Abstract: 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.

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 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: 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.

Abstract: 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.

Abstract: 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.