MQTT & OPC-UA Integration
InfluxDB / TimescaleDB
LoRaWAN Wide-Area Sensor
Edge Computing On-Site
Safety Wearable Integration
Mining IoT Architecture

Mining IoT Architecture — From Sensors at the
Mine Face to Dashboards in the Control Room

A mining IoT platform consists of four layers working together: (1) Sensor and device layer — physical sensors, GPS trackers, wearables, and equipment telemetry systems distributed across the mine site, sending data via wireless protocols (4G LTE, Wi-Fi, LoRaWAN, Zigbee, proprietary radio) appropriate to the coverage requirements and data volumes involved. (2) Edge computing layer — on-premise servers and compute devices at the mine site that receive raw sensor data, perform real-time processing, trigger alerts, and store data locally — ensuring the IoT platform functions during connectivity outages. (3) Cloud platform layer — AWS IoT Core or Azure IoT Hub receiving data from the mine site edge, providing long-term data storage, ML model execution, and integration APIs. (4) Application layer — real-time dashboards, mobile apps for field workers, automated alert notifications, and reporting tools that surface actionable intelligence to mine managers, maintenance teams, environmental officers, and safety personnel.

Algosoft's mining IoT technology stack: MQTT broker (Eclipse Mosquitto or HiveMQ on mine site edge server) as the central data bus for all sensor streams; OPC-UA for SCADA and PLC data integration; InfluxDB or TimescaleDB for high-volume time-series sensor data storage (purpose-built for the write patterns of continuous sensor data — far more efficient than storing sensor readings in PostgreSQL row-by-row); Apache Kafka for high-throughput real-time streaming where data volumes exceed MQTT broker capacity (mineral processing plant vibration sensors generating thousands of readings per second); Grafana for operational dashboards (open-source, highly customisable, native InfluxDB and TimescaleDB support); React custom dashboards for public-facing or highly branded interfaces; Python (FastAPI) for real-time alert processing, ML model serving, and integration APIs; AWS IoT Core or Azure IoT Hub for cloud connectivity; and AWS ap-southeast-2 (Sydney) or Azure Australia East for Australian data residency requirements.

  • Edge-First for Remote Mine Site Reliability — Algosoft designs all mining IoT platforms as edge-first: the critical operational functions (real-time alerts, fleet position dashboard, environmental monitoring displays in the control room) run on edge computing hardware at the mine site and function regardless of satellite or cellular connectivity status. Cloud connectivity enhances the platform (long-term analytics, corporate dashboards, ML model updates) but is never a dependency for core operational alerting. For remote Australian mine sites in the Pilbara, Goldfields, Bowen Basin, or Hunter Valley — where satellite internet may drop for hours — this architecture distinction is the difference between a reliable operational tool and an unreliable one that mine teams stop trusting.
  • Australian Environmental Compliance Integration — Australian mine sites operate under environmental monitoring licence conditions that require continuous or periodic recording of dust, noise, and water quality measurements — with regulatory reporting obligations to state environmental regulators (EPA NSW, DWER WA, DESI QLD). Algosoft's environmental monitoring IoT systems automate this compliance data collection: continuous PM10/PM2.5 particulate sensors feeding directly into automated compliance reports in the format required by the site's environmental licence conditions, noise monitoring at site boundary points with automated exceedance alerts, and mine water quality monitoring (pH, electrical conductivity, turbidity, heavy metals via in-situ sensors) for sites with tailings storage facilities or mine dewatering discharge. Automated compliance reports generated in regulatory agency format for submission at required frequencies — eliminating manual data compilation and reducing environmental non-compliance risk.
Edge
First Architecture
LoRaWAN
Wide-Area Coverage
OPC-UA
SCADA Integration
Real-time
Alerts <1s
MQTT (Eclipse Mosquitto) OPC-UA InfluxDB / TimescaleDB Apache Kafka Grafana Dashboards LoRaWAN / Zigbee AWS IoT Core Azure IoT Hub Python FastAPI
Get Mining IoT Quote →
Mining IoT Solutions

