Cloud-Fog-Edge Integrated Network Architecture for Drone Cluster Combat Systems

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Modern warfare increasingly relies on drone technology to achieve tactical superiority. Unmanned Aerial Vehicle (UAV) cluster combat systems integrate multiple autonomous drones to execute complex missions through networked coordination. As computational demands surge, distributed resource provisioning becomes critical. We propose a novel network architecture integrating cloud, fog, and edge computing paradigms to optimize decentralized resource utilization across drone clusters. Our design incorporates dynamic load balancing to address inherent challenges in distributed systems, enhancing robustness against battlefield uncertainties.

Introduction

Drone technology has revolutionized military operations, with Unmanned Aerial Vehicle clusters enabling collaborative reconnaissance, strike missions, and electronic warfare. These systems demand real-time data processing, yet onboard computational constraints limit autonomy. Existing solutions struggle with bandwidth volatility and load imbalances. Cloud computing offers vast resources but introduces latency; edge computing enables rapid response but lacks scalability. Fog computing bridges this gap by decentralizing resources closer to drones. We synthesize these paradigms into a cohesive architecture, significantly improving the efficiency and resilience of Unmanned Aerial Vehicle operations.

System Network Architecture Design

Combat System Hierarchy

Our hierarchical combat system (Figure 1) integrates layered command structures:

  • Edge Layer: Small drone clusters controlled by infantry, supported by portable multifunctional UAVs.
  • Fog Layer: Large drone clusters with specialized UAVs: fog access nodes (hosting servers), communication relays, and combat units.
  • Cloud Layer: Remote high-performance computing centers for strategic analysis.

Large clusters provide signal coverage and resource allocation, while small clusters deliver infantry support. This structure optimizes drone technology deployment across tactical and strategic levels.

Cloud-Fog-Edge Collaborative Architecture

The integrated architecture (Figure 3a) operates as follows:

  • Cloud Computing: Processes multi-source battlefield intelligence using $$t_{\text{cloud}} = \frac{D}{B_{\text{downlink}}} + \frac{D \cdot I}{C_{\text{cloud}}}}$$ where \(D\) = data size, \(B\) = bandwidth, \(I\) = instructions/byte, \(C\) = compute capacity (MIPS).
  • Fog Computing: Filters, compresses, and routes data. Tasks requiring global analysis are offloaded to the cloud: $$\text{Offload}_{\text{decision}} = \begin{cases} 1 & \text{if } \frac{S_{\text{data}}}{B_{\text{fog-cloud}}} + t_{\text{cloud}} < t_{\text{local}}} \\ 0 & \text{otherwise} \end{cases}$$
  • Edge Computing: Handles latency-sensitive tasks (e.g., path planning) via local devices.

This synergy ensures efficient resource use in Unmanned Aerial Vehicle networks.

UAV Cluster Network Architecture

Edge Computing Network

Small UAVs offload tasks to nearby edge servers (Figure 4a). The network topology forms a star configuration where each drone connects to \(K\) infantry-carried servers. Task latency is minimized when: $$t_{\text{edge}} = \min_{k \in \{1,\dots,K\}} \left( \frac{D}{b_k} + \frac{D \cdot I}{C_k} \right)$$ where \(b_k\) = link bandwidth, \(C_k\) = server compute capacity.

Fog-Edge Collaborative Network

Large UAV clusters form a multi-hop mesh (Figure 5a). Communication follows: $$\text{Link}_{\text{active}} = \begin{cases} 1 & \text{if } \| \text{pos}_i – \text{pos}_j \| < R_{\text{comm}} \\ 0 & \text{otherwise} \end{cases}$$ Data routing uses gradient-based paths toward fog nodes. This design enhances scalability for dynamic Unmanned Aerial Vehicle swarms.

