SENSOR KITS AND ASSOCIATED METHODS FOR MONITORING AND MANAGING UNDERWATER INDUSTRIAL SETTINGS

1 . A method of monitoring an underwater industrial setting using a sensor kit including an edge device and a plurality of sensors, the method comprising:

receiving, by an edge processing system of the edge device, reporting packets from a plurality of sensors via a self-configuring sensor kit network, each reporting packet containing routing data and one or more instances of sensor data captured by a respective sensor of the plurality of sensors, wherein the plurality of sensors includes two or more sensor types selected from the group comprising: infrared sensors, sonar sensors, LIDAR sensors, water penetrating sensors, light sensors, strain sensors, rust sensors, biological sensors, temperature sensors, chemical sensors, valve integrity sensors, vibration sensors, flow sensors, cavitation sensors, pressure sensors, weight sensors, and camera sensors;

performing, by the edge processing system, one or more edge operations on the instances of sensor data in the reporting packets;

generating, by the edge processing system, sensor kit packets based on the one or more edge operations on the instances of sensor data in the reporting packets; and

transmitting, by the edge processing system, the sensor kit packets to an edge communication system of the edge device, wherein the edge communication system transmits the sensor kit packets to a backend system via a public network.

2 . The method of claim 1 , wherein the sensor kit further comprises a gateway device, wherein the gateway device is configured to receive the sensor kit packets from the edge device via a wired communication link and transmit the sensor kit packets to the backend system via the public network on behalf of the edge device.

3 . The method of claim 2 , wherein the gateway device includes a satellite terminal device that is configured to transmit the sensor kit packets to a satellite that routes the sensor kits to the public network.

4 . The method of claim 2 , wherein the gateway device includes a cellular chipset that is pre-configured to transmit sensor kit packets to a cellphone tower of a preselected cellular provider.

5 . The method of claim 1 , wherein receiving the reporting packets from the one or more respective sensors is performed using a first communication device of the edge device that receives reporting packets from the plurality of sensors via a self-configuring sensor kit network and transmitting the sensor kit packets to the backend system is performed using a second communication device of the edge device.

6 . The method of claim 5 , wherein the second communication device of the edge device is a satellite terminal device that is configured to transmit the sensor kit packets to a satellite that routes the sensor kits to the public network.

7 . The method of claim 5 , further comprising:

capturing, by the plurality of sensors, sensor data; and

transmitting, by the plurality of sensors, the sensor data to the edge device via the self-configuring sensor kit network.

8 . The method of claim 7 , wherein transmitting the sensor data via the self-configuring sensor kit network includes directly transmitting, by each sensor of the plurality of sensors, instances of sensor data with the edge device using a short-range communication protocol, wherein the self-configuring sensor kit network is a star network.

9 . The method of claim 8 , further comprising initiating, by the edge processing system, configuration of the self-configuring sensor kit network.

10 . The method of claim 7 , wherein the self-configuring sensor kit network is a mesh network and each sensor of the plurality of sensors includes a communication device.

11 . The method of claim 10 , further comprising:

establishing, by the communication device of each sensor of the plurality of sensors, a communication channel with at least one other sensor of the plurality of sensors;

receiving, by at least one sensor of the plurality of sensors, instances of sensor data from one or more other sensors of the plurality of sensors; and

routing, by the at least one sensor of the plurality of sensors, the received instances of the sensor data towards the edge device.

12 . The method of claim 7 , wherein the self-configuring sensor kit network is a hierarchical network and the sensor kit includes one or more collection devices.

13 . The method of claim 12 , further comprising:

receiving, by at least one collection device of the one or more collection devices, reporting packets from one or more sensors of the plurality of sensors; and

routing, by the at least one collection device of the one or more collection devices, the reporting packets to the edge device.

14 . The method of claim 12 , wherein each collection device is installed in a different respective section of the underwater industrial setting and collects sensor data from sensors of the plurality sensors that are deployed in the respective section.

15 . The method of claim 1 , further comprising storing, by one or more storage devices of the edge device, instances of sensor data captured by the plurality of sensors of the sensor kit.

16 . The method of claim 1 , wherein the edge device further comprises one or more storage devices that store a model data store that stores one or more machine-learned models that are each trained to predict or classify a condition of a component of the underwater industrial setting and/or the underwater industrial setting based on a set of features that are derived from instances of sensor data captured by one or more of the plurality of sensors.

