OTA Architecture

 

                                    Photo by Pixabay

Developing an end-to-end Over-the-Air (OTA) update architecture for IoT devices in equipment like

escalators and elevators involves several components. This architecture ensures that firmware updates

can be delivered seamlessly and securely to the devices in the field. Here's an outline of the architecture

with explanations and examples:

1. Device Firmware: - The IoT devices (escalators, elevators) have embedded firmware that needs to be updated over the air. - Example: The firmware manages the operation of the device, and we want to update it to fix bugs or

add new features. 2. Update Server: - A central server responsible for managing firmware updates and distributing them to the devices. - Example: A cloud-based server that hosts the latest firmware versions. 3. Update Package: - The firmware update packaged as a binary file. - Example: A compressed file containing the updated firmware for the escalator controller. 4. Device Management System: - A system to track and manage IoT devices, including their current firmware versions. - Example: A cloud-based device management platform that keeps track of each escalator's firmware

version. 5. Communication Protocol: - A secure and efficient protocol for communication between the devices and the update server. - Example: MQTT (Message Queuing Telemetry Transport) for lightweight and reliable communication. 6. Authentication and Authorization: - Security mechanisms to ensure that only authorized devices can receive and install firmware updates. - Example: Token-based authentication, where devices need valid tokens to request updates. 7. Rollback Mechanism: - A mechanism to rollback updates in case of failures or issues. - Example: Keeping a backup of the previous firmware version on the device. 8. Deployment Strategy: - A strategy to deploy updates gradually to minimize the impact on operations. - Example: Rolling deployment where updates are deployed to a subset of devices first, and if successful,

expanded to others. 9. Update Trigger: - Mechanism to initiate the update process on devices. - Example: A scheduled time for updates or an event-triggered update based on certain conditions. 10. Logging and Monitoring: - Comprehensive logging and monitoring to track the update process and identify any issues. - Example: Logging each update attempt, monitoring device status during updates. 11. Edge Computing (Optional): - For large-scale deployments, edge computing can be used to distribute updates more efficiently. - Example: Edge devices in the facility can act as local update servers, reducing the load on the central

server. 12. Network Considerations: - Ensuring that the devices have reliable and secure connectivity for downloading updates. - Example: Using secure protocols like HTTPS for update downloads. Explanation: The architecture ensures that firmware updates can be securely and efficiently delivered to IoT devices.

The update process is orchestrated, logged, and monitored to maintain the reliability and security of the

devices in the field.

The deployment strategy and rollback mechanism add resilience to the update process. Example Scenario: Let's consider an example where an escalator management company wants to update the firmware of all

escalators to improve energy efficiency. The central server hosts the updated firmware, and the device

management system tracks the current firmware version on each escalator. Using a secure communication

protocol, the escalators request updates, and the deployment strategy ensures a smooth transition. If any

issues arise during the update, the rollback mechanism reverts the escalator to the previous firmware

version.

Today, industrial companies seek to ingest, store, and analyze IoT data closer to the point of generation.

This enhances predictive maintenance, improves quality control, ensures worker safety, and more.

Industrial Edge computing, focusing on stationary edge gateways in industrial environments, plays a

crucial role in connecting Operational Technology (OT) systems with the cloud. This whitepaper outlines

design considerations for industrial IoT architectures using the industrial edge, addressing low latency,

bandwidth utilization, offline operation, and regulatory compliance. The edge gateway serves as an

intermediary processing node, integrating industrial assets with the AWS Cloud, and addressing security

challenges for less-capable OT systems without authentication, authorization, and encryption support.

The following section examines key imperatives in edge computing. This architecture provides a structured approach to managing OTA updates for IoT devices, ensuring they

stay up-to-date, secure, and efficient.

