Different IoT Protocols

 

                                    Photo by Christina Morillo

Protocols in IoT: In the realm of the Internet of Things (IoT), communication protocols play a crucial role in

enabling devices to exchange data seamlessly. The choice of protocols depends on various

factors such as the nature of devices, network constraints, and the specific requirements of

the IoT application. Here's a contextual overview of how protocols fit into the IoT landscape: 1. Diverse Ecosystem: - IoT encompasses a diverse ecosystem of devices ranging from sensors and actuators to

smart appliances and industrial machines. - Different devices may have distinct communication needs, influencing the selection of

protocols. 2. Resource Constraints: - Many IoT devices operate under resource constraints, including limited processing power, memory, and energy. - Protocols designed for IoT must be optimized to function efficiently in resource-constrained environments. 3. Wireless Connectivity: - The majority of IoT devices rely on wireless communication due to the dynamic and

distributed nature of IoT deployments. - Protocols must address challenges like low bandwidth, high latency, and intermittent

connectivity. 4. Message Patterns: - IoT applications often involve various communication patterns, such as point-to-point,

publish-subscribe, and request-response. - Protocols are chosen based on their suitability for specific message patterns. 5. Standardization and Interoperability: - Standardization of protocols enhances interoperability, allowing devices from different

manufacturers to communicate seamlessly. - Protocols like MQTT, CoAP, and AMQP have gained popularity for their standardized

approaches. 6. Security Concerns: - IoT devices are susceptible to security threats, and communication protocols must

incorporate robust security measures. - Protocols like MQTT and CoAP often include features for secure data exchange. 7. Scalability: - Scalability is a critical consideration as IoT networks may involve a massive number of

devices. - Protocols should support scalability to accommodate the growth of the IoT ecosystem. 8. Application-Specific Requirements: - IoT applications span various domains, including smart homes, healthcare, industrial

automation, and agriculture. - Protocols are chosen based on the specific requirements of each application domain. 9. Evolution of Standards: - The landscape of IoT communication protocols continues to evolve with the emergence

of new standards and enhancements to existing ones. - Organizations and communities work towards developing protocols that address the

evolving needs of IoT deployments.

Differences Between CoAP, AMQP, MQTT, and Zigbee:

1. CoAP (Constrained Application Protocol):

   - Use Case:

     - Designed for resource-constrained devices and low-power networks in IoT applications.

   - Architecture:

     - Lightweight request-response protocol.

     - Suitable for scenarios where minimizing protocol overhead is crucial.

   - Communication Model:

     - Typically request-response, but can be used in publish-subscribe patterns.

   - Transport:

     - Operates over UDP, minimizing overhead.

   - Complexity:

     - Simplified compared to AMQP, suitable for constrained environments.

   - Typical Industry Usage:

     - Widely used in IoT applications, especially where low-power and efficiency are key.

2. AMQP (Advanced Message Queuing Protocol):

   - Use Case:

     - Ideal for enterprise messaging, ensuring reliable, asynchronous communication.

   - Architecture:

     - Message-oriented protocol with queuing and routing.

     - Suitable for scenarios where message order and reliability are critical.

   - Communication Model:

     - Publish-Subscribe and Point-to-Point.

   - Transport:

     - Typically operates over TCP.

   - Complexity:

     - More feature-rich and complex compared to CoAP.

   - Typical Industry Usage:

     - Commonly used in financial services, healthcare, and other enterprise applications.

3. MQTT (Message Queuing Telemetry Transport):

   - Use Case:

     - Designed for low-bandwidth, high-latency, or unreliable networks, making it suitable for

IoT and M2M communication.

   - Architecture:

     - Lightweight publish-subscribe messaging protocol.

     - Ideal for scenarios where minimizing overhead is crucial.

   - Communication Model:

     - Publish-Subscribe.

   - Transport:

     - Typically operates over TCP but can be adapted to other protocols.

   - Complexity:

     - Simpler compared to AMQP, focused on minimizing data transfer.

   - Typical Industry Usage:

     - Widely used in IoT, home automation, and mobile applications.

