Discover the range of services offered by Microsoft’s Azure platform for the Internet of Things, including IoT Hub and Digital Twins.
The Internet of Things (IoT) has pervaded homes and organisations, connecting devices to enhance daily life and productivity, respectively. IoT architecture is based on three major components.
Edge client: Collates data from connected devices and sends to the gateway.
Gateway: Validates, identifies and classifies data, and forwards it to the cloud platform for storage and processing.
Cloud: Receives data blocks from the gateway, stores them, and transfers them for processing.
 The Internet of Everything (IoE) is the amalgamation of IoT (Internet of Things), IoD (Internet of Digital) and IoH (Internet of Humans) to create a connection between people, smart devices and organisations. Internet of Digital is the group of data service applications that handle social networks, media services, analytics on data, and cloud platforms (simply termed as SMAC). Internet of Humans deals with smart devices that interact with humans (like smart bands and medical monitors) to monitor and collect various datasets from human interaction. These devices can be used for remote patient monitoring using cloud-based platforms or remote health advisory solutions through contact centre AI services. Internet of Everything (IoE) is an emerging trend in new age architecture and requires costly infrastructure, but is touted to become popular soon. |
Building security into IoT architecture
Since many third party and native services are involved in IoT architecture, security design plays a vital role. This is done at four levels. Infrastructure security manages security at the physical device level using device controllers, edge, gateway and cloud. Platform security manages security at runtime in edge and gateway to handle access control and protect data privacy requirements. Network security manages and monitors data encryption during network transfers — a two-way handshake ensures secured clients are connected, and security logs are audited for anomalous behaviour, if any. Application security manages native and third-party services handling data, and helps protect IoT services from cyberattacks.
Potential challenges in IoT security include data privacy compromised when using third-party services, unprotected data storage, lack of role based access control (RBAC), weak cloud architecture to protect public services, and insecure or outdated IoT device components being exposed to cyberattacks.
Guidelines for developing a security strategy for IoT applications
Some of the core principles for developing a security strategy for IoT applications and infrastructure services are:
- Data encryption at rest to protect data when it is stored in the data storage or application storage facility. This could be through encryption or device level cipher protection.
- Data encryption in transit or in motion to protect data when transporting it through a communication protocol. This could be through a secured tunnel or safeguarding network services using a secured protocol like SSL, TLS, FTPS, etc.
- Device identity to protect unauthorised devices being connected to the IoT network of services — each device must have a unique identity. This will be used to authenticate (verify device access) and authorise (permit device connection) in the IoT ecosystem of connected devices.
- Using a token-based authentication and authorisation framework like OAuth 2.0 to use API based services for connecting and communicating with IoT applications.
- Policy management and role-based access control to enable resource level access management for all the connected services.
- Two-way protection in all services through certificate-based authentication like Public Key Infrastructure (PKI) and Organization based Access Control (OrBAC) for capability-based access control.
Azure IoT Hub and Digital Twins
All electronic devices in a smart home are connected in a controllable network. This allows users to interact with their homes through these connected devices from anywhere in the world. The Internet of Things (IoT) exponentially expands the web of devices connected to the Internet and allows them to talk to each other. Automated handling of electronic devices existed long before IoT and smart devices came to life, but IoT enables smart functions like voice enabled control and devices talking to each other (e.g., Alexa, Siri, Google Home) using low-cost wireless protocols including Wi-Fi and Bluetooth, and now ZigBee and Z-Wave protocols.
ZigBee and Z-Wave protocols are ideal for smart devices and smart home architecture as they are optimised for low data transmission and low power consumption as compared to Bluetooth or Wi-Fi protocols. Also, they can be transmitted in closed areas like smart homes covered with walls and furniture.
In a smart home architecture, smart devices are connected with a wireless network through a central hub which can be controlled by mobiles and tablets. Azure IoT Hub is such a gateway transmission service for connected devices in a cloud-based system.
CAD or computer aided design was widely used earlier for product development. Now, with digital architecture and device management in use, digital twins help to create dynamic, flexible, configurable and real-time extensible simulators. With IoT-based application designs, they play a vital role in product simulation, testing and inspection.
Microsoft Azure has introduced a sophisticated Digital Twin platform for such simulated modelling, based on a flexible open modelling language called Digital Twins Definition Language (DTDL). Its construct is the JSON-LD standard, and it can be tailored to any use case as needed.
Azure Digital Twins is more than a product simulator and can be used for service or process simulation. It utilises IoT reference architecture including Azure IoT Hub and other Azure functions and REST API services from the Azure platform. Azure Digital Twins can flexibly integrate with time series insights, storage, data lake services, or analytics services to prepare a complete (unified) simulated service or environment for product engineering services.
A lot of features are being included in Azure Digital Twins to build service simulation on-the-fly by combining other powerful features of the Azure platform.
Industrial use cases of Azure IoT services
- Predictive maintenance
Real-time monitoring: Azure IoT enables continuous monitoring of equipment health through sensors and IoT devices. This allows for the collection of data such as temperature, vibration, and pressure.
Failure prediction: By applying machine learning algorithms, companies can predict equipment failures before they happen, reducing downtime and maintenance costs.
Cost efficiency: Proactive maintenance scheduling helps in optimising resource allocation and extending the lifespan of machinery.
- Supply chain optimisation
Inventory management: IoT devices provide real-time insights into inventory levels, helping businesses manage stock efficiently and reduce excess inventory.
