The integration of emerging uncrewed aerial vehicle (UAV) with artificial intelligence (AI) and ground-embedded robots (GERs) has transformed emergency rescue operations in unknown environments. However, the high computational demands of such missions often exceed the capacity of a single UAV, making it difficult for the system to continuously and stably provide high-level services. To address these challenges, this paper proposes a novel cooperation framework involving UAVs, GERs, and airships. This framework enables resource pooling through UAV-to-GER (U2G) and UAV-to-airship (U2A) communications, providing computing services for UAV offloaded tasks. Specifically, we formulate the multi-objective optimization problem of task assignment and exploration optimization in UAVs as a dynamic long-term optimization problem. Our objective is to minimize task completion time and energy consumption while ensuring system stability over time. To achieve this, we first employ the Lyapunov optimization method to transform the original problem, with stability constraints, into a per-slot deterministic problem. We then propose an algorithm named HG-MADDPG, which combines the Hungarian algorithm with a generative diffusion model (GDM)-based multi-agent deep deterministic policy gradient (MADDPG) approach, to jointly optimize exploration and task assignment decisions. In HG-MADDPG, we first introduce the Hungarian algorithm as a method for exploration area selection, enhancing UAV efficiency in interacting with the environment. We then innovatively integrate the GDM and multi-agent deep deterministic policy gradient (MADDPG) to optimize task assignment decisions, such as task offloading and resource allocation. Simulation results demonstrate the effectiveness of the proposed approach, with significant improvements in task offloading efficiency, latency reduction, and system stability compared to baseline methods.

uncrewed aerial vehicles (UAVs) offer high mobility and flexible deployment capabilities, making them ideal for Internet of Things (IoT) applications. However, the substantial amount of data generated by various applications within the existing low-altitude network requires processing through deep neural networks (DNN) on UAVs, which is challenging due to their limited computational resources. To address this issue, we propose a two-stage optimization method for flight path planning and task allocation based on a mother-child UAV swarm system. In the first stage, we employ a greedy algorithm to solve the path planning problem by considering the task size of the target area to be inspected and the shortest flight path as constraints. The goal is to minimize both the flight path of the UAV and the overall cost of the system. In the second stage, we introduce a novel DNN task assignment algorithm that combines multiagent deep deterministic policy gradient (MADDPG) and generative diffusion models (GDMs), named GDM-MADDPG. This algorithm takes advantage of the reverse denoising process of GDM to replace the actor network in MADDPG. It enables UAVs to generate specific DNN task assignment actions based on agents’ observations in a dynamic environment, thereby improving the efficiency of task assignment and overall system performance. The simulation results demonstrate that our algorithm outperforms the benchmarks in terms of path planning, Age of Information (AoI), task completion rate, and system utility, demonstrating its effectiveness.

This paper proposes a feedback based algorithm for congestion control and bandwidth allocation in the presence of either TCP traffic or both TCP and real time traffic. In this proposal, the network layer conveys bandwidth and propagation delay measurements to data sources, for instance using IPv6 optional fields. TCP sources use this bandwidth delay product to control their congestion window, while video sources use the available bandwidth feedback to adjust their data sending rate. The experiments show that with this approach, the network achieves stable equilibrium, and users, either transmitting TCP traffic or real-time traffic, can share network resources fairly. Furthermore, since TCP sources learn about the available bandwidth independent of packet loss, there is no need to reduce the congestion window following a packet loss. This provides a way to improve the TCP performance in wireless networks where it is difficult to distinguish between congestion loss and radio channel interference loss.

In the presence of satellite channels, one of the most challenging problems for TCP is to achieve fair bandwidth sharing among several competing connections with round-trip propagation delays that may differ by more than two orders of magnitude. The bandwidth-aware TCP (BA-TCP) provides a fair solution while maintaining the end-to-end semantics of transport protocols. At the same time, it allows backward compatibility and it does not require substantial changes to the existing TCP. In BA-TCP, the network layer is assumed to be able to convey propagation delay and available bandwidth measurements to end users, for instance using IPv6 optional fields. TCP receivers employ this information to compute a generalized advertised window, which, in turn, controls the amount of data the source injects into the network. Experimental results show that a satellite connection can fairly share a bottleneck with wired connections. Furthermore, since BA-TCP exploits the bandwidth-delay product to control the source congestion window, the queuing delay at the bottleneck link tends to zero at steady state and buffer overflow is negligible in most situations.

Fairness in the Internet is becoming a major obstacle for the introduction of commercial services. In this paper we address the problem of fairness with a slightly non-standard approach: we show that, in order to achieve fairness in the Internet, it is enough to provide good congestion control, without the need for explicit computation of fairness indexes. We first introduce a TCP-based congestion scheme that takes into account available bandwidth resources on links, describing how this scheme can be easily introduced as a TCP option in end-systems; it requires neither a centralized approach in routers, nor a per-flow management of traffic. Then several scenarios are analyzed via simulation, showing how and to what degree fairness is achieved. Results are compared with a traditional TCP-Reno implementation with RED routers. Finally, the coexistence of the new TCP version with traditional implementations is discussed.