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IoT applications in Application Development

IoT applications in Application Development

$250.00
Toolkit Included:
Includes a practical, ready-to-use toolkit containing implementation templates, worksheets, checklists, and decision-support materials used to accelerate real-world application and reduce setup time.
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Course access is prepared after purchase and delivered via email
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What does the IoT applications in Application Development course cover?

IoT applications in Application Development is covered here in 8 modules: Architecting Scalable IoT System Topologies, Secure Device Identity and Access Management, Data Ingestion and Stream Processing Pipelines and 5 more. The outline lists 48 specific topics, opening with selecting between edge-centric, fog, and cloud-based processing based on latency requirements and data volume constraints.

How do you approach IoT applications in Application Development step by step?

The work is sequenced in 8 stages. It starts with Architecting Scalable IoT System Topologies, moves through Secure Device Identity and Access Management and Data Ingestion and Stream Processing Pipelines, and ends at Edge Application Deployment and Orchestration. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the IoT applications in Application Development course?

Module 1 is Architecting Scalable IoT System Topologies. It works through selecting between edge-centric, fog, and cloud-based processing based on latency requirements and data volume constraints., designing device-to-gateway communication patterns using MQTT, CoAP, or HTTP depending on network reliability and power constraints., implementing hierarchical device grouping to manage thousands of endpoints across geographically distributed sites. and 3 more.

How is the IoT applications in Application Development course delivered?

The IoT applications in Application Development course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the IoT applications in Application Development course cost?

The IoT applications in Application Development course is $251 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

Closely related courses: IoT Applications Toolkit, IoT Applications in Application Performance Monitoring Kit, IoT devices in Application Development, IoT applications in Big Data.

More answers: what you get with every course, refund policy, all help answers.

This curriculum spans the technical breadth of a multi-phase IoT deployment initiative, equivalent to the architecture, security, and operations work conducted in enterprise advisory engagements for industrial and commercial IoT systems.

Module 1: Architecting Scalable IoT System Topologies

  • Selecting between edge-centric, fog, and cloud-based processing based on latency requirements and data volume constraints.
  • Designing device-to-gateway communication patterns using MQTT, CoAP, or HTTP depending on network reliability and power constraints.
  • Implementing hierarchical device grouping to manage thousands of endpoints across geographically distributed sites.
  • Choosing between publish-subscribe and request-response models for command propagation in heterogeneous device fleets.
  • Integrating legacy industrial protocols (e.g., Modbus, BACnet) with modern IoT platforms through protocol translation gateways.
  • Allocating compute responsibilities across edge nodes and central services to balance real-time response and centralized analytics.

Module 2: Secure Device Identity and Access Management

  • Enrolling devices at scale using certificate-based authentication with automated PKI integration.
  • Implementing just-in-time registration (JITR) to securely onboard devices without pre-provisioned credentials.
  • Rotating device keys and certificates through automated lifecycle management to meet compliance requirements.
  • Enforcing role-based access control (RBAC) for device groups to limit command execution to authorized services.
  • Configuring mutual TLS between devices and backend services to prevent spoofing in untrusted networks.
  • Designing fallback authentication mechanisms for offline operation while maintaining auditability.

Module 3: Data Ingestion and Stream Processing Pipelines

  • Configuring message brokers (e.g., Kafka, AWS IoT Core) to handle bursty telemetry from high-frequency sensors.
  • Validating and filtering malformed payloads at ingestion to prevent pipeline contamination.
  • Implementing schema versioning for telemetry data to support backward compatibility during firmware updates.
  • Applying time-windowed aggregation to reduce data volume before long-term storage.
  • Handling out-of-order events in distributed systems using watermarking and buffering strategies.
  • Designing dead-letter queues and retry policies for failed message processing without data loss.

Module 4: Firmware Over-the-Air (FOTA) Management

  • Segmenting device fleets into canary and production groups to test firmware updates in production-like conditions.
  • Signing firmware images cryptographically to prevent unauthorized or tampered code execution.
  • Monitoring update success rates and rollback triggers using health telemetry from devices.
  • Managing bandwidth constraints by scheduling delta updates during off-peak network hours.
  • Implementing boot integrity checks to ensure firmware persistence after failed update attempts.
  • Designing rollback mechanisms that preserve device functionality when new firmware fails validation.

Module 5: Device Lifecycle and Operational Monitoring

  • Tracking device state transitions (provisioned, active, decommissioned) in a centralized registry.
  • Configuring heartbeat intervals and anomaly detection thresholds to identify unresponsive devices.
  • Correlating device logs with network telemetry to isolate connectivity issues from application faults.
  • Automating decommissioning workflows to revoke credentials and remove devices from monitoring systems.
  • Establishing thresholds for storage and memory usage alerts to preempt device failures.
  • Integrating device health metrics into existing IT operations dashboards (e.g., Splunk, Datadog).

Module 6: Regulatory Compliance and Data Governance

  • Implementing data retention policies that align with GDPR, HIPAA, or industry-specific mandates.
  • Masking personally identifiable information (PII) in telemetry before storage or analysis.
  • Documenting data lineage from sensor to dashboard for audit and regulatory reporting.
  • Enabling data subject access requests (DSARs) for IoT-generated personal data across distributed systems.
  • Encrypting data at rest and in transit using FIPS-compliant algorithms for regulated environments.
  • Conducting third-party penetration testing on device and cloud components to meet compliance standards.

Module 7: Integration with Enterprise Application Ecosystems

  • Exposing IoT data streams via REST or GraphQL APIs for consumption by ERP and CRM systems.
  • Synchronizing device metadata with CMDBs to maintain accurate asset inventories.
  • Triggering workflow automation (e.g., ServiceNow tickets) based on device fault events.
  • Mapping telemetry events to business KPIs in enterprise data warehouses for executive reporting.
  • Implementing idempotent message processing to prevent duplicate actions in downstream systems.
  • Negotiating SLAs with internal stakeholders for data freshness, availability, and incident response.

Module 8: Edge Application Deployment and Orchestration

  • Deploying containerized microservices to edge devices using Kubernetes variants (e.g., K3s, OpenYurt).
  • Managing configuration drift across edge nodes using GitOps-based deployment pipelines.
  • Reserving system resources for critical applications to prevent resource starvation during peak loads.
  • Implementing local service discovery to enable inter-application communication on edge gateways.
  • Monitoring edge application performance with lightweight agents that minimize overhead.
  • Coordinating batch processing jobs across edge clusters to optimize energy and compute usage.
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