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M2M and the Supply Chain
| March 1, 2016
Wireless Things are proud to announce that having been acquired by 365 Agile Ltd in February of 2015, the company has floated on AIM, and is now part of the publicly listed 365 Agile Group PLC.
Article | April 16, 2020
Tech companies are stepping up Internet of Things technologies to protect against COVID-19 and future viruses by using LiDAR and infrared cameras to detect a person’s body temperature from a distance or even handwashing. Keeping the data secure in such detection is also going to be a challenge. One approach is to put a chip inside an IoT device when it is manufactured to enable strong authentication and secure communication, mainly to guard against device counterfeiting. Hitachi Vantara has touted forward looking infrared cameras (FLIR) cameras to detect the temperature of a person from a distance. That way a passenger on a train or a worker or a customer in a store can be non-intrusively screened, according to a blog from Mark Jules, global vice president of smart spaces and video intelligence.
In the wake of the COVID-19 pandemic, manufacturing is roaring back to life, and with it comes a renewed focus on Digital Transformation initiatives. The industry stands on the doorstep of its much-anticipated renaissance, and it’s clear that manufacturing leaders need to not only embrace but accelerate innovation while managing critical processes like increasing capacity while maintaining product quality. Effective collaboration will be key to doing both well, but it’s even more critical as workforces have gone and are still largely remote.
As the virus swept the globe, it became apparent quickly that there would be winners and losers. Many manufacturers were caught off-guard, so to speak. Before manufacturing’s aforementioned reckoning, the industry had already been notorious for its slow adoption of the digital, data-centric mindset that has transformed other industries.
The book Design, Launch, and Scale IoT Services classifies the components of IoT services into technical modules. One of the most important of these is Artificial Intelligence (AI). This article is intended to supplement the book by providing insight into AI and its applications for IoT. After many years in the wilderness, AI is back on the hype curve and will change the world again. Or, will it? AI has always been interesting, but what has changed to justify the current hype? There are several contributing factors. The volumes of data that will be produced by many IoT services suggest that this data cannot be managed by humans with traditional analytics tools. Therefore, AI can offer opportunities for IoT services to extract maximum value from the data. IoT cloud platforms are now offering AI services via APIs and application development tools, making AI more accessible for many IoT services. Now, AI can be incorporated without requiring extensive development or excessive costs.
Edge computing refers to information being processed at the edge of the network, rather than being sent to a central cloud server. The benefits of edge computing include reduced latency, reduced costs, increased security and increased business efficiency. Transferring data from the edge of a network takes time, particularly if the data is being collected in a remote location. While the transfer may usually take less than a second, glitches in the network or an unreliable connection may increase the time required. For some IoT applications, for example, self-driving cars, even a second may be too long. Imagine a security camera that’s monitoring an empty hallway. There’s no need to send hours of large video files of an empty hallway to a cloud server (where you will need to pay to store them). With edge computing, the video could be sent to the cloud only if there is movement detected in the hallway.
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