Lesson 1Security fundamentals end-to-end: device identity, secure boot, secure storage, TLS, OTA signingThis section introduces end-to-end security for industrial sensors, covering device identity, secure boot, secure storage, TLS, OTA signing, and key management, and explains how to integrate these controls into architecture without breaking usability or uptime.
Provisioning device identity and certificatesSecure boot chains and firmware integritySecure storage of keys and secretsTLS configuration for constrained devicesSigned OTA updates and rollback safetyLesson 2Embedded systems architecture: MCUs, real-time OS, drivers, bootloaderThis section covers embedded architectures for industrial sensors, including MCU selection, memory layout, real-time operating systems, drivers, bootloaders, and how to structure firmware for reliability, testability, and safe over-the-air updates in the field.
Choosing MCU families and peripheralsMemory maps, flash, and RAM planningRTOS tasks, scheduling, and prioritiesDriver abstraction and hardware isolationBootloaders and firmware update flowsLesson 3Operational nonfunctional requirements: availability, latency, scalability, throughput, maintainabilityThis section details nonfunctional requirements for industrial sensors, including availability, latency, throughput, scalability, and maintainability, and shows how to translate them into concrete design targets, SLAs, and architectural tradeoffs across the stack.
Defining SLAs and SLOs for sensor fleetsModeling latency and end-to-end timingThroughput, batching, and backpressureDesigning for availability and failoverMaintainability, observability, and supportLesson 4Sensor hardware components: transducers, conditioning, power, enclosuresThis section examines key hardware elements of industrial sensors, including transducers, signal conditioning, power supply and management, and mechanical enclosures, with emphasis on reliability, noise performance, and environmental protection in harsh sites.
Selecting transducers for target phenomenaSignal conditioning and analog front endsPower budgeting and energy harvestingBattery life, regulators, and protectionEnclosure design and environmental sealingLesson 5Connectivity options: Ethernet, Wi-Fi, BLE, LoRaWAN, cellular (NB-IoT, LTE-M) and tradeoffsThis section compares connectivity options for industrial sensors, including Ethernet, Wi-Fi, BLE, LoRaWAN, and cellular variants, and explains tradeoffs in range, bandwidth, power, cost, reliability, and regulatory constraints for different deployment scenarios.
Ethernet and industrial fieldbus integrationWi-Fi for high-throughput local networksBLE for commissioning and local accessLoRaWAN and sub-GHz long-range linksNB-IoT and LTE-M cellular deploymentsLesson 6Cloud architecture patterns for IoT: ingestion, message queues, time-series storage, APIsThis section presents cloud architecture patterns for IoT sensor data, including ingestion endpoints, message queues, time-series storage, APIs, and stream processing, and explains how to design scalable, secure, and cost-efficient backends for large fleets.
Designing ingestion endpoints and gatewaysMessage queues, topics, and routingTime-series databases and retentionAPIs for data access and integrationStream processing and alert pipelinesLesson 7Understanding industrial sensor requirements and typical use casesThis section clarifies how to capture industrial sensor requirements and map them to use cases, including process monitoring, predictive maintenance, safety, and regulatory needs, while balancing cost, power, accuracy, and integration constraints.
Eliciting stakeholder and field requirementsDefining accuracy, range, and sampling needsEnvironmental and regulatory constraintsPower, cost, and lifetime trade studiesTranslating use cases into specs and KPIsLesson 8Edge processing and data reduction: sampling, filtering, aggregation, local ML inferenceThis section explains how edge devices sample, filter, and aggregate sensor data, when to discard or compress information, and how to apply lightweight machine learning inference locally to reduce bandwidth and latency while preserving key insights.
Designing sampling rates and windowsFiltering noise and outlier rejectionAggregation, compression, and downsamplingLocal ML inference and model selectionBalancing edge and cloud responsibilities
