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 messing up 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 reliability, 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 details nonfunctional needs for industrial sensors like availability, latency, throughput, scalability, and maintainability, and how to turn them into solid design goals, SLAs, and trade-offs across the 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 parts of industrial sensors, like transducers, signal conditioning, power supply and management, and enclosures, stressing reliability, noise handling, and protection in rough 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 part compares connectivity choices for industrial sensors like Ethernet, Wi-Fi, BLE, LoRaWAN, and cellular types, explaining 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 part shares cloud patterns for IoT sensor data, like ingestion points, message queues, time-series storage, APIs, and stream processing, and how to design 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 part makes clear how to gather industrial sensor needs and link them to use cases like process monitoring, predictive maintenance, safety, and rules, 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 part explains how edge devices sample, filter, and group sensor data, when to drop or compress info, and how to use simple machine learning locally 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
