There seems to be a general consensus that interdisciplinary education for engineers (sometimes referred to simply as IEE) is highly desirable given the complex nature of many modern engineering problems. Most agree that those issues with significant societal, environmental, or ethical impacts, such as climate change, sustainable development, and even the use of artificial intelligence, can only be effectively addressed through a multi-disciplinary combination of technical and non-technical insight. The notion that interdisciplinary education can lead to more effective problem-solving, teamwork, and communication skills is frequently mentioned. In recent years, senior leaders in both government and industry have been quite vocal in their support of interdisciplinary education for engineers.
This article continues the series of interviews with the Officers of the IEEE ComSoc Member and Global Activities (MGA) Council for the term 2026–2027, published every month in the Global Communications Newsletter.
The airspace field in the range of 3,000 meters and below becomes a new operating domain for the digital economy. Unmanned aerial vehicles (UAVs), cooperative swarms, and urban air mobility platforms are moving from isolated demonstrations toward persistent roles in public safety, logistics, infrastructure inspection, environmental monitoring, and emergency response. Yet a low-altitude wireless network (LAWN) cannot be built by simply lifting a terrestrial network into the sky. Three-dimensional mobility continually reshapes topology and propagation; sensing and control actions affect the network state they depend on; and limited payload, energy, and computing resources must coexist with stringent requirements for latency, reliability, security, and safety.
In this issue of IEEE Communication Magazine, I am pleased to introduce Shui Yu, Vice President for Technical and Educational Activities (TEA), who will share his plans for advancing IEEE Communication Society (ComSoc) and every member of the professional family in terms of technology and education in this fast-developing and dynamic Big Science age.
The IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN) was organized in Tempe, Arizona, USA, June 15–16, 2026, and was the 32nd edition of the conference. More than 40 participants attended the conference in person, and several more joined remotely due to visa issues. Dr. Eirini Eleni Tsiropoulou General Chair, and Dr. Wenfei Wu General Co-Chair, led the Organizing Committee. The committee also included Dr. Anna Maria Vegni and Dr. Parth Pathak TPC Co-Chairs; Dr. Abdallah Shami and Dr. Ahmed Refaey Hussein, Posters, Demos, and PhD Forum Co-Chairs; Dr. Sharief Oteafy and Dr. Cheng Li, Tutorials Co-Chairs; Dr. Dimitrios Michael Manias, Publication Chair; Aruzhan Sabyrbek, Web Chair; and Dr. Aris Leivadeas, Publicity Chair. IEEE continuously supported the conference through Stephanie Poli, ComSoc Project Manager, and Bruce Worthman, Financial Chair.
The development of smart cities worldwide is placing tremendous demands on transportation systems, challenging cities to provide safe, efficient, sustainable, and resilient mobility services. Central to such transformation lies the convergence of advanced communication networks, intelligent transportation technologies, and data-driven decision-making, which form a vital part of smart city infrastructure. Smart transportation has emerged as an important part of smart cities, enabling connected vehicles, intelligent infrastructure, real-time traffic management, and enhanced public services. This special issue brings together cutting-edge research and innovative applications that address the critical communication challenges and opportunities underlying next-generation urban mobility.
The history of an engineering institution or organization responsible for important technical innovations can play an important role in helping us more completely understand the history of technology. Moreover, by capturing the traditions, principles, and beliefs that have made the organization both successful and unique, such histories can also help to preserve the identity of the institution or organization while contributing to its future success.
As summer draws to a close, this August 2026 issue of IEEE Communications Magazine turns our attention to another challenge facing our community: how to build communications systems that are not only more capable, but also more sustainable, dependable, and beneficial to society. The articles in this issue examine a broad spectrum of topics, including net-zero 6G networks, deterministic networking, integrated sensing and communications, network sovereignty, human digital twins, and trustworthy AI. Together, these efforts reflect a growing emphasis on creating communications infrastructures that balance performance, resilience, sustainability, and human needs.
The sixth generation (6G) of wireless communications is envisioned as a transformative platform that will seamlessly integrate communication, sensing, computing, and intelligence to support immersive, autonomous, and human-centric services. Through capabilities such as terabit-per-second connectivity, pervasive artificial intelligence, integrated sensing and communication, and global coverage across terrestrial and non-terrestrial infrastructures, 6G is expected to become a cornerstone of future digital societies. However, realizing this ambitious vision poses a fundamental challenge: sustainability. The rapid growth of network traffic, edge intelligence, cloud computing, and connected devices threatens to significantly increase the energy consumption and carbon footprint of communication systems if sustainability is not embedded into their design from the outset.
