Module identity fetch issue
I have registered an edge device[gateway] to Azure IoTHub using x509 self signed certificate. The device got registered fine and modules [edgeAgent,edgeHub] got deployed along with some custom edge modules- with deployment status 200, device and modules status reporting. The modules are running on the edge device but the modules keep restarting as they couldnt authenticate. edge Device registration is through x509 self signed certificate, with below properties in config.toml # Manual provisioning with x.509 certificates [provisioning] source = "manual" iothub_hostname = "REQUIRED_IOTHUB_HOSTNAME" device_id = "REQUIRED_DEVICE_ID_PROVISIONED_IN_IOTHUB" [provisioning.authentication] method = "x509" identity_cert = "REQUIRED_URI_OR_POINTER_TO_DEVICE_IDENTITY_CERTIFICATE" identity_pk = "REQUIRED_URI_TO_DEVICE_IDENTITY_PRIVATE_KEY" Logs from edgeHub: [INF] - Unable to authenticate client <deviceid>/<custom_edge_module> with cached service identity <deviceid>/<custom_edge_module> (Found: False). Resyncing service identity... <4> 2025-09-19 00:29:56.415 +00:00 [WRN] - Error while refreshing the service identity: <deviceid>/<custom_edge_module> OnBehalfOf: <deviceid> System.Collections.Generic.KeyNotFoundException: The given key '<deviceid>/<custom_edge_module>' was not present in the dictionary. at Microsoft.Azure.Devices.Edge.Hub.Core.DeviceScopeIdentitiesCache.RefreshServiceIdentityInternal(String refreshTarget, String onBehalfOfDevice, Boolean invokeServiceIdentitiesUpdated) in /mnt/vss/_work/1/s/edge-hub/core/src/Microsoft.Azure.Devices.Edge.Hub.Core/DeviceScopeIdentitiesCache.cs:line 187 device twin status: "deviceScope": "ms-azure-iot-edge://<devicescope>", "modelId": "", "status": "enabled", "statusUpdateTime": "0001-01-01T00:00:00.0000000Z", "lastActivityTime": "2025-09-19T00:47:10.0840495Z", "connectionState": "Connected", "cloudToDeviceMessageCount": 0, "authenticationType": "selfSigned", "x509Thumbprint": { "PrimaryThumbprint": "<thumbprint>" } Module identity twin of edgeHub: "modelId": "", "status": "enabled", "statusUpdateTime": "0001-01-01T00:00:00.0000000Z", "lastActivityTime": "2025-09-19T00:42:23.4967322Z", "connectionState": "Connected", "cloudToDeviceMessageCount": 0, "authenticationType": "sas", "x509Thumbprint": {} module identity twin of edgeAgent and other modules: "modelId": "", "status": "enabled", "statusUpdateTime": "0001-01-01T00:00:00.0000000Z", "lastActivityTime": "2025-09-19T00:54:15.6085296Z", "connectionState": "Disconnected", "cloudToDeviceMessageCount": 0, "authenticationType": "sas", "x509Thumbprint": {} The modules couldnt communicate to hub as they couldnt authenticate, where as the same modules works fine when the edge device is registered via shared access signature and send telemetry to iot hub. Please let me know where could the issue be for modules not able to communicate with iotHubPantry Log–Microsoft Cognitive, IOT and Mobile App for Managing your Fridge Food Stock
First published on MSDN on Mar 06, 2018 We are Ami Zou (CS & Math), Silvia Sapora(CS), and Elena Liu (Engineering), three undergraduate students from UCL, Imperial College London, and Cambridge University respectively.
Built a Real-Time Azure AI + AKS + DevOps Project – Looking for Feedback
Hi everyone, I recently completed a real-time project using Microsoft Azure services to build a cloud-native healthcare monitoring system. The key services used include: Azure AI (Cognitive Services, OpenAI) Azure Kubernetes Service (AKS) Azure DevOps and GitHub Actions Azure Monitor, Key Vault, API Management, and others The project focuses on real-time health risk prediction using simulated sensor data. It's built with containerized microservices, infrastructure as code, and end-to-end automation. GitHub link (with source code and documentation): https://github.com/kavin3021/AI-Driven-Predictive-Healthcare-Ecosystem I would really appreciate your feedback or suggestions to improve the solution. Thank you!
