With the boom in artificial intelligence (AI) and Internet-of-Things (IoT), thousands of smart devices are interconnected with each other and deeply applied into human society. This prosperity has significantly improved public service and management, which were traditionally based on manual work. As a notable scenario, librarianship has embraced an era of “Smart Libraries” enabled by AI and IoT. Unlike existing surveys, our work comprehensively overviews the AI- and IoT-based technologies in three fundamental aspects: smart service, smart sustainability, and smart security. We then further highlight the trend towards future smart libraries.

Detecting spoofing attacks on the positions of unmanned aerial vehicles (UAVs) within a swarm is challenging. Traditional methods relying solely on individually reported positions and pairwise distance measurements are ineffective in identifying the misbehavior of malicious UAVs. This paper presents a novel systematic structure designed to detect and mitigate spoofing attacks in UAV swarms. We formulate the problem of detecting malicious UAVs as a localization feasibility problem, leveraging the reported positions and distance measurements. To address this problem, we develop a semidefinite relaxation (SDR) approach, which reformulates the non-convex localization problem into a convex and tractable semidefinite program (SDP). Additionally, we propose two innovative algorithms that leverage the proximity of neighboring UAVs to identify malicious UAVs effectively. Simulations demonstrate the superior performance of our proposed approaches compared to existing benchmarks. Our methods exhibit robustness across various swarm networks, showcasing their effectiveness in detecting and mitigating spoofing attacks. Specifically, the detection success rate is improved by up to 65%, 55%, and 51% against distributed, collusion, and mixed attacks, respectively, compared to the benchmarks.

This paper provides a comprehensive exploration of network resilience in next-generation (xG) cellular communication infrastructures. It begins with an introduction to the network science framework and its relevance to modern communication systems, and then delves into the theoretical foundations of network resilience, including key concepts from graph theory, complex network analysis, and cascading failure models. A detailed examination of network failures in xG networks follows, employing graph-theoretic approaches to analyze failure propagation, identify critical nodes, and assess network vulnerabilities. The paper also outlines practical methodologies for enhancing resilience, such as adaptive topology design, failure prediction, and decentralized architectural frameworks. The paper further discusses future research directions, emphasizing emerging challenges and opportunities in network resilience. It also reviews ongoing standardization efforts aimed at integrating network science principles into communication infrastructure design. Lessons learned and open challenges are summarized that require further investigation, making it a valuable resource for researchers, engineers, and practitioners seeking to advance the resilience and adaptability of xG networks.

A large number of Internet of Things (IoT) devices have been interconnected for information collection and exchange. The data are only meaningful if it is captured at the expected location (i.e., the IoT devices or sensors are not removed accidentally or intentionally). This article presents a new algorithm, which cooperatively locates multiple IoT devices deployed in a 3-D space based on pairwise Euclidean distance measurements. When the distance measurement noises are negligible, a new feasibility problem of rank-3 variables is formulated. We solve the problem using the difference-of-convex (DC) programming to preserve the rank-3 constraints, rather than relaxing the constraints, using semidefinite relaxation (SDR). When the distance measurements are corrupted by additive noises and nonlight-of-sight (NLOS) propagation, a maximum-likelihood estimation (MLE) problem is formulated and transformed to a DC program solved with the rank-3 constraints preserved. Simulation results indicate that the proposed approach can achieve satisfactory accuracy results with a low complexity and strong robustness to the irregular topology, poor connectivity, and measurement errors, as compared to existing SDR-based alternatives.

Multidisciplinary sustainable development is an important and complex system for comprehensive universities. Typically, a comprehensive university’s objective is to create a free, open, and diversified ecosystem of disciplines. Given finite available resources, e.g., funding or investment, configuring the formation of disciplines is critical. Understanding the interrelationships among different disciplines is challenging. Rather than directly wading through massive high-dimensional interrelated data, we judiciously formulate the cumbersome configurations of disciplines as a discipline recommendation problem. In this paper, we propose a novel data-driven approach to the configuration of disciplines based on a recommendation to predict and recommend an appropriate configuration of disciplines. The proposed approach exhibits good performance against standard metrics on real-world public data sets. It can be implemented as an attractive engine for constructing disciplines for universities.

This paper focuses on the essential global issue of protecting and transmitting indigenous knowledge. It reveals the challenges in this area and proposes a sustainable supply chain framework for indigenous knowledge. The paper reviews existing technological solutions and identifies technical challenges and gaps. It then introduces cutting-edge technologies to protect and disseminate indigenous knowledge more effectively. The paper also discusses how the proposed framework can address real-world challenges in protecting and transmitting indigenous knowledge, and explores future research applications of the proposed solutions. Finally, it addresses open issues and provides a detailed analysis, offering promising research directions for the protection and transmission of indigenous knowledge worldwide.