36 CoRAD Drone Data Collection

36.1 Start Simple

This section: Start Simple — Start with a mission that moves, loses energy, and changes its radio path while it works.

Start with a mission that moves, loses energy, and changes its radio path while it works. In CoRAD Drone Data Collection, the practical question is what the aircraft must sense, relay, decide, and prove before the flight or network role is safe enough to trust.

Phoebe’s Field Notes: The Battery Line Item Is Hiding a Watt Number, Not Just Seconds

The mathematical gist. The chapter’s 37 s flight plus 50 s hover uses 87/1800=4.83% of a time-only battery budget. If hover current is 1.3× cruise current, the same mission costs 37+1.3(50)=102 equivalent cruise seconds, or 5.67%—17.24% more than the time-only estimate. This is a teaching correction, not a flight certificate.

Figure 36.6 shows the LiPo pack whose derated energy capacity limits the drone mission budget.

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Math Bridge · guided foundationsWhy can 87 seconds consume 102 equivalent seconds?Let Motion Marley weight cruise and hover by current instead of pretending every second costs the same. Learn the maths

36.2 Learning Objectives

This section: Learning Objectives

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

• Explain CoRAD Architecture: Describe 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

36.3 Prerequisites

This section: 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

Sensor Squad Rescue Drone

36.4.1 When Sensors Cannot Phone Home

Oh no! It was raining SO hard at Farmer Chen’s sunflower field that something strange happened. All the little sensor friends could still FEEL the rain and measure how wet the soil was, but they couldn’t TALK to the base station anymore!

Sammy the Soil Sensor was worried: “I’ve been saving important data for THREE WHOLE DAYS, but I can’t send it anywhere! The rain made my radio whisper instead of shout!”

Light Lucy nodded: “Me too! I can only talk to things really, REALLY close---like 5 steps away instead of 100!”

That’s when Danny the Drone flew in to save the day!

“Don’t worry, friends!” Danny buzzed. “I’ll fly right over each of you, hover close enough to hear your whispers, download all your saved data, and carry it back to the base station!”

36.4.2 Danny’s Smart Route

Danny had to be clever about where to fly---drones can’t fly forever! Their batteries run out, just like when your tablet runs out of charge.

Danny’s Plan:

1. Start at the charging station (home base)
2. Visit the CLOSEST sensor first
3. Then fly to the next closest one that hasn’t been visited
4. Keep going until all sensors are visited
5. Fly back home before the battery runs out!

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The Magic Formula: Danny had to make sure: Flying Time + Hovering Time < Battery Time

If Danny’s battery lasted 30 minutes, and flying took 10 minutes and hovering over 4 sensors took 8 minutes (2 minutes each), Danny had: 30 - 10 - 8 = 12 minutes to spare! Plenty of time to get home safely!

36.4.3 Fun Challenge

Imagine YOU are Danny the Drone! You have 20 minutes of battery. You need to visit 3 sensors that are each 2 minutes of flying apart, and you hover for 3 minutes at each sensor. Can you complete the mission?

• Flying: 2 + 2 + 2 + 2 = 8 minutes (to visit all 3 and return)
• Hovering: 3 + 3 + 3 = 9 minutes
• Total: 8 + 9 = 17 minutes

Yes! You have 3 minutes left to spare!

36.5 The Challenge: Disconnected Sensor Nodes

This section: The Challenge: Disconnected Sensor Nodes

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.

36.5.1 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.

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36.6 TSP Flight Path Optimization

This section: TSP Flight Path Optimization — Objective: Find the shortest flight path visiting all disconnected nodes and returning to base.

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

Inspect Figure 36.3 and ask whether the planned route fits inside the usable battery time after reserve.

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Read Figure 36.3 from field coordinates and aircraft constraints into the nearest-neighbor route, then verify the mission calculation. The four path segments total 329.9 m. At 10 m/s, flight time is $329.9\ \text{m}/10\ \text{m s}^{-1}=32.99\ \text{s}$, rounded to 33.0 s. Four ten-second visits add 40 s of hover, producing a 73.0 s mission. A 20% reserve leaves $1800\ \text{s}\times0.8=1440\ \text{s}$ usable, so the plan fits. The heuristic does not prove global optimality; it supplies a checkable route whose distance, hover assumptions, reserve, wind, and return condition can be challenged before launch.

