Lesson 1Security fundamentals end-to-end: device identity, secure boot, secure storage, TLS, OTA signingDis section go introduce end-to-end security for industrial sensors, covering device identity, secure boot, secure storage, TLS, OTA signing, and key management, and go explain how to put dese controls inside architecture 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, bootloaderDis section go cover embedded architectures for industrial sensors, including MCU selection, memory layout, real-time operating systems, drivers, bootloaders, and how to arrange firmware for reliability, testability, and safe over-the-air updates for field work.
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, maintainabilityDis section go detail nonfunctional requirements for industrial sensors, including availability, latency, throughput, scalability, and maintainability, and go show how to turn dem into concrete design targets, SLAs, and architectural tradeoffs across di whole stack.
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, enclosuresDis section go look at key hardware elements of industrial sensors, including transducers, signal conditioning, power supply and management, and mechanical enclosures, wid strong focus on reliability, noise performance, and environmental protection for harsh sites.
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 tradeoffsDis section go compare connectivity options for industrial sensors, including Ethernet, Wi-Fi, BLE, LoRaWAN, and cellular variants, and go explain tradeoffs in range, bandwidth, power, cost, reliability, and regulatory constraints for different deployment scenarios.
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, APIsDis section go present cloud architecture patterns for IoT sensor data, including ingestion endpoints, message queues, time-series storage, APIs, and stream processing, and go explain how to design scalable, secure, and cost-efficient backends for large 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 casesDis section go clear how to capture industrial sensor requirements and map dem to use cases, including process monitoring, predictive maintenance, safety, and regulatory needs, while balancing cost, power, accuracy, and integration constraints.
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 inferenceDis section go explain how edge devices sample, filter, and aggregate sensor data, when to throw away or compress information, and how to apply lightweight machine learning inference locally to reduce bandwidth and latency while keeping key insights.
Designing sampling rates and windowsFiltering noise and outlier rejectionAggregation, compression, and downsamplingLocal ML inference and model selectionBalancing edge and cloud responsibilities