Mining IoT Solutions for Open-Cut,
Underground, and Mineral Processing Operations

Fleet Telematics & Real-Time Equipment Tracking

Real-time GPS vehicle tracking and telematics platform for mine site heavy equipment — haul trucks, excavators, graders, water carts, service vehicles, and light vehicles. Hardware options: OEM fleet telematics (Caterpillar Product Link, Komatsu KOMTRAX, Hitachi ConSite, Volvo CareTrack — Algosoft integrates with existing OEM telemetry APIs); third-party GPS hardware (Queclink, Teltonika, Calamp — Algosoft recommends hardware for new deployments); mine site camera systems for operator fatigue detection (Seeing Machines, SmartCap integration). Data collected per vehicle: GPS position (5–30 second polling), speed, heading, engine hours, fuel consumption, idle time, payload weight (from OBW sensors), fault codes (SAE J1939 CAN bus data). Real-time mine site map dashboard: vehicle positions on satellite imagery, colour-coded by status (moving/idle/off), assignment label (dig face, dump point, workshop), geofence alerts for unauthorised zone entry, and speed alerts for haul road speed limit compliance. Fatigue management: hours-tracking per operator with alert triggers at regulatory thresholds.

Environmental Monitoring IoT Platform

Continuous environmental monitoring systems for mine site compliance with environmental licence conditions and regulatory requirements. Dust monitoring: PM10 and PM2.5 particulate monitoring at site boundary and dust source locations — Algosoft integrates with BAM (Beta Attenuation Mass) monitors, DustTrak instruments, or lower-cost optical particle counters for indicative monitoring. Noise monitoring: real-time dBA monitoring at site boundary noise monitoring points — integrated with Australian Standard AS 1055 noise measurement requirements. Water quality: pH, electrical conductivity (EC), turbidity, dissolved oxygen, and temperature monitoring for mine water (pit dewatering discharge, tailings storage facility seepage monitoring, stormwater runoff). Air quality: gas detection for underground mines (CO, CO₂, CH₄, NO₂, O₂ — gas monitoring with LoRaWAN wireless transmission from underground headings where cabling is impractical). Meteorological data: anemometer wind speed and direction for dust dispersion modelling, rainfall for stormwater management. Automated exceedance alerts: SMS and email to environmental officer and mine manager when readings exceed licence condition limits — with automated regulatory notification workflows for reportable exceedances.

Safety Wearable & Personnel Tracking System

Integrated personnel safety system combining wearable devices and mine site infrastructure to provide real-time worker location, safety alert, and emergency response capability. Personnel tracking hardware options: Bluetooth Low Energy (BLE) beacon-based indoor location (accurate to 5–10m in underground headings with fixed BLE reader infrastructure), UWB (Ultra-Wideband) real-time location (accurate to 0.1–0.3m — high cost, suitable for highest-risk areas), GPS-based personal tracking for open-cut and surface operations. Safety wearables supported: SmartCap fatigue monitoring (EEG-based drowsiness detection), Blackline Safety G7 lone worker safety device (man-down detection, fall detection, GPS, two-way voice), MSA Safety ALTAIR connected gas detection (CO, O₂, H₂S — with wireless data to central monitoring), physiological monitoring wearables for heat stress management (heart rate, skin temperature — particularly relevant for Pilbara and Northern Territory mine sites). Mine evacuation system: emergency muster board showing all personnel locations at time of emergency evacuation, real-time reconciliation of evacuated vs. missing persons to guide emergency response team.

