution in a simulation environment considering the mobility principle of FANET nodes. Besides, the effectiveness of the optimization solution and the performance gains are evaluated analytically. The simulation and numerical results show that the proposed optimization model is efficient as compared to other solutions, in maximizing throughput and minimizing computational latency, delay, and packet loss.