Patent Description:
Internet of Things (IoT) devices are proliferating in society. These devices generally have specific functionality and limited processing power. In many instances, the processing power is not sufficient to support security or maintenance applications. As such, IoT devices are particularly vulnerable to cyber-attaches. In addition, maintenance and replacement of IoT devices may be delayed or altogether missed because IoT device owners and IoT device vendors cannot easily communicate with one another. Limited communication between owners and vendors may occur for a variety of reasons including, for example, security implementations that limit or exclude data gathering from entities outside of the system owner's IoT network.

As such, systems for managing IoT devices are presented herein.

Systems and methods for using the same are disclosed for managing Internet of Things (IoT) devices in accordance with embodiments of the present invention. In particular, embodiments presented provide platforms featuring an architecture for user and device authentication as well as IoT system self-healing. In an embodiment the system for managing Internet of Things (IoT) devices comprises a plurality of IoT devices operable by a user; a hub for electronically coupling the plurality of IoT devices to the user; an IOT server for electronically coupling the plurality of IoT devices with a plurality of IoT vendors; a plurality of application programming interfaces (API) for enabling data sharing between the plurality of IoT vendors and the plurality of IoT devices; and a plurality of dashboard graphical user interfaces (GUI) for enabling communication between the plurality of IoT vendors and the user, wherein the IoT server enables the plurality of APIs.

Embodiments of the present invention will now be described in detail with reference to a few embodiments thereof as illustrated in the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without some or all of these specific details. In other instances, well known process steps and/or structures have not been described in detail in order to not unnecessarily obscure the present invention.

As will be appreciated by one skilled in the art, the present invention may be a system, a method, and/or a computer program product.

A computer readable storage medium, as used herein, is not to be construed as being transitory signals /per se/, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

<FIG> is an illustrative representation of a system <NUM> for managing Internet of Things (IoT) devices in accordance with embodiments of the present invention. In particular, embodiments presented provide platforms featuring an architecture for user and device authentication as well as IoT system self-healing. As illustrated, system <NUM> includes: human-to-machine (H2M) authentication layer <NUM>; machine-to-machine (M2M) encryption and authentication layer <NUM>; and self-healing layer <NUM>. In embodiments, user <NUM> may access a variety of user IoT devices <NUM> via hub <NUM>. H2M authentication layer embodiments include: smartphone-based login using ultra-sound and ready for voice-activated IoT hubs that provide no user credentials for hackers to steal and prevent phishing or man-in-the-middle attacks. In further embodiments, user IoT devices <NUM> are in communication with IoT server <NUM>, which in turn is enabled with a variety of application program interfaces <NUM> that provide communication between users and IoT vendors <NUM>. M2M encryption and authentication layer embodiments include: minimum computation IoT cypher that authenticates and protects simple end-point devices and data. In addition, self-healing layer embodiments include predictive Artificial Intelligence (AI) analytics to detect anomalous behavior and anticipate device/system failure, which will be discussed in further detail below for <FIG>.

<FIG> is an illustrative representation of a self-healing layer <NUM> for managing IoT devices in accordance with embodiments of the present invention. In particular, <FIG> further illustrates self-healing layer as illustrated in <FIG>, <NUM>. In general, self-healing layer embodiments allow an IoT system to predict, identify and remedy system failures due to a cyberattack, due to a communications failure, or due to physical failures of some of IoT components. As illustrated, server database <NUM> provides a data repository for system <NUM> as enabled by an IoT server (see <NUM>, <FIG>). Relevant data may be retrieved and sent to a variety of entities including IoT hub <NUM>, IoT device vendors <NUM>, <NUM>rd party sources <NUM>, and an AI engine <NUM>. Communication is facilitated between IoT device vendors and IoT device owners via dashboard <NUM> and API <NUM>.

In embodiments, the self-healing layer <NUM> may be thought of as a data analytics layer, which includes a rules engine and flight envelope parameters, an AI engine <NUM> for predictive analytics and anomaly detection, and any number of IoT device vendor interface APIs. The rules engine and flight envelope parameters allow the system to monitor device outputs or inputs to determine if they are within a safe operating range. In embodiments, a rules engine receives data from all connected devices and runs the data against the IoT device operating limits. The rules engine then applies rules specified by the system owner or user to control the IoT system and to evaluate system state and health. In some embodiments, the rules engine alerts a system owner if a device is operating outside of normal parameters. This may be part of an AI decision to replace a device pre-failure or just alert the system owner. The rules engine can also shut down devices which are behaving erroneously.

In general, the rules engine is based on a flight-envelope-limit or operational parameters. In aviation, the term flight envelope refers collectively to the operating parameters and capabilities of a specific model or type of aircraft. The various parameters that make up a flight envelope include the aircraft's maximum altitude, maximum and minimum speed, the maximum amount of g-forces the craft can withstand, climb rate, glide ratio and other factors that define the aircraft's flight characteristics. Just like aircraft, IoT ecosystems and devices also need flight envelopes - pre-defined operating and performance parameters. Any device performing outside of these is an indication that the device is either <NUM>) failing/has failed or <NUM>) the device has been hacked. A device operating within the flight envelope parameters is considered normal. In embodiments, the flight envelope includes at least two sets of "not to exceed" parameters. One is a maximum operating range parameter, the other is a pre-settable operating range parameter which a user may want to mandate for a particular device. In embodiments, if flight envelope parameters are exceeded, the rules engine can activate a particular system reaction, which will depend on operator settable rules.