36.6.1 TSP Algorithm Options

Choose the solver based on node count and planning urgency:

• Brute force: O(n!) and optimal, but only practical for about 10 nodes or fewer.
• Branch and bound: exponential worst case; useful for about 20 nodes or fewer when pruning is effective.
• Nearest neighbor: O(n^2), quick for this small planner, but its route can be longer than the best route.
• 2-opt improvement: O(n^2) local improvement pass; often trims nearest-neighbor routes for moderate node counts.
• Genetic algorithms: generation count times population size; useful for larger networks when a near-optimal route is enough.

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

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36.7 Worked Example: Agricultural Field Mission

This section: Worked Example: Agricultural Field Mission

Example Calculation:

Agricultural field with 4 disconnected nodes:

• Nodes: (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:

1. Start at base (0,0)
2. Visit nearest unvisited node
3. Repeat until all nodes visited
4. Return to base

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

Flight time calculation:

• Distance: 58.3m + 42.4m + 41.2m + 89.4m + 98.5m = 329.9 meters
• Flight time: 329.9m / 10m/s = 33.0 seconds
• Hover time: 4 nodes x 10s = 40 seconds
• Total mission time: 73.0 seconds
• Battery usage: 73.0s / 1800s = 4.1%
• Feasible: Yes, with 1727.0s (95.9%) battery remaining

Output:

Planned flight path: [(0, 0), (50, 30), (80, 60), (120, 50), (40, 90), (0, 0)]
Estimated flight distance: 329.9 meters
Estimated flight time: 73.0 seconds (flight: 33.0s, hover: 40.0s)
Battery capacity: 1800 seconds
Battery usage: 4.1%
Feasibility: Feasible with 1727.0s (95.9%) battery remaining
Safety margin (20% reserve): 73.0s mission <= 1440s usable -- OK

36.8 Battery Feasibility Analysis

This section: Battery Feasibility Analysis — Critical constraint: Drone must return to base before battery depletes.

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

Battery Budget Formula:

T_mission = T_flight + T_hover

Where:

• T_flight = D_total / v_cruise (distance / speed)
• T_hover = N_nodes x t_download (nodes x time per node)
• T_usable = 0.8 x T_battery (hold back 20% of battery as reserve)

Feasibility check:

T_mission <= T_usable

36.9 Selection Workflow: Trade-offs in CoRAD Planning

This section: Selection Workflow: Trade-offs in CoRAD Planning

Planning trade-offs:

• Optimal TSP gives the shortest path but becomes computationally expensive as node count grows.
• Heuristics produce fast approximate routes, often close enough for field deployment.
• Battery constraints may force large networks into multiple flights even when the route is efficient.
• Real-time adaptation improves resilience, but every mid-flight change must be rechecked against return-to-home reserve.

Alternative View: CoRAD Mission Timeline

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

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Operational Metrics: A small CoRAD mission for 8 disconnected nodes can take about 40 minutes end-to-end when weather and airspace are clear. Rain, high wind, or poor visibility can delay launch and extend detection-to-recovery by several hours.

36.10 Implementation Considerations

This section: Implementation Considerations

36.11 Knowledge Check

This section: Knowledge Check

36.12 Ice Storm Drone Data Recovery

This section: Ice Storm Drone Data Recovery

36.13 CoRAD as Recovery Claim

This section: CoRAD as Recovery Claim — A CoRAD route is useful only when it proves a recovery claim: these isolated nodes still hold valuable data, a drone can reach them safely, the transfer can finish in range, and the aircraft can return with reserve.

A CoRAD route is useful only when it proves a recovery claim: these isolated nodes still hold valuable data, a drone can reach them safely, the transfer can finish in range, and the aircraft can return with reserve. The TSP route is one piece of that claim, not the whole production decision.

The chapter examples show how route distance, hover time, node count, and safety margin interact. A production planner adds the evidence that makes the sortie launchable: weather, airspace, geofence, data deadline, transfer authentication, backup landing path, and a stop rule when any assumption changes.

Figure 36.7 shows CoRAD connects monitoring, route planning, drone collection, gateway upload, and status refresh into one recovery chain.