17 . The method of claim 16 , wherein performing one or more edge operations includes:

generating, by the edge processing system, a feature vector based on one or more instances of sensor data received from one or more sensors of the plurality of sensors;

inputting, by the edge processing system, the feature vector to the machine-learned model to obtain a prediction or classification relating to a condition of a particular component of the underwater industrial setting or the underwater industrial setting and a degree of confidence corresponding to the prediction or classification; and

selectively encoding, by the edge processing system, the one or more instances of sensor data prior to transmission to the backend system based on the prediction or classification.

18 . The method of claim 17 , wherein selectively encoding the one or more instances of sensor data includes:

compressing, by the edge processing system, the one or more instances of sensor data using a lossy codec in response to obtaining one or more predictions or classifications relating to conditions of respective components of the underwater industrial setting and the underwater industrial setting that collectively indicate that there are likely no issues relating to any component of the underwater industrial setting and the underwater industrial setting.

19 . The method of claim 18 , wherein compressing the one or more instances of sensor data using a lossy codec includes:

normalizing, by the edge processing system, the one or more instances of sensor data into respective pixel values;

encoding, by the edge processing system, the respective pixel values into a media content frame; and

compressing, by the edge processing system, a block of media content frames using the lossy codec to obtain a compressed block, wherein the lossy codec is a video codec and the compressed block includes the media content frame and one or more other media content frames that include normalized pixel values of other instances of sensor data.

20 . The method of claim 19 , wherein the backend system receives the compressed block in one or more sensor kit packets and determines the sensor data collected by the sensor kit by decompressing the compressed block using the lossy codec.

21 . The method of claim 17 , wherein selectively encoding the one or more instances of sensor data includes:

compressing, by the edge processing system, the one or more instances of sensor data using a lossless codec in response to obtaining a prediction or classification relating to a condition of a particular component or the underwater industrial setting that indicates that there is likely an issue relating to the particular component or the underwater industrial setting.

22 . The method of claim 17 , wherein selectively encoding the one or more instances of sensor data includes:

refraining, by the edge processing system, from compressing the one or more instances of sensor data in response to obtaining a prediction or classification relating to a condition of a particular component or the underwater industrial setting that indicates that there is likely an issue relating to the particular component or the underwater industrial setting.

23 . The method of claim 17 , wherein selectively encoding the one or more instances of sensor data includes selecting, by the edge processing system, a stream of sensor data instances for uncompressed transmission.

24 . The method of claim 16 , wherein performing one or more edge operations includes:

generating, by the edge processing system, a feature vector based on one or more instances of sensor data received from one or more sensors of the plurality of sensors;

inputting, by the edge processing system, the feature vector to the machine-learned model to obtain a prediction or classification relating to a condition of a particular component of the underwater industrial setting or the underwater industrial setting and a degree of confidence corresponding to the prediction or classification; and

selectively storing, by the edge processing system, the one or more instances of sensor data in a storage device of the one or more storage devices based on the prediction or classification.

25 . The method of claim 24 , wherein selectively storing the one or more instances of sensor data includes:

storing, by the edge processing system, the one or more instances of sensor data in the storage device with an expiry in response to obtaining one or more predictions or classifications relating to conditions of respective components of the underwater industrial setting and the underwater industrial setting that collectively indicate that there are likely no issues relating to any component of the underwater industrial setting and the underwater industrial setting, wherein storing the one or more instances of sensor data in the storage device with an expiry is performed such that the one or more instances of sensor data are purged from the storage device in accordance with the expiry.

26 . The method of claim 24 , wherein selectively storing the one or more instances of sensor data includes:

storing, by the edge processing system, the one or more instances of sensor data in the storage device indefinitely in response to obtaining a prediction or classification relating to a condition of a particular component or the underwater industrial setting that indicates that there is likely an issue relating to the particular component or the underwater industrial setting.

27 . The method of claim 1 , wherein the plurality of sensors includes a first set of sensors of a first sensor type and a second set of sensors of a second sensor type selected from the group comprising: infrared sensors, sonar sensors, LIDAR sensors, water penetrating sensors, light sensors, strain sensors, rust sensors, biological sensors, temperature sensors, chemical sensors, valve integrity sensors, vibration sensors, flow sensors, cavitation sensors, pressure sensors, weight sensors, and camera sensors.