The AI engine <NUM> for predictive analytics and anomaly detection is part of the self-healing mechanism, which can predict IoT device failure in the system based on Meantime Between Failure (MTBF) statistics provided by IoT device vendors. Anomaly detection monitors system activity data to detect anomalous behavior and potential hacks as well as identifying other system failure modes. System specific failure modes can be determined by machine learning algorithms informed by extended observations of system data. In embodiments, AI engines include machine learning which can establish a baseline of a system owner's IoT system behavioral pattern and detect abnormal system behavior. The AI can then attempt to match the detected abnormal system behavior to known cyberattack patterns. The AI may then take actions to remediate the damage done by an identified cyberattack.

IoT device vendor interface APIs <NUM> are portals to IoT device vendor databases which include IoT device characteristics and MTBF data to be utilized by the AI engine for predictive analytics operations. These APIs <NUM> are supported by dashboards <NUM> allowing IoT System Owners (SO) and IoT Device Vendors (DV) <NUM> to interact to keep IoT systems operational and to request a variety of data and services from each other. MTBF data is one example embodiments, but DVs can also request actual field operations data from SOs to refine their MTBF numbers and even to monitor real time system performance. The same interface is able to process an upgrade, purchase a replacement, or a service as may be needed. This creates a relationship between SOs and DVs with aligned interests to keep IoT systems operating reliably. In effect, both parties have a stake in each other's success. The vendor MTBF Interface API and dashboard UI diagram are discussed in further detail below for <FIG> and <FIG>.

<FIG> is an illustrative representation of a system owner dashboard <NUM> utilized in a system for managing IoT devices in accordance with embodiments of the present invention. In general, a dashboard, as contemplated herein, is a set of intuitive screens through which the SO can interact with the IoT hub using a web browser. A dashboard further allows the SO to monitor current IoT network state and manage rules in the flight envelope. Dashboard embodiments display device performance and operational limits that the device should not go over including one provided by the device manufacturer which if exceeded, will damage the device. As noted above, API embodiments are supported by dashboards that allow IoT SOs and IoT DVs to interact to keep IoT systems operational and to request a variety of data and services from each other. SO dashboard embodiments illustrated include any number of sections including, for example: device information section <NUM>, device sale history table <NUM> and device data request section <NUM>. In embodiments, device data requests may include MTBF data request <NUM>, failure statistics request <NUM>, and real-time telemetry request <NUM>. Other data requests may be included without limitation and without departing from embodiments provided herein. These data requests provide data to an AI engine for predictive analytics operations as disclosed herein. In addition, API also includes an IoT marketplace through which an SO can purchase replacement devices from a DV (any device vendor) once AI makes the decision to replace.

<FIG> is an illustrative representation of a device vendor dashboard <NUM> utilized in a system for managing IoT devices in accordance with embodiments of the present invention. As noted above, API embodiments are supported by dashboards that allow IoT SOs and IoT DVs to interact to keep IoT systems operational and to request a variety of data and services from each other. DV dashboard embodiments illustrated include any number of sections including, for example: system owner information section <NUM>, device sale history table <NUM> and device data request section <NUM>. In embodiments, device data requests may include MTBF data request <NUM>, failure statistics <NUM>, and real-time telemetry <NUM>. The API also acts as a secure contact point between SO and DV, which allows the DV to update/patch their devices in the field to be in compliance with current IoT laws and directives requiring this type of maintenance.

The terms "certain embodiments", "an embodiment", "embodiment", "embodiments", "the embodiment", "the embodiments", "one or more embodiments", "some embodiments", and "one embodiment" mean one or more (but not all) embodiments unless expressly specified otherwise. The terms "including", "comprising", "having" and variations thereof mean "including but not limited to", unless expressly specified otherwise. The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms "a", "an" and "the" mean "one or more", unless expressly specified otherwise.

Claim 1:
A method for managing Internet of Things, IoT, devices in an IoT system, the IoT system (<NUM>) comprising:
a plurality of IoT devices (<NUM>) operable by a user (<NUM>);
a hub (<NUM>) for electronically coupling the plurality of IoT devices (<NUM>) to the user (<NUM>);
an IOT server (<NUM>) for electronically coupling the plurality of IoT devices (<NUM>) with a plurality of IoT vendors (<NUM>);
a plurality of application programming interfaces, APIs, (<NUM>) for enabling data sharing between the plurality of IoT vendors (<NUM>) and the plurality of IoT devices (<NUM>);
a plurality of dashboard graphical user interfaces, GUI, (<NUM>) for enabling communication between the plurality of IoT vendors (<NUM>) and the user (<NUM>),
the IoT server (<NUM>) enabling the plurality of APIs (<NUM>), and comprising an artificial intelligence, AI, engine (<NUM>);
wherein the method comprises:
establishing, by the AI engine (<NUM>), a baseline of the IoT system behavioral patterns;
detecting, by the AI engine (<NUM>), abnormal system behavior based on the baseline;
matching, by the AI engine (<NUM>), the detected abnormal system behavior to any of a plurality of known cyberattack patterns;
remediating, by the AI engine (<NUM>), any damage done that corresponds with any of the matching known cyberattack patterns; and
performing, by the AI engine (<NUM>), predictive analytics and anomaly detection to predict IoT device failure in the IoT system (<NUM>) based on Meantime Between Failure, MTBF, statistics provided by the plurality of IoT device vendors (<NUM>).