With escalating inflation and supply chain challenges, retailers need to consider adding new technologies to their businesses to both enrich their offerings to attract new customers, amplify customer loyalty, and increase process efficiencies, while reducing costs in the long run. Automation and technology can change both customers’ and employees’ experience in the store, from new ways for consumers to find and pay for items, to information systems for workers.

The ISD-O370 (from Lanner) is an edge-rugged cellular router. It is compliant with IP67 and the MIL-STD standards (to ensure the device operates in harsh environments) and also supports 5G and Wi-Fi 6 for demanding wireless applications. The appliance can also provide edge computing capabilities. It runs on Linux on Intel’s Atom C3000 with up to 64GB DDR4 (64GM eMMC).

With the continuously growing demand for consumer electronic applications, creating increased data transfers from emails, video conferencing, and remote applications, often utilizing unprotected public networks, makes productivity application tools vulnerable to attacks. The increasing number of network security breaches, increasing adoption of cloud technologies, the trend of network automation, and 5G networks, makes protecting network transmitted data imperative, further increasing the demand to improve network security.

As enterprises eagerly adopt new technologies relating to IIoT and mobile Edge computing, they often stumble upon unforeseen challenges.  

Challenges such as the exponential growth in the number of connected mobile devices, increased needs for bandwidth, more and more sophisticated attacks and sometimes unexpected operational overheads. 

Autonomous lawn mowers are designed to reduce lawn maintenance labor costs and time. But an inefficient or improperly programmed or operated robotic lawn mower would defeat the whole purpose of “autonomy.” An unproductive and unsafe robot will turn a gardener into a programmer, taking away the focus on what matters: the grass.

Intelligence (AI) and Machine Learning (ML) technology is undoubtedly the best path to efficient and precise autonomous driving systems but also one of the most challenging. The traditional in-vehicle technology doesn't have the necessary capacity to perform high processing workloads, especially intensive AI and ML decision-making.

Enterprises nowadays are migrating to the Cloud in droves and more and more software are being licensed, delivered and deployed using SaaS; such migration and deployment have become the norm for the so-called hybrid workplace model that mixes in-office and remote work, especially during the pandemic.  

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