Distributed Fibre Sensing
Continuous, long-distance, passive sensing along the route.
01 · Why Imbak Canyon
A high-value forest requires a different research approach.
Rainforest research requires continuous observation, spatial distribution and longitudinal context. More observation should not automatically mean more physical presence.
Continuous Observation
Extend scientific visibility between field visits instead of replacing field researchers.
Distributed Measurement
Move from isolated measurements toward spatially meaningful research coverage.
Minimum Necessary Presence
Deploy only where scientific value justifies ecological presence.
Research determines the technology. Technology does not determine the research.
02 · Research Gap
Continuous visibility between field visits.
Field expeditions, fixed stations, satellites and periodic drone surveys remain essential. IDRCIN fills the gap: continuous, distributed, relocatable and low-presence monitoring.
Field Expedition
Strong judgement and sampling, but episodic and presence-intensive.
Fixed Station
Continuous at one location, but spatially inflexible and persistent.
Remote Survey
Large-area context, but not continuous local measurement.
IDRCIN
Distributed continuous measurements with a temporary, relocatable field layer.
03 · Proposition
A moving scientific grid.
The sensing layer can be retrieved, inspected, calibrated and redeployed as research questions evolve.
MAP
DEPLOY
OBSERVE
DETECT
RETRIEVE
RELOCATE
LEARN
04 · How IDRCIN Works
Map first. Fibre first. Deploy selectively.
Reconnaissance, Digital Twin, ecological routing, ultra-light fibre, Distributed Fibre Sensing, selective point instruments, the observatory layer and NeuralOps work as one research infrastructure.
04A · Fibre-First Sensing
One observatory. Two complementary sensing modes.
Fibre remains primary. Autonomous pods are sparse, specialised and used only where a physical probe is justified.
Fibre First
Research-defined deploymentAutonomous Specialised Point Sensing
Selective, battery-powered, low-duty-cycle and retrievable.
Fibre senses continuously across space.
Autonomous pods measure specialised properties at selected points.
Sparse by design
river
soil
tree
canopy
No New Forest Gateway NetworkNo towerNo relayNo communications hutNo in-forest gateway networkDesigned to avoid new terrestrial gateway infrastructure inside the protected forest. Satellite / NTN, if considered, remains subject to field and regulatory validation.
Can fibre measure it?YES → FIBRE
If NO, does research require a physical probe?Research determines technology.
NO → DO NOT DEPLOYWireless capability is not permission.
YES → POD PILOTSmall, retrievable, subject to validation.
Scientific Sensor
Ultra-Low-Power Controller
Local Buffer
LoRa
Battery
Antenna
Sensor
Local Processing
Small Payload / Event
IDRCIN Platform
NeuralOps
Researcher
Fibre First · Specialised Measurements Only · Less Permanent Infrastructure · Retrieve. Reuse. Redeploy.
05 · Canopy Deployment
Extremely lightweight at the research point.
The spool deploys and recovers the primary fibre infrastructure. Autonomous Scientific Pods are separate, small research instruments used only at approved specialist points.
Fibre Remains Primary
The integrated spool supports the ultra-light fibre route, controlled payout and recovery.
Specialised Point Only
One specialised sensor maps to one small autonomous pod where fibre is scientifically insufficient.
Retrieve / Reuse / Redeploy
Pods are battery-powered, locally buffered and retrieved for inspection, calibration and relocation.
06 · Research Capacity
Sets A / B / C.
Redundancy is spatial, not duplicated inside each research point. The separate Autonomous Scientific Pod Pilot Pool is pending asset-level reconciliation and is not added to these locked counts.
4Research Zones
48Active Sensor Points
36Primary Points
12Redundancy / Control
07 · Reliability
Reliability is budgeted from day one.
Phase 2 includes a complete replacement reserve rather than assuming tropical field hardware will never fail.
48Active
+481-to-1 Replacement
+6Emergency Pool
102 total sensor assemblies.
08 · Drone Fleet
Operational redundancy where it matters.
One reconnaissance platform plus two deployment/retrieval-capable aircraft.
