As sixth-generation (6G) wireless networks evolve into increasingly Artificial Intelligence (AI)-driven, user-centric ecosystems, traditional reactive handover mechanisms demonstrate limitations, especially in mobile edge computing and autonomous agent-based service scenarios. This manuscript introduces the Wireless AI Agent Network (WAAN), a cross-layer framework designed to enable intent-aware and proactive handovers in 6G networks. WAAN embeds lightweight Tiny Machine Learning (TinyML) agents as autonomous, negotiation-capable entities across heterogeneous edge nodes that contribute to intent propagation and network adaptation. To ensure continuity across mobility-induced disruptions, WAAN incorporates semi-stable Rendezvous Points (RPs) that serve as coordination anchors for context transfer and state preservation. The framework’s operational capabilities are demonstrated through a multimodal environmental control case study, highlighting its effectiveness in maintaining user experience under mobility. Finally, the article discusses key challenges and future opportunities associated with the deployment and evolution of WAAN.

Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the resource-constrained edges of the Internet. Meanwhile, the resource requirements of digital computing and deep learning are growing exponentially, in an unsustainable manner. One possible way to bridge this gap is the adoption of resource-efficient brain-inspired “neuromorphic” processing and sensing devices, which use event-driven, asynchronous, dynamic neurosynaptic elements with colocated memory for distributed processing and machine learning. However, since neuromorphic systems are fundamentally different from conventional von Neumann computers and clock-driven sensor systems, several challenges are posed to large-scale adoption and integration of neuromorphic devices into the existing distributed digital–computational infrastructure. Here, we describe the current landscape of neuromorphic computing, focusing on characteristics that pose integration challenges. Based on this analysis, we propose a microservice-based conceptual framework for neuromorphic systems integration, consisting of a neuromorphic-system proxy, which would provide virtualization and communication capabilities required in distributed systems of systems, in combination with a declarative programming approach offering engineering-process abstraction. We also present concepts that could serve as a basis for the realization of this framework, and identify directions for further research required to enable large-scale system integration of neuromorphic devices.

The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.

Information-centric networking (ICN) technology matches many major requirements of vehicular ad hoc networks (VANETs) in terms of its connectionless networking paradigm accordant with the dynamic environments of VANETs and is increasingly being applied to VANETs. However, wireless transmissions of packets in VANETs using ICN mechanisms can lead to broadcast storms and channel contention, severely affecting the performance of data dissemination. At the same time, frequent changes of topology due to driving at high speeds and environmental obstacles can also lead to link interruptions when too few vehicles are involved in data forwarding. Hence, balancing the number of forwarding vehicular nodes and the number of copies of packets that are forwarded is essential for improving the performance of data dissemination in information-centric networking for vehicular ad-hoc networks. In this paper, we propose a context-aware packet-forwarding mechanism for ICN-based VANETs. The relative geographical position of vehicles, the density and relative distribution of vehicles, and the priority of content are considered during the packet forwarding. Simulation results show that the proposed mechanism can improve the performance of data dissemination in ICN-based VANET in terms of a successful data delivery ratio, packet loss rate, bandwidth usage, data response time, and traversed hops.

Many common DTN routing protocols are replication-based, which have relatively good performance in terms of message delivery ratio but high overhead, and leave the issue of garbage collection open. In this paper, we propose Named Data Distance Routing (NDDR), a named data based DTN routing approach which makes routing decisions for named data based on topological distance information. This helps to reduce the overhead of routing. We have implemented NDDR in the ONE simulation environment and the simulation results show that the proposed routing method has better performance in terms of message delivery ratio and network overhead compared with several typical replication-based DTN routing protocols

Information dissemination is one of the most important tasks of vehicular networks. Therefore, information-centric networking (ICN) technology is being more and more widely used in vehicular networks due to the connectionless and lightweight characteristics of this networking paradigm. Caching plays an essential role in information-centric networks, but current caching techniques for ICN are not ideal for use in vehicular networks on account of the dynamicity and wireless transmission of vehicular networks. This paper presents a caching approach for ICN-based vehicular networks that takes into account both the dynamicity of vehicular networks and the popularity of the information to be distributed. By introducing the “interval” metric and estimating the popularity of information and current networking conditions of the vehicles on road, cooperative caching among nodes can be realized without exchanging cache management information among them. Simulation results show that the proposed approach can increase the storage space utilization and has low data response time for vehicular networks.

This paper outlines the tradeoffs involved in utilizing Information-Centric Networking (ICN) for Internet of Things (IoT) scenarios. It describes contexts and applications where the IoT would benefit from ICN, and where a host-centric approach would be better. Requirements imposed by the heterogeneous nature of IoT networks are discussed in terms of connectivity, power availability, computational and storage capacity. Design choices are then proposed for an IoT architecture to handle these requirements, while providing efficiency and scalability. An objective is to not require any IoT specific changes of the ICN architecture per se, but we do indicate some potential modifications of ICN that would improve efficiency and scalability for IoT and other applications

Information-centric networking (ICN) is being applied to the vehicular networks by more and more researchers on account of its lightweight and connectionless networking paradigm and in-network caching characteristics, making it suitable for the dynamic environments of vehicular networks. However, wireless transmission of interest packets to find content in the network may lead to broadcast storms that can affect the performance of information dissemination severely. This paper proposes a distance assisted data dissemination method with broadcast storm suppressing mechanism (DASB) for supporting rapid and efficient information dissemination in ICN-based vehicular ad hoc networks (VANETs). Geo-position data of vehicles are used to accelerate packet forwarding, and vehicular nodes in certain areas are restricted to forward packets in order to suppress the broadcast storm. Simulation results show that the proposed method can greatly reduce the total number of packets transmitted in the network, and the successful information delivery ratio and information delivery time can also be improved.