DE202022105895U1 - Performance optimization in wireless devices through artificial intelligence and machine learning - Google Patents

Abstract

A performance-optimized wireless device (100) that uses artificial intelligence and machine learning, consisting of:
a wireless device (101);
a communications chip (102) embedded in the wireless device (101);
a wireless hotspot (103); and
a wireless network (104).

Description

FIELD OF THE INVENTION

The present disclosure relates to a system for performance optimization, and more particularly to performance optimization in wireless devices using artificial intelligence and machine learning.

BACKGROUND

The wireless devices have network settings that are generally kept static. These are optimized settings that attempt to maintain compatibility with a range of wireless hotspots and optimize power delivery. There are specific protocols for implementing communications in wireless local area networks. These protocols provide wireless hotspot providers with significant latitude in various aspects of wireless hotspot operations. It is up to the provider's discretion how they handle the parameters of their wireless hotspots.

The patent application with the number US20190342835A1 discloses techniques for power management of devices in wireless networks. For example, a device may include a power management module and a transceiver module. The power management module determines a beacon interval and a wakeup interval. The transceiver module sends a transmission to one or more remote devices that includes the beacon interval and the wakeup interval.

The patent application with the number US20170019865A1 discloses a method and apparatus for performing efficient network transitions.

One or more of the probe request frames that an AP can receive or process as valid may be modified by including a transmit power for the probe request frame.

Recent technologies such as artificial intelligence and machine learning can help in effectively optimizing the performance of wireless devices. Therefore, there is a need for a system for optimizing the performance of wireless devices. In this regard, a performance-optimized wireless device utilizing artificial intelligence and machine learning is disclosed.

The described power-optimized wireless device is configured to change Wi-Fi microcircuit settings to reduce power consumption. It is configured to determine the map interval for displaying wireless hotspot delivery traffic. The power-optimized wireless device uses machine learning algorithms to determine and change the wake-up interval of its Wi-Fi microcircuit. The machine learning algorithms are one or more neural network algorithms, regression algorithms, regularization algorithms, Bayesian algorithms, clustering algorithms, deep learning algorithms, dimensionality reduction algorithms, decision tree algorithms, rule-based algorithms or artificial intelligence.

All patent publications contained herein are incorporated by reference to the same extent as if each publication or patent application were expressly and individually identified as incorporated by reference. If any definition or use of a term in an incorporated reference is inconsistent or inconsistent with the definition of that term given herein, the definition of that term given herein shall control. Accordingly, in some embodiments, the numerical parameters set forth in the written description and appended claims are approximate and may vary depending on the desired properties to be achieved by a particular embodiment.

OBJECT OF THE INVENTION

The main objective of the invention is to provide performance optimization in a wireless device using artificial intelligence and machine learning.

Another object of the invention is to provide a power optimized wireless device that is configured to change settings of Wi-Fi microcircuits to reduce power consumption.

SUMMARY OF THE INVENTION

The present invention discloses a performance-optimized wireless device using artificial intelligence and machine learning, comprising a wireless device, a communication chip embedded in the wireless device, a wireless hotspot, and a wireless network.

In one aspect of the present disclosure, the wireless device is a router, a routing switch, an integrated access device, a multi plexer or an access device for a wide area network.

In one aspect of the present disclosure, the wireless device is configured to modify settings of the Wi-Fi microcircuits to reduce power consumption.

In one aspect of the present disclosure, the wireless device uses machine learning algorithms to determine and change the wake-up interval of its Wi-Fi microcircuit.

In one aspect of the present disclosure, the wireless device may employ one or more neural network algorithms, regression algorithms, regularization algorithms, Bayesian algorithms, clustering algorithms, deep learning algorithms, dimensionality reduction algorithms, decision tree algorithms, rule-based algorithms, or artificial intelligence.

In one aspect of the present disclosure, the wireless device is configured to determine the map interval for the delivery traffic display of the wireless hotspot.

In one aspect of the present disclosure, the wireless device is configured to determine broadcast traffic on Address Resolution Protocol (ARP) responses to analyze the ARP packets.

It should be noted that although the present disclosure has been discussed in terms of a defined set of functional modules, any other module or set of modules may be added/deleted/changed/combined and all such architectural/in structure of the proposed system fall entirely within the scope of the present disclosure. Each module can also be broken down into one or more functional sub-modules, all of which also fall fully within the scope of the present disclosure.

Various objects, features, aspects and advantages of the subject invention will become more apparent from the following detailed description of the preferred embodiments.

character list

• zeigt ein Blockdiagramm eines leistungsoptimierten drahtlosen Geräts (100), das künstliche Intelligenz und maschinelles Lernen gemäß einer Ausführungsform der vorliegenden Erfindung verwendet.

The accompanying drawing is provided for further understanding of the present disclosure and is a part of this specification. The drawings illustrate example embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

• Figure 12 shows a block diagram of a performance-optimized wireless device (100) using artificial intelligence and machine learning according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Aspects of the present disclosure relate to performance optimization in wireless devices using artificial intelligence and machine learning.