Scaling Smart with Azure: Architecture That Works
Hi Tech Community! I’m Zainab, currently based in Abu Dhabi and serving as Vice President of Finance & HR at Hoddz Trends LLC a global tech solutions company headquartered in Arkansas, USA. While I lead on strategy, people, and financials, I also roll up my sleeves when it comes to tech innovation. In this discussion, I want to explore the real-world challenges of scaling systems with Microsoft Azure. From choosing the right architecture to optimizing performance and cost, I’ll be sharing insights drawn from experience and I’d love to hear yours too. Whether you're building from scratch, migrating legacy systems, or refining deployments, let’s talk about what actually works.Announcing the Firmware Analysis Public Preview
Consider an organization with thousands of smart sensors, IoT/OT and network equipment deployed on factory floors. Most of these devices are running full operating systems, but unlike traditional IT endpoints which often run security agents, IoT/OT and network devices frequently function as “black boxes”: you have little visibility into what software they’re running, which patches are applied, or what vulnerabilities might exist within them. This is the challenge many organizations face with IoT/OT and networking equipment - when a critical vulnerability is disclosed, how do you know which devices are at risk? To help address this challenge, we are excited to announce the public preview of firmware analysis, a new capability available through Azure Arc. This extends the firmware analysis feature we introduced in Microsoft Defender for IoT, making it available to a broader range of customers and scenarios through Azure. Our goal is to provide deeper visibility into IoT/OT and network devices by analyzing the foundational software (firmware) they run. Firmware analysis will also help companies that build firmware for devices better meet emerging cybersecurity regulations on their products. In this post, we’ll explain how the service works, its key features, and how it helps secure the sensors and edge devices that feed data into AI-driven industrial transformation. Securing Edge Devices to Power AI-Driven Industrial Transformation In modern industrial environments, data is king. Organizations are embracing Industry 4.0 and AI-driven solutions to optimize operations, leveraging advanced analytics and machine learning. The path to AI-driven industrial transformation is fueled by data – and much of that data comes from sensors and smart devices at the edge of the network. These edge devices measure temperature, pressure, vibration, and dozens of other parameters on the factory floor or in remote sites, feeding streams of information to cloud platforms where AI models turn data into insights. In fact, sensors are the frontline data collectors in systems like predictive maintenance, continuously monitoring equipment and generating the raw data that powers AI predictions. However, if those edge devices, sensors, and networking equipment are not secure and become compromised, the quality and reliability of the data (and thus the AI insights) cannot be guaranteed. Vulnerable devices can also be used by attackers to establish a foothold in the network, allowing them to move laterally to compromise other critical systems. In an industrial setting this could mean safety hazards, unplanned downtime, or costly inefficiencies. This is why securing the smart devices and networking equipment at the foundation of your industrial IoT data pipeline is so critical to digital transformation initiatives. By using firmware analysis on the devices’ firmware before deployment (and regularly as firmware updates roll out), the manufacturer and plant operators gain visibility into the security posture of their environment. For example, they might discover that a particular device model’s firmware contains an outdated open-source library with a known critical vulnerability. With that insight, they can work with the vendor to get a patched firmware update before any exploit occurs in the field. Or the analysis might reveal a hard-coded passwords for maintenance account in the device; the ops team can then ensure those credentials are changed or the device is isolated in a network segment with additional monitoring. In short, firmware analysis provides actionable intelligence to fortify each link in the chain of devices that your industrial systems depend on. The result is a more secure, resilient data foundation for your AI-driven transformation efforts – leading to reliable insights and safer, smarter operations on the plant floor. Firmware analysis is also a key tool used by device builders – by analyzing device firmware images before they are delivered to customers, builders can make sure that new releases and firmware updates meet their and their customers’ security standards. Firmware analysis is a key component to address emerging cybersecurity regulations such as the EU Cyber Resilience Act and the U.S. Cyber Trust Mark. How Firmware Analysis Works and Key Features Firmware analysis takes a binary firmware