Below are a few nice articles about Azure, AWS for IoT and OTA

Azure IoT

AWS IoT

How Digital Twin can help to solve Traffic Congestion

 

                                Traffic jam in Bengaluru, photo by BBC

Traffic Congestion and Commuter Suffering: A Growing Dilemma

In today's fast-paced world, traffic congestion has become an all too familiar adversary for commuters across the globe. The once-dreaded morning and evening rush hours have transformed into daily ordeals, as millions of people find themselves trapped in a labyrinth of slow-moving vehicles, their daily routines marred by frustrating delays. This relentless gridlock not only tests the patience of commuters but also takes a substantial toll on their quality of life.

The consequences of traffic congestion extend far beyond mere inconvenience. It contributes to increased fuel consumption, leading to higher emissions of greenhouse gases and worsening air quality, thereby posing serious environmental concerns. Moreover, it results in lost productivity, economic setbacks, and a significant drain on the mental and physical well-being of those caught in its grip.

In this era of technological innovation, addressing the issue of traffic congestion has become an urgent imperative. It is a challenge that calls for innovative solutions, collaboration among various stakeholders, and the harnessing of cutting-edge technologies to transform the way we navigate our daily commutes. This article explores how a holistic approach to traffic management, bolstered by real-time connectivity, proactive government involvement, and weather forecasting integration, can be a beacon of hope in the quest to alleviate the suffering caused by traffic congestion. Let's embark on a journey to discover the potential of a smarter, more efficient traffic management system—one that empowers commuters and helps us reclaim our precious time from the clutches of traffic woes.

Reducing traffic congestion using digital twin technology involves several steps and can significantly improve traffic management. Here's a step-by-step breakdown of how digital twins can help alleviate traffic congestion:

Step 1: Data Collection and Integration

- Data Sources: Gather data from various sources, including traffic cameras, sensors, GPS devices, and mobile apps.

- Data Integration: Integrate data from these sources into a unified platform or system. This includes real-time traffic data, historical traffic patterns, weather conditions, and events data.

Step 2: Creating a Digital Twin

- Virtual Replica: Build a digital twin or virtual replica of the road network, including streets, intersections, highways, and public transportation systems.

- Data Mapping: Overlay real-time data onto the digital twin to create a dynamic representation of the current traffic conditions.

Step 3: Traffic Simulation

- Traffic Modeling: Use advanced traffic modeling and simulation algorithms to predict traffic patterns based on historical data, events, and real-time information.

- What-If Scenarios: Simulate different scenarios, such as accidents, road closures, or special events, to anticipate their impact on traffic flow.

Step 4: Predictive Analytics

- Predictive Algorithms: Employ machine learning and predictive analytics to forecast future traffic congestion and identify potential trouble spots.

- Anomaly Detection: Detect unusual traffic behavior or incidents that may lead to congestion.

Step 5: Traffic Management and Optimization

- Traffic Control: Implement intelligent traffic management systems that can adjust traffic signals, lane directions, and signage in real-time based on the digital twin's insights.

- Dynamic Routing: Provide drivers with real-time traffic information and suggest alternative routes to divert traffic from congested areas.

Step 6: Communication and Alerts

- Public Communication: Share real-time traffic information and updates with the public through various channels, such as mobile apps, websites, and electronic signs.

- Emergency Alerts: Send alerts about accidents, road closures, or severe weather conditions to drivers to prevent them from entering congested areas.

Step 7: Continuous Monitoring and Feedback Loop

- Real-Time Monitoring: Continuously monitor traffic conditions and update the digital twin with new data.

- Feedback Loop: Analyze the effectiveness of traffic management interventions and adjust strategies accordingly.

Step 8: Collaboration

- Government Agencies: Collaborate with local government agencies, law enforcement, and transportation authorities to coordinate traffic management efforts.

- Private Sector: Partner with private companies that provide navigation and ride-sharing services to leverage their data and resources.

Step 9: Scalability and Future Expansion

- Scalability: Ensure that the digital twin infrastructure can handle increased data volumes and expand to cover larger geographic areas.

- Innovation: Stay up-to-date with advancements in AI, IoT, and data analytics to continuously improve traffic management.

