Lesson 1Security fundamentals end-to-end: device identity, secure boot, secure storage, TLS, OTA signingThis part teaches full security for industrial sensors, from device identity, safe starting, safe data keeping, TLS links, OTA signing, and key handling, and how to fit these into designs without spoiling ease or steady 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 looks at device designs for industrial sensors, picking MCUs, memory plans, real-time systems, drivers, bootloaders, and building firmware for trust, testing, 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 explains steady work needs for industrial sensors like always on, quick response, growth, flow rate, and easy upkeep, turning them into real targets, promises, and choices across the whole setup.
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 main hardware parts of industrial sensors like sensors, signal fixing, power supply, and casings, stressing trust, low noise, and safety from harsh weather.
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 link choices for industrial sensors like Ethernet, Wi-Fi, BLE, LoRaWAN, cell nets, looking at reach, speed, power use, price, trust, and rules for different spots.
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 designs for IoT sensor data, like intake points, queues, time data stores, APIs, stream work, for big groups that scale safe and cheap.
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 note industrial sensor needs and match to uses like watching processes, fixing ahead, safety, rules, balancing price, power, right measure, fit.
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 shows how edge tools take samples, clean, group sensor data, drop or squeeze info, use light machine learning nearby to cut link use and wait while keeping main points.
Designing sampling rates and windowsFiltering noise and outlier rejectionAggregation, compression, and downsamplingLocal ML inference and model selectionBalancing edge and cloud responsibilities
