Percepio®, the leader in visual trace diagnostics for embedded systems and the Internet of Things (IoT), has been awarded the prestigious Elektra Award 2021 for its visual trace diagnostics tool Tracealyzer for Linux. Tracealyzer for Linux was voted best product in the “Design Tools and Development Software” category by the jury, ahead of developer tools from five other companies, including Cadence and Synopsys.

This is the second part of the two-part blog series covering embedded Linux systems and the challenges brought about by the proliferation of Internet of Things (IoT) devices. In Part I, we surveyed the embedded ecosystem and the role Linux plays within that space. This blog takes you on the next step in the journey, where we explore the most demanding challenges facing manufacturers of tightly embedded IoT devices.

As the number of connected gadgets in our homes, offices, and industrial networks continues to grow exponentially, keeping IoT devices secure has become a vital part of our everyday lives. However, our webcams, printers, and smart plugs often lack security features due to their fast time to market, making them particularly vulnerable to attack. And because security metrics themselves can be tricky to assess, tracking IoT device security is increasingly a challenge.

Imagine there’s an embedded system that needs to persist some state when the processor restarts (either intentionally or due to a catastrophic error). This could be some external hardware information (what’s the position of a motor or actuator?) or a method to communicate the reset to the user (display some information on the device’s display). A simple way to store information through a reboot is to use what’s called“non-initialized” memory.

Percepio®, a leader in visual trace diagnostics for embedded systems and the Internet of Things (IoT), today announced improved support for Microsoft Azure and Azure RTOS ThreadX in Tracealyzer, two enhancements that will ease the development and debugging of Azure IoT systems.

Smart devices are everywhere nowadays, from refrigerators and coffee makers to security cameras and thermostats. A significant percentage of these Internet-of-Things (IoT) devices are designed for consumers. However, they have also become a mainstay in various other fields, including healthcare, where their use has become prolific. The Internet-of-Medical-Things (IoMT) market has exploded in value, forecasted to reach approximately $158.1 billion by 2022, according to Deloitte.

With the widespread use of LTE (Long Term Evolution), we are seeing more IoT devices come online in remote regions of our planet. Picture this scenario: A country is currently experiencing a national emergency due to an electrical grid failure. To mitigate the power shortage the government has deployed generators in the remote regions of their country to power the most remote villages. The problem? The villages are still reporting outages due to the emergency generators running out of fuel.

The Internet of Things adoption is growing faster than ever before. As connected devices become more affordable, they find their place in many aspects of our lives. Users worldwide can benefit from a large ecosystem of IoT solutions. However, this rapid growth comes at a cost. Different IoT edge devices have different interfaces, speak different languages and many are not supported soon after manufacturing. Over time, this presents challenges not only to usability, but also to security and privacy.

In the last 2 installments (Part 1 & Part 2), we discussed the basics of IoT and an example of how the components can be connected and used to provide basic automation and alerting. These seemingly simple steps can build up to provide very advanced controls of all aspects of the physical world. The challenge can become managing situations that were not expected.