4. Zigbee:

   - Use Case:

     - A wireless communication standard designed for short-range, low-power devices in IoT

and home automation.

   - Architecture:

     - Zigbee is a wireless communication protocol operating on IEEE 802.15.4 standard.

     - Mesh networking capabilities, allowing devices to communicate with each other to

extend range.

   - Communication Model:

     - Typically point-to-point or point-to-multipoint for short-range communication.

   - Transport:

     - Utilizes low-power wireless communication.

   - Complexity:

     - Zigbee is optimized for low-power devices, with simpler communication compared to

AMQP or MQTT.

   - Typical Industry Usage:

     - Commonly used in smart home devices, industrial automation, and healthcare.

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

Inference a Model in Small Microcontroller

 

                                            Photo by Google DeepMind

To improve model processing speed on a small microcontroller, you can consider the following strategies:

1. Optimize Your Model:

- Use a model that is optimized for edge devices. Some frameworks like TensorFlow and PyTorch

offer quantization techniques and smaller model architectures suitable for resource-constrained

devices.

- Prune your model to reduce its size by removing less important weights or neurons.

2. Accelerated Hardware:

- Utilize hardware accelerators if your Raspberry Pi has them. For example, Raspberry Pi 4

and later versions have a VideoCore VI GPU, which can be used for certain AI workloads.

- Consider using a Neural Compute Stick (NCS) or a Coral USB Accelerator, which can

significantly speed up inferencing for specific models.

3. Model Quantization:

- Convert your model to use quantized weights (e.g., TensorFlow Lite or PyTorch Quantization).

This can reduce memory and computation requirements.

4. Parallel Processing:

- Use multi-threading or multiprocessing to parallelize tasks. Raspberry Pi 4, for example, is a

quad-core device, and you can leverage all cores for concurrent tasks.

5. Use a More Powerful Raspberry Pi:

- If the model's speed is critical and you're using an older Raspberry Pi model, consider upgrading

to a more powerful one (e.g., Raspberry Pi 4).

6. Optimize Your Code:

- Ensure that your code is well-optimized. Inefficient code can slow down model processing. Use

profiling tools to identify bottlenecks and optimize accordingly.

7. Model Pruning:

- Implement model pruning to reduce the size of your model without significantly affecting its

performance. Tools like TensorFlow Model Optimization can help with this.

8. Implement Model Pipelining:

- Split your model into smaller parts and process them in a pipeline. This can improve throughput

and reduce latency.

9. Lower Input Resolution:

- Use lower input resolutions if acceptable for your application. Reducing the input size will speed

up inference but may reduce accuracy.

10. Hardware Cooling:

- Ensure that your Raspberry Pi has adequate cooling. Overheating can lead to thermal throttling

and reduced performance.

11. Distributed Processing:

- If you have multiple Raspberry Pi devices, you can distribute the processing load across them to

achieve higher throughput.

12. Optimize Dependencies:

- Use lightweight and optimized libraries where possible. Some deep learning frameworks have

optimized versions for edge devices.

13. Use Profiling Tools:

- Tools like `cProfile` and `line_profiler` can help you identify performance bottlenecks in your code.

Keep in mind that the level of improvement you can achieve depends on the specific model, hardware,

and application. It may require a combination of these strategies to achieve the

desired speedup.

Resource Draining Issues on Microservice Applications Running on ARM

Addressing resource-heavy issues in a microservices application running in Dockerized containers on an ARM-based Toradex microcontroller requires a systematic approach. Here are steps to check, verify, and fix these issues:

1. Resource Monitoring:

   - Use monitoring tools like `docker stats`, `docker-compose top`, or specialized monitoring tools like Prometheus and Grafana to monitor resource usage within Docker containers.

   - Check CPU, memory, and disk utilization for each container to identify which service or container is causing resource bottlenecks.

2. Identify Resource-Hungry Containers:

   - Look for containers that are consuming excessive CPU or memory resources.

   - Pay attention to specific microservices that are consistently using high resources.

