Unmanned Aerial Vehicles (UAVs) are critical for maintaining communication in disaster-affected areas, where terrestrial infrastructure is often unavailable and network connectivity becomes highly fragile. To address this challenge, the paper proposes decentralized data forwarding and trajectory alignment in UAV-assisted networks using an information-centric paradigm. Specifically, we propose a Mobility-Aware QoS Information-Centric Networking (MA-QoS ICN) framework, wherein each UAV embeds Quality-of-Service information (QoS info), including priority, deadline, and packet lifetime—as well as UAV Mobility State Information (UMSI), such as velocity, direction, position, and battery level, directly into the ICN packet headers. To model the dynamic and uncertain environment, we formulate the UAV coordination problem as a stochastic game for maximizing long-term content delivery performance, where each UAV acts as a learning agent and makes forwarding and trajectory alignment decisions based on its local state. We then develop a multi-agent reinforcement learning (MARL) framework in which each UAV independently discovers its optimal strategy through local observations and experience, without requiring centralized control. In particular, we adopt an agent-independent learning method based on Independent Q-Learning, leveraging ICN’s packet-level handling for implicit coordination. This design significantly reduces communication overhead while preserving coordination among agents. Simulation results demonstrate that the proposed framework achieves substantial improvements in delivery reliability, latency reduction, and retransmission minimization, along with enhanced scalability under diverse disaster conditions.

In-network caching stands as one of the core pillars of the Named Data Networking (NDN) paradigm, where every internet router has the capability to store the incoming data packets in its cache along the path between data sources and consumers. This capability is particularly vital in Internet of Things (IoT) environments, where efficient and timely data retrieval is critical. Numerous strategies have been designed for caching IoT data and often rely on the Exact Match Policy (EMP), which strictly matches content names for data retrieval. However, semantic considerations of content names in EMP remain underexplored. Additionally, several caching mechanisms are modeled after traditional IP-based communication approaches, such as neighbor or topology-based centrality methods for content placement and data eviction for replacement. These approaches hold limited relevance in NDN, as content names can originate from anywhere in the network. To this end, we propose a novel semantic-aware naming resolution as an alternative to EMP in cache design. Furthermore, the proposed scheme introduces a content-based centrality mechanism, along with a backup strategy for cache management. Integration of semantics into caching strategies offers a more optimal approach for supporting NDN IoT. This combination can boost overall network performance. Simulation results illustrate that the proposed scheme outperformed the advanced existing studies CaDaCa and PoolCache in cache hit ratios, semantic satisfaction rate, latency, and route elongation factor.

Vehicular ad-hoc networks (VANETs) are characterized by high mobility, dynamic topology changes, and unstable connections, and additionally, they require stringent communication requirements. To make VANET communication more efficient, this poster proposes a Lagrange-based optimized forwarding strategy (LOFS) for information-centric VANETs. The LOFS accounts for several parameters, including link expiration time (LET), link congestion, quality of service (QoS), and traffic conditions to select the next vehicle for Interest and Data packet forwarding. In addition, LOFS mitigates the broadcast storm problem of the existing multicast forwarding strategy, designed for information-centric VANETs.

Real-time healthcare applications demand stringent communication resources to meet the QoS requirements. To meet the application resource demands, this poster proposes a Load and Bandwidth aware Forwarding strategy (LBF-ICN) in Information-Centric Networks (ICN). The LBF-ICN considers router interfaces' pending entries and available bandwidth resources in order to select the next hop for interest forwarding. The LBF-ICN strategy aims to distribute the network load along with bandwidth allocation in interest forwarding.

To efficiently allocate communication and computation resources among enhanced Mobile Broadband (eMBB) and ultra–Reliable Low Latency Communication (uRLLC) applications, this article proposes a joint communication and computation resource allocation scheme in 6th Generation (6G) Information-Centric Networks named: CCR-ICN. To achieve efficient communication and computation resources sharing, the CCR-ICN scheme designs a microservice-centric Interest naming structure to enable named-based communication. Furthermore, the CCR-ICN scheme designs a robust forwarding strategy specifically tailored to efficiently allocate communication resources such as bandwidth among eMBB and uRLLC application requests. In addition, for the allocation of computation resources, the proposed scheme designs a priority-driven computation resources allocation mechanism ensuring effective allocation of Multi-Access Edge Computing (MEC) computation resources. To evaluate the proposed scheme, we performed extensive simulations in a widely used ns-3-based ndnSIM simulator and the results reveal the CCR-ICN achieves 66.82% higher communication resource allocation, and a 17.51% lower communication overhead compared to the TS-6G scheme. Moreover, the CCR-ICN performs computation request satisfaction at 42.03% and 12.72% higher than the TS-6G and EWFQ/LC schemes respectively. Furthermore, the CCR-ICN achieves computation resource utilization of 35.54% and 21.63% lower than the EWFQ/LC and TS-6G schemes respectively.