This article continues the series of interviews with the Officers of the IEEE ComSoc Member and Global Activities (MGA) Council for the term 2026–2027, published every month in the Global Communications Newsletter.
Conferences are where ideas come to life. They are the cornerstone of our technical community, bringing together researchers, engineers, practitioners, industry leaders, and students from around the world to exchange knowledge, challenge conventional thinking, and inspire the next generation of innovations. Whether through technical sessions, workshops, tutorials, industry panels, or informal hallway conversations, conferences create an environment where collaborations begin, partnerships flourish, and the future of communications technology is shaped.
Federated learning enables collaborative model training without centralizing raw data, supporting privacy-sensitive applications ranging from mobile assistants to healthcare systems. However, model updates may still leak sensitive information. Differential privacy mitigates this risk by injecting noise into local updates, although existing approaches typically rely on static, globally scheduled, or fixed client-level privacy budgets that may poorly align privacy expenditure with learning utility. This work introduces an agentic privacy management framework in which autonomous client-side agents dynamically allocate privacy budgets based on gradient informativeness, loss variation, and remaining budget constraints. Unlike predefined allocation schemes, the proposed mechanism treats privacy spending as a state-aware and context-aware control process embedded within federated learning. Experimental results show consistent improvements in convergence and final accuracy compared to representative differential privacy allocation baselines, while reducing cumulative privacy expenditure.
With the rapid emergence of low-altitude wireless networks for the sixth generation (6G), terrestrial networks are extending their coverage into three-dimensional airspace. Different from traditional terrestrial communication, the energy consumption in groundto- air (G2A) coverage increases exponentially with the aerial node's altitude, constraining the scalability and sustainability of the network. In this work, a transformative evolution toward embodied intelligent agent (EIA)-enabled networks is envisioned, where terrestrial base stations are no longer passive communication nodes but intelligent entities capable of sensing, reasoning, and acting upon both wireless and energy environments. By opening interfaces between wireless and energy networks, energy is elevated from a static constraint to a controllable physical resource. This shift enables networks to dynamically redistribute energy, coordinate cooperative actions among agents, and align spatial energy availability with G2A coverage demand through a closed sensing-decision-action loop. An energy-intelligence- communication co-designed architecture is introduced to support this evolution, incorporating cross-layer orchestration, agent-to-agent coordination, and interpretable cooperative mechanisms that prevent agents' self-preserving behaviors under conflicting objectives. By embedding embodiment and energy awareness into network design, this paradigm offers a scalable and low-carbon pathway for sustainable G2A coverage, shaping the long-term development of embodied low-altitude wireless networks.
Today’s wireless networks remain fundamentally reactive, relying primarily on instantaneous observations and short-term optimization. Such a paradigm becomes increasingly insufficient in the highly dynamic, non-stationary, and tightly coupled environments envisioned for 6G. This article introduces Living Digital Twin Networks (L-DTNs), a bio-inspired architecture that redefines the digital twin as the cognitive core of closed-loop network intelligence. Unlike conventional AI-native or digital-twin-assisted approaches, L-DTNs integrate structured memory, experience-aware adaptation, and long-timescale evolution within a unified operational framework, enabling networks to continuously learn from past interactions, anticipate future conditions, and refine their behavior over time. We further formalize network “aliveness” through seven measurable system-level criteria characterizing stability preservation, multi-timescale organization, closed-loop information transformation, capability growth, context-aware adaptation, real-time responsiveness, and reusable operational knowledge. Treating operational experience as an explicit system-level entity, rather than as information implicitly embedded in model parameters, is the central design principle that distinguishes L-DTNs from prior approaches. The proposed framework is mapped onto O-RAN architectures through a multi-timescale realization spanning Near-RT and Non-RT RIC control loops. Representative system-level evaluations for autonomous mobility scenarios demonstrate improved throughput stability, handover reliability, recovery behavior, multi-timescale coordination, and cross-context adaptation compared with reactive, DRL-based, and conventional DT-assisted baselines. Collectively, L-DTNs provide a practical roadmap for transforming network intelligence from an auxiliary optimization layer into an intrinsic, continuously evolving capability for future 6G networks.
