480 CoRAD Drone Flight Planning

480.1 Learning Objectives

By the end of this chapter, you will be able to:

Understand CoRAD concept: Explain how drones restore connectivity to isolated sensor nodes
Apply TSP algorithms: Use Traveling Salesman Problem solutions for efficient flight path planning
Calculate flight budgets: Estimate drone battery requirements including flight time, hover time, and safety margins
Plan recovery missions: Design complete drone data collection missions with multiple disconnected nodes
Handle constraints: Account for battery limits, weather conditions, and real-time route adaptation

MVU: Minimum Viable Understanding

Core concept: When sensors can sense but cannot communicate (due to interference or failures), drones fly overhead to collect buffered data via short-range radio. Why it matters: A single drone mission can recover days of data from dozens of isolated sensors, maintaining network continuity during adverse conditions. Key takeaway: Use TSP heuristics (nearest neighbor) for quick route planning; verify battery feasibility including 20% safety margin before deploying.

480.2 Prerequisites

Before diving into this chapter, you should be familiar with:

Duty Cycle Fundamentals: Understanding why sensors may have buffered data waiting for collection
UAV Networks: Drone capabilities, flight constraints, and communication ranges
Wireless Sensor Networks: Network topologies and why nodes may become disconnected

For Beginners: What is CoRAD?

Problem scenario: Agricultural network with 500 sensors. Heavy rain causes “dumb nodes”—sensors that work but can’t transmit (radio range drops from 100m to 5m due to water interference). How do you collect data from isolated nodes?

CoRAD solution: Send a drone to fly overhead, get within 5m of each isolated sensor, download buffered data via short-range radio, return to base. Drone acts as mobile data collector when normal multi-hop routing fails.

Term	Simple Explanation
CoRAD	Connectivity Re-establishment with Aerial Drones—using drones to collect data from unreachable sensors
Dumb Node	Sensor that can sense but can’t communicate (environmental interference)
TSP	Traveling Salesman Problem—finding shortest path visiting all locations (drone flight route optimization)
Hover Time	Time drone spends stationary above each sensor downloading data

Why drones? They can reach sensors anywhere, bypass terrain obstacles, and cover large areas quickly. One 30-minute drone flight can visit 20+ isolated sensors.

480.3 The Challenge: Disconnected Sensor Nodes

⏱️ ~12 min | ⭐⭐⭐ Advanced | 📋 P05.C02.U02

CoRAD (Connectivity Re-establishment with Aerial Drones) addresses the problem of collecting data from isolated sensor nodes (“dumb nodes”) that can sense but cannot communicate due to interference or topology fragmentation.

480.3.1 Why Sensors Become Disconnected

In real-world deployments, sensors may become temporarily unreachable:

Environmental interference: Rain/fog reduces radio range (100m → 5m)
Node failures: Battery depletion or hardware failures create coverage gaps
Terrain obstacles: Buildings, hills, or vegetation block line-of-sight
Mobile scenarios: Nodes move out of communication range

Solution: Deploy a drone as a mobile data collector to visit disconnected nodes and download buffered sensor data.

480.4 Flight Path Optimization: Traveling Salesman Problem (TSP)

Objective: Find the shortest flight path visiting all disconnected nodes and returning to base.

This is the classic Traveling Salesman Problem (TSP):

Input: Coordinates of \(N\) disconnected nodes + base station
Goal: Minimize total flight distance/time
Constraint: Drone battery capacity limits flight duration

480.4.1 TSP Algorithm Options

Algorithm	Complexity	Quality	Best For
Brute Force	O(n!)	Optimal	≤10 nodes (testing)
Nearest Neighbor	O(n²)	10-25% from optimal	Real-time planning
2-opt Improvement	O(n²)	5-10% from optimal	Moderate node counts
Genetic Algorithm	O(generations × population)	2-5% from optimal	Large networks (>50 nodes)
Branch and Bound	O(2ⁿ) worst case	Optimal	≤20 nodes with pruning

For CoRAD applications, nearest neighbor heuristic provides a good balance of speed and solution quality for real-time drone deployment.

480.5 Worked Example: Agricultural Field Mission

Example Calculation:

Agricultural field with 5 disconnected nodes:

Nodes: (0,0), (50,30), (80,60), (120,50), (40,90) meters
Base: (0,0)
Drone specs: 10 m/s cruise speed, 10s hover per node, 1800s battery (30 min)

Using nearest neighbor heuristic:

Start at base (0,0)
Visit nearest unvisited node
Repeat until all nodes visited
Return to base

Path found: Base → Node1(50,30) → Node2(80,60) → Node4(40,90) → Node3(120,50) → Base

Flight time calculation:

Distance: 58.3m + 42.4m + 72.1m + 94.9m + 130.4m = 398.1 meters
Flight time: 398.1m / 10m/s = 39.8 seconds
Hover time: 5 nodes × 10s = 50 seconds
Total mission time: 89.8 seconds
Battery usage: 89.8s / 1800s = 5.0%
Feasible: Yes, with 1710.2s (95%) battery remaining

Output:

Planned flight path: [(0, 0), (50, 30), (80, 60), (40, 90), (120, 50), (0, 0)]
Estimated flight distance: 398.1 meters
Estimated flight time: 89.8 seconds (flight: 39.8s, hover: 50.0s)
Battery capacity: 1800 seconds
Battery usage: 5.0%
Feasibility: Feasible with 1710.2s (95.0%) margin
Safety margin (20% reserve): 1440s required, 1710.2s available ✓

480.6 Battery Feasibility Analysis

Critical constraint: Drone must return to base before battery depletes.