The invention will now be described with reference to the accompanying drawing, which illustrates, by way of non-limiting example, preferred embodiments of this invention.

In one embodiment of the present disclosure a block diagram of a performance-optimized wireless device (100) using artificial intelligence and machine learning. The device (100) consists of a wireless device (101), a communication chip (102) embedded in the wireless device (101), a wireless hotspot (103) and a wireless network (104).

The wireless device (101) can be a device for wireless communication with the wireless hotspot (103). The wireless device (101) is a router, routing switch, integrated access device, multiplexer, or wide area network access device. The wireless device (101) can also be an electronic lock, an exit device, an auto operator, a barrier control device or any type of access control device. The wireless device (101) may include a physical locking mechanism configured for access control.

The wireless network (104) provides a communication network for the wireless device (101) and the wireless hotspot (103). A communication chip (102) is embedded in the wireless device (101) to enable wireless communication. The wireless device (101) includes a processor that controls the Wi-Fi microcircuits of the wireless device (101).

The wireless device (101) is able to communicate with the wireless hotspot (103). The wireless device (101) is configured to be able to change Wi-Fi microcircuit settings to reduce power consumption. The battery consumption is determined by the Changing Wi-Fi microcircuit settings is reduced and battery life is increased.

The wireless device (101) changes the settings of the Wi-Fi microcircuit to reduce the power transmitted to the Wi-Fi microcircuit in relation to the maximum power of the Wi-Fi microcircuit. The wireless device (101) can also determine the limit on the number of beacons sent by the wireless hotspot (103) that can be ignored by the wireless device (101) without disconnecting the wireless hotspot (103).

In addition, the wireless device (101) determines the interval for displaying the delivery traffic of the wireless hotspot (103). Based on the results, the wireless device (101) changes the wake-up interval of its Wi-Fi microcircuit. This reduces the power consumption of the device. The wireless device (101) can also determine broadcast traffic on Address Resolution Protocol responses to analyze the ARP packets, which can be ignored without disconnecting the wireless hotspot (103). The wireless device (101) can then change settings to change the boundaries of the ARP packets.

The wireless device (101) uses machine learning algorithms to determine the wake-up interval of its Wi-Fi microcircuits. The wireless device (101) also uses machine learning to determine the limits for ARP packets. It then determines the appropriate settings of its Wi-Fi microcircuits for a change. The wireless device (101) may use one or more neural network algorithms, regression algorithms, regularization algorithms, Bayesian algorithms, clustering algorithms, deep learning algorithms, dimensionality reduction algorithms, decision tree algorithms, rule-based algorithms, or artificial intelligence.

While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope of the invention. The scope of the invention is determined by the following claims. The invention is not limited to the described embodiments, variants or examples, which are included to enable a person of ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person of ordinary skill in the art.

Reference List

100

Performance-optimized wireless device with artificial intelligence and machine learning

101

wireless device

102

communication chip

103

wireless hotspot

104

wireless network

QUOTES INCLUDED IN DESCRIPTION

This list of the documents cited by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent Literature Cited

• US20190342835A1 [0003]
• US20170019865A1 [0004]

Claims (7)

A performance-optimized wireless device (100) that uses artificial intelligence and machine learning, consisting of: a wireless device (101); a communications chip (102) embedded in the wireless device (101); a wireless hotspot (103); and a wireless network (104). The performance-optimized wireless device (100) that uses artificial intelligence and machine learning as in claim 1 as claimed, wherein the wireless device (101) is a router, a routing switch, an integrated access device, a multiplexer or a wide area network access device. The power-optimized wireless device (100) that uses artificial intelligence and machine learning as in claim 1 as claimed, wherein the wireless device (101) is configured to modify the settings of the Wi-Fi microcircuits to reduce power consumption. The power-optimized wireless device (100) that uses artificial intelligence and machine learning as in claim 1 is claimed, wherein the wireless device (101) uses machine learning algorithms to determine and modify the wake-up interval of its Wi-Fi microcircuit. Performance-optimized wireless device (100) with artificial intelligence and machine learning claim 4 , wherein the wireless device (101) uses one or more neural network algorithms, regression algorithms, regularization algorithms, Bayesian algorithms, clustering algorithms, deep learning algorithms, dimensionality reduction algorithms, decision tree algorithms, rule-based algorithms, or artificial intelligence. Performance-optimized wireless device (100) using artificial intelligence and machine learning claim 1 wherein the wireless device (101) is configured to determine the map interval for displaying delivery traffic of the wireless hotspot (103). The performance-optimized wireless device (100) powered by artificial intelligence and machine learning claim 1 used, wherein the wireless device (101) is configured to determine broadcast traffic on Address Resolution Protocol (ARP) responses to analyze the ARP packets.

Images