Logistics tracking: Azure IoT facilitates the tracking of shipments, ensuring timely deliveries and improved logistics management.
Demand forecasting: By analysing data trends, companies can forecast demand more accurately, optimising production schedules and reducing waste.
- Energy management
Smart grids: Azure IoT supports the development of smart grids that adjust energy distribution based on real-time demand, improving efficiency and reducing costs.
Consumption analysis: Industries can monitor energy consumption patterns and identify areas for improvement, leading to significant cost savings.
Sustainability goals: IoT solutions help industries meet sustainability targets by optimising energy use and reducing carbon footprints.
- Asset tracking
Real-time location: Azure IoT enables real-time tracking of assets across facilities, improving asset utilisation and reducing losses.
Condition monitoring: Sensors can monitor the condition of assets, ensuring they are maintained properly and reducing the risk of damage.
Operational efficiency: Improved asset tracking leads to enhanced operational efficiency and reduced operational costs.
- Quality control and assurance
Automated inspections: IoT devices can automate the inspection process, ensuring consistent quality checks and reducing human error.
Data-driven insights: By analysing data from production lines, companies can identify defects early and take corrective actions promptly.
Continuous improvement: Ongoing data collection and analysis support continuous improvement initiatives, enhancing product quality over time.
- Remote monitoring and management
Facility management: Azure IoT allows for the remote monitoring of facilities, enabling quick responses to issues without the need for onsite personnel.
Operational visibility: Enhanced visibility into operations helps in identifying inefficiencies and optimising processes.
Scalability: IoT solutions can be scaled to include additional devices and sensors as business needs grow, providing flexibility and adaptability.
- Enhanced safety and compliance
Workplace safety: IoT devices can monitor environmental conditions and alert workers to potential hazards, improving workplace safety.
Regulatory compliance: Automated data collection and reporting ensure compliance with industry regulations, reducing the risk of penalties.
Incident response: Real-time alerts and monitoring enable swift incident response, minimising the impact of safety breaches.
How to develop IoT solutions with Azure
Developing data-intensive IoT solutions with Microsoft Azure involves a strategic approach to harnessing the power of connected devices and advanced analytics. It starts by defining clear objectives and identifying key data sources, such as sensors and IoT devices, to ensure comprehensive data collection. You can utilise Azure IoT Hub to securely connect, manage, and ingest data from various endpoints.
- Implement robust data storage solutions like Azure Data Lake or Azure Blob Storage to handle large volumes of structured and unstructured data efficiently. Leverage Azure Stream Analytics for real-time data processing and Azure Machine Learning to derive predictive insights, enhancing decision-making capabilities.
- Ensure data security and compliance by integrating Azure Security Center and Azure Active Directory for identity management and threat protection. Optimise performance and scalability with Azure Functions and Azure Logic Apps, enabling seamless integration and automation across services.
- Finally, visualise insights using Power BI, providing stakeholders with interactive and actionable dashboards.
By strategically combining these Azure services, businesses can develop scalable data-driven IoT solutions that drive innovation and operational excellence.
Scalable event-driven architecture (EDA) using Azure Event Grid
Event-driven architecture (EDA) is an architectural pattern commonly used for event processing, enrichment, consumption and reaction to events. Asynchronous message processing can be achieved using EDA at scale. It can also help to broadcast events to multiple consumers (event handlers) at the same time to execute the triggered action.
In cloud platforms like Azure, enterprise application architecture requires scalable, high performing event driven engines like Azure Event Grid. The latter can simplify event delivery, build reliable cloud applications, and enable inter-service communication as a seamless experience.
In an Azure event-driven architecture, event producers can be an IoT hub, machine learning service, cloud event sources, Azure Service Bus, or Azure Blob Storage, to name a few. These events are broadcast to multiple consumers registered with an event grid. These event consumers can be an Azure cloud function, logic apps, event hubs, storage queues or application services like the Azure Automation service.
Each event handler that receives an event and processes it needs to subscribe to event topics. The topic is the endpoint where event sources send/publish the events. When the event grid receives the events, it finds the list of subscribed handlers to route events, and triggers the event handling action appropriately.
An event grid has advanced event filters which make sure that only relevant events are received by event handlers. Sending events to more than one event handler is called fan-out. An event grid makes multiple copies of events to as many handlers as needed.
Azure Event Grid is highly reliable since it ensures delivery of events even when there are high-volume workloads — a high throughput of events is managed at scale. Also, it is relatively cheaper as compared to a persistent queueing mechanism, which comes at a fixed cost. Azure Event Grid enables pay-per-event for the events handled. It is generally used for automation of operations like infrastructure workflow-based provisioning, application integration like reporting and logging, and event handling with serverless architecture like Azure Functions.
The scope for Azure IoT applications
The future of Azure IoT applications is bright, with significant advancements on the horizon. Key areas include enhanced predictive analytics through advanced machine learning, enabling proactive decision-making and automation. The expansion of edge computing will allow real-time data processing and improved security by handling data locally.
Integration with AI and blockchain will provide sophisticated insights and secure transactions. In sustainability, Azure IoT will optimise energy use and facilitate environmental monitoring. Healthcare will benefit from remote patient monitoring and smart medical devices, improving patient care. Smart cities will leverage IoT for infrastructure management and public safety, enhancing urban living. Industrial automation will see smart manufacturing and optimised supply chains, boosting efficiency and reducing costs.
Overall, Azure IoT is set to drive innovation and efficiency across various sectors, addressing global challenges with cutting-edge technology.
