The embodiments of the present disclosure provide method and Internet of Things (IoT) systems for predicting maintenance materials of a smart gas pipeline network. The method may be implemented based on a smart gas safety management platform of an Internet of Things (IoT) system for predicting maintenance materials of a smart gas pipeline network. The method may comprise: obtaining a pipeline network feature of a gas pipeline network; predicting fault probabilities of one or more point positions of the gas pipeline network based on the pipeline network feature, the fault probabilities including probabilities of one or more preset fault types of faults occurring at the point positions; and determining demand for the maintenance materials based on the fault probabilities of the one or more point positions.

The embodiments of the present disclosure provide method and Internet of Things (IoT) systems for predicting maintenance materials of a smart gas pipeline network. The method may be implemented based on a smart gas safety management platform of an Internet of Things (IoT) system for predicting maintenance materials of a smart gas pipeline network. The method may comprise: obtaining a pipeline network feature of a gas pipeline network; predicting fault probabilities of one or more point positions of the gas pipeline network based on the pipeline network feature, the fault probabilities including probabilities of one or more preset fault types of faults occurring at the point positions; and determining demand for the maintenance materials based on the fault probabilities of the one or more point positions.

Methods, apparatus, and processor-readable storage media for detecting physical anomalies of a computing environment using machine learning techniques are provided herein. An example computer-implemented method includes monitoring a physical environment corresponding to at least one component of a distributed computing system using at least one sensor that is one or more of: at least partially within the at least one component and attached to the at least one component; performing, by the at least one component, a machine learning process comprising: analyzing data generated by the at least one sensor to detect one or more physical anomalies associated with the physical environment, and in response to detecting a physical anomaly, selecting at least one automated action, involving at least one additional component of the distributed computing system, to at least partially mitigate the physical anomaly; and initiating a performance of the at least one automated action.

Methods, apparatus, and processor-readable storage media for detecting physical anomalies of a computing environment using machine learning techniques are provided herein. An example computer-implemented method includes monitoring a physical environment corresponding to at least one component of a distributed computing system using at least one sensor that is one or more of: at least partially within the at least one component and attached to the at least one component; performing, by the at least one component, a machine learning process comprising: analyzing data generated by the at least one sensor to detect one or more physical anomalies associated with the physical environment, and in response to detecting a physical anomaly, selecting at least one automated action, involving at least one additional component of the distributed computing system, to at least partially mitigate the physical anomaly; and initiating a performance of the at least one automated action.

Wirelessly networked systems of implantable and non-implantable medical devices with networking protocols, software, and hardware that allow for communications and energy transfer between different the medical devices (free standing, implants and wearables) using ultrasonic waves. The networks and methods of use are used to construct cardiac pacing, deep brain stimulation, and neurostimulation networks based on ultrasonic wide band technology.

Disclosed embodiments relate to distributed, crowd-sourced Internet of Things (IoT) discovery using Block Chain. In one example, a method includes scanning a network and generating a signature based on IoT device traits discovered, determining whether the signature is already in a verified or an unverified Block Chain, when the signature exists in the verified Block Chain, providing a verified entry including at least the IoT device type, otherwise, when the signature exists in the unverified Block Chain, providing an unverified entry including at least the IoT device type, incrementing a count, and promoting the unverified entry to the verified Block Chain when the count reaches a threshold, and otherwise, when the signature is in neither Block Chain, using the traits to guess the IoT device type, generating a new entry including the IoT device type, a location, and a timestamp, and storing the new entry in the unverified Block Chain.

Systems and methods are provided for performing privacy transformation of data to protect privacy in data analytics under the multi-access edge computing environment. In particular, a policy receiver in an edge server receives privacy instructions. Inference determiner in the edge server in a data analytics pipeline receives data from an IoT device and evaluates the data to recognize data associated with personally identifiable information. Privacy data transformer transforms the received data with inference for protecting data privacy by preventing exposure of private information from the edge server. In particular, the privacy data transformer dynamically selects a technique among techniques for removing information that is subject to privacy protection and transforms the received data using the technique. The techniques includes reducing resolution of image data such that inference enables object recognition without sufficient details to prevent other servers in the data analytics pipeline to determine identifies of the object deeper inferences.

Systems and methods are provided for performing privacy transformation of data to protect privacy in data analytics under the multi-access edge computing environment. In particular, a policy receiver in an edge server receives privacy instructions. Inference determiner in the edge server in a data analytics pipeline receives data from an IoT device and evaluates the data to recognize data associated with personally identifiable information. Privacy data transformer transforms the received data with inference for protecting data privacy by preventing exposure of private information from the edge server. In particular, the privacy data transformer dynamically selects a technique among techniques for removing information that is subject to privacy protection and transforms the received data using the technique. The techniques includes reducing resolution of image data such that inference enables object recognition without sufficient details to prevent other servers in the data analytics pipeline to determine identifies of the object deeper inferences.

The disclosure relates to system and method for facilitating designing of an Internet of Things (IoT) infrastructure for deploying an IoT application. The method includes determining a Manhattan distance between each of a plurality of existing requirements and a new requirement, identifying one or more of the plurality of existing requirements corresponding to a minimum Manhattan distance, determining a relevancy score for each of the one or more identified existing requirements based on a similarity between the each of the one or more identified existing requirements and the new requirement, and providing one or more IoT components and one or more IoT designs corresponding to a similar existing requirement for facilitating designing of the IoT infrastructure. The similar existing requirement comprises one of the one or more identified existing requirement with a maximum relevancy score.

The disclosure relates to system and method for facilitating designing of an Internet of Things (IoT) infrastructure for deploying an IoT application. The method includes determining a Manhattan distance between each of a plurality of existing requirements and a new requirement, identifying one or more of the plurality of existing requirements corresponding to a minimum Manhattan distance, determining a relevancy score for each of the one or more identified existing requirements based on a similarity between the each of the one or more identified existing requirements and the new requirement, and providing one or more IoT components and one or more IoT designs corresponding to a similar existing requirement for facilitating designing of the IoT infrastructure. The similar existing requirement comprises one of the one or more identified existing requirement with a maximum relevancy score.