Performance Improvement of Wireless Multimedia Sensor Networks using MIMO and Compressive Sensing

Wireless Multimedia Sensor Networks (WMSN) are designed to transmit audio and video streams, still images, and scalar data. Multimedia transmission over wireless sensor networks has many applications, such as video surveillance, object tracking, telemedicine, theft control systems, and traffic monitoring. Researchers face many challenges, such as higher data rates, lower energy consumption, reliability, signal detection and estimation, uncertainty in network topology, quality of service (QoS), and security- and privacy-related issues to accomplish various applications of WMSN. This paper presents multiple input multiple output (MIMO) along with compressive sensing (CS) properties to improve system performance in terms of energy consumption and QoS in