01Recon / Mapping
LiDAR, RGB, Digital Twin, route verification and inspection.
02Primary Deployment
Spool deployment, sensor placement, fibre operations and retrieval.
03Backup Deployment
Operational redundancy and retrieval resilience.
09 · Intelligence Architecture
Keep sensors simple. Move intelligence inward.
Distributed fibre and small received or recovered pod records converge at the IDRCIN platform. LoRa carries compact scientific payloads or events, not raw high-bandwidth streams.
IDRCIN Platform
Acquisition, local storage, health monitoring and deterministic validation.
NeuralOps
Cross-sensor context and AI reasoning only when useful, not a continuous LLM per pod.
Research Intelligence
Digital Twin, dashboards, traceable evidence and researcher access.
10 · Research scope
Explore 60 research programmes.
The pitch stays summary-level. LoRa is not forced into all 60 studies: fibre, autonomous point sensing, hybrid and remote-sensing studies use the mode their research requires.
Open the Research Observatory for the full technical matrix.
10 · Presence vs Impact
No monitoring system has zero impact.
IDRCIN targets low routine human presence, temporary technology presence and high spatial flexibility. Wireless capability does not create ecological permission.
Fibre First
Use the distributed infrastructure before adding another physical instrument.
Specialised Only
Deploy a pod only for a research-defined property fibre cannot adequately provide.
Less Permanent Infrastructure
Designed to avoid new towers, relays, communications huts and gateway networks inside the forest.
Retrieve / Reuse
Temporary, relocatable instrumentation with explicit recovery responsibility.
Drone Noise
Wildlife Interaction
Fibre Snagging
Battery / Electronics
RF Uncertainty
Retrieval Failure
Do-Not-Deploy Principle: if fibre is adequate, a physical probe is unnecessary or scientific value is insufficient, do not deploy.
11 · ESG & Carbon
Less presence, less carbon.
IDRCIN budgets energy and carbon rather than assuming them away.
IDRCIN vs Manual
Fewer human trips
Drone-deployed, retrievable sensing reduces repeated access and persistent footprint.
NeuralOps vs Full AI
Local-first
Validation on-premise; heavy cloud LLM used sparingly, on demand.
Carbon & Energy
Measured, including TNB
Grid electricity for DAQ/HQ budgeted and disclosed, not assumed.
Two Layers of Operational Carbon Reduction
IDRCIN targets carbon reduction at the physical research layer by reducing repeated field mobilisation. NeuralOps targets carbon reduction at the digital intelligence layer by reducing unnecessary AI processing.
“Reduce unnecessary movement in the forest. Reduce unnecessary computation in AI.”
Physical Research Layer
IDRCIN vs Conventional / Manual Monitoring
Preliminary scenario estimate
Manual Monitoring
12 campaigns2 × 4×4
7,200 km × 0.256 = 1,843.2 kg
0
tonnes CO₂e / year
VS
IDRCIN
6 inspections1 × 4×4
drone charging · 180 kWh / yr
460.8 + 97.0 = 557.8 kg
0
tonnes CO₂e / year
≈ 70% LOWERoperational field emissions
This comparison focuses on operational field emissions, primarily ground transport and drone electricity. It does not yet include full embodied-carbon lifecycle emissions from manufacturing vehicles, drones, sensors, fibre, batteries or infrastructure. Actual project values should later be replaced with measured vehicle kilometres, fuel litres, drone battery charging kWh, field mission count and retrieval missions.
Digital Intelligence Layer
NeuralOps vs Full-AI Processing
Working workload comparison
Full AI
large AI workload32B tokens
VS
NeuralOps
after routing / detached filtering2.5B tokens
≈ 92.2% LESSvariable AI workload
Whole-system energy model: 30% fixed infrastructure + 70% variable. NeuralOps = 30% + (70% × 2.5/32) = 35.47%.
≈ 64.5% LOWERestimated whole-system compute footprint
Illustrative example based on an existing 360 kg CO₂e annual Full-AI baseline: Full AI ≈ 360 kg; NeuralOps ≈ 128 kg; estimated avoided ≈ 232 kg CO₂e / year. Not externally audited data.
