Containerization has transformed application deployment across diverse environments in the edge-cloud computing continuum, providing lightweight, portable solutions that integrate seamlessly across various cloud-native environments. However, default scheduling often leads to resource underutilization due to overestimated requests. This results in inaccurate demand prediction and static allocation strategies. This paper enhances a bottom-up, self-organizing approach with an NNbased peer selection mechanism, enabling intelligent resource allocation while maintaining decentralization. The proposed NNbased approach enables a data-driven selection strategy that significantly improves resource utilization and system performance; a 21% improvement in resource utilization is reported in a high-traffic scenario compared to the predecessor framework. The findings contribute to the broader research community by demonstrating the potential of combining agent-based modeling with machine learning techniques, paving the way for more adaptive and efficient edge orchestration systems.

This paper presents RAPID-TSP, a distributed coordination algorithm for multi-drone scanning missions in dynamic environments. It enables drones to balance area coverage efficiency with network connectivity by leveraging a tunable parameter λ. This parameter allows each drone to adaptively choose its next scanning target based on proximity and the positions of neighboring drones, while aiming to maintain connectivity with a base station for mission updates. RAPID-TSP incorporates conflict resolution and supports two swarming strategies to minimize redundant scanning and ensure efficient collaboration. A realistic scenario involving the scanning of container stacks in a logistics hub serves as evaluation scenario. Extensive simulations show how RAPID-TSP performs robustly under varying conditions and can reduce mission completion time by up to 40% compared to traditional mTSP coordination methods. A key design insight is that mission time is significantly reduced by adding more drones only when communication range is sufficient; otherwise, sparse connectivity limits their effectiveness.

We propose a multi-objective optimization algorithm to allocate tasks and plan paths for a team of UAVs. The UAVs must find a target in a bounded area and then continuously communicate the target information to the ground personnel. Our genetic algorithm approach aims to minimize the mission completion time, which includes the time to find the target (area coverage) and the time to setup a communication path (network connectivity). We evaluate strategies using a data mule, a relay chain, and a novel hybrid approach to communicate with the ground personnel. The algorithm can be tuned to prioritize coverage or connectivity, depending on the mission demands. Simulation results show reduced overall mission completion times (up to 65%), with more improvement as the UAV density increases.

The commercial availability of small unmanned aerial vehicles (UAVs) opens new horizons for applications in disaster response, search and rescue, event monitoring, and delivery of goods. An important building block is the wireless communication between UAVs and to base stations. Design of such a wireless network may vary vastly from existing networks due to aerial network characteristics such as high mobility of UAVs in 3D space. This paper presents experimental performance results with commercially available UAVs. First, we show throughput results for IEEE 802.11ac in a UAV setting. Second, we demonstrate that IEEE 802.11n can have much higher throughput over longer ranges than reported in [1] and [2]. Third, we analyze the fairness in a multi-sender aerial network. Fourth, we test a real-world coverage scenario with two mobile UAVs sending to a single receiver. Performance analysis considers the rate adaptation mechanism in both indoor and outdoor line-of-sight scenarios.

Small-scale multicopters operating as autonomous teams in the air are envisioned for aerial monitoring and transport of goods in a variety of applications, including disaster management and environmental monitoring. For such applications to become reality, a high-throughput wireless network is needed. This paper presents experimental performance results with commercially available quadrocopters communicating via IEEE 802.11a. In particular, we compare the infrastructure and mesh modes of 802.11 for one-hop and two-hop communications, thus analyzing network layer versus MAC layer relaying. Results illustrate that changes are required in the mesh mode to support applications demanding high throughput with low jitter.