Step 10: Public Awareness and Education

- Awareness Campaigns: Educate the public about the benefits of using digital twin-powered traffic management systems and encourage their active participation in reducing congestion.

- Behavioral Changes: Promote alternative modes of transportation, carpooling, and public transit to reduce the number of vehicles on the road.

Implementing a digital twin for traffic management involves a combination of technology, data analysis, and proactive decision-making. By following these steps and embracing innovation, cities and transportation authorities can work towards significantly reducing traffic congestion and improving the overall quality of urban life.

This system can connect real time in all the following ways.

Certainly, here are the details for each of the points:

1. Real-Time Commuter Connectivity:

   - GPS Integration: Commuters can connect to the system in real-time through GPS-enabled mobile apps. These apps provide real-time traffic data and navigation instructions.

   - Offline Support: To address issues like GPS signal loss during heavy rain or tunnels, apps can store offline maps and use dead reckoning algorithms to estimate the user's position.

   - Predictive Navigation: The system can provide commuters with accurate predictive directions before they start their commute by analyzing historical data, current traffic conditions, and expected congestion.

2. Government Collaboration:

   - Data Sharing: Government organizations and departments can connect to the system by sharing traffic data, road closure information, and construction updates.

   - Traffic Management: Traffic authorities can use the system to remotely control traffic signals, access real-time traffic data for proactive decision-making, and deploy law enforcement where needed.

   - Emergency Response: The system can be integrated with emergency services to quickly respond to accidents and medical emergencies in congested areas.

3. Weather Forecast Integration:

   - Weather Data Sources: Connect the system to various weather data sources, including meteorological agencies, weather satellites, and IoT weather sensors.

   - Weather Alerts: Integrate weather alerts into the system to provide real-time information on weather-related road hazards such as heavy rain, snow, ice, and flooding.

   - Traffic Adaptation: The system can use weather forecasts to predict how weather conditions will impact traffic and provide alternative routes and warnings to commuters.

4. Additional Considerations:

   - Environmental Data: Consider integrating air quality monitoring sensors and data to help commuters make informed decisions based on air quality.

   - Public Transport Integration: Integrate public transport schedules, real-time bus/train tracking, and fare information into the system to encourage the use of alternative modes of transport.

   - User Education: Develop campaigns to educate commuters about the benefits of the system, how to use it effectively, and encourage responsible driving behaviours.

   - Privacy and Data Security: Implement robust privacy measures to protect user data, ensuring that location information is anonymized and not misused.

   - Scalability: Plan for system scalability to accommodate growing user numbers and data volumes as more commuters join the network.

   - Feedback Mechanism: Create channels for commuters to provide feedback, report issues, and suggest improvements to the system.

By addressing these points, a comprehensive traffic management system can effectively connect commuters, government organizations, weather forecasting agencies, and other stakeholders to collaboratively tackle traffic congestion and enhance overall transportation experiences.

Real-Time Data With IoT & MQTT

 

unplush

For real time data analysis we need real time data to be collected. I would like to share with you this small project for capturing real time temperature and humidity data from sensors and showing that in a local dashboard.

You can connect to the real time data analysis dashboard as well. If you are interested in how to can read my other article here.

We will collect data by temperature and humidity sensor DHT11. Which is very small and cheap. However, work perfectly.

We will be using a microcontroller Arduino with ESP8266 wifi on board. You can use Uno+WiFi R3 AtMega328p+NodeMSU ESP8266 8mb Memory USB-TTL CH340G Compatible for Arduino UNO

Also, we need a local or free MQTT broker. And node-red. Node-RED is a flow-based development tool for visual programming. It makes it easy to wire together different hardware devices, APIs, and online services to create applications. Node-RED is a great tool for developers who want to create IoT applications quickly and easily.

Here are some of the things you can do with Node-RED:

* Control hardware devices, such as lights, sensors, and motors.