3. Optimize Microservices:

   - Review the Docker container configurations for each microservice. Ensure that you have allocated the appropriate amount of CPU and memory resources based on the microservice's requirements.

   - Adjust resource limits using Docker Compose or Kubernetes configuration files to prevent over-provisioning or under-provisioning of resources.

4. Horizontal Scaling:

   - Consider horizontal scaling for microservices that are particularly resource-intensive. You can use orchestration tools like Kubernetes or Docker Swarm to manage scaling.

   - Distributing the workload across multiple containers can help alleviate resource bottlenecks.

5. Optimize Docker Images:

   - Check if Docker images are optimized. Images should be as small as possible, and unnecessary packages or files should be removed.

   - Utilize multi-stage builds to reduce the size of the final image.

   - Ensure that Docker images are regularly updated to include security patches and optimizations.

6. Memory Leak Detection:

   - Investigate if there are any memory leaks in your microservices. Tools like Valgrind, Go's `pprof`, or Java's Memory Analyzer can help identify memory leaks.

   - Ensure that resources are properly released when they are no longer needed.

7. Container Cleanup:

   - Implement regular container cleanup procedures to remove unused containers and images. Docker provides commands like `docker system prune` for this purpose.

   - Stale containers and images can consume valuable resources over time.

8. Use Lightweight Base Images:

   - Choose lightweight base images for your Docker containers. Alpine Linux and BusyBox-based images are often more resource-efficient than larger distributions.

9. Microcontroller Configuration:

   - Review the configuration of your ARM-based Toradex microcontroller. Ensure that it's optimized for your workload.

   - Check if there are any kernel parameters or settings that can be adjusted to better allocate resources.

10. Logging and Monitoring:

   - Implement proper logging and monitoring within your microservices. Log only essential information and use log rotation to prevent log files from consuming excessive disk space.

   - Set up alerts for resource thresholds to proactively identify and address issues.

11. Benchmarking and Load Testing:

   - Perform benchmarking and load testing to simulate high loads and identify bottlenecks under stress conditions. Tools like Apache JMeter or wrk can help with this.

12. Continuous Optimization:

   - Regularly review and optimize your microservices and Docker configurations. Microservices applications are dynamic, and their resource requirements may change over time.

13. Consideration for ARM Architecture:

   - Keep in mind that ARM-based architectures have specific optimization considerations. Ensure that your application and dependencies are compiled and configured appropriately for ARM.

By following these steps, you can systematically identify and address resource-heavy issues in your microservices application running on ARM-based Toradex microcontrollers in Dockerized containers. It's essential to monitor resource usage continuously and optimize your setup to ensure efficient resource allocation and improved system performance.

Photo by Kelly

OTA for IOT

 

Photo by Markus Winkler on Unsplash

Often you need to implement Machine Learning application into the EDGE devices. So IoT devices running your machine learning or other artificial intelligence application required updates time to time when you update ML models or back end application or some other part of the application running on IoT.

Over-the-Air (OTA) updates for IoT devices running machine learning applications refer to the capability of remotely updating the software and machine learning models deployed on IoT devices. This enables device manufacturers and developers to deliver bug fixes, security patches, feature enhancements, and even model updates to deployed devices without physically accessing or manually updating each device.

Implementing OTA for IoT devices running machine learning applications involves the following key steps:

1. Remote Software Management: OTA updates require a robust infrastructure to remotely manage and distribute software updates to IoT devices. This typically involves a cloud-based server or platform that maintains a repository of updates and communicates with the devices.

2. Update Packaging: The updates, including new software versions, bug fixes, security patches, or model updates, need to be packaged appropriately for efficient delivery. The packages may include the updated machine learning models, libraries, configuration files, or any other relevant software components.

3. Secure Communication: OTA updates should be transmitted securely to prevent unauthorized access or tampering. Encryption protocols such as Transport Layer Security (TLS) can be used to establish secure communication channels between the server and the devices.

4. Device Compatibility and Verification: The OTA system should handle device-specific variations in terms of hardware, firmware, and software versions. Compatibility checks should be performed before initiating updates to ensure that the updates are applicable to the target device.

