Abstract: A home computing cloud (HCC) supports one or more Internet of Things (IoT) devices, possibly with different connectively protocols, in a local environment. The HCC often reduces the amount of data traffic sent to a public computing cloud (PCC) by locally processing collected device data rather than by sending the device data to the PCC for processing. This approach reduces the amount of data traffic sent over the network, improves data privacy and helps to maintain a desired quality of service level. In order to do so, the HCC may download an appropriate data analytic model from the PCC, train the model, execute the trained model to obtain prediction information from collected IoT device data, and upload the trained model to the PCC. Alternatively, the HCC and PCC may execute sub-models of the analytic model and exchange the outputs of the sub-models with each other.

Abstract: A home computing cloud (HCC) supports one or more Internet of Things (IoT) devices, possibly with different connectively protocols, in a local environment. The HCC often reduces the amount of data traffic sent to a public computing cloud (PCC) by locally processing collected device data rather than by sending the device data to the PCC for processing. This approach reduces the amount of data traffic sent over the network, improves data privacy and helps to maintain a desired quality of service level. In order to do so, the HCC may download an appropriate data analytic model from the PCC, train the model, execute the trained model to obtain prediction information from collected IoT device data, and upload the trained model to the PCC. Alternatively, the HCC and PCC may execute sub-models of the analytic model and exchange the outputs of the sub-models with each other.

Abstract: A home computing cloud (HCC) supports one or more Internet of Things (IoT) devices, possibly with different connectively protocols, in a local environment. The HCC often reduces the amount of data traffic sent to a public computing cloud (PCC) by locally processing collected device data rather than by sending the device data to the PCC for processing. This approach reduces the amount of data traffic sent over the network, improves data privacy and helps to maintain a desired quality of service level. In order to do so, the HCC may download an appropriate data analytic model from the PCC, train the model, execute the trained model to obtain prediction information from collected IoT device data, and upload the trained model to the PCC. Alternatively, the HCC and PCC may execute sub-models of the analytic model and exchange the outputs of the sub-models with each other.