The integration of low-altitude mobile edge computing (MEC) and integrated sensing and communication (ISAC) holds immense potential for empowering applications such as aerial inspection and emergency rescue. However, compared to terrestrial networks, low-altitude environments pose severe challenges to sensing robustness due to high dynamics, complex channel clutter, and resource constraints. In this article, we investigate Agentic AI-based solutions to achieve robust sensing in low-altitude ISAC-MEC networks (ISAC-MECs). Specifically, we first analyze the core challenges of achieving sensing robustness in low-altitude scenarios and discuss the limitations of conventional AI methods in addressing these complexities. Then, we introduce the modular architecture and workflow of Agentic AI, followed by an analysis of its role in addressing sensing robustness challenges. To demonstrate the practical benefits of Agentic AI for robust sensing in low-altitude ISAC-MECs, we propose a physics-reasoning-enhanced Agentic AI-based optimization framework via a representative case study. The framework uses potential-guided and self-refinement mechanisms to iteratively refine state representations and reward functions, which improves reinforcement learning performance for robust sensing in low-altitude ISAC-MECs. Simulation results validate the effectiveness of the proposed framework. Finally, we outline future directions for integrating Agentic
The geopolitical landscape is becoming increasingly complex and tense, with a largely silent cyber conflict unfolding on a daily basis. Consequently, traditional defenses based on detection and postevent analysis are therefore becoming insufficient. This situation has compelled both public and private entities to adopt a more proactive approach to addressing digital threats in order to anticipate them. This is particularly important in the Critical Infrastructure (CI) sector, given its strategic geopolitical role and the severe consequences that attacks on such infrastructures may entail. In this context, this article proposes the Operational Platform for Offensive Replication (OPFOR), a modular framework for executing Adversary Emulation (AE) based on attacker behaviors attributable to real malicious actors with specific objectives. Likewise, we introduce the concept of Adaptive Adversary Emulation (AAE), defined as the execution of an actor's behavior adapted to a different context while preserving the similarity of the actions and the Tactics, Techniques, and Procedures (TTP) employed by adversaries.
Low Earth orbit (LEO) mega-constellations, such as Starlink and Kuiper, are revolutionizing global connectivity by providing internet access to remote and oceanic regions. To meet explosive transmission demands for 6G applications, including the metaverse, the Internet of Things (IoT), and holographic communication, hybrid freespace optical (FSO) and radio frequency (RF) links offer a promising solution. FSO provides ultra-high throughput, and RF offers weather-resilient reliability. To address these conflicts, we introduce an intelligent, user-centric connectivity architecture for hybrid FSO/RF LEO satellite networks, leveraging software-defined satellite networking (SDSN) and collaborative deep reinforcement learning (DRL). We guide readers through entire connection lifecycles, key technical challenges, and an intelligent solution that enables high-quality service in dynamic space environments. A case study illustrates intelligent orchestration in a mega-constellation environment, highlighting the trade-offs between switching frequency and link stability. Concurrently, we explore development directions for intelligent 6G Space-Air-Ground Integrated Networks (SAGIN).
Future low-altitude wireless networks (LAWNs) are evolving from simple connectivity layers into intelligent fabrics populated by goal-driven aerial agents. In this emerging landscape, unmanned aerial vehicles (UAVs) must autonomously navigate complex tradeoffs between mission-critical objectives, such as timely delivery, and opportunistic utility, such as sensing data harvesting. However, existing control paradigms face a dilemma: model-based optimization is often computationally prohibitive and overly conservative, while blackbox DRL offers limited interpretability and does not readily provide the operational assurance required for regulation-sensitive airspace. This article proposes a structured agentic intelligence framework that bridges this gap. By embedding the analytical structure of optimal control into a learnable neural architecture, we develop a greybox policy with interpretable threshold adaptation and structured execution logic. Our case study of time-critical urban logistics shows that the proposed structured approach can adapt effectively to stochastic environments and maintain a high mission success rate under the considered simulation settings. A favorable tradeoff is demonstrated between delivery reliability, sensing utility, and energy efficiency, while supporting lightweight inference in the tested edge-hardware setting.
A query in the Domain Name System (DNS) can result in a negative response from an authoritative server. A negative response indicates a non-existent name (NXDOMAIN) in a zone. Although resolvers can cache negative responses, queried domain names associated with malicious traffic typically vary and make cached items useless. This variability effectively bypasses resolver caches, resulting in high volume NXDOMAIN traffic that can overwhelm authoritative servers. To reduce the volume of negative queries between the resolver and the authoritative server, aggressive caching has been widely adopted by resolvers. This technique uses previously cached denial records (a record type that proves name non-existence) to reduce query traffic. However, privacy requirements have led to the design of several denial record variants that are either inefficient or incompatible with aggressive caching. To overcome this limitation, we propose a novel denial record design that utilizes a Bloom filter to embed more information for the resolver. Our design provides stronger privacy than the legacy denial record approach and exhibits better compatibility than other denial record variants in supporting aggressive caching. Experimental results show that our approach reduces traffic between resolvers and authoritative servers under massive NXDOMAIN queries, improves resolver response performance, and requires fewer cache entries to achieve the same negative cache hit rates compared with the legacy denial record technique.