Conveyor, Pump & Fixed Plant IoT Monitoring

Vibration, temperature, and operational monitoring for fixed plant equipment in mineral processing plants and mine infrastructure — conveyors, SAG mills, ball mills, pumps, crushers, stackers, and reclaimers. Condition monitoring sensors: vibration accelerometers (Emerson CSI, SKF Microlog, Sensata — or low-cost MEMS accelerometers on Algosoft IoT gateway hardware for indicative monitoring), temperature (thermocouple or RTD on bearings, motor windings, gearbox oil), current draw (current transducers on motor drives detecting load changes indicating blockage or wear), belt slip detection (encoder speed comparison between drive and tail pulleys). Data protocol: OPC-UA from Rockwell or Siemens PLC collecting field sensor data, or MQTT from Algosoft IoT gateway hardware deployed on existing analog sensor outputs. Time-series storage: InfluxDB capturing vibration RMS, peak, and spectral data (FFT frequency analysis stored for bearing fault frequency analysis). Predictive analytics: ML models detecting bearing inner/outer race fault frequencies (BPFI/BPFO patterns), imbalance, misalignment — with 72-hour advance warning of bearing failure enabling planned replacement rather than unplanned breakdown of conveyor system critical to mine production.

Geotechnical & Blast Monitoring IoT

Geotechnical monitoring systems for slope stability monitoring in open-cut mines and underground mining — critical for managing the risk of slope failure or ground collapse events. Instrumentation and IoT integration: piezometers (groundwater pressure in slope — wireless data loggers transmitting via LoRaWAN to mine site edge server), slope inclinometers (sub-surface ground movement detection — monitored for acceleration patterns indicating slope instability), crack gauges on slope crest cracking (displacement monitoring), robotic total station (automated survey instrument continuously monitoring survey prism arrays on high-risk slopes — generating Northing/Easting/RL coordinate data at 5-minute intervals), prism monitoring arrays (100+ reflective prisms on slope face, surveyed by robotic total station or LIDAR scanner). Alert thresholds: configurable velocity alerts (mm/day movement rate triggering amber/red alert levels — aligned with mine's slope management plan trigger action response plan). Blast monitoring: vibration monitoring at sensitive receptors (mine infrastructure, nearby communities, protected heritage sites) — peak particle velocity (PPV) compliance monitoring for blast notifications and regulatory reporting. Seismic monitoring for underground mines: microseismic array detecting rock mass stress changes indicating seismic hazard buildup.

Mine IoT Data Platform & Operations Dashboard

Centralised IoT data platform integrating all mine site data streams into a single operational intelligence interface — replacing the fragmented situation where fleet data is in one system, environmental data in spreadsheets, safety data in another system, and maintenance data in a CMMS disconnected from real-time sensor feeds. IoT data platform architecture: MQTT broker as central bus receiving all sensor streams → InfluxDB time-series storage (high-throughput write path for continuous sensor data) + PostgreSQL for operational metadata (equipment registry, alert configuration, maintenance records) → Apache Kafka for stream processing pipelines (real-time alert evaluation, anomaly detection, data transformation) → REST API layer (FastAPI) exposing data to dashboard and mobile applications. Operations dashboard: configurable for each role (mine manager: production KPIs + safety overview; maintenance engineer: equipment health matrix + upcoming maintenance; environmental officer: compliance monitoring status + exceedance alerts; dispatch controller: real-time fleet map + production vs. plan). White-labelled IoT platform branded to your mine site — not a generic third-party IoT cloud platform with your mine logo added.

Mining IoT Pricing

Mining IoT Platform Investment
Software Development + Integration Costs

TIER 01

Single-Domain IoT Module

$20,000–$45,000

10–14 week delivery
Fleet tracking, or environmental monitoring, or safety wearable integration MQTT / OPC-UA integration to existing sensors InfluxDB time-series storage Grafana dashboard with your mine site map SMS/email alert system for exceedances 6-month post-launch support