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Read the CoRAD plan as a chain of falsifiable claims. The gateway heartbeat proves which nodes are isolated, the buffer estimate proves which readings are still worth recovering, the TSP or 2-opt route candidate proves a short visit order, the hover budget proves each download can finish, and the return reserve proves the aircraft can stop collecting data and come home. If any link in that chain is missing, the output is only a route sketch. It is not yet a mission release.

This matters because CoRAD sits between networking and flight safety. A networking-only plan may optimize packet recovery while ignoring battery aging, wind, geofence limits, or a degraded landing path. A flight-only plan may keep the aircraft safe while visiting nodes whose buffers have already overwritten the needed readings. The production version joins both views into one go/no-go decision.

36.14 CoRAD Mission Release Record

This section: CoRAD Mission Release Record — A launchable recovery mission needs one record that connects the routing calculation to field evidence.

A launchable recovery mission needs one record that connects the routing calculation to field evidence. The record should let an operator say why the sortie was allowed, what would stop it, and how recovered data will be reconciled after upload.

Build that record before the operator arms the aircraft. Start with the isolated-node report, then freeze a route candidate, a usable battery budget, the per-node hover timeout, the communications authentication method, and the recovery priority order. Keep the raw assumptions beside the route: airspeed, wind forecast, no-fly boundaries, altitude constraints, payload mass, expected link range, and the battery reserve rule. A route that only says "visit these coordinates" cannot answer whether the sortie should launch; a release record can.

During execution, treat changes as release-record updates rather than informal operator judgment. If a node takes longer than its hover timeout, log the failed transfer and recompute the remaining route with that battery already spent. If a new heartbeat arrives from a target node, remove it from the recovery set unless the data deadline still justifies a visit. If wind or airspace changes, recalculate the return-to-home threshold from current distance home instead of relying on a fixed battery percentage. The record is what makes those decisions auditable after the data is uploaded.

36.15 Reserve as Return Constraint

This section: Reserve as Return Constraint — The route calculation is usually done before launch, but the safety constraint changes during flight.

The route calculation is usually done before launch, but the safety constraint changes during flight. The drone spends energy moving away from home, hovering, fighting wind, handling retries, and sometimes detouring around airspace or obstacles. A fixed route that was feasible at takeoff can become infeasible after the first few transfers.

Under the hood, the reserve check is not one subtraction from nominal battery life. The planner is maintaining a state estimate: current battery, distance home, expected return energy, remaining target value, transfer-time distribution, and the cost of missed nodes. A nearest-neighbor route can look efficient at launch and still be the wrong route after a high-priority node fails to transfer, because the remaining tour, return leg, and data value have changed. A production planner therefore recomputes the suffix of the route, not just the whole original loop, each time mission assumptions drift.

The energy model should separate flight, hover, communications, reserve, and contingency. Flight time depends on distance, airspeed, wind, climb/descent, and detours. Hover time depends on payload size, radio link quality, authentication handshake time, and retry policy. Reserve depends on current distance home and landing options, not a comforting fixed percentage. Contingency covers practical field effects such as a blocked landing area, a rejected geofence crossing, a second transfer attempt, or a forced skip around a low-value node. Keeping those terms separate helps the operator know which constraint actually failed.

This is also why reconciliation belongs in the mission model. The aircraft may return safely while the data mission is incomplete: one node may be skipped, one transfer receipt may fail to match a gateway upload, or one reading batch may arrive with an ambiguous time window. The under-the-hood success condition is therefore two-part: the aircraft preserves safe recovery, and the data ledger preserves traceability. If either part fails, the system should produce an exception record and a follow-up route, not silently mark the recovery complete.

36.16 Checkpoint: Summary

This section: Checkpoint: 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, transfer retries, and return reserve before launch and during flight
• Multi-flight planning: Splitting large networks into sequential drone missions
• Operational workflow: From detection through release evidence, planning, deployment, collection, reconciliation, and data recovery

36.17 Related Chapters: What’s Next

This section: Related Chapters: What’s Next

• Learn UAV network fundamentals: UAV Network Fundamentals
• Study UAV trajectory control: UAV Trajectory Control
• Explore FANET fundamentals: FANET Fundamentals
• Learn UAV swarm coordination: UAV Swarm Coordination
• Study energy-aware UAV missions: UAV Energy-Aware Mission Planning