Image Transmission Over Decode and Forward Based Cooperative Wireless Multimedia Sensor Networks for Rayleigh Fading Channels in Medical Internet of Things (MIoT) for Remote Health-Care and Health Communication Monitoring - Al Hayani, Bilal; Ilhan, Haci (AMER SCIENTIFIC PUBLISHERS, 2020)
For wireless multimedia sensor networks (WMSNs), energy efficiency is the most critical research problem as the high dimensional digital images consumes more processing capabilities of sensor nodes. In WMSN, the image is transmitted through large number of nodes, thus larger the transmissions more the energy consumption. The use of compressive sensing (CS) for periodic data transmissions is proven as an effective solution for Wireless Sensor Networks (WSNs) already. The CS based sensor communications reduces the data transmissions and improves the energy efficiency significantly. However, another concern while using the compression is data loss due to different security threats during the transmission. In this paper, a novel model for one-way decode-and-forward (DF) based helpful image transmission in sensor networks using advanced terminologies for image compression and encryption is presented. First at transmitter side, the images are pre-processed and compressed using the advanced JPEG2000 compression method, and then secure the compressed image data using the modified elliptical curve cryptography (ECC). In cooperative communications, proposed ECC based security techniques prevents any vulnerabilities on compressed data. Relay node performs the task of DF technique on received data. At the receiver side, all reversed process is performed to reconstruct the original image and then again applying pre-processing filter to improve the image quality. The simulation results show that proposed model showing performances improved compared to condition of-craftsmanship techniques.
Keywords: Unified Communication; Relay Communication; Modulation; Compressive Sensing; Encryption; Energy Efficiency; COMPRESSION; DIVERSITY; Mathematical & Computational Biology; Radiology, Nuclear Medicine & Medical Imaging