INTENT-BASED NETWORK COMPUTING JOB ASSIGNMENT

    公开(公告)号:US20240129195A1

    公开(公告)日:2024-04-18

    申请号:US18481988

    申请日:2023-10-05

    CPC classification number: H04L41/0896 H04L41/122

    Abstract: Described is a novel framework, we call intent-based computing jobs assignment framework, for efficiently accommodating a clients' computing job requests in a mobile edge computing infrastructure. We define the intent-based computing job assignment problem, which jointly optimizes the virtual topology design and virtual topology mapping with the objective of minimizing the total bandwidth consumption. We use the Integer Linear Programming (ILP) technique to formulate this problem, and to facilitate the optimal solution. In addition, we employ a novel and efficient heuristic algorithm, called modified Steiner tree-based (MST-based) heuristic, which coordinately determines the virtual topology design and the virtual topology mapping. Comprehensive simulations to evaluate the performance of our solutions show that the MST-based heuristic can achieve an efficient performance that is close to the optimal performance obtained by the ILP solution.

    MAPPING USING OPTICAL FIBER SENSING

    公开(公告)号:US20220333956A1

    公开(公告)日:2022-10-20

    申请号:US17720245

    申请日:2022-04-13

    Abstract: Distributed fiber optic sensing (DFOS) systems and methods that automatically detect vibration signal patterns from waterfall data recorded by DFOS system operations in real- time and associate the detected vibration signal patterns to GPS location coordinates without human intervention or interpretation. When embodied as a computer vision-based operation according to aspects of the present disclosure, our inventive systems and method provide accurate, cost-efficient, and objective determination without relying on humans and their resulting bias' and inconsistencies.

    AUTOMATIC CALIBRATION FOR BACKSCATTERING-BASED DISTRIBUTED TEMPERATURE SENSOR

    公开(公告)号:US20240302225A1

    公开(公告)日:2024-09-12

    申请号:US18598386

    申请日:2024-03-07

    CPC classification number: G01K15/005 G01K11/32

    Abstract: Disclosed are vehicle-infrastructure interaction systems and methods employing a distributed fiber optic sensing (DFOS) system operating with pre-deployed fiber-optic telecommunication cables buried alongside/proximate to highways/roadways which provide 24/7 continuous information stream of vehicle traffic at multiple sites; only require a single optical sensor cable that senses/monitors multiple locations of interest and multiple lanes of traffic; the single optical sensor cable measures multiple related information (multi-parameters) about a vehicle, including driving speed, wheelbase, number of axles, tire pressure, and others, that can be used to derive secondary information such as weight-in-motion; and overall information about a fleet of vehicles, such as traffic congestion or traffic-cargo volume. Different from merely traffic counts, our approach can provide the count grouped by vehicle-types and cargo weights. Precise measurements are facilitated by high temporal sampling rates of the distributed acoustic sensing and a dedicated peak finding algorithm for extracting the timing information reliably.

    DYNAMIC INTENT-BASED NETWORK COMPUTING JOB ASSIGNMENT USING REINFORCEMENT LEARNING

    公开(公告)号:US20230376783A1

    公开(公告)日:2023-11-23

    申请号:US18319472

    申请日:2023-05-17

    CPC classification number: G06N3/092 G06N3/045

    Abstract: An advance in the art is made according to aspects of the present disclosure directed to a method that determines virtual topology design and resource allocation for dynamic intent-based computing jobs in a mobile edge computing infrastructure when client requests are dynamic. Our method according to aspects of the present disclosure is an unsupervised machine learning approach, so that there is no need for manual labeling or pre-processing in advance, while a training process and decision making is performed online. In sharp contrast to the prior art, our method according to aspects of the present disclosure utilizes reinforcement learning techniques to make an efficient assignment in which two neural networks—a policy neural network and a value neural network—are used interactively to achieve the assignment. A training process is performed through a batch (or group) processing style in an online manner.

    COLORLESS DISTRIBUTED FIBER OPTIC SENSING / DISTRIBUTED VIBRATION SENSING

    公开(公告)号:US20230152151A1

    公开(公告)日:2023-05-18

    申请号:US17987822

    申请日:2022-11-15

    CPC classification number: G01H9/004 G01D5/35361

    Abstract: Systems, methods, and structures for colorless distributed fiber optic sensing/distributed vibration sensing (DOFS/DVS) over dense wavelength division multiplexing (DWDM) telecommunications facilities that operate over a C-band wavelength range spanning from 1525 nm to 1565 nm wherein the DOFS/DVS systems exhibit suitable reconfigurability of its wavelength to match a wavelength of a desired testing channel and may advantageously provide DOFS/DVS capabilities to existing DWDM communications infrastructure as a retrofit. Colorless DFOS/DVS systems according to the present disclosure include a length of optical sensor fiber; a colorless DFOS/DVS interrogator in optical communication with the optical sensor fiber, said colorless DFOS/DVS interrogator configured to generate optical pulses, introduce the generated pulses into the length of optical sensor fiber, and receive backscattered signals from the length of the optical sensor fiber; and an intelligent analyzer configured to analyze colorless DFOS/DVS data received by the DFOS/DVS interrogator and determine from the backscattered signals, vibrational activity occurring at locations along the length of the optical sensor fiber.

    FLEXIBLE AND RAPID DEPLOYABLE FIELD MONITORING SYSTEM

    公开(公告)号:US20240118116A1

    公开(公告)日:2024-04-11

    申请号:US18311881

    申请日:2023-05-03

    CPC classification number: G01D5/35358

    Abstract: A flexible, rapid deployable perimeter monitoring system and method that employs distributed fiber optic sensing (DFOS) technologies and includes a deployment/operations field vehicle including an interrogator and analyzer/processor. The deployment/operations field vehicle is configured to field deploy a ruggedized fiber optic sensor cable in an arrangement that meets a specific application need, and subsequently interrogate/sense via DFOS any environmental conditions affecting the deployed fiber optic sensor cable. Such sensed conditions include mechanical vibration, acoustic, and temperature that may be advantageously sensed/evaluated/analyzed in the deployment/operations vehicle and subsequently communicated to a central location for further evaluation and/or coordination with other monitoring systems. Upon completion, the field vehicle and DFOS reconfigure a current location or redeployed to another location.

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