Lesson 1Security fundamentals end-to-end: device identity, secure boot, secure storage, TLS, OTA signingThis part brings in full security for industrial sensors, covering device identity, secure boot, secure storage, TLS, OTA signing, and key handling, and shows how to fit these into your setup without messing up ease of use or keeping things running.
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 part goes over embedded setups for industrial sensors, including picking MCUs, memory plans, real-time OS, drivers, bootloaders, and how to organise firmware for reliability, testing, and safe updates over the air right where it's installed.
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 part breaks down nonfunctional needs for industrial sensors, like availability, latency, throughput, scalability, and maintainability, and demonstrates how to turn them into solid design goals, SLAs, and trade-offs across the whole system.
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 part looks closely at main hardware parts of industrial sensors, including transducers, signal conditioning, power supply and management, and casings, stressing reliability, handling noise, and protection from harsh conditions out there.
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 part weighs up connectivity choices for industrial sensors, like Ethernet, Wi-Fi, BLE, LoRaWAN, and cellular types, and lays out trade-offs in range, bandwidth, power use, cost, reliability, and rules for various setup situations.
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 part shares cloud patterns for IoT sensor data, covering intake points, message queues, time-series storage, APIs, and stream handling, and explains building scalable, secure, cost-smart backends for big groups of devices.
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 part makes clear how to gather industrial sensor needs and link them to common uses, like monitoring processes, predictive upkeep, safety, and rules, while juggling cost, power, accuracy, and fitting in 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 part explains how edge devices take samples, filter, and group sensor data, when to drop or squeeze info, and how to run simple machine learning right there to cut bandwidth and delay while keeping important insights.
Designing sampling rates and windowsFiltering noise and outlier rejectionAggregation, compression, and downsamplingLocal ML inference and model selectionBalancing edge and cloud responsibilities