TIER 02

Integrated Operations Platform

$55,000–$120,000

18–26 week delivery
Fleet tracking + environmental + safety wearable integration Centralised MQTT broker + InfluxDB platform Custom React operations dashboard (not Grafana) Mobile app for field workers and supervisors AWS IoT Core / Azure IoT Hub cloud connectivity 12-month support + alert tuning

TIER 03

Full Mine IoT + Predictive AI

$130,000–$280,000

9–15 month delivery
All domains: fleet, environmental, safety, geotechnical, fixed plant Predictive maintenance ML models (Python) Geotechnical monitoring (prism, piezometer, inclinometer) Blast monitoring and blast exclusion zone enforcement Full SCADA OPC-UA integration (historian, PLCs) 24-month support + continuous model improvement

SUPPORT

IoT Platform Managed Service

$5,000–$15,000/month

Ongoing managed service
Platform hosting on AWS/Azure (included) 24/7 monitoring of IoT platform health Alert threshold tuning and optimisation Monthly feature releases from mining feedback ML model retraining on new failure data Dedicated account manager
Prices are Algosoft software development and integration costs. Hardware (sensors, GPS trackers, edge servers, gateways) costs are additional — Algosoft provides hardware specification and procurement support. AWS/Azure IoT platform running costs: typically $800–$5,000/month depending on device count and data volume. Get mining IoT scoping quote →
Why Algosoft for Mining IoT

Why Mining Companies Choose Algosoft
for Mining IoT Platform Development

01

Industrial Protocol Expertise — OPC-UA, MQTT, Modbus

Most software development companies understand HTTP and REST APIs. Mining IoT requires expertise in industrial communication protocols that general software developers don't encounter. Algosoft's mining IoT team is experienced in the protocols that define the mine site technology landscape: OPC-UA (the modern industrial interoperability standard — Algosoft develops OPC-UA clients connecting to SCADA servers from AVEVA, Ignition, Rockwell FactoryTalk, and Siemens WinCC); MQTT (the dominant IoT messaging protocol — Algosoft deploys Eclipse Mosquitto and HiveMQ brokers and develops MQTT publishers and subscribers for sensor gateways and application integrations); Modbus TCP and RTU (the legacy industrial protocol still present in most Australian mineral processing plants — Algosoft develops Modbus TCP masters reading sensor registers from PLCs); and DNP3 (common in Australian mine site SCADA for remote RTU communication). This industrial protocol competency means Algosoft can connect to your existing mine site systems — not require you to replace functional operational technology for IoT connectivity.

02

Time-Series Database Architecture for High-Volume Sensor Data

Mining sensor data has characteristics that defeat conventional database design: very high write volumes (a 200-truck fleet + 500 environmental sensors + 1,000 fixed plant sensors generates millions of data points per day), high cardinality (thousands of unique sensor identifiers), and time-indexed query patterns (give me the last 72 hours of Truck 47's engine temperature, show me PM10 readings at boundary monitor B3 during the last 30 days). Conventional relational databases (MySQL, PostgreSQL without extensions) become impractically slow for these access patterns at mine site data volumes. Algosoft deploys purpose-built time-series databases: InfluxDB 2.x or TimescaleDB (PostgreSQL extension) depending on project requirements. InfluxDB's Flux query language is optimised for time-range queries with downsampling (aggregate from 1-second raw data to 5-minute averages for trend displays). TimescaleDB integrates with the PostgreSQL ecosystem the team already knows while adding time-series performance. Algosoft calibrates the appropriate retention policy (raw 1-second data for 30 days, 1-minute aggregates for 1 year, daily aggregates for 5 years) — balancing storage cost against analytical flexibility.

