Techniques are described that combine machine learning with an edge network that includes IoT devices to yield an effective and efficient method of assessing a condition of an environment.  An inference module that includes a machine-learning algorithm, installed and executing on the IoT devices, assesses a condition detected from multiple, different geographic locations.  The IoT devices transfer sets of data and inferences as well as respective sets of confidence levels to converge on a verified set of inferences.  The verified set of inferences is arrived at quickly and with a high confidence level

Shankar, Karthik Ravi

Singh, Hanumant Prasad

English

Technical Disclosure Common

Technical Disclosure CommonsDefensive Publications SeriesOctober 30, 2018Transfer Inference LearningKarthik Ravi ShankarHanumant Prasad SinghFollow this and additional works at: https://www.tdcommons.org/dpubs_seriesThis work is licensed under a Creative Commons Attribution 4.0 License.This Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive PublicationsSeries by an authorized administrator of Technical Disclosure Commons.Recommended CitationShankar, Karthik Ravi and Singh, Hanumant Prasad, "Transfer Inference Learning", Technical Disclosure Commons, (October 30,2018)https://www.tdcommons.org/dpubs_series/1618Transfer Inference Learning Karthik Ravi Shankar Hanumant Singh  Abstract:   Techniques are described that combine machine learning with an edge network that includes IoT devices to yield an effective and efficient method of assessing a condition of an environment.  An inference module that includes a machine-learning algorithm, installed and executing on the IoT devices, assesses a condition detected from multiple, different geographic locations.  The IoT devices transfer sets of data and inferences as well as respective sets of confidence levels to converge on a verified set of inferences.  The verified set of inferences is arrived at quickly and with a high confidence level. Keywords:  Internet-of-Things (IoT), machine learning, inference, edge network, edge device  Background: Today, with advancements in communication technologies and with computing/sensing electronics embedded in a myriad of devices, the ability for devices to collect and exchange data with one another is escalating.  Devices such as smart phones, voice-recognizing personal assistants, computers, automobiles, home entertainment systems/appliances, and the like, are able to communicate with one another either directly, in a machine-to-machine environment, or indirectly over a network.  Such communications and exchange of data across the myriad of devices is commonly referred to as the Internet-of-Things (IoT).  The communications and exchange of data can have purposes that include, for example, collecting usage data for vendor 2Shankar and Singh: Transfer Inference LearningPublished by Technical Disclosure Commons, 2018analytics, remote initiation/shut-down of an operating system, automating a home environment, monitoring a person’s health, and so forth. A view of an example IoT environment is represented in Fig. 1 below:  74Internet of Things (IoT) Fig. 1 In the IoT environment of Fig. 1, data may be collected by sensors of a device and shared with another device.  Processing of data may be performed local to the device collecting the data or remote from the device collecting the data.  Combinations of hardware (e.g., sensors, 3Defensive Publications Series, Art. 1618 [2018]https://www.tdcommons.org/dpubs_series/1618microprocessors, memory), software (e.g., algorithms, GUI’s), and services (e.g., communication networks) may be used to sense, collect, and exchange data.  Large amounts of data are expected to be exchanged, as part of the IoT, across a horizon that is developing and changing frequently. Detection mechanisms that may be built into IoT devices, such as light sensors, radar systems, proximity sensors, imaging sensors, cameras, or microphones, may measure conditions of an environment surrounding the IoT devices.  Furthermore, and in some instances, computing algorithms may be applied to the conditions, as measured by the IoT devices, to assess aspects of the environment, examples of which include identifying a person who might be within the environment, quantifying movement of an object within the environment, or detecting a manufacturing anomaly within the environment.   Typically, a centralized computing system, such as a cloud-computing server or a network server, applies the computing algorithms and machine learning techniques to the conditions detected by the IoT devices to determine or quantify the elements within the environment.  However, the use of such a centralized computing system with the IoT devices has multiple drawbacks.  As one example, extensive use of computing power of the centralized computing system may circumvent other, higher priority operations or computations the computing system may be tasked to perform.  As another example, communication latencies between the IoT devices and the centralized computing system may delay results.  And, as yet another example, if a single IoT device provides the detected conditions to the centralized computing systems, inferences drawn on the detected conditions may be in error if the single IoT device is distant from the conditions within the environment that it is trying to detect.  In summary, combining the centralized computing system with IoT devices yields neither an efficient nor effective manner to assess aspects of the environment.  4Shankar and Singh: Transfer Inference LearningPublished by Technical Disclosure Commons, 2018Description: An edge computing model, in general, relies on computations divided across multiple, geographically distributed computing nodes of an edge network.  In some cases, the edge network may be radio access network (RAN) such as a as third-generation partnership project long-term evolution (3GPP LTE) network or fifth-generation new radio (5G NR) network.  Furthermore, in edge computing, the geographically distributed computing nodes are referred to as edge devices.  In certain instances, a geographic distribution of the previously described IoT devices may constitute the edge devices of the edge network. Machine-learning techniques may be combined with the edge network (including the IoT devices) to yield an effective and efficient method of assessing conditions of an environment.  For example, a machine-learning algorithm executing on a first IoT device may assess a condition that is detected by detection mechanisms of the IoT device and generate a first set of data and inferences.  Based on a first geographic location of first IoT device with respect to the condition as well as capabilities of the IoT device’s detection mechanisms, a set of first confidence levels may be established for the first set of data and inferences.  The first set of confidence levels, along with the first set of data and inferences, may be transferred to a second IoT device that is currently detecting and assessing (or will subsequently detect and assess) the condition from another geographic location and yield a second set of data and inferences with a second set of confidence levels.  The assessment of the condition, as performed by the second IoT device, may factor in the first set of confidence levels, along with the first set of data and inferences, to expedite its assessment of the condition.  