* Connect to APIs, such as weather data, stock prices, or social media feeds.

* Send and receive data from online services, such as Google Cloud Platform or Amazon Web Services.

* Create dashboards and visualizations to display data.

* Automate tasks and workflows.

Node-RED is a powerful tool that can be used to create a wide variety of applications. If you are interested in IoT development, Node-RED is a great place to start.

Here are some of the benefits of using Node-RED:

• Easy to learn and use. Node-RED is a visual programming tool, which means that you don’t need to know how to code to use it. You can simply drag and drop nodes together to create your application.
• Flexible. Node-RED is very flexible and can be used to create a wide variety of applications.
• Extensible. There is a large community of Node-RED users who have created a wide range of nodes that can be used in your applications.

Free and open source. Node-RED is free and open source software, which means that it is available to everyone.

MQTT stands for Message Queuing Telemetry Transport. It is a lightweight messaging protocol that is commonly used for Internet of Things (IoT) applications. MQTT is a publish/subscribe protocol, which means that devices can publish messages to topics, and other devices can subscribe to topics to receive messages.

To run MQTT locally, you will need to install an MQTT broker. An MQTT broker is a software application that manages the communication between devices that are publishing and subscribing to topics. There are many different MQTT brokers available, both open source and commercial. Some popular MQTT brokers include:

* Mosquitto

* Eclipse Paho

* HiveMQ

* ActiveMQ

Once you have installed an MQTT broker, you can start publishing and subscribing to topics. To publish a message, you need to specify the topic name and the message payload. The message payload is the actual data that you want to send. To subscribe to a topic, you simply specify the topic name.

Here is an example of how to publish a message to a topic using the Mosquitto broker:

mosquitto_pub -h localhost -t mytopic -m "Hello world!"

This command will publish the message “Hello world!” to the topic “mytopic” on the MQTT broker running on the localhost.

Here is an example of how to subscribe to a topic using the Mosquitto broker:

mosquitto_sub -h localhost -t mytopic

This command will subscribe to the topic “mytopic” on the MQTT broker running on the localhost. When a message is published to the topic “mytopic”, the mosquitto_sub command will print the message payload to the console.

MQTT is a simple and lightweight messaging protocol that is well-suited for IoT applications. It is easy to install and use, and it can be used to connect a wide variety of devices.

Hope you already installed the Arduino IDE in your system. Add the following libraries from the IDE.

• ESP8266WiFi
• PubSubClient
• DHT

Connect to your microcontroller and port in IDE. Create a new sketch with this code do not forget to update your wifi username, password etc.

Upload the code to your microcontroller.

Run the mqtt server.

Run node-red to create two pub of temperature and humidity. And two subscribers for node-red dashboard. Below is the node-red flow chart for this application.

Temperature node:

{
        "id": "8af3f91e138dfc7f",
        "type": "mqtt in",
        "z": "7c0214a9d7501627",
        "name": "temp mqtt",
        "topic": "sensor/temperature",
        "qos": "0",
        "datatype": "auto-detect",
        "broker": "89cb5cc4b9d9ccc2",
        "nl": false,
        "rap": true,
        "rh": 0,
        "inputs": 0,
        "x": 160,
        "y": 120,
        "wires": [
            [
                "91b12e2e350dc914",
                "fe8591a0be99386b"
            ]
        ]
    },
    {
        "id": "89cb5cc4b9d9ccc2",
        "type": "mqtt-broker",
        "name": "remote broker",
        "broker": "broker.emqx.io",
        "port": "1883",
        "clientid": "dhiraj",
        "autoConnect": true,
        "usetls": false,
        "protocolVersion": "4",
        "keepalive": "60",
        "cleansession": true,
        "birthTopic": "test",
        "birthQos": "0",
        "birthPayload": "",
        "birthMsg": {},
        "closeTopic": "",
        "closeQos": "0",
        "closePayload": "",
        "closeMsg": {},
        "willTopic": "",
        "willQos": "0",
        "willPayload": "",
        "willMsg": {},
        "userProps": "",
        "sessionExpiry": ""
    }
]

