Edge solutions: Consumer sector

Edge solutions in the consumer sector are possible thanks to increasingly powerful embedded technologies. As a result, they make it possible to equip even everyday devices such as stoves or televisions with edge intelligence. This not only means greater ease of use but also leads to improved operation of the devices. Smart applications with local data processing can also provide added security in the home, for example. Or allow simple payments using a smartphone.

Smart devices with their own intelligence are increasingly also conquering the everyday world of the consumer, be it in the form of wearables, in home appliances or with assistance systems that make life simpler and safer for the elderly. There are already some edge solutions in the consumer sector.

Miele has launched a solution on the market called Motionreact, for example. Which the oven uses to anticipate what the user wants to do next. For example, the oven alerts you to the end of the program by emitting an acoustic signal. As the user approaches the device, two things happen at the same time. The acoustic signal falls silent and the light in the oven chamber switches on. Or, the appliance and oven chamber light switch on when approached and the main menu appears on the display. From a technical perspective, the system operates via infrared sensors in the appliance panel. They respond to movements at a distance of between approx. 20 and 40 centimetres in front of the appliance.

Edge solutions in the consumer sector

Artificial Intelligence capabilities are being integrated increasingly into consumer devices. Not only do they allow greater ease of use, operability is also improved.

An example of this is the new generation of TV top models from LG Electronics. They have intelligent processors which, thanks to the integrated AI, improve the visual quality. Using deep-learning algorithms, the TVs analyse the quality of the signal source. And accordingly choose the most suitable interpolation method for optimum picture playback. Additionally, the processor performs a dynamic fine calibration of the tone-mapping curve for HDR contents in accordance with ambient light. The picture brightness is optimised dynamically based on values for how the human eye perceives images under different lighting conditions. Even in the darkest scenes, high-contrast and detailed pictures with excellent colour depth can be reproduced. And even in rooms with high ambient brightness. The room brightness is captured by means of an ambient light sensor in the TV.

Smart devices are also of tremendous assistance when it comes to making life safer and more comfortable for the elderly. This is shown by the example neviscura from nevisQ. The discreet sensor system integrated in the apartment’s skirting boards allows falls, for example. To be detected automatically and without any additional equipment attached to the body. The nurse-call system then informs the nurse in real time.

The data from the infrared sensors is recorded in a base station with smart functions and analysed. Local data processing immediately detects critical situations in the room. With the base station acting as an interface to the call system at the same time. In the future, the AI-sensor system will also use activity analyses to detect whether a person’s condition is changing. And thus prevent critical situations.

Banking sector – one of the largest users of edge technologies

The IoT with its array of consumer wearables along with Edge Computing are also altering life outside of the home. This is especially true of how banks conduct their business.

According to a study by ResearchAndMarkets, the financial and banking sector worldwide is actually one of the biggest users of Edge Computing. Growing acceptance of digital and mobile banking initiatives and payment using wearables are significantly increasing the demand for Edge-Computing solutions. Such wearables are, for example, the Apple Watch, Fitbit or the smartphone. That’s because banking networks need to be as secure and reliable as possible in order to gain from the advantages offered by IoT technologies. But IoT devices themselves are difficult to secure. To achieve the security level required for banking applications, advanced cryptographic algorithms are needed. However, these CPU-intensive operations are extremely complex to implement, for IoT devices.

The use of security agents at the edge is therefore recommended for this reason. For example, this could be a router or a base station installed in the vicinity of the user for processing security algorithms and encrypting data from and to IoT devices. Customers can therefore complete banking transactions securely even with simpler wearables. But as use of IoT devices for processing banking transactions and payments continues to grow, so too does the need to store and process data in edge data centres. Because they are closer to the user, these micro data centres allow data to be processed closer to the source, thus reducing the response times of the system (latency) and also the costs for data transfer. Paying via smartphone is therefore just as fast as or even faster than using cash from a wallet.

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