03

LoRaWAN for Wide-Area Mine Site Coverage

Open-cut mine sites present a wireless connectivity challenge that standard wireless technologies don't solve well: very large geographic areas (1–50+ km²), remote from cellular tower infrastructure, with line-of-sight disruption from mine benches and waste dumps, and a need to connect low-power sensors that must run on batteries for years (environmental monitoring sensors at pit crest, geotechnical prism data loggers, perimeter fence monitoring). LoRaWAN (Long Range Wide Area Network) is designed exactly for this application: sensors transmit data 2–15 km line-of-sight using sub-GHz radio bands (915 MHz in Australia — the AU915 frequency plan) at very low power consumption (battery life 2–10 years for sensors reporting every 15 minutes). Algosoft designs LoRaWAN networks for mine sites: gateway placement planning (typically 2–5 gateways covering a medium mine site), LoRaWAN Network Server deployment (ChirpStack — open-source, on-premise mine site server or AWS), IoT gateway hardware specification and procurement support, and integration of LoRaWAN sensor data to the central MQTT broker for downstream processing and dashboard display.

04

Practical ML — Predictive Maintenance That Mining Teams Actually Use

Predictive maintenance ML models in mining have a practical implementation problem: they are developed by data scientists who have never stood near a SAG mill bearing, and deployed to maintenance planners who don't understand why the model says a bearing will fail. The result is that models are ignored because mine maintenance teams don't trust them. Algosoft's approach to mining predictive maintenance ML: start with rule-based alerts (known OEM fault codes, temperature threshold exceedances, vibration overall RMS limits) that provide immediate value without ML; then layer ML anomaly detection on top of the stable rule base after 3–6 months of clean data collection; involve mine maintenance engineers in defining what constitutes a true positive failure event (so models are trained on the right labels, not just any SAE J1939 fault code); and deliver predictions in maintenance-team language ("Truck 23 rear left suspension cylinder shows pressure decay pattern consistent with seal failure — recommend inspection before next scheduled service") rather than model confidence scores. Maintenance teams who understand why the alert was generated act on it. Predictive accuracy typical result: 70–85% of predicted failures actually occur within predicted timeframe at 90% recall (catching most failures before breakdown).

05

Autonomous Vehicle Integration Readiness

Australian mining is the global leader in autonomous haulage system (AHS) deployment — BHP's Pilbara iron ore operations run the world's largest autonomous truck fleet (Caterpillar 793F AHS), Rio Tinto's AutoHaul is the world's first fully autonomous long-distance rail system, and Fortescue has committed to autonomous fleet conversion across multiple mine sites. Algosoft's IoT platforms are designed for compatibility with autonomous vehicle infrastructure: AHS integration layer receives position and status data from autonomous vehicle management systems via REST API or MQTT; custom traffic management interfaces show mixed autonomous/manual traffic on mine site GIS; blast exclusion zone integration with AHS (automated exclusion zone enforcement via API to AHS stopping mixed fleets from entering blast zones without human radio communication); and reporting interfaces that show AHS operational data (utilisation %, tonnes per hour, autonomous mode %) alongside manual fleet KPIs for direct comparison. For mines planning autonomous vehicle transition, Algosoft can build the IoT infrastructure foundation now that will integrate cleanly with the AHS platform when deployed.

06

Australian Data Residency for Mine Site Operational Data

Australian mine site operational data — equipment positions, production data, environmental monitoring, geotechnical data — is increasingly subject to data sovereignty considerations. For large mining companies (BHP, Rio Tinto, Glencore, South32 listed ASX entities), board-level IT governance frameworks require that operational data reside in Australian data centres. For mine sites with Australian government joint venture arrangements (particularly uranium and coal operations), data residency requirements may be contractual. Algosoft deploys all Australian mine site IoT platforms on Australian-region cloud infrastructure: AWS ap-southeast-2 (Sydney — three availability zones), Azure Australia East (New South Wales) and Azure Australia Southeast (Victoria). All data processing occurs on Australian soil — no data transits to US-region cloud for processing. On-premise deployment option: for mines with strict data sovereignty requirements or limited cloud connectivity, Algosoft deploys the full IoT platform stack on mine site servers — edge processing, MQTT broker, time-series database, dashboard, and API server all running on mine site hardware under the mine's direct IT control.