Battery Budget Formula:

\[ T_{total} = T_{flight} + T_{hover} + T_{safety} \]

Where:

\(T_{flight} = \frac{D_{total}}{v_{cruise}}\) (distance ÷ speed)
\(T_{hover} = N_{nodes} \times t_{download}\) (nodes × time per node)
\(T_{safety} = 0.2 \times T_{battery}\) (20% reserve)

Feasibility check:

\[ T_{total} \leq 0.8 \times T_{battery} \]

480.6.1 Multi-Flight Planning

When a single drone flight cannot cover all disconnected nodes:

Cluster nodes by geographic proximity
Plan separate missions for each cluster
Prioritize by data urgency or buffer fullness
Schedule sequential flights with battery swap/recharge

Example: 50 disconnected nodes over 2km² area

Drone range: 15 minutes effective (with 20% safety)
Nodes per flight: ~12-15 (depending on spacing)
Required flights: 4 sequential missions
Total recovery time: ~2 hours (including recharge)

480.7 Trade-offs in CoRAD Planning

Factor	Trade-off
Optimal TSP	Guarantees shortest path but computationally expensive (O(n!) complexity)
Heuristics	Fast approximation (within 10-20% of optimal)
Battery constraint	May require splitting large networks into multiple drone flights
Real-time adaptation	Re-plan route if new disconnected nodes discovered mid-flight

Alternative View: CoRAD Mission Timeline

This variant shows a complete CoRAD mission lifecycle from detection to data recovery, illustrating the operational workflow.

%% fig-alt: "CoRAD mission timeline showing complete workflow: Phase 1 Detection at T=0 where fog gateway detects nodes offline and weather API confirms rain interference. Phase 2 Planning at T+5min where TSP route computed and battery feasibility verified. Phase 3 Deployment at T+10min where drone launches and flies to first cluster. Phase 4 Collection at T+15min where drone hovers within 5m range, downloads 2MB buffered data, moves to next node. Phase 5 Return at T+35min where drone returns to base and uploads 15MB collected data. Phase 6 Recovery at T+40min where gateway reintegrates data and network coverage restored."
%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#2C3E50', 'primaryTextColor': '#fff', 'primaryBorderColor': '#16A085', 'lineColor': '#16A085', 'secondaryColor': '#E67E22', 'tertiaryColor': '#7F8C8D', 'fontSize': '14px'}}}%%
timeline
    title CoRAD Mission Lifecycle
    section Detection Phase
        T=0 : Gateway detects nodes offline : Rain interference confirmed
    section Planning Phase
        T+5min : TSP route computed : 8 nodes, 2.3km path
        T+7min : Battery feasibility verified : 18min flight, 65% capacity
    section Deployment Phase
        T+10min : Drone launches : Auto-navigation active
        T+12min : First node cluster reached : Hover at 3m altitude
    section Collection Phase
        T+15min : Node 1-3 data downloaded : 4.5 MB collected
        T+22min : Node 4-6 data downloaded : 3.2 MB collected
        T+28min : Node 7-8 data downloaded : 2.1 MB collected
    section Return Phase
        T+32min : Return flight initiated : Direct path to base
        T+38min : Landing and data upload : 9.8 MB to gateway
    section Recovery Complete
        T+40min : Data reintegrated : Network coverage restored

Operational Metrics: A typical CoRAD mission for 8 disconnected nodes takes ~40 minutes end-to-end, recovering 10-15 MB of buffered sensor data. Mission success rate exceeds 95% in clear weather; rain delays may extend detection-to-recovery to 2-4 hours.

480.8 Implementation Considerations

480.8.1 Node Discovery Protocol

Before drone deployment, the gateway must identify which nodes are disconnected:

Heartbeat monitoring: Nodes send periodic “I’m alive” messages
Timeout detection: Missing heartbeats trigger “disconnected” status
Location lookup: Gateway retrieves last known coordinates from database
Route planning: TSP solver receives disconnected node list

480.8.2 Data Download Protocol

When drone arrives at each node:

Proximity trigger: Node detects drone within 5m range
Wake-up: Node exits low-power mode
Authentication: Drone sends credentials, node verifies
Bulk transfer: Node transmits buffered data (BLE or short-range 802.15.4)
Acknowledgment: Drone confirms receipt, node clears buffer
Power down: Node returns to sleep mode

480.8.3 Weather Constraints

Condition	Impact	Action
Light rain	Reduced radio range (50%)	Increase hover time
Heavy rain	Unsafe for flight	Delay mission
Wind > 15 m/s	Reduced stability	Reduce payload weight
Fog	Navigation difficulty	Use GPS-only navigation

480.9 Knowledge Check

Quiz: CoRAD Flight Planning

Question: What is the primary purpose of using TSP algorithms in CoRAD drone planning?

💡 Explanation: B. TSP (Traveling Salesman Problem) algorithms find the shortest route that visits all required locations exactly once and returns to the starting point. In CoRAD, this minimizes drone battery consumption by reducing total flight distance, enabling more nodes to be visited per mission. While data prioritization (D) is important, it’s a separate concern from route optimization.

480.10 Summary

This chapter covered CoRAD (Connectivity Re-establishment with Aerial Drones) for collecting data from isolated sensor nodes:

Disconnection causes: Environmental interference, node failures, terrain obstacles, and mobile scenarios
TSP optimization: Using algorithms like nearest neighbor to find efficient flight paths
Battery budgeting: Calculating flight time, hover time, and safety margins to ensure mission completion
Multi-flight planning: Splitting large networks into sequential drone missions
Operational workflow: From detection through planning, deployment, collection, and data recovery

480.11 What’s Next

Continue to Topology Management Techniques to learn about event-aware topology reconfiguration, information-theoretic transmission decisions, and social sensing integration for adaptive duty cycling.