Overview

This concept extends log-file analysis by deploying a sensor network across critical assets, shifting from scheduled checks to continuous condition awareness. Edge devices capture and transmit event-based data on vibration, temperature, power quality, and environmental conditions. Within an RCM framework, this reduces the need for manual inspections, turning many inspection tasks into automated, evidence-driven processes. The analysis layer converts raw readings into asset performance insights that guide both human operators and automated systems, enabling more accurate maintenance decisions and more reliable service outcomes. Intelligence at the edge and secure event pipelines ensure developing defects are identified and mitigated before they cause failure.

While large vendors promote expensive consultancy and pre-packaged “black-box” IoT solutions, it is not difficult to build an effective sensor network with a little learning, some trial and error, and the right guidance. Affordable single-board processors, off-the-shelf sensors, and low-cost IoT communication modules are widely available and relatively straightforward to engineer. This approach allows organisations to develop capability in-house, control their own data, and focus investment on what really matters: extracting insight that improves asset performance and maintenance outcomes.

Architecture

• Edge Devices — modular sensor nodes positioned where measurements are needed, capturing and transmitting data in real time.
• Connectivity — LTE, fibre, Wi-Fi, and LoRaWAN can be used depending on data volume, power availability, location, and existing infrastructure.
• Ingestion — cloud APIs receive authenticated data and store it in time-series and NoSQL databases, providing flexibility to handle any data format.
• Processing — condition rules and potentially ML inference layer translate raw readings into health indicators, alarms, and work orders.
• Visualisation — dashboards federate the live data into condition, performance, and control room views.

The sensors I have deployed so far are typically built around Arduino or ESP controllers combined with off-the-shelf sensors, enclosed in IP65 housings for resilience. Data is transmitted either via Wi-Fi or through IoT-dedicated SIMs and components. The code, written in C++, provides powerful edge-processing capabilities before transmission, reducing noise and enriching the data at source. In the cloud (AWS), I run an application called Spark comprised of a Node.js web service and MongoDB with basic security controls. MongoDB is configured as a time-series database with documents automatically expiring after two weeks, ensuring lean data storage. For visualisation, I have developed custom charts as well as integrated the dataset into Power BI for flexible analysis and reporting.

The chart below is a live example of the system in operation. Three temperature sensors installed in my home transmit data to Spark every ten minutes, providing a continuous feed that demonstrates real-world performance.

Live Temperature Readings

Implementation Considerations

• Systems Engineering - systems engineering provides the structured approach that ties together requirements, design, integration, and validation, ensuring the sensor network functions as a coherent, reliable, and secure whole.
• Site Installation — generic site installation designs provide standardised layouts for power, communications, enclosures, and sensor interfaces, enabling repeatable, reliable, and cost-effective deployments across multiple locations.
• Data Management - ensuring sensor readings are securely collected, stored, and structured so they remain accurate, accessible, and actionable throughout their lifecycle.
• Cyber Security — protects the sensor network through secure devices, encrypted communications, and controlled access, ensuring data integrity and system resilience.
• Maintenance — defining the maintenance requirements, including calibration, ensures the sensor network remains accurate, reliable, and sustainable throughout its lifecycle.
• Change Management — technicians and controllers need training and support to interpret new data flows and act on the insights.

Final Thoughts

Implementing a sensor network is as much about planning and governance as it is about technology. Success depends on establishing a clear business case, defining functional and system requirements, and designing with the whole lifecycle in mind — from installation and data management to cyber security and maintenance. By standardising site designs, clarifying data strategies, and embedding calibration and maintenance requirements upfront, organisations can ensure their networks deliver consistent, reliable, and actionable insights. Ultimately, a sensor network should not just collect data, but provide trusted information that supports decision-making, drives efficiency, and strengthens long-term asset performance.