In general, the method may “cascade” amongst multiple devices to quickly converge on an assessment that meets a determined confidence level. 5Defensive Publications Series, Art. 1618 [2018]https://www.tdcommons.org/dpubs_series/1618Consider the example illustrated by Fig. 2 below.  As illustrated by Fig. 2, a series of IoT devices (cameras) are detecting and assessing a condition in an environment.  In this example, the condition corresponds to a person walking along a path that locates the person at different distances (and angles) relative to different, respective cameras. Cam 1Cam 2Cam 3D1D2D3First set of data and inferences, first set of confidence levelsSecond set of data and inferences, second set of confidence levelsNotification, including verified set of inferences Smart Phone Fig. 2 6Shankar and Singh: Transfer Inference LearningPublished by Technical Disclosure Commons, 2018As illustrated in Fig. 2, detection mechanisms, such as a proximity sensor, a light sensor, and an image sensor that may be integrated into a first camera (Cam1), detect that the person is walking a distance (D1) from the first camera under poor lighting conditions.  Performing a first iteration of computing operations local to itself (e.g., edge computing), the first camera Cam1 may assess the condition to yield a first set of data that might include, for example, viewing angles, lighting conditions, focal distances, and the like.  The camera may also assess the condition to yield inferences that might include, for example, how tall the person is based on pixel scaling, who the person is through image recognition techniques, if the person is injured via kinematic motion analysis, and the like.  Furthermore, and based on the distance D1, the first camera Cam1 may assign a first confidence level for each of the first set of data and inferences.   As part of an edge network, the first camera Cam1 then transfers the first set of data and inferences, as well as the first set of confidence levels, to the second camera (Cam2).  As illustrated in Fig. 2, wireless links (e.g., wireless links of a 3GPP LTE network or a 5G NR network) transfer the first set of data and inferences and  the first set of confidence levels (for simplicity, base stations of the edge network are not illustrated).  In some alternate variations, transferring may be supported by a wired, local area network (LAN), Bluetooth ® wireless communication technologies, or the like. After receiving the first set of data and inferences, as well as the first set of confidence levels, the second camera Cam2 may modify settings associated with its detection mechanisms (e.g., its proximity sensor, light sensor, and image sensor) or parameters used as part of assessment operations (e.g., pixel scaling, image recognition, and kinematic motion analysis).  It is also possible for the second camera Cam2 to activate an additional or other detection mechanism it 7Defensive Publications Series, Art. 1618 [2018]https://www.tdcommons.org/dpubs_series/1618might have over the detection mechanisms of the first camera Cam1 (an example would be a microphone to detect audio data for voice recognition).   As the person is within its field of view, the second camera Cam2 may then perform a second iteration of computing operations (e.g., edge computing operations that assess the condition of the person walking near the second camera Cam2) based on settings, parameters, and activated detection mechanisms derived from the first set of data and inferences and the first set of confidence levels to yield a second set of data and inferences.  In some instances, the second camera Cam 2 may omit computing operations to assess a particular inference if an associated, received confidence level (e.g., a confidence level from the first set of confidence levels) meets a threshold.  For example, if the confidence level of the person’s height as assessed by the first Cam1 meets the threshold, the second camera Cam2 may omit performing computing operations directed to assessing the person’s height and conserve computing resources.  Based on the distance D2, the second camera Cam2 may assign a second set of confidence levels to the second set of data and inferences, after which it transfers the second set of data and inferences and the second set of confidence levels to a third camera Cam3. The third camera Cam3 may perform computing operations again (e.g., edge computing).  This third iteration of computing operations, using the transferred second set of data and inferences and the second set of confidence levels, is more efficient and effective than both the first iteration of computing operations (performed by the first camera Cam1) the second iteration of computing operations (performed by the second camera Cam2).  As illustrated, the third iteration of computing operations yields a verified set of inferences (e.g., the identity, height, and injury status) that meet a confidence level threshold and transmits a notification, including the verified set of inferences, to another device (e.g., a smart phone) via the edge network. 8Shankar and Singh: Transfer Inference LearningPublished by Technical Disclosure Commons, 2018In general, an IoT device that supports transfer inference learning will include several components.  Examples of the components in a camera-based IoT device (e.g., any of Cam1, Cam2, or Cam 3 of the example described in Fig. 2) are illustrated in Fig. 3 below. TransceiverDetection MechanismsProcessor Computer-Readable Storage MediaInference Module  Fig. 3 As illustrated in Fig. 3, the camera-based IoT device includes a transceiver, detection mechanisms, a processor, and a computer-readable storage media than includes code or instructions in the form of an inference module.  In the context of the example described by Fig. 2, 9Defensive Publications Series, Art. 1618 [2018]https://www.tdcommons.org/dpubs_series/1618the transceiver might be a transceiver that is capable of transmitting or receiving communications as part of an edge network that is a 3GPP LTE or 5GNR network.  The detection mechanisms may correspond to the proximity sensor, a light sensor, and an image sensor.  Furthermore, and as part of the edge computing operations of Fig. 2 (e.g., Cam1, Cam2, or Cam3 assessing conditions to yield sets of data and inferences and sets of confidence levels), the processor may execute the code of the inference module.  The inference module, in general, includes a machine-learning algorithm. The above material, directed to a camera-based IoT device, is an example of transfer inference learning within an edge-computing environment.  However, the camera-based IoT device is by way of example only, and is non-limiting.  For example, edge devices which are not IoT devices, yet possess a transceiver, detection mechanisms, a processor, and a computer-readable storage media having an inference module, may perform transfer inference learning within an edge-computing environment using the techniques described herein.  10Shankar and Singh: Transfer Inference LearningPublished by Technical Disclosure Commons, 2018

Transfer Inference Learning

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Transfer Inference Learning

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