Distributed Computing Strategy

We address load imbalance using Generalized Diffusion Algorithm (GDA). Define system topology as graph \(G = (V, E)\) with:

  • \(V = \{v_0, v_1, \dots, v_p\}\): Compute nodes (UAVs/servers)
  • \(E\): Communication links
  • \(W = \{w_{ij}\}\): Link weights
  • \(C = \{C_i\}\): Node compute capacities

Total latency combines:

$$t = t_p + t_d + t_c + t_b \approx t_d + t_c$$

where:

$$t_d = \sum_{k=1}^n \max_{(v_i,v_j) \in E} \left( \frac{|\Delta_{ij}^k|}{b_{ij}} \right), \quad t_c = \max_{i \in \{0,\dots,p\}} \left( \frac{l_i}{C_i} \right)$$

Optimization minimizes:

$$\min \left[ \sum_{k=1}^n \max_{(v_i,v_j) \in E} \left( \frac{|\Delta_{ij}^k|}{b_{ij}} \right) + \max_{i} \left( \frac{l_i}{C_i} \right) \right] \quad \text{s.t.} \sum_{i=0}^p l_i = L$$

Algorithm 1: Generalized Diffusion Load Balancing

Input: \(G, L, W, C, l\), threshold \(\delta\)
1: Overloaded node \(v_{ol}\) computes diffusion matrix \(M(\epsilon_0^1)\)
2: Initialize status[|V|] = false
3: while true do
4:   for all \(v_i \in V\) parallel do
5:     Exchange \(l_i^k\) with neighbors
6:     Compute load differential: \(\Delta_{ij}^k = m_{ji} l_i^k - m_{ij} l_j^k\)
7:     Update load: \(l_i^{k+1} = l_i^k - \sum_{j} \Delta_{ij}^k\)
8:     if \(|l_i^{k+1} - l_i^k| < \delta\) then status[i] = true
9:   if ∀status[i] = true then break
10:  else k = k + 1
Output: Balanced load distribution

Simulation Results and Analysis

We simulated 12-node topology (Table 1) with varying compute capacities and bandwidths (80–110 Mbps). Data processing assumed 20 instructions/byte.

Table 1: Compute Node Specifications
Node Type Capacity (MIPS)
\(v_0\)–\(v_4\) Edge Server 48–102
\(v_5,v_8\) Basic UAV 93–94
\(v_6,v_7\) Combat UAV 500
\(v_9,v_{10}\) Fog UAV 97–104
\(v_{11}\) Cloud 5000

Latency and Robustness

Figure 6 demonstrates:

  • Edge computing outperforms cloud-terminal (CT) below 25MB data (109.7Mbps required for CT parity).
  • Fog-edge collaboration reduces latency by 38% vs. CT at 100Mbps bandwidth.
  • Cloud-Fog-Edge (CFE) maintains stable latency under bandwidth fluctuations (Figure 6d), with 68% lower latency variability than CT.
Table 2: Latency Comparison (ms) at 100Mbps
Data Size Edge Fog-Edge CFE Cloud-Terminal
5MB 142 210 305 512
25MB 680 734 820 1,892
50MB 1,325 1,402 1,518 3,805

Fault Tolerance

System resilience was validated under component failures (Figure 7):

  • Edge computing: 1-server connection reduces latency by 63% vs. local processing. Redundancy (\(K \geq 2\)) cuts failure impact by ≥41%.
  • Fog-edge: Loss of strong compute nodes (fog UAVs) degrades performance 3.2× more than losing basic UAVs.
  • CFE: Maintains <80% performance in degraded states (no network/UAV losses), proving battlefield resilience for Unmanned Aerial Vehicle clusters.

Load Balancing Efficiency

GDA reduced latency by 19–52% versus benchmarks (Figure 8):

$$ \text{GDA Gain} = \frac{t_{\text{benchmark}} – t_{\text{GDA}}}{t_{\text{benchmark}}} \times 100\% $$

Table 3: Load Balancing Performance (50MB Data)
Algorithm Edge (ms) Fog-Edge (ms) CFE (ms)
GDA (Ours) 1,325 1,402 1,518
SWRR 1,638 1,721 1,864
Pick-KX 1,892 1,950 2,105
GreedyLB 2,774 2,843 2,981

Conclusion

Our cloud-fog-edge architecture significantly enhances drone technology capabilities for cluster combat systems. By integrating distributed computing paradigms and GDA load balancing, we achieve:

  1. 46% average latency reduction versus conventional cloud-terminal systems
  2. Bandwidth variation robustness with <22% performance fluctuation under ±30% bandwidth changes
  3. Graceful degradation during node/link failures, maintaining >80% functionality

This research advances networked Unmanned Aerial Vehicle operations, enabling efficient, resilient autonomy in contested environments. Future work will explore AI-driven task partitioning and quantum-resistant security protocols.

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