Microsoft Technology Licensing, LLC

Yuanchao Shu

Ganesh Ananthanarayanan

Paramvir Bahl

Zhuqi Li

Thomas Moscibroda

Nikolaos Karianakis

Tsu-wang Hsieh

Börje Felipe Fernandes Karlsson

Yundie Zhang

Romil Bhardwaj

Ramarathnam R Venkatesan

Yoganand Rajasekaran

Yu Yan

Tsuwang Hsieh

Kiran Muthabatulla

Anand Padmanabha Iyer

Yi Jiang

Longfei Shangguan

Ruogu Du

Arthi Hema Padmanabhan

In implementations of the subject matter described herein, a solution for providing an indoor location-based service is provided. In this solution, a floor plan about a first floor of a first building comprising at least one floor is obtained. A first map for the first floor is generated based on the floor plan. The first map includes a plurality of vertices and edges, where one vertex corresponds to a position on the first floor, an edge connecting two vertices represents a physical object or a passable path on the first floor, and two positions corresponding to the two vertices are located at the physical object or on the passable path. Moreover, a location-based service is provided to a user at least based on the first map.

Indoor location-based service

Systems and methods are provided for continuous learning of models across hierarchies under a multi-access edge computing. In particular, an on-premises edge server, using a model, generates inference data associated with captured stream data. A data drift determiner determines a data drift in the inference data by comparing the data against reference data generated using a golden model. The data drift indicates a loss of accuracy in the inference data. A gateway model maintains one or more models in a model cache for update the model. The gateway model instructs the one or more servers to train the new model. The gateway model transmits the trained model to update the model in the on-premises edge server. Training the new model includes determining an on-premises edge server with computing resources available to train the new model while generating other inference data for incoming stream data in the data analytic pipeline.

Continuous learning models across edge hierarchies

The techniques described herein use an edge device to manage the security for a data stream being ingested by a tenant and a cloud platform. The creation of the data stream for ingestion occurs in an environment that is trusted by a tenant (e.g., an on-premises enterprise network). The cloud platform that is part of the data stream ingestion process is outside this trusted environment, and thus, the tenant loses an element of security when ingesting data streams for cloud storage and/or cloud processing. Accordingly, the edge device is configured on a trust boundary so that the data stream ingestion process associated with a cloud platform is secured, or trusted by the tenant. The edge device is configured to encrypt the data stream using a data encryption key and/or manage the protection of the data encryption key.

Secure data ingestion with edge computing

Provided are aspects relating to methods and computing devices for allocating computing resources and selecting hyperparameter configurations during continuous retraining and operation of a machine learning model. In one example, a computing device configured to be located at a network edge between a local network and a cloud service includes a processor and a memory storing instructions executable by the processor to operate a machine learning model. During a retraining window, a selected portion of a video stream is selected for labeling. At least a portion of a labeled retraining data set is selected for profiling a superset of hyperparameter configurations. For each configuration of the superset of hyperparameter configurations, a profiling test is performed. The profiling test is terminated, and a change in inference accuracy that resulted from the profiling test is extrapolated. Based upon the extrapolated inference accuracies, a set of selected hyperparameter configurations is output.

Allocating computing resources during continuous retraining

Systems and methods are provided for reducing stream data according to a data streaming protocol under a multi-access edge computing. In particular, an IoT device, such as a video image sensing device, may capture stream data and generate inference data by applying a machine-learning model trained to infer data based on the captured stream data. The inference data represents the captured stream data in a reduced data size based on performing data analytics on the captured data. The IoT device formats the inference data according to the data streaming protocol. In contrast to video data compression, the data streaming protocol includes instructions for transmitting the reduced volume of inference data through a data analytics pipeline.

Data streaming protocols in edge computing

Computing resources are managed in a computing environment comprising a computing service provider and an edge computing network. The edge computing network comprises computing and storage devices configured to extend computing resources of the computing service provider to remote users of the computing service provider. The edge computing network collects capacity and usage data for computing and network resources at the edge computing network. The capacity and usage data is sent to the computing service provider. Based on the capacity and usage data, the computing service provider, using a cost function, determines a distribution of workloads pertaining to a processing pipeline that has been partitioned into the workloads. The workloads can be executed at the computing service provider or the edge computing network.

Orchestrating edge service workloads across edge hierarchies

Methods and computing devices for allocating computing resources and selecting hyperparameter configurations during continuous retraining and operation of a machine learning model. In one example, a computing device configured to be located at a network edge between a local network and a cloud service includes a processor and a memory storing instructions executable by the processor to operate a machine learning model. During a retraining window, a selected portion of a video stream is selected for labeling. At least a portion of a labeled retraining data set is selected for profiling a superset of hyperparameter configurations. For each configuration of the superset of hyperparameter configurations, a profiling test is performed. The profiling test is terminated, and a change in inference accuracy that resulted from the profiling test is extrapolated. Based upon the extrapolated inference accuracies, a set of selected hyperparameter configurations is output.

A multi-hop relay network comprises a set of data nodes arranged in a relay topology, wherein the set of data nodes communicate with one another via a data plane comprising a set of high frequency data links, such as mmWave links. An edge node communicates with one or more of the set of data nodes via the data plane and communicates with the set of data nodes over a control plane comprising a set of low frequency wireless links, such as a Wi-Fi network. The edge node determines a path utilization for the set of high frequency wireless links. When one of the high frequency wireless links is over-utilized, the edge node communicates, to a data node in the set of data nodes via the control plane, a command to change the relay topology. The edge node also determines whether the relay topology is operating at a target accuracy. When it is not, the edge node adjusts a data analytics parameter for a node to improve the overall accuracy of the network.

Live video analytics over high frequency wireless networks

In accordance with implementations of the subject matter described herein, a new approach for generating indoor navigation is proposed. Generally speaking, a reference signal that includes time series data collected by at least one environment sensor along a reference path from a start point to a destination is obtained. For example, the reference signal may be obtained by environment sensors equipped in a user's mobile device or another movable entity. Then, a movement event by identifying a pattern from the reference signal, the pattern describing measurements of the at least one environment sensor associated with a specific movement is extracted. Next, a navigation instruction is generated to indicate that the movement event occurs during a movement of the at least one environment sensor along the reference path. Further, the navigation instruction may be provided to a person for indoor navigation.

Indoor navigation

In accordance with implementations of the subject matter described herein, a new approach for creating a navigation trace is proposed. In these implementations, a reference signal is obtained, where the reference signal includes time series data collected by at least one environment sensor along a reference path from a start point to a destination. Then, a navigation trace is created for the reference path based on the obtained reference signal, where magnitudes at locations of the navigation trace reflect measurements collected by the at least one environment sensor at respective time points of the reference signal. In accordance with implementations of the subject matter described herein, a new approach for generating a navigation instruction from a navigation trace.

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Kirkland, WA, United States of America

Yuanchao Shu