5. Rollback and Redundancy: To handle potential issues or failures during the update process, an OTA system should have mechanisms for rollback or redundancy. This allows devices to revert to the previous working state in case of update failures or unexpected behavior.

6. User Consent and Control: Users may need to be informed and given control over the OTA update process. Devices should provide options to schedule updates, delay them, or manually trigger updates based on user preferences.

7. Monitoring and Analytics: OTA systems can incorporate monitoring and analytics capabilities to track the success of updates, detect anomalies, and gather data for future improvements.

Implementing OTA updates for IoT devices running machine learning applications helps ensure that devices remain up-to-date, secure, and optimized for improved performance. It enables developers to continuously improve the machine learning models deployed on the devices and provide a seamless user experience without the need for physical intervention.

Basic steps you can follow to do the automated OTA.

1. Create a .service file which will eventually run the flask REST server application.
2. Create one shell script which will check and fetch the latest code from github. And replace the old code with latest code. Then restart the service file.
3. In that case, you can set up a cron job to execute the shell script at a specific interval.

Here’s an example of how you can configure a cron job to run the OTA model update script:

1. Open your terminal and run the following command to edit the cron jobs:

```

crontab -e

```

2. In the cron file, add a new entry to specify the schedule and command for the OTA model update script. For example, to run the script every day at 2:00 AM, add the following line:

```

0 2 * * * /bin/bash /path/to/ota_model_update.sh >> /path/to/logfile.log 2>&1

```

Make sure to replace `/path/to/ota_model_update.sh` with the actual path to your OTA model update shell script. The `>> /path/to/logfile.log 2>&1` part redirects the script output to a log file.

3. Save and exit the file.

The cron job is now configured to execute the OTA model update shell script at the specified interval. The script will run automatically without any manual intervention, allowing you to perform regular updates to your IoT device’s model.

Note: Ensure that the shell script (`ota_model_update.sh`) has the correct file paths and necessary permissions to execute. You may need to modify the script to include any additional steps required for updating the model from the zip file in the Git repository.

Create update shell script to fetch the model from a GitHub repository and then restart the `.service` file, you can modify the OTA model update shell script as follows:

```

#!/bin/bash

# Script for OTA Model Update

# Variables

DEVICE_IP=”192.168.0.100" # IP address of the IoT device

REMOTE_DIR=”/path/to/remote/directory” # Directory on the IoT device where the model is stored

BACKUP_DIR=”/path/to/backup/directory” # Directory to store backup of the previous model

GITHUB_REPO=”https://github.com/your_username/your_repository.git" # URL of the GitHub repository

# Backup existing model

ssh user@$DEVICE_IP “cp $REMOTE_DIR/model.pkl $BACKUP_DIR/$(date +%Y%m%d%H%M%S)_model.pkl”

# Clone the latest model from the GitHub repository

git clone $GITHUB_REPO /tmp/latest_model

# Move the latest model to the remote directory

scp /tmp/latest_model/model.pkl user@$DEVICE_IP:$REMOTE_DIR/model.pkl

# Restart the .service file (replace with your specific command)

ssh user@$DEVICE_IP “systemctl restart your_service.service”

```

In this updated script, the following changes have been made:

1. Added the `GITHUB_REPO` variable to specify the URL of the GitHub repository where the latest model is stored.

2. Added the backup step to copy the existing model to the backup directory using the current date and time as part of the backup file name.

3. Cloned the latest model from the specified GitHub repository to the local `/tmp/latest_model` directory.

4. Used `scp` to copy the latest model to the remote directory on the IoT device.

5. Restarted the `.service` file on the IoT device. Replace `your_service.service` with the actual name of the service file that needs to be restarted.

Ensure that you have the necessary SSH access and permissions set up for executing these commands remotely on the IoT device. Customize the script according to your specific requirements, such as file paths and service names.

After modifying the shell script, you can schedule the updated script as a cron job, as mentioned in the previous instructions, to automatically fetch the latest model from the GitHub repository and restart the `.service` file on your desired schedule.

Ota

IoT

Machine Learning

Artificial Intelligence

Edge