An Efficient and Robust MIoT Communication Solution using a Deep Learning Approach

Abstract

Due to the volume of multimedia sensed data, a network of Multimedia Internet of Things (MIoT) devices faces various challenging constraints, most notably in terms of communication overhead, power consumption, and memory usage. A set of these MIoT devices is unable to overcome the large data-size challenge via the use of the Lossy Multimedia Compression (LMC) such as JPEG and BPG since they are limited in memory and computation. Instead, in this paper, we propose to down-scale images at MIoT devices with a factor of 2, 3 or ≥ 4, which reduces the memory consumption, computation, and communicated data size and consequently the latency and energy consumption. To recuperate high-quality images, we apply a Deep Learning (DL) denoising/super-resolution model at the server-side. On the other hand, as MIoT devices use a wireless connection, there is a higher risk of transmission packets loss compared to a wired connection. Almost, packets loss are managed through costly data re-transmissions or data redundancy. However, these solutions with intrinsically voluminous data such as the multimedia one are costly, especially for limited MIoT devices. To overcome this challenge, the denoising/super-resolution model did also undergo a training model to retrieve high-quality images from down-scaled erroneous ones. The obtained results show how effective this proposed solution is, especially when it comes to the enhancement of visual quality of down-scaled and erroneous images with minimum communication, latency, and consequently resource overhead.