IDRCIN reduces repeated physical mobilisation.
NeuralOps reduces unnecessary AI computation.
NeuralOps reduces unnecessary AI computation.
More scientific intelligence with less operational overhead efficiency at both the forest edge and the compute layer.
From Estimated → Measured
Physical Layer
- vehicle kilometres
- fuel consumption
- drone battery kWh
- number of missions
- human field hours
Digital Layer
- total tokens
- model calls
- server / DAQ electricity
- cloud workload · storage · network
Future KPI
kg CO₂e / research pointkg CO₂e / month of monitoringkg CO₂e / GB validated datakg CO₂e / research output
Goal: replace preliminary scenario estimates with measured operational ESG data during POC / pilot.
11 · Pilot Programme
Prove it before scaling it.
The programme is stage-gated. The Autonomous Scientific Pod element is a small validation pilot, not approved full deployment.
Phase 1 · POC
RM500K3–4 Months
Validate core deployment, communication, retrieval and ecological assumptions.
Phase 2 · Full Pilot
RM2.0M12 Months
4 zones, 48 active points, 102 sensor assemblies, 3-aircraft fleet, DAQ, NeuralOps, TM Cloud and full lifecycle validation. The separate pod pilot pool remains pending asset reconciliation.
RF + Battery
Validate link feasibility, low-duty operation and measured consumption.
Science + Survivability
Validate sensor usefulness, enclosure performance and ecological footprint.
Retrieval
Recover, inspect, calibrate and decide whether reuse or redeployment is justified.
Pilot subject to BOM validation and reallocation within the existing Phase-2 envelope. Decision Gate: GO · MODIFY · STOP.
12 · The Ask
RM2.5M potential programme stage gated.
Not an unconditional RM2.5M commitment. RM500K funds the POC. RM2.0M proceeds only after successful validation.
Phase 1
RM500KProof of Concept
GO
MODIFY
STOP
MODIFY
STOP
Phase 2
RM2.0M12-Month Full Pilot
13 · Where the RM2M Goes
Funding creates a complete research capability.
Preliminary planning budget. Final values remain subject to detailed design, site assessment, research requirements and vendor quotations.
Drone Fleet, LiDAR & Aerial Systems
RM390,000
Canopy Fibre Corridor & Recovery
RM204,000
Spool, Fibre, Quick Release & Recovery
RM210,000
Observatory Backbone & Connectivity
RM220,000
NeuralOps & Digital Platform
RM200,000
Mapping & Temporal Digital Twin
RM90,000
Field Operations & Logistics
RM150,000
Scientific & Ecological Monitoring
RM120,000
Regulatory, Safety, Insurance & Training
RM70,000
Calibration, QA & Spares
RM70,000
AINNA Systems Integration / PM
RM220,000
Contingency / Field Risk Reserve
RM56,000
TOTAL PHASE 2 BUDGET: RM2,000,000
14 · After Phase 2
Successful Imbak deployment moves directly into steady state.
After the full pilot, Imbak moves directly into an operating steady state with predictable, modest annual planning costs.
~RM1.0MPlanning Baseline / Year
Indicative steady-state range: RM0.8M–RM1.2M annually.
Annual OPEX
Operate · Maintain · Calibrate · Retrieve · Redeploy · Cloud / Data · Research Support · Ecology
15 · Optional Expansion
Expansion is optional not automatic.
A future landscape such as Maliau Basin would be a separate programme with its own mapping, sensor deployment, DAQ infrastructure, ecological baseline and validation.
IMBAKOperational Network
MALIAU?Separate Optional Programme
16 · Governance
Science first. Conservation always.
Scientific, ecological and engineering governance connect through joint steering and explicit GO / MODIFY / STOP authority.
Scientific
Research questions determine whether fibre, a specialist pod or no additional instrument is required.
Ecological
Restricted zones, disturbance limits, presence budget and stop authority.
Engineering
Drone, fibre, separate pilot inventory, power, RF validation, NeuralOps and retrieval.