image (the low-level software running on an IoT/OT and network device) and conducts an automated security analysis. You can upload an unencrypted, embedded Linux-based firmware image to the firmware analysis portal. The service unpacks the image, inspects its file system, and identifies potential hidden threat vectors – all without needing any agent on the device. Here are the main capabilities of the firmware analysis service: Identifying software components and vulnerabilities: The first thing the analysis does is produce an inventory of software components found inside the firmware, generating a Software Bill of Materials (SBOM). This inventory focuses especially on open-source packages used in the firmware. Using this SBOM, the service then scans for known vulnerabilities by checking the identified components against public Common Vulnerabilities and Exposures (CVEs) databases. This surfaces any known security flaws in the device’s software stack, allowing device manufacturers and operators to prioritize patches for those issues. Analyzing binaries for security hardening: Beyond known vulnerabilities, our firmware analysis examines how the firmware’s binaries were built and whether they follow security best practices. For example, it checks for protections like stack canaries, ASLR (Address Space Layout Randomization), and other compile-time defenses. This “binary hardening” assessment indicates how resistant the device’s software might be to exploitation. If the firmware lacks certain protections, it suggests the device could be easier to exploit and highlights a need for improved secure development practices by the manufacturer. In short, this feature acts as a gauge of the device’s overall security hygiene in its compiled code. Finding weak credentials and embedded secrets: Another critical aspect of the analysis is identifying hard-coded user accounts or credentials in the firmware. Hard-coded or default passwords are a well-known weakness in IoT devices – for instance, the Mirai botnet famously leveraged a list of over 60 factory-default usernames and passwords to hijack IoT devices for DDoS attacks. Firmware analysis will flag any built-in user accounts and the password hash algorithms used, so manufacturers can remove or strengthen them, and enterprise security teams can avoid deploying devices with known default credentials. Additionally, the firmware analysis looks for cryptographic materials embedded in the image. It will detect things like expired or self-signed TLS/SSL certificates, which could jeopardize secure communications from a device. It also searches for any public or private cryptographic keys left inside the firmware – secrets that, if found by adversaries, could grant unauthorized access to the device or associated cloud services. By uncovering these hidden secrets, the service helps eliminate serious risks that might otherwise go unnoticed in the device’s software. All these insights – from software inventory and CVEs to hardening checks and secret material detection – are provided in a detailed report for each firmware image you analyze. Firmware analysis provides deep insights, clear visibility, and actionable intelligence into your devices' security posture, enabling you to confidently operate your industrial environments in the era of AI-driven industrial transformation. Getting Started and What’s Next If you have IoT/OT and network devices in your environment, use firmware analysis to test just how secure your devices are. Getting started is easy: access firmware analysis public preview by searching on “firmware analysis” in the Azure portal, or access using this link. In the future, firmware analysis will be more tightly integrated into the Azure portal. Onboard your subscription to the preview and then upload firmware images for analysis - here is a step-by-step tutorial. The service currently supports embedded Linux-based images up to 1GB in size. In this preview phase, there is no cost to analyze your firmware – our goal is to gather feedback. We are excited to share this capability with you, as it provides a powerful new tool for securing IoT/OT and network devices at scale. By shedding light on the hidden risks in device firmware, firmware analysis helps you protect the very devices that enable your AI and digital transformation initiatives. Firmware is no longer just low-level code—it’s a high-stakes surface for attack, and one that demands visibility and control. Firmware analysis equips security teams, engineers, and plant operators with the intelligence needed to act decisively—before vulnerabilities become headlines, and before attackers get a foothold. Please give the firmware analysis preview a try and let us know what you think.How IoT is a game changer for the Sustainability Metaverse
The sustainability metaverse is less about any specific type of technology, and much more about how we interact with technology. It has become a truly viable notion that technology permits the degree of interaction required to support a unified experience that simultaneously transcends both physical and digital.
