Executive Summary
Mobile tower failures are costly, unpredictable, and disruptive. Our team set out to explore how real time data and machine learning could change that.
IQZ built a Proof of Concept on Databricks that simulates mobile tower operations under real world conditions. The system processes live streaming data, applies geospatial analytics, and predicts failures before they happen.
The goal was to show how proactive maintenance powered by data can improve uptime, reduce costs, and help network teams stay ahead of problems.
The Challenge
Maintaining thousands of mobile towers is never simple. Failures can be caused by all kinds of factors like equipment faults, environmental conditions, even fuel shortages at remote sites.
For this POC, we wanted to go beyond simple system monitoring. We set out to simulate realistic tower operations, including:
- Sudden signal strength drops.
- Equipment faults and malfunctions.
- Weather impacts like heavy rain, high winds, or low atmospheric pressure.
- Resource issues such as low fuel at generator-powered sites.
The challenge was to create a platform that could process all this data in real time, apply advanced analytics, and predict failures before they disrupted service.
Solution Overview
Our team delivered an end-to-end streaming platform on Databricks, designed to simulate real world tower data and test predictive maintenance at scale.
Real Time Data Simulation
We developed a flexible data generator that creates synthetic tower events. These include both normal operations and realistic failure scenarios like:
- Signal drops due to weather.
- Power fluctuations from fuel shortages.
- Sensor anomalies triggered by atmospheric pressure changes.
- Simulated failure events such as antenna faults or cooling issues.
This ensured our system was stress tested with data that mimicked real tower conditions as closely as possible.
Streaming Pipeline and Medallion Architecture
We built a reliable streaming pipeline using Databricks Structured Streaming and Delta Lake, with bronze, silver, and gold layers for data quality and readiness:
- The bronze layer captures raw simulated events continuously.
- The silver layer cleans and enriches the data, adding location and context.
- The gold layer provides ready to use insights for analytics and machine learning.
Geospatial Insights with Mosaic
Tower locations, coverage areas, and environmental factors were processed using Mosaic. This allowed our team to map towers, run proximity analysis, and spot geographic patterns in failures.
Predictive Maintenance with Machine Learning
We integrated a machine learning model trained on simulated historical patterns. It analyzes factors like:
- Recent signal and power trends.
- Environmental conditions.
- Small anomalies building over time.
The model predicted potential failures before they happened, enabling proactive maintenance decisions.
Dashboards and Alerts
Interactive dashboards in Databricks SQL provided a live view of tower health, risk levels, and failure predictions. Alerts notified teams when the model flagged high risk situations.
Why This POC Mattered
Our team designed this POC to mirror the complexity of real-world tower operations as closely as possible. By simulating diverse failure scenarios and environmental impacts, we could:
- Test how real time data pipelines handled realistic conditions.
- Validate the accuracy and usefulness of predictive maintenance models.
- Explore how geospatial analytics added value for operations teams.
- Demonstrate how network reliability could improve the way data, AI, and real time insights come together.
This POC was the foundation for a scalable, production ready solution that could transform mobile tower management..
Predictive Power in Action
During the simulation, our predictive model flagged towers showing early signs of risk. For example:
A group of towers in a region with simulated heavy rainfall showed elevated risk scores. The system flagged them for inspection.
Towers running low on simulated fuel triggered alerts for preventative refuelling, avoiding potential outages.
Gradual sensor anomalies linked to atmospheric conditions led the model to predict likely equipment failures in advance.
These scenarios demonstrate how predictive maintenance powered by real time data can give operations teams time to act before customers are impacted.
What’s Next
This POC lays the groundwork for a full-scale, real-world deployment. Next, our team is focused on:
- Expanding the simulation to cover more environmental and operational scenarios.
- Refining the machine learning models with even more realistic data.
- Exploring integrations with actual tower data sources.
- Continuing to enhance dashboards and geospatial insights for even faster decision making.
The ultimate goal is clear, help network operators shift from reactive firefighting to proactive, data driven maintenance.
Conclusion
IQZ has shown how Databricks, combined with machine learning and real time data, can create a smarter approach to tower operations.
This POC proves that predictive maintenance is not just possible, it is practical, scalable, and ready to deliver real business value.
We are excited to build on this foundation and help operators keep networks running reliably, even under the most challenging conditions.
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