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.

Evaluation of energy consumption and device lifetime in battery-powered wireless sensor networks (WSN) is almost exclusively based on estimates of the total charge (i.e. mA-h) consumed by the device. In reality, batteries are complex electro-chemical systems and their discharge behavior depends heavily on the timing and intensity of the applied load. However, there is very little empirical data or reliable models available for the kinds of batteries and loads that are typically used in WSN. The effect of battery dynamics on sensor lifetime is therefore not well understood. We characterize CR2032 Li coin cells using carefully controlled synthetic loads and a wide range of WSN-typical load parameters. Our results are the first to quantify in-depth the discharge behavior of primary batteries in the WSN context. We report that in some common cases, observed lifetimes can differ from predicted ones by almost a factor of three. Furthermore, loads with similar average currents - which would be expected to have similar lifetimes - can vary significantly in the amount of capacity they can utilize, with short duration loads generally faring better. The results show that energy evaluation based on a mA-h consumed model has significant limitations. This has important implications for the design and evaluation of WSN applications, as well as for practical problems in network dimensioning and lifetime prediction.

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.

Interactive applications are latency-sensitive systems that enable dynamic responses to user inputs in domains such as robotics, industrial automation, and autonomous control. These applications require efficient application protocols for communication, with the pub/sub model being one of the most promising approaches. However, existing pub/sub systems are architecturally constrained, particularly by limited memory capacity and inefficiencies in dynamic environments. Addressing these challenges requires effective distributed memory management, yet this aspect has received limited attention in existing research. This paper addresses the gap by proposing MemIndex , an adaptive and autonomous distributed memory-management framework with an intent-indexed bipartite graph architecture. It is designed for an LM-based multi-agent pub/sub systems, enabling agents to autonomously negotiate memory operations in real time through dynamic index spaces for efficient reasoning. We evaluate our proposed MemIndex using diverse models against two baselines. Experimental results show MemIndex outperforms both baselines across storage, retrieval, update, and deletion operations, achieving average reductions of about 34% and 56% in elapsed time, 57% and 75% in CPU utilization, 23% and 76% in memory usage. Scalability tests further demonstrate that MemIndex maintains low end-to-end delay as submissions and agents grow, confirming that its negotiation-driven offloading enables efficient distributed memory management in interactive applications.

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.

The rising complexity and data generation in cyber-physical systems and the Internet of Things require a shift towards an edge-to-cloud computing continuum ecosystem with enhanced artificial intelligence (AI) capabilities. Furthermore, resource limitations and the projected doubling of the electricity used by information and communication technologies until 2030 require innovative resource-efficient solutions. Neuromor-phic computing and intelligence are prominent examples that take inspiration from the function of brains, the most energy-efficient processors known. However, neuromorphic systems differ from conventional von Neumann computers and clock-driven sensor systems and likely require different continuum-enabling abstractions and concepts for large-scale and high-utility neuromorphic systems integration. This paper focuses on communication aspects and summarises recent developments in this area, including a problem analysis aiming to identify challenges and open issues for integrating neuromorphic systems in the digital continuum. Our main contribution is a description of event- and query-based communication models commonly used in microservice architectures, with mappings to defining characteristics of neuromorphic systems and the continuum. In this, we outline an abstraction where each neuromorphic system has a digital counterpart called a Gemi. A Gemi keeps an abstract digital model of its corresponding neuromorphic system, is programmable, and provides digital interfaces. Spike and temporal embedding representations in the neuromorphic systems are mapped to an efficient event-based publish-subscribe model in the digital domain.