FAQs

Mining IoT Solutions
Frequently Asked Questions

Protocol selection depends on sensor type, coverage range, data volume, and power availability. MQTT over 4G LTE: high-bandwidth, reliable where cellular coverage exists — fleet telematics, control room systems. LoRaWAN (AU915): low-power, long-range (2–15 km), low data rate — environmental monitors, geotechnical data loggers, perimeter sensors needing multi-year battery life. Wi-Fi (IEEE 802.11): high bandwidth, short range — fixed plant equipment connected to mine site Wi-Fi infrastructure. OPC-UA over Ethernet: SCADA/PLC integration on mine site LAN. Zigbee: underground sensor mesh networks where LoRaWAN penetration is limited. Bluetooth Low Energy (BLE): underground personnel tracking with fixed BLE reader infrastructure. Most mine site IoT deployments use 3–4 protocols depending on the application — Algosoft's IoT platform normalises all streams to a central MQTT broker.
Yes — SCADA integration via OPC-UA is standard Algosoft mining IoT practice. Supported SCADA platforms: AVEVA System Platform / InTouch (Wonderware), Ignition by Inductive Automation (Ignition exposes a native MQTT Sparkplug B interface — clean integration), Rockwell FactoryTalk View, Siemens WinCC. Historian integration: OSIsoft PI (PI Web API REST, PI AF SDK), AVEVA Historian. OPC-UA connection: Algosoft deploys an OPC-UA client subscribing to data items on your SCADA OPC-UA server — your SCADA vendor must expose an OPC-UA server endpoint (most modern SCADA platforms do). For older SCADA without OPC-UA: Algosoft develops Modbus TCP/RTU clients reading directly from PLCs where SCADA doesn't expose OPC-UA. Technical assessment of your specific SCADA version and integration options is part of the initial scoping consultation.
Edge-first architecture: all real-time operational functions run on edge computing hardware at the mine site with no internet dependency. Specifically: MQTT broker, InfluxDB time-series database, Grafana dashboards, and alert processing all run on mine site edge server(s) — connected to the mine site local area network. Mine site control room dashboards display in real-time from the local network. Cloud synchronisation uses a store-and-forward pattern: data is queued locally during connectivity outages and synchronised to AWS or Azure when connectivity is restored. LoRaWAN sensors have embedded data logging — up to 50,000 readings stored on sensor (months of data at 15-minute intervals) with upload on next gateway connectivity. Edge server hardware: Algosoft specifies and the client procures appropriate industrial-grade servers (Kontron, Advantech, Dell Rugged Edge) suitable for mine site environmental conditions (dust, temperature, humidity, vibration).
Algosoft's pricing covers software development and integration — not hardware. Single-domain IoT module (e.g., environmental monitoring only): $20,000–$45,000 software development. Integrated operations platform (fleet + environmental + safety): $55,000–$120,000. Full mine IoT + predictive AI: $130,000–$280,000. Hardware costs (sensors, GPS trackers, LoRaWAN gateways, edge server) are additional — Algosoft provides specification and procurement support with 3 hardware supplier quotes. AWS/Azure running costs: typically $800–$5,000/month. Hardware capital cost varies widely by mine size and instrumentation density — a LoRaWAN environmental monitoring network for a medium mine site (8 dust monitors, 4 noise monitors, 2 weather stations, 4 LoRaWAN gateways): approximately $60,000–$120,000 hardware. Algosoft provides a scoping report itemising both software development and hardware cost estimates before commitment.
Yes — underground mining IoT has specific requirements Algosoft addresses. Air quality monitoring: continuous gas detection (CO, CO₂, CH₄, NO₂, O₂, H₂S) with wireless data transmission — LoRaWAN for longer headings, Zigbee mesh for complex underground networks. Personnel tracking: BLE beacon infrastructure (fixed BLE readers on underground roads at 50–100m intervals, workers carry BLE tags) providing 5–10m location accuracy for muster and emergency response. Blast monitoring: seismic sensor arrays for microseismic monitoring of rock mass stability — critical for underground mines in challenging geotechnical conditions. Underground equipment tracking: RFID readers at underground junction points for coarse location tracking; WiFi-based positioning where underground Wi-Fi is installed. Battery-powered sensors: ATEX/IECEx certified intrinsically safe sensor hardware required for underground gas environments — Algosoft specifies only compliant hardware for underground explosive atmosphere zones.
