Lesson 1Security fundamentals end-to-end: device identity, secure boot, secure storage, TLS, OTA signingThis section introduces complete security for industrial sensors, covering device identity, secure boot, secure storage, TLS, OTA signing, and key management, and shows how to fit these protections into your design without spoiling usability or uptime.
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 section covers embedded setups for industrial sensors, including picking MCUs, memory arrangement, real-time operating systems, drivers, bootloaders, and how to organise firmware for reliability, testability, and safe over-the-air updates in the field.
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 section details nonfunctional needs for industrial sensors, like availability, latency, throughput, scalability, and maintainability, and shows how to turn them into solid design goals, SLAs, and trade-offs across the entire 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 section looks at key hardware parts of industrial sensors, including transducers, signal conditioning, power supply and management, and casings, stressing reliability, noise control, and protection in tough 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 section compares connection choices for industrial sensors, like Ethernet, Wi-Fi, BLE, LoRaWAN, and mobile options, explaining trade-offs in range, bandwidth, power use, 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 section shows cloud design patterns for IoT sensor data, including intake points, message queues, time-series storage, APIs, and stream processing, and how to build scalable, secure, cost-effective 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 section makes clear how to gather industrial sensor needs and link them to use cases like process monitoring, predictive maintenance, safety, and compliance, 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 section explains how edge devices sample, filter, and summarise sensor data, when to drop or compress info, and how to run 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
