  Applying image sensors in automation of Industrial Internet of Things (IIoT)
technology is on the rise, day by day. In such companies, a large number of
high volume images are transmitted at any moment; therefore, a significant
challenge is reducing the amount of transmitted information and consequently
bandwidth without reducing the quality of images. Image compression in sensors,
in this regard, will save bandwidth and speed up data transmitting. There are
several pieces of research in image compression for sensor networks, but,
according to the nature of image transfer in IIoT, there is no study in this
particular field. In this paper, it is for the first time that a new reusable
technique to improve productivity in image compression is introduced and
applied. To do this, a new adaptive lossy compression technique to compact
sensor-generated images in IIoT by using K- Means++ and Intelligent Embedded
Coding (IEC) as our novel approach, is presented. The new method is based on
the colour of pixels so that pixels with the same or nearly the same colours
are clustered around a centroid and finally, the colour of the pixels will be
encoded. The experiments are based on a reputable image dataset from a real
smart greenhouse; i.e. KOMATSUNA. The evaluation results reveal that, with the
same compression rate, our approach compresses images with higher quality in
comparison with other methods such as K-means, fuzzy C-means and fuzzy
C-means++ clustering.
