We have entered the next phase of the digital revolution in which the data center has stretched to the edge of the network and where myriad Internet of Things (IoT) devices gather and process data with the aid of artificial intelligence (AI).
IoT devices have become ubiquitous. Given the number of new devices being deployed all over the world and far from the desks of developers, it is imperative to have a solid set of tools to manage them without being directly connected to them via JTAG, USB, or SSH. The necessary tasks in the IoT device lifecycle include device deployment and management, remote monitoring, and over-the-air (OTA) software updates.
Large IoT environments are highly complex and comprise multiple layers of disparate devices that must move data between each other, across potentially unreliable connections. Having visibility into each layer of your IoT environment is critical for quickly identifying problems with your deployment that could negatively impact user experience.
Continuing in our series of InfluxDays recaps, we turn our attention to Brian Gilmore’s presentation on Industrial IoT. This is an area that uses time series data extensively and has a lot of room to expand the way it uses this data. Here’s a quick breakdown of where things stand today.
Companies need to consider both how fast they can put edge applications into action and update them, and how quickly they can process incoming data. Industrial processes are becoming increasingly automated as sensors on machines collect a growing amount of data. Much of this data is time-stamped and can help companies improve processes. This large volume of sensor data can become unwieldy if companies don’t manage it properly.
Time series data is a sequence of data points generated through repeated measurements indexed over time. The data points originate from the same source and track changes at different points in time. Times series data includes data like stock exchange data, monthly inflation data, quarterly gross domestic product (GDP) data, and logs from IoT sensors.
Lua is one of the many great interpreters that can be run on embedded devices. It’s fast, uses little memory, is written in ANSI C, and is known by plenty of developers. These are a few of the many reasons why the team at Panic chose to include a Lua interpreter on their Playdate device and allow games to be written in it. You can think of Lua as an alternative to the MicroPython (Python) or JerryScript (Javascript) interpreters. However, there’s a problem.
15 November 2022: Canonical announced today the availability of new enterprise-grade Ubuntu images designed for next-gen Intel IoT platforms. Purpose-built for industrial environments and use cases, the latest Ubuntu images on Intel hardware deliver the performance, safety, and end-to-end security enterprises expect from the most widely used Operating System (OS) among professional developers with latest Intel technologies pre-enabled and available.
IoT Device Management Guide The Internet of Things has been raising lots of discussions and debates (of course for the benefits it offers). According to IoT Analytics, the number of connected IoT devices crossed 12 billion in 2020, which was 2 billion more than the estimated devices. Recent trends indicate that this technological transformation will not just be restricted to random “things” but will be ubiquitous, or what few are calling the Internet of Everything.
The internet of things is one of my favorite topics. IOT enables low-powered connected devices that opens gateways from the digital to the real world. While I love tinkering away with an Arduino sketch and the latest Espressif or Arduino board, there is always an air of frustration when trying to build out what at first seems like simple functionality using one of these “smart devices” because of the limited view we have into their operations.
