Domain framework: edt_framework
Scope
Enabling meta-layer — AI, IoT, and connectivity infrastructure that enhances resource management and coordination across all other EDT domains (D01–D08). Not a resource domain in its own right: a community cannot be self-sufficient in digital intelligence the way it can be self-sufficient in energy or food. Technologies here amplify the performance of every other domain. CR_004 CR_005
Key technologies
AI for energy, water, and food optimisation; IoT sensor networks; satellite internet (Starlink, LEO); community mesh networks (LoRa/Wi-Fi/Bluetooth); smart meters and monitoring; digital twins of community systems; community data platforms; predictive maintenance; edge computing; edge AI chips; LoRa and low-power communications (Zigbee, Z-Wave); drone technology (thermal inspection, agricultural monitoring); Reality 2 platform (community mesh, Mariko Earthgrids); Trust Groups (encrypted data sovereignty); satellite Earth observation (SAR, optical, multispectral — crop monitoring, terrain, flood mapping, environmental health); Earth observation access models (partner, productised tool, embedded specialist); spatial ML analytics platforms.
Evidence
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AI grid management identified as the next major revolution in energy — Spanish blackout (April 2025) caused by poor manual regulation, not renewables; “no reason” grid management shouldn’t be fully computer-controlled. Germany operates 3–4 TSOs simultaneously, enabled by digitalisation. Interview II [INT_002]
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Satellite technology is already baseline for energy scheduling/forecasting — current practice in the energy industry, not an emerging technology. Interview II [INT_002]
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Drone technology: thermal camera inspection of PV panels for fault detection; early crop disease/infection detection in agricultural fields. Robotic panel cleaning relevant in dusty/desert environments; natural precipitation sufficient in temperate climates (e.g., Hungary — 1–2 cleanings/year in utility-scale contracts, often not used). Interview II [INT_002]
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Hybrid decentralised-first mesh architecture: “decentralised first, can talk centralised when it needs to or when it’s able to” — designed specifically for storm/disaster scenarios where centralised infrastructure fails. Pure centralisation is fragile; pure decentralisation forces every node to do everything. Interview III [INT_003]
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Mariko Earthgrids Wairoa pilot: ~10 LoRa mesh sensors at approximately 1/10 council sensor cost, ~40 more imminent; solar + battery, ~2 weeks autonomy without sun; public dashboard plus tailored emergency controller views; community annotation. Interview III [INT_003]
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Reality 2 platform: new technology stack organised by proximity/location/context; auto-configures on startup; runs from ESP32 microcontroller to Linux machine; same codebase across hardware range. Planned open source. Interview III [INT_003]
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Edge AI is now off-grid deployable — small devices with built-in camera and AI processing run completely off-grid; “the key thing is moving all of it from the central to the edge.” Interview III [INT_003]
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Data sovereignty via Trust Groups: community-controlled encrypted data sharing; “the river owns its data, administered by Kaitiaki”; community sets rules for what data is shared with whom; enables a community data marketplace as a long-term self-funding mechanism. Interview III [INT_003]
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Satellite Earth observation operates on three access tiers: free optical imagery (Google Earth) for anyone; free SAR data (Sentinel-1) for technically skilled users; high-resolution crop/soil/atmospheric data only via paid licensing. NZ receives 0–1 Sentinel-1 passes per 6-day cycle (vs. ~every 6 days for Europe) — a structural coverage disadvantage from NZ’s geopolitical invisibility in European constellation priorities. Interview IV [INT_004]
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NZ has no sovereign satellite capability — zero nationally owned or controlled satellites; no national EO data-gathering platform. Government space strategy documents mention this as an aspiration; nothing is formally committed. A structural D09 constraint for NZ communities. Interview IV [INT_004]
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The practical community access model for satellite EO is via a translator layer — partner organisation, productised analytics tool, or embedded specialist. AI lowers the code-writing barrier but not the domain knowledge requirement; without expertise in data limitations, AI-generated satellite analytics produce confident but unreliable results. Interview IV [INT_004]
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Traditional rural energy models have “the drawback of not considering digital technology and renewable energy” — bibliometric review of 259 studies (1979–2024) identifies absence of digital integration as a structural gap in rural energy planning; validates D09 as a necessary enabling layer, not optional feature. LIT_004
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AI-driven multi-energy coupling model for rural systems integrates capacity planning + operation scheduling optimization; improves speed of response and adaptability; “rendered it more flexible to cope with the diversified energy sources and complex operational scheduling situations involved in rural energy systems.” LIT_004
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Blockchain enables decentralized energy trading with traceability, fair payments, and prosumer reputation scheme — emerging in the literature from 2021 onward as a key mechanism for community energy markets and P2P trading alongside microgrids. LIT_004