Humidity node:

{
        "id": "62cd8fffc101abf7",
        "type": "mqtt in",
        "z": "7c0214a9d7501627",
        "name": "humidity mqtt",
        "topic": "sensor/humidity",
        "qos": "0",
        "datatype": "auto-detect",
        "broker": "89cb5cc4b9d9ccc2",
        "nl": false,
        "rap": true,
        "rh": 0,
        "inputs": 0,
        "x": 170,
        "y": 260,
        "wires": [
            [
                "91b12e2e350dc914",
                "bb13832944ebe60a"
            ]
        ]
    },
    {
        "id": "89cb5cc4b9d9ccc2",
        "type": "mqtt-broker",
        "name": "remote broker",
        "broker": "broker.emqx.io",
        "port": "1883",
        "clientid": "dhiraj",
        "autoConnect": true,
        "usetls": false,
        "protocolVersion": "4",
        "keepalive": "60",
        "cleansession": true,
        "birthTopic": "test",
        "birthQos": "0",
        "birthPayload": "",
        "birthMsg": {},
        "closeTopic": "",
        "closeQos": "0",
        "closePayload": "",
        "closeMsg": {},
        "willTopic": "",
        "willQos": "0",
        "willPayload": "",
        "willMsg": {},
        "userProps": "",
        "sessionExpiry": ""
    }
]

Temperature gauge node:

{
        "id": "fe8591a0be99386b",
        "type": "ui_gauge",
        "z": "7c0214a9d7501627",
        "name": "",
        "group": "30b7c6cd1061cb67",
        "order": 0,
        "width": 0,
        "height": 0,
        "gtype": "gage",
        "title": "Temperature ",
        "label": "Celsius ",
        "format": "{{value}}",
        "min": 0,
        "max": "100",
        "colors": [
            "#00b500",
            "#e60039",
            "#e93407"
        ],
        "seg1": "",
        "seg2": "",
        "diff": false,
        "className": "",
        "x": 650,
        "y": 300,
        "wires": []
    },
    {
        "id": "30b7c6cd1061cb67",
        "type": "ui_group",
        "name": "Group 1",
        "tab": "6cc408fc9b1895d7",
        "order": 1,
        "disp": true,
        "width": "6",
        "collapse": false,
        "className": ""
    },
    {
        "id": "6cc408fc9b1895d7",
        "type": "ui_tab",
        "name": "Tab 1",
        "icon": "dashboard",
        "order": 1,
        "disabled": false,
        "hidden": false
    }
]

Humidity gauge node:

{
        "id": "bb13832944ebe60a",
        "type": "ui_gauge",
        "z": "7c0214a9d7501627",
        "name": "",
        "group": "d8f94d45e6e6f994",
        "order": 0,
        "width": 0,
        "height": 0,
        "gtype": "gage",
        "title": "Humidity",
        "label": "%RH",
        "format": "{{value}}",
        "min": 0,
        "max": "100",
        "colors": [
            "#0ef10e",
            "#e6e600",
            "#ca3838"
        ],
        "seg1": "",
        "seg2": "",
        "diff": false,
        "className": "",
        "x": 640,
        "y": 380,
        "wires": []
    },
    {
        "id": "d8f94d45e6e6f994",
        "type": "ui_group",
        "name": "Group 2",
        "tab": "6cc408fc9b1895d7",
        "order": 2,
        "disp": true,
        "width": 6
    },
    {
        "id": "6cc408fc9b1895d7",
        "type": "ui_tab",
        "name": "Tab 1",
        "icon": "dashboard",
        "order": 1,
        "disabled": false,
        "hidden": false
    }
]

Dashboard

I am a Software Architect | AI, Data Science, IoT, Cloud ⌨️ 👨🏽 💻

I love the learn and share knowledge. Thank you.

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