How integreate Azure IoT Hub with Azure Synapse in RealTime
Hello, I'm researching how to connect Azure IoT Hub with Azure Synapse, I've already used IoT Hub a bit but I don't have any knowledge of Synapse, it is also required that the data be in RT, so if someone has already done something similar or knows where I can find answers I would appreciate it. Have a good day.Windows 10 EOS for Windows IoT Enterprise
Do you manage Windows IoT Enterprise devices and wonder how theyll be impacted by Windows 10 end of support (EOS)? As we approach October 14, 2025, it's important to understand that not all of these devices will be impacted. Understanding Windows 10 end of support When Windows 10 reaches EOS on October 14, 2025, Microsoft will no longer provide bug fixes, security updates, time zone updates, or technical support for most devices running Windows 10. However, not all Windows 10 editions will be impacted. Windows 10 EOS and Windows IoT Enterprise Windows IoT Enterprise is used for building fixed-function, specialized devices such as automated teller machines (ATMs), point of sale (POS), digital signs, factory automation devices, and healthcare devices. It comes in two versions, each of which are impacted differently by the Windows 10 EOS date. Windows IoT Enterprise LTSC The Long-Term Servicing Channel (LTSC) is the most common version of Windows IoT Enterprise. Each LTSC release has its own support lifecycle, most of which will continue to receive full support for several years to come. Edition End of support Impacted Windows 10 IoT Enterprise LTSC 2021 January 13, 2032 No Windows 10 Enterprise LTSC 2019 [1] January 9, 2029 No Windows 10 Enterprise LTSB 2016 [1] October 13, 2026 No Windows 10 Enterprise LTSB 2015 [1] October 14, 2025 Yes Windows 10 Enterprise LTSB 2015 10-year support lifecycle EOS aligns with the Windows 10 EOS. Owners of devices running this version should contact the device maker to determine if there is an upgrade option available or if the device would need to be replaced. Windows 10 Enterprise LTSB 2016 has a year remaining on its 10-year support lifecycle. Owners of devices running this version should begin working with their device maker on plans for upgrade or replacement if they haven't already. Windows IoT Enterprise (not LTSC) Windows 10 IoT Enterprise, version 22H2, is the final version of Windows 10 and will remain in support with monthly security updates through October 14, 2025. There will be no ESU for Windows 10 IoT Enterprise, version 22H2. The support lifecycles for prior versions such as 21H2 or earlier have already expired. Owners of devices running version 22H2 may be able to upgrade to Windows 11. To determine if your device is eligible for upgrade, see Can I upgrade to Windows 11? Some devices may be locked down to an appliance-like experience, in which case a self-service upgrade may not be available, and you will need to reach out to your device maker or original equipment manufacturer (OEM) to determine available options. How ready are your Windows IoT devices? Most devices running Windows 10 IoT Enterprise are NOT impacted by the Windows 10 EOS. For devices that are impacted, contact your device maker for options to upgrade or replace. Now is a great time to consider upgrading to a device running Windows 11 IoT Enterprise LTSC 2024, which will be supported through October 10, 2034. [1] IoT was not used in the official product name until LTSC 2021. LTSB stands for Long-Term Servicing Branch, a former name for LTSC.
Need help with updating disconnected devices
hey, I am new to azure and IOT and I need help with knowing how to do this. The scenario is that: I have a set of Linux devices that can't be connected to the internet ever, these devices should be connected to another device (will have internet) which will act as parent to all these disconnected devices. The challenge is to update these child devices using Azure IOT, the updates will be deployed in the hub, and it has to passed to child devices via parent device and automatically needs to be installed in the child devices. The parent might not require this update or might. How will I do this? also I can't use any scripting mechanisms. Now when I surfed a bit through azure documentation, I found out that I can use device update for this, What I found was: 1) setup every device in IOT hub 2)set the device with internet as parent and others a child 3)set up MCC module in parent 4)Connect the devices physically (Lan or Wi-Fi) 5)Roll out updates Now I don't know whether this is true or not, it's just my understanding. I am having few doubts: 1)do we also add the child devices (disconnected devices in IOT hub), if yes what if we have 1000 devices? (I'm asking about scalability) 2)How do I actually physically connect the parent and child devices, do I just plug in Lan/Wi-Fi, or do I have to do anything else? 3)How to add MCC Module? 4)how does this actually works? is it feasible?
