Network Architecture for Unmanned Aerial Vehicle Swarm Operations with Cloud-Fog-Edge Integration

By /

1. Introduction

Modern warfare increasingly relies on unmanned aerial vehicle (UAV) swarms for missions requiring distributed intelligence, real-time response, and resilience. However, limitations in onboard computational resources and dynamic battlefield conditions necessitate innovative network architectures. We propose a cloud-fog-edge integrated network architecture to optimize distributed computing efficiency and robustness for unmanned aerial vehicle swarm combat systems. This architecture synergizes:

  • Cloud computing for global data analytics,
  • Fog computing for agile resource orchestration,
  • Edge computing for latency-sensitive task execution.

2. Hierarchical System Design

2.1. Operational Structure

The unmanned aerial vehicle swarm combat system adopts a layered command hierarchy:

  • Large UAV clusters act as fog nodes, providing signal coverage and resource allocation.
  • Small UAV clusters support infantry units via portable multi-functional unmanned aerial vehicles.

Table 1: UAV Cluster Composition

Cluster TypeComponentsPrimary Role
Large UAVFog server UAVs, relay UAVs, fire-support UAVsFog layer resource provisioning
Small UAVInfantry-portable multi-functional UAVsEdge data collection & local compute

2.2. Cloud-Fog-Edge Network Architecture

The three-tiered topology enables efficient task distribution:

  • Cloud Layer: Centralized data centers for predictive analytics.
  • Fog Layer: Large unmanned aerial vehicle clusters processing filtered data.
  • Edge Layer: Small UAVs/soldier terminals for real-time preprocessing.

*Table 2: Layer-Specific Functions*

LayerComponentsFunction
CloudHigh-performance serversStrategic decision-making, big data analysis
FogLarge UAV clustersData aggregation, bandwidth-sensitive task offloading
EdgeSoldier devices/small UAVsData preprocessing, ultra-low-latency responses (e.g., path planning)

3. Distributed Computing Modes

3.1. Edge Computing

For latency-critical tasks (e.g., navigation), small unmanned aerial vehicle units offload computations to nearby edge servers:tedge=DBedge+D⋅ICedgetedge​=Bedge​D​+Cedge​D⋅I​

where DD = data size, BedgeBedge​ = bandwidth, II = instructions/byte, CedgeCedge​ = server compute power.

3.2. Fog-Edge Collaborative Computing

Localized analysis avoids cloud latency. Tasks route through fog-layer unmanned aerial vehicles using multi-hop links:tfog-edge=max⁡(DBij)+max⁡(LjCj)∀(vi,vj)∈Etfog-edge​=max(Bij​D​)+max(Cj​Lj​​)∀(vi​,vj​)∈E

3.3. Cloud-Fog-Edge Coordination

Global tasks leverage all layers:

  • Edge: Data filtering.
  • Fog: Task prioritization.
  • Cloud: Heavy computations.

4. Generalized Diffusion Load Balancing (GDA)

4.1. Problem Formulation

The unmanned aerial vehicle network is modeled as an undirected graph G=(V,E)G=(V,E), where VV = nodes (UAVs/servers), EE = communication links. Total task delay comprises:ttotal=tp+td+tc+tbttotal​=tp​+td​+tc​+tb​

  • tptp​: Load-info exchange delay.
  • tdtd​: Load-transfer delay.
  • tctc​: Compute delay.
  • tbtb​: Result-return delay.

4.2. Optimization Objective

Minimize ttotalttotal​ subject to load equilibrium:min⁡[∑k=1nmax⁡(vi,vj)∈E(∣Δijk∣bij)+max⁡i(liCi)]min[k=1∑n​(vi​,vj​)∈Emax​(bij​∣Δijk​∣​)+imax​(Ci​li​​)]s.t. ∑i=0pli=Ls.t. i=0∑p​li​=L

4.3. GDA Algorithm

Input: \(G, L, W, C, l\), threshold \(\delta\)  
1. Overloaded node \(v_{ol}\) computes diffusion matrix \(M(\epsilon)\)  
2. **while** TRUE:  
3.   **for** each node \(v_i \in V\):  
4.      Exchange load \(l_i^k\) with neighbors  
5.      Compute load transfer \(\Delta_{ij} = m_{ji}l_i - m_{ij}l_j\)  
6.      Update load: \(l_i^{k+1} = l_i^k - \sum \Delta_{ij}\)  
7.      **if** \(|l_i^{k+1} - l_i^k| < \delta\): mark balanced  
8.   **if** all nodes balanced: BREAK

5. Performance Validation

5.1. Simulation Setup

  • Nodes: 5 edge, 6 fog, 1 cloud (Table 3).
  • Bandwidth: 80–110 Mbps (heterogeneous).
  • Workload: 5–25 MB.

Table 3: Compute Node Capabilities (MIPS)

Nodev0v0​v1v1​…v11v11​
MIPS10250…5000

5.2. Key Results

  • Edge vs. Cloud-Only:
    • For 25 MB tasks, edge computing reduces latency by 40% vs. cloud-only (requires ≥109.7 Mbps).
  • Fog-Edge vs. Cloud:
    • Fog-edge achieves 60% lower delay at 100 Mbps bandwidth.
  • Robustness:
    • Cloud-fog-edge maintains stable latency (±8%) under bandwidth fluctuations (Fig 6c-d).

5.3. Fault Tolerance

  • Edge Mode: Adding 1 edge server cuts latency by 35% (Fig 7a).
  • Fog-Edge Mode: Loss of weak compute nodes (low MIPS) has marginal impact (Fig 7b).
  • System States: Degrades gracefully under node failures (Fig 7c).

5.4. Load Balancing Efficiency

GDA outperforms alternatives:

  • 25% faster than Smooth Weighted Round Robin (SWRR).
  • 30% faster than GreedyLB in fog-edge scenarios (Fig 8).

6. Conclusion

Our cloud-fog-edge integrated architecture significantly enhances unmanned aerial vehicle swarm combat systems by:

  1. Reducing Latency: Edge/fog layers handle 70% of tasks below 100 ms.
  2. Improving Robustness: Tolerates 20% node failure with <15% performance drop.
  3. Optimizing Resource Usage: GDA cuts task delays by 30–40% vs. benchmark algorithms.
    This work enables resilient, efficient unmanned aerial vehicle operations in bandwidth-constrained battlefields. Future extensions will integrate AI-driven task prediction.
Scroll to Top