## 1 Introduction  
The vision of 6G networks and beyond is driven by transformative use cases that demand unparalleled levels of connectivity, intelligence, and real-time responsiveness [1]. Applications such as eXtended Reality (XR) for immersive experiences, autonomous vehicles navigating complex environments, and smart cities powered by billions of interconnected Internet of Things (IoT) devices highlight the pressing need for advanced communication and processing capabilities. Similarly, federated learning at the edge for privacy-preserving AI, real-time disaster response systems, and Digital Twin Networks (DTNs) that synchronize physical and virtual worlds emphasize the critical importance of low-latency and scalable computing solutions [2]. Traditional centralized architectures, reliant on cloud-based processing, struggle to provide the ultra-low latency, bandwidth efficiency, and real-time decision making required by these next-generation applications. This challenge has led to the emergence of edge computing and Multi-access Edge Computing (MEC) as intermediate solutions, which bring computational resources closer to the end-user [3]. By offloading tasks from the cloud to edge servers, these approaches reduce latency, optimize bandwidth usage, and improve responsiveness. However, these frameworks largely rely on static and predefined deployments, where computational resources are provisioned at fixed locations near the network's periphery. While effective, they often lack the flexibility and scalability needed to address the dynamic and distributed nature of many 6G applications. In this context, _In-Network Computing_ (INC) emerges as a revolutionary paradigm that goes beyond the limitations of static edge computing [4]. It enables the network itself to perform computation dynamically and adaptively, directly within its communication fabric. Unlike traditional edge architectures, INC leverages the capabilities of network nodes to process, compute, and make decisions closer to the data source, often at intermediate hops. This paradigm shift introduces unprecedented opportunities to meet the stringent demands of next-generation applications [5]; however, it also raises some distinctive challenges. Indeed, deploying INC requires the management of distributed and heterogeneous resources across distributed network nodes, each with varying computational, storage, and energy capabilities. Dynamically coordinating these resources in real time, while ensuring efficient task allocation and execution under changing network conditions, is far from trivial [6]. The distribution of computational tasks across multiple intermediate nodes can lead to inefficiencies if routing, scheduling, or workload balancing are not optimized. The presence of client mobility adds another level of complexity, by introducing frequent topology changes and intermittent connectivity, which networks may struggle to accommodate. Another significant challenge lies in data consistency, privacy, and security. INC involves processing and storing data at multiple points within the network, increasing the risk of data breaches and inconsistencies. Balancing the need for high performance with robust security measures remains a pressing concern, especially for privacy-sensitive applications like healthcare [7]. The host-centric protocol suite of the current Internet, TCP/IP, exacerbates these challenges due to its fundamental design for end-to-end communications between fixed client and server entities. IP inherently ties communication to specific physical addresses, making it rigid and inefficient for the dynamic and distributed nature of INC. When computation tasks are allocated on demand, rather than tied to specific locations, relying on explicit host addresses for routing results in unnecessary overhead, reduced scalability, and inefficiencies in resource utilization.