Ambeent Inc.

Ambeent Wireless

Argela Yazilim Ve Bilisim Teknolojileri San Ve Tic A.s.

Argela Yazilim Be Bilişim Teknolojlleri San. Ve Tic. A.ş

Mustafa Ergen

Sinem Çöleri Ergen

Hasan Şildir

Mehmet Fatih Tuysuz

Seyhan Civanlar

Onur Ergen

Bulent Kaytaz

Nazli Guney

Melih Karaman

The invention generally relates to a method and system for distribution of computational capacity for a plurality of end-devices in different localities using a decentralized edge architecture. The method and system clusters a plurality of moving nodes capable of offering highly virtualized computational and storage resources utilizing an Artificial Intelligence (AI) model. The clustering is performed by utilizing two prediction models: a mobility prediction model and a theoretical framework. The mobility prediction model learns timing and direction of movements such as mobility patterns of each of the plurality of moving nodes, as to ascertain computational capacity for the given locale at a certain time. The theoretical framework performs sequential to parallel conversion in learning, optimization and caching algorithms of the AI model under contingent circumstances. Upon clustering, the cluster of moving nodes is utilized to process a plurality of workloads co-operatively for the set of end-devices in the given locale.

Method and system for distribution of computational and storage capacity using a plurality of moving nodes in different localities: a new decentralized edge architecture

The invention generally relates to a method and system for enabling low-latency data communication by aggregating a plurality of network interfaces, each network interface associated with a different network. The method and system measures in real-time, network performance capabilities associated with the networks via the respective network interfaces. The method and system then assigns two or more multi-threading processors in a multi-processor architecture configured to execute a plurality of threads for processing one or more data streams. The threading in each processor is interlinked with two or more network interfaces based on the measured network performance capabilities and network performance capability requirements of the one or more data streams, thereby enabling threading-based cooperation among multi-core processors in the multi-processor architecture and the plurality of network interfaces. The one or more data streams are then transmitted to the two or more network interfaces and thereon to the associated networks for transport.

Method and system for enabling low-latency data communication by aggregating a plurality of network interfaces

The invention generally relates to a method and system for utilizing an Artificial Intelligence (AI)-based technology for tracking human mobility patterns and contact points, via a Software Development Kit (SDK) integrated into client device applications. The SDK performs contact tracing by recording past and current signal data/measurements of Wi-Fi access points encountered by a client device and stores these measurements in a local memory of the client device. The SDK further analyses the recorded past and current signal data/measurements of the Wi-Fi access points using an AI module to derive mobility patterns of user of the client device. The AI module of the SDK then identifies other client devices that the client device may have encountered based on analyzing the mobility patterns of the user of the client device and the recorded past and current signal data/measurements and via performing Wi-Fi network sniffing.

Method and system for contact tracing using a software development kit (SDK) integrated into client devices

Provided is a method and system for a network-assisted Quality of Experience (QoE)-based smart and proactive video streaming framework to be deployed at Multi-access Edge Computing (MEC) servers. Quality of Experience (QoE) levels for a plurality of video sessions streamed from a cloud server to computing devices through a central edge server are estimated based on one or more metrics associated with the plurality of video sessions. Additionally, channel status of one or more neighboring edge servers proximate to the central edge server is determined. The QoE levels of the plurality of video sessions are then maximized based on the estimating, by employing a local optimization, a global optimization and a combination of both the local optimization and the global optimization based on the one or more metrics and the channel status determined for the one or more neighboring edge servers.

Optimizing quality of experience (QoE) levels for video streaming over wireless/cellular networks

Provided is a method and system for optimizing utilization and performance of a Wi-Fi network for one or more subscriber client devices through a Wi-Fi console application by monitoring an RF environment of the Wi-Fi network to detect interference to a subscriber client device from one or more neighboring client devices that include non-subscriber client devices and other subscriber client devices and allocating a spectrum/channel for the subscriber client device to access the Wi-Fi network using an AI model based on the interference detected, throughput requirements of applications running on the subscriber client device and importance/priority of an activity on the subscriber client device. The AI model constructs a relational aggregated graph and decompose the relational aggregated graph into dynamic clusters. A heuristic deep-learning method is applied to analyze the dynamic clusters to reduce a computation time for recommendation of a suitable spectrum/channel for accessing the Wi-Fi network.

Optimizing utilization and performance of Wi-Fi networks

The invention provides a method and system for automatically managing a plurality of Wi-Fi access points using a network management cloud. In order to manage the plurality of Wi-Fi access points, a connection is established between one or more Wi-Fi access points and a client device using the network management cloud. The network management cloud, then, receives and analyzes information from the client device related to controlling one or more Wi-Fi access points. Thereafter, the network management cloud determines one or more operation settings for the one or more Wi-Fi access points based on the analyzed information. The one or more operation settings of the one or more Wi-Fi access points are then configured using the network management cloud. Thus, the one or more Wi-Fi access points are configured by the client device through the network management cloud.

Method and system for automatically managing a plurality of Wi-Fi access points using a network management cloud

The invention provides a method and system for managing a plurality of Wi-Fi access points using a cloud based adaptive software defined network (A-SDN). In order to manage the plurality of Wi-Fi access points, measurement data corresponding to a plurality of network variables are collected from one or more Wi-Fi access points and one or more client devices using a cloud based network application. Once the measurement data are collected, one or more optimal settings of one or more decision variables are derived for the one or more Wi-Fi access points using one or more network variables from the plurality of network variables. The one or more optimal settings are derived by formulation of optimization problems through heuristic solutions and a machine learning based optimization using the one or more network variables.

Method and system for managing a plurality of Wi-Fi access points using a cloud based adaptive software defined network

The invention provides a method and system for establishing a connection between a client device of a plurality of client devices and a Wi-Fi access point of a plurality of Wi-Fi access points using a cloud platform. In an embodiment, the cloud platform identifies one or more target Wi-Fi access points and transmits an SSID-password tuple associated with the client device to the one or more target Wi-Fi access points. The one or more target Wi-Fi access points, then, broadcast an SSID of the client device and upon detecting the broadcast, the client device selects a target Wi-Fi access point and transmits a message with a pre-stored SSID-password tuple to the target Wi-Fi access point. Thereafter, the target Wi-Fi access point authorizes the client device to access the target Wi-Fi access point based on a comparison of the pre-stored SSID-password tuple and the SSID-password tuple received from the cloud platform.

Method and system for establishing a connection between a client device and a Wi-Fi access point using a cloud platform

The invention provides a method and system for tracking a position of one or more Wi-Fi devices of a plurality of Wi-Fi devices. For tracking the position of the one or more Wi-Fi devices, a Triggering Router triggers the one or more Wi-Fi devices to transmit one of a broadcast probe and a directed probe through broadcasting hidden SSID networks and common SSIDs that increase a probing tendency of the one or more Wi-Fi devices. The plurality of Wi-Fi sniffers, then, collect measurement data associated with the one or more Wi-Fi devices in response to detecting the broadcast probe transmission and the directed probe transmission. Thereafter, the position of the one or more Wi-Fi devices is estimated using an adaptive machine learning model based on an indoor model whose parameters are tuned based on radio frequency (RF) measurements from the plurality of Wi-Fi devices and the measurement data.

Method and system for tracking position of one or more Wi-Fi devices

The invention provides a method and system for establishing a connection between a client device of a plurality of client devices and a Wi-Fi access point of a plurality of Wi-Fi access points using a cloud platform. One or more cellular devices and one or more non-Subscriber Identity Module (non-SIM) devices of a user are registered at the cloud platform. The one or more non-SIM devices are then associated to the one or more cellular devices. Further, each Wi-Fi access point of the plurality of Wi-Fi access points is also registered at the cloud platform. A location of the client device is identified and one or more Wi-Fi access points that are closest to the client device is identified. The one or more Wi-Fi access points then authorize the client device to access the one or more Wi-Fi access points based on the information provided by the cloud platform.

Method and system for authenticating cellular devices and non-SIM devices for accessing a Wi-Fi access point using a cloud platform

The invention provides a method and system for sharing Wi-Fi in a Wi-Fi network using a cloud platform. To start with, the method and system enables registration of one or more Wi-Fi access points on the cloud platform and enables a client device to rent a Wi-Fi connection from owner of a Wi-Fi access point. Subsequently, the method and system enables the client device to select a Wi-Fi access point registered on the cloud platform based on usage fee, properties and signal quality. Further, the method and system performs offload optimization using an AI module by deciding whether data offloading from licensed spectrum to unlicensed spectrum is desirable or not. In response to deciding that data offload is desirable, the client device is allowed to connect to the selected Wi-Fi access point and transact with the Wi-Fi access point owner using cryptocurrencies in a blockchain-based network.

Method and system for sharing Wi-Fi in a Wi-Fi network using a cloud platform

The invention provides a method and system for controlling a plurality of Wi-Fi access points in a wireless network using a cloud platform. To start with, a connection is established between one or more Wi-Fi access points of the plurality of Wi-Fi access points and a client device using the cloud platform. Subsequently, details pertaining to the plurality of Wi-Fi access points are collected and analyzed using an Artificial Intelligence (AI) model. The AI model then derives configurations for the plurality of Wi-Fi access points in response to analyzing the details. Thereafter, the derived configurations are transmitted to the client device. The transmitted configurations are finally implemented at the plurality of access points using either the cloud platform or the client device. The derived configurations include optimization of Wi-Fi access point network parameters based on a frequency and a set of constraints.

Method and system for controlling a plurality of Wi-Fi access points in a wireless network using a cloud platform

A method and system for determining an optimal wireless channel for a Wi-Fi access point in a cloud-based software defined network (SDN) using an Application Programming Interface (API) is described. Initially, the API collects the measurement information and media access control (MAC) addresses from a plurality of nearby Wi-Fi access points to a client device. The measurement information is analyzed to determine a location of the client device using an Artificial Intelligence (AI) model. To determine the location of the client device, the API creates a plurality of clusters of Wi-Fi access points and updates the plurality of clusters with one or more newly identified Wi-Fi access points. Thereafter, the API calculates an optimal wireless channel for a Wi-Fi access point from the cluster using the AI model and enables the client device to automatically switch to the optimal wireless channel for connecting with the Wi-Fi access point.

Method and system for determining an optimal wireless channel for a Wi-Fi access point in a cloud-based software defined network using an application programming interface

The invention provides a method and system for establishing a connection between a client device of a plurality of client devices and a Wi-Fi access point of a plurality of Wi-Fi access points using a cloud platform. To start with, the plurality of client devices and the plurality of Wi-Fi access points are registered with the cloud platform. Subsequently, the client device transmits a network demand request to one or more target Wi-Fi access points. The one or more target Wi-Fi access points receive account details containing pre-registered credentials associated with the client device from the cloud platform. Upon receiving the account details, the one or more target Wi-Fi access points authorize the client device and broadcast a personalized SSID. The client device uses the personalized SSID to access a Wi-Fi access point of the one or more target Wi-Fi access points using the pre-registered credentials.

Method and system for establishing a connection between a client device and a Wi-Fi access point based on personalized SSIDs using a cloud platform

The invention provides a method and system for enhancing signal qualities within a Wi-Fi network using an adaptive-software defined network (A-SDN) and a cloud platform. The Wi-Fi network includes client devices connected to a Wi-Fi access point and a plurality of nearby Wi-Fi access points to the one or more client devices. The method and system collects radio frequency (RF) measurements pertaining to each client device, the connected Wi-Fi access point, and the plurality of nearby Wi-Fi access points, and an Artificial Intelligence (AI) model is utilized to derive interference measurements based on the RF measurements. The AI model then derives configurations related to Wi-Fi network parameters associated with the connected Wi-Fi access point based on the RF measurements and the interference measurements by solving a complex optimization problem. The Wi-Fi network parameters are then updated based on the derived configurations.

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Istanbul, Turkey

Mustafa Ergen