An apparatus (105, 110) comprising: a transceiver communication module (122, 135) configured to transmit or receive compressed data over a communication link; and a compression configuration module (155, 175) coupled with the transceiver communication module, the compression configuration module configured to: identify one or more compression/decompression parameters, a length of time and energy of compression/decompression associated with the compression of data to form the compressed data or decompression of the compressed data to form the data, and a transmission/reception energy associated with transmission or reception of the compressed data over the communication link; predict with a trained machine learning algorithm a predicted energy consumption, based on an energy consumption trade-off among the identified one or more compression/decompression parameters, the length of time and energy of compression/decompression, and the transmission/reception energy, by the apparatus, for each of a plurality of compression configurations; and identify, based on the predicted energy consumption, a compression configuration to be used to reduce energy consumption from the plurality of compression configurations, wherein the compression configuration is based on one or more of the compression/transmission related parameters; obtain an energy and time outcome of the compression configuration, wherein a difference between the predicted energy consumption and the obtained energy and time outcome of the identified compression configuration is used to retrain the trained machine learning algorithm. a transceiver communication module (122, 135) configured to transmit or receive compressed data over a communication link; and a compression configuration module (155, 175) coupled with the transceiver communication module, the compression configuration module configured to: identify one or more compression/decompression parameters, a length of time and energy of compression/decompression associated with the compression of data to form the compressed data or decompression of the compressed data to form the data, and a transmission/reception energy associated with transmission or reception of the compressed data over the communication link; predict with a trained machine learning algorithm a predicted energy consumption, based on an energy consumption trade-off among the identified one or more compression/decompression parameters, the length of time and energy of compression/decompression, and the transmission/reception energy, by the apparatus, for each of a plurality of compression configurations; and identify, based on the predicted energy consumption, a compression configuration to be used to reduce energy consumption from the plurality of compression configurations, wherein the compression configuration is based on one or more of the compression/transmission related parameters; obtain an energy and time outcome of the compression configuration, wherein a difference between the predicted energy consumption and the obtained energy and time outcome of the identified compression configuration is used to retrain the trained machine learning algorithm. identify one or more compression/decompression parameters, a length of time and energy of compression/decompression associated with the compression of data to form the compressed data or decompression of the compressed data to form the data, and a transmission/reception energy associated with transmission or reception of the compressed data over the communication link; predict with a trained machine learning algorithm a predicted energy consumption, based on an energy consumption trade-off among the identified one or more compression/decompression parameters, the length of time and energy of compression/decompression, and the transmission/reception energy, by the apparatus, for each of a plurality of compression configurations; and identify, based on the predicted energy consumption, a compression configuration to be used to reduce energy consumption from the plurality of compression configurations, wherein the compression configuration is based on one or more of the compression/transmission related parameters; obtain an energy and time outcome of the compression configuration, wherein a difference between the predicted energy consumption and the obtained energy and time outcome of the identified compression configuration is used to retrain the trained machine learning algorithm.