Lesson 1Security fundamentals end-to-end: device identity, secure boot, secure storage, TLS, OTA signingThis part introduces full security for industrial sensors, covering device identity, secure boot, secure storage, TLS, OTA signing, and key handling, and how to fit these into designs without spoiling ease of use or reliability.
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 looks at embedded setups for industrial sensors, including picking MCUs, memory plans, real-time OS, drivers, bootloaders, and organising firmware for steadiness, testing ease, and safe field updates.
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 details nonfunctional needs for industrial sensors like availability, latency, throughput, scalability, and upkeep, showing 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 checks key hardware bits for industrial sensors, like transducers, signal conditioning, power supply and handling, and casings, stressing reliability, low noise, and protection in rough outdoor spots.
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 mobile options, explaining trade-offs in reach, speed, power use, cost, steadiness, and rules for various setups.
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, like intake points, message queues, time-series storage, APIs, and stream handling, and how to craft scalable, safe, 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 process watching, upkeep prediction, safety, and rules, while juggling cost, power, accuracy, and fitting-in limits.
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 gear samples, filters, and sums up sensor data, when to drop or squeeze info, and using simple machine learning on-site to cut bandwidth and wait times while keeping vital insights.
Designing sampling rates and windowsFiltering noise and outlier rejectionAggregation, compression, and downsamplingLocal ML inference and model selectionBalancing edge and cloud responsibilities
