—Reliable and ubiquitous communications, offering high data rates, low latency and supporting large numbers of connected devices, are critical requirements for modern emergency rescue missions. Multiple teams of First Responders, operating at remote areas, on rough terrain or under harsh conditions (e.g. wildfires, earthquakes, flooding etc.) need seamless connectivity to send/receive mission data and organize their operations. Decentralized swarm computing architectures offer a wide range of capabilities to enhance and accelerate edge processing for critical use case scenarios. This paper presents a converged approach on swarm computing and intelligence using Decentralized Autonomous Organizations for emergency communications, and how a swarm of drones can leverage different edge accelerators for different applications.

This work presents an energy-efficient implementation of Unmanned Aerial Vehicle (UAV)-based systems over 5G networks with on-board accelerated processing capabilities and provides a preliminary evaluation of the integrated solution. The study is a two-fold comparative analysis focused on connectivity and edge processing for UAVs. Two discrete deployment scenarios are implemented, where standard 5G configuration with artificial neural network (ANN) processing is evaluated against 5G Reduced Capability (RedCap) connectivity, paired with Spiking Neural Networks (SNNs). Both proposed energy-efficient alternative solutions are designed to offer significant energy savings; this paper examines whether they are suitable candidates for energy-constrained environments, i.e., UAVs, and quantifies their impact on the overall energy consumption of the system. The integrated solution, with 5G RedCap/SNNs, achieves energy-use reductions approaching 60%, which translates to an approximate 35% increase in flight time. The experimental evaluations were performed in a real-world deployment using a 5G-equipped UAV with edge-processing capabilities based on NVIDIA’s Jetson Orin.

Extensive small-ruminant grazing sustains food production on the Mediterranean land with few alternative uses, yet supervision is intermittent and welfare oversight weak. Digital monitoring is expected to close that gap, but detection alone does not produce intervention. In remote rangelands the link is unreliable, so an inference may reach the farmer late, degraded or not at all, and an alert arriving after the animal has moved is not a weaker alert but a different decision. This study develops a human-in-the-loop decision framework in which the delivery state of the link becomes a decision variable rather than a transport detail, coupling technology, farmer behaviour and governance. Constructed through design science research and structured analysis of an EU-funded project’s specifications and evaluation plans, it comprises a seven-layer architecture, a decision-episode schema, a connectivity-aware alert lifecycle, a decision-provenance matrix, and an evidence-maturity classification constraining what may be claimed. Five decision classes from a Cretan sheep and goat pilot instantiate it. After applying it, specification inconsistencies invisible to component-level review surfaced, among which is an alert payload lacking the coordinates, confidence and severity that its own indicators require. The framework is an instantiated specification, not a validated intervention: no detection, delivery, acknowledgement, usability or sustainability outcome is measured.

This work presents an energy efficient implementation for UAV-based systems over 5G networks with on-boarded accelerated processing capabilities and provides a preliminary evaluation analysis of the integrated solution. A two-fold comparative study focused on connectivity and edge processing for UAVs, realizes two discrete deployment scenarios, where standard 5G configuration with Artificial Neural Networks processing is evaluated against 5G RedCap connectivity paired with Spiking Neural Networks. Both proposed alternative energy efficient solutions, are designed to offer significant energy saving, and this paper examines if they are fit candidates for energy stringent environments, i.e., UAVs, and also quantify the impact on the overall energy consumption of the system. The integrated solution with 5G RedCap/SNN realizes energy-use reductions approaching 60%, which translated to approximately 35% of increased flight time. The experimental evaluations were performed in a real-world deployment using a 5G equipped UAV with edge processing capabilities based on NVIDIA’s Jetson Orin.