Category: IoT

Hyperconnecting Here to There

As enterprise buildings and campuses grow more intelligent (think IoT, building automation and network convergence), facility and network managers are deploying massive numbers of new devices, sensors, controllers, and other devices. The challenge is adapting the data and power cabling infrastructure to keep it efficient, capable and flexible.

From the factory to the field: AgTech comes of age

With the world’s population on track to reach 9.7 billion people by 2050, farmers will need to grow twice the amount of food they do today to avoid scarcity issues and social disruption. Agriculture technology or “AgTech” will play a pivotal role in alleviating the building pressure on farmers to produce more by providing improved control and real-time data insights of farming operations for more efficient management of crops, resources, and livestock.
A product of the Industrial Internet of Things (IIoT), AgTech refers to the use of connected technology in agriculture, horticulture, and aquaculture. It empowers farmers to operate “smarter,” leading to higher revenues, lower costs, and increased margins. Like the IIoT, AgTech seeks to do more with less in leveraging a cross-section of networked devices to uncover ways to boost productivity without burdening already overtaxed natural resources.

White Paper: Solving Edge Computing Infrastructure Challenges

Edge computing installations have become increasingly business critical. Deploying and operating IT at the edge of the network comes with unique challenges. Solving them requires a departure from the traditional means of selecting, configuring, assembling, operating, and maintaining these systems.
This paper describes a new emerging model that involves an integrated ecosystem of cooperative partners, vendors, and end users. This ecosystem and the integrated micro data center solution it produces, help mitigate the unique challenges of edge applications. Download this white paper to help mitigate edge challenges.

Researchers create an algorithm that maximizes IoT sensor inference accuracy using edge computing

We are in a fascinating era where even low-resource devices, such as Internet of Things (IoT) sensors, can use deep learning algorithms to tackle complex problems such as image classification or natural language processing (the branch of artificial intelligence that deals with giving computers the ability to understand spoken and written language in the same way as humans).