Lesson 1Security fundamentals end-to-end: device identity, secure boot, secure storage, TLS, OTA signingThis lesson covers full end-to-end security for industrial sensors, including device identity, secure boot, secure storage, TLS, OTA signing, and key handling, and how to weave these into designs without messing up usability 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 lesson dives into embedded setups for industrial sensors, covering MCU choices, memory plans, real-time OS, drivers, bootloaders, and firmware structure for reliability, 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 lesson breaks down nonfunctional needs for industrial sensors like availability, latency, throughput, scalability, and maintainability, turning 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 lesson looks at main hardware parts for industrial sensors: transducers, signal conditioning, power supply and management, enclosures, stressing reliability, low noise, and protection in harsh environments.
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 lesson weighs up connectivity choices for industrial sensors like Ethernet, Wi-Fi, BLE, LoRaWAN, cellular options, and trade-offs in range, bandwidth, power, cost, reliability, 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 lesson shares cloud patterns for IoT sensor data: ingestion points, message queues, time-series storage, APIs, stream processing, and designing scalable, secure, cost-smart backends for big 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 lesson helps grasp industrial sensor needs and map them to cases like process monitoring, predictive maintenance, safety, regulations, balancing cost, power, accuracy, and integration 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 lesson explains edge sampling, filtering, aggregating sensor data, when to drop or compress, and local lightweight ML to cut bandwidth and delay 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