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Satellite EO and IoT ground-based sensing are complementary D09 layers: satellite provides broad area coverage and historical change detection; LoRa/IoT provides real-time hyperlocal sensing. Optimal community architecture integrates both rather than choosing between them. Interview IV [INT_004]
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GIS-based spatial energy-option assessment (IntiGIS-Local): a method (ArcGIS ModelBuilder) that maps the levelized energy cost (LEC) of off-grid options — individual PV, microgrid, solar–diesel hybrid — for every location in a community, picking the least-cost option per spot from the local resource and the dispersion of homes. A concrete reference approach for an NI spatial energy siting/sizing layer, complementing satellite/EO and the model’s energy costing; demonstrated for off-grid rural electrification (Guasasa, Cuba). LIT_048
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Probabilistic remote-sensing for building-level electricity access & demand (BEACON / LItLDF): a multimodal-data-fusion methodology (the full method behind the Open Energy Maps platform — OT_023) that infers, for every building in a region, both electrification status (BEACON: 80.7% accuracy in Rwanda, beating prior models) and electricity demand (LItLDF) from satellite imagery + nighttime lights + building footprints + land use + internet-speed data fused with sparse utility ground truth, using a neural network embedded in a Bayesian model. Two transferable D09 lessons for NI: (1) the output should be a distribution (mean + uncertainty), not a point estimate — uncertainty is itself the planning input; and (2) such inference is a flagged future NI layer for NZ contexts where utility data is structurally unavailable (off-grid Māori land, papakāinga, pre-smart-meter rural areas), not an active feed for the grid-connected Tasman pilot. Demand-estimation accuracy alone can swing modelled electricity unit cost ~3× — a quantified argument for right-sizing against a defensible demand profile. African/LMIC method demonstration; numbers are not NZ benchmarks. LIT_052
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Adverse outcomes of AI technologies ranked #30 short-term but #5 long-term — the largest upward shift across all 33 global risks (GRPS 2025–2026); “jobless productivity” scenario projects 50% of US entry-level white-collar jobs eliminated within 5 years; labour displacement → inequality → societal polarization identified as a reinforcing loop. AI deployment in community systems must be designed to augment rather than replace community labour and decision-making — D09 tools carry a responsibility not to reproduce the macro-scale displacement risk at community scale. OT_014
SSI connections
- I08 Innovation & appropriate technology — digital intelligence is the primary expression of technology adoption and innovation capacity at community scale. CR_001 Interview III [INT_003]
- I06 Resistance to external shocks — real-time monitoring and decentralised-first mesh architecture improve adaptive response to disruptions; AI grid management and LoRa mesh both provide resilience against centralised system failure. Interview II [INT_002] Interview III [INT_003]
- I05 Good governance & transparency — Trust Groups provide a working technical implementation of community data sovereignty; community dashboards increase transparency and participatory access to information. Interview III [INT_003]
- I01 Financial & economic sufficiency — optimisation of energy, water, and food systems via AI reduces resource waste and operating costs; community data marketplace offers a self-funding mechanism for digital infrastructure. Interview II [INT_002] Interview III [INT_003]
- I02 Food security & sustainable agriculture — satellite crop quality, soil moisture, and vegetation health monitoring provides area-wide food production intelligence accessible via partner or productised-tool model. Interview IV [INT_004]
- I09 Environmental sustainability — satellite biodiversity, waterway health, land-change, and agricultural environmental monitoring enables ongoing environmental stewardship without expensive ground-based survey programmes. Interview IV [INT_004]
Relevance to Neobiome
D09 technologies are not standalone design choices — they are applied within each domain’s design as optimisation and monitoring tools. A technology page filed under D09 should always note which primary domain(s) it enables.
AI grid management in particular is the enabling layer that makes a decentralised multi-source community energy system (D01) operationally viable at scale. Interview II [INT_002]
LoRa mesh networks provide the baseline communications resilience for remote NZ communities where fibre and GSM are unreliable — specifically designed to function during the flood/storm events that most threaten community safety. Interview III [INT_003]
Satellite EO is not currently a DIY community technology — the access barrier (expertise, bandwidth, NZ coverage gap) means it operates as a D09 enabling layer accessed via partners or productised tools rather than managed by the community directly. Neobiome’s D09 architecture should plan for satellite-delivered environmental and agricultural monitoring as a periodic audit layer complementing the real-time, hyperlocal LoRa mesh network.
Open questions
[Rural connectivity infrastructure in NZ (Starlink coverage, fibre gaps), data sovereignty and community ownership of sensor data, links to questions/ pages. Reality 2 open-source release timeline.]
Connections
Links to
Sources (8): CR_001 · CR_004 · CR_005 · LIT_004 · LIT_048 · LIT_052 · OT_014 · OT_023
Concepts (1): EDT Framework — Emerging & Disruptive Technolo…
Technologies (1): Satellite Earth Observation (Remote Sensing)
Referenced by
Technologies (1): LoRa Mesh Network (Community Sensor & Comms In…
Sources (1): LIT_004