Australian mine environmental licences (typically issued under state environmental protection legislation — Protection of the Environment Operations Act 1997 NSW, Environmental Protection Act 1986 WA, Environmental Protection Act 1994 QLD) contain specific monitoring and reporting requirements: monitoring point locations, measurement frequencies, exceedance action levels, and reporting formats for submission to the regulator. Algosoft's environmental monitoring IoT system is configured against the specific conditions in the mine's environmental licence: monitoring at the prescribed monitoring points, at the required frequency (continuous, hourly, daily), with exceedance levels triggering automated alerts (SMS/email to environmental officer) and automated regulatory notification workflows for reportable exceedances. Monthly/quarterly/annual environmental monitoring reports are generated automatically from the accumulated data in the IoT platform — eliminating manual spreadsheet compilation and reducing the risk of data handling errors in regulatory submissions.
ML model types by application: Failure prediction from structured telemetry (haul truck failure prediction from engine hours, fuel rate, coolant temperature trends, oil analysis results) — XGBoost/Random Forest classifier trained on historical OEM fault codes as labels; typical accuracy 75–85% precision at 90% recall. Vibration anomaly detection (conveyor bearing, SAG mill, pump) — time-series ML using FFT spectral analysis with bearing fault frequency pattern detection (BPFI/BPFO/BSF/FTF from SKF bearing geometry data); autoencoder anomaly detection on 1-second vibration waveforms for early fault detection before spectral patterns emerge. Lifespan remaining estimation for consumables (haul truck tyres from payload history and haul road distance data; grinding media in SAG mill from feed tonnage and hardness data). Models are retrained quarterly on new failure events from the mine site — model performance improves with more mine-specific failure history. Each prediction comes with a natural-language alert for maintenance planners: component identified, fault type, confidence, and recommended action — not just a probability score.
Timeline by tier: Single-domain module (fleet tracking or environmental monitoring): 10–14 weeks software development. Integrated operations platform (3–4 domains): 18–26 weeks. Full mine IoT + predictive AI: 9–15 months. Timeline does not include hardware procurement and installation (which depends on lead times for sensors, gateways, and edge servers — typically 8–16 weeks). Recommended sequencing: Week 1–4: requirements gathering and integration assessment. Week 4–6: hardware specification and procurement order. Week 6–18: software development (runs parallel to hardware procurement). Week 18–22: hardware installation on site (by client or local electrical contractor). Week 22–26: software deployment to edge server, integration testing, alert configuration, user training. This parallel sequencing minimises total calendar time from engagement to live system. Get in touch with your mine's primary IoT requirement and we'll provide a detailed implementation timeline within 48 hours.
Mining IoT Solutions

Connect Your Mine Site's Data — Real-Time IoT Platform for Mining

Fleet telematics and real-time dispatch visibility. Continuous environmental monitoring with automated compliance reporting. Safety wearable and personnel tracking systems. Conveyor and fixed plant predictive maintenance IoT. Geotechnical slope stability monitoring. MQTT, OPC-UA, and LoRaWAN integration. InfluxDB and TimescaleDB time-series platforms. Edge-first architecture for remote mine sites. AWS ap-southeast-2 and Azure Australia East data residency. Software development from $20,000. Get a mining IoT scoping quote within 48 hours.

Get Mining IoT Quote Mining Software Solutions

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