Abstract: Provided is a method and system for optimizing utilization and performance of one or more unlicensed bands in a network (Wi-Fi and Citizens Broadband Radio Service (CBRS)) through a client device application that communicates with a central node deployed on a cloud platform. The method and system collects Radio Frequency (RF) measurements from a plurality of client devices and from a plurality of nearby point of attachments (PoAs) and transmits the collected RF measurements to the cloud platform. An optimal transmission channel/network in the one or more unlicensed bands is then calculated for one or more nearby PoAs using an Artificial Intelligence (AI) module based on the RF measurements, which utilizes a Dynamic Frequency Selection (DFS), an Automated Frequency Coordinator (AFC) and a Spectrum Access System (SAS) to select the optimal transmission channel/network based on the data-rate result received from the DFS, AFC and SAS.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Provided is a method and system for optimizing utilization and performance of one or more unlicensed bands in a network (Wi-Fi and Citizens Broadband Radio Service (CBRS)) through a client device application that communicates with a central node deployed on a cloud platform. The method and system collects Radio Frequency (RF) measurements from a plurality of client devices and from a plurality of nearby point of attachments (PoAs) and transmits the collected RF measurements to the cloud platform. An optimal transmission channel/network in the one or more unlicensed bands is then calculated for one or more nearby PoAs using an Artificial Intelligence (AI) module based on the RF measurements, which utilizes a Dynamic Frequency Selection (DFS), an Automated Frequency Coordinator (AFC) and a Spectrum Access System (SAS) to select the optimal transmission channel/network based on the data-rate result received from the DFS, AFC and SAS.

Abstract: 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.

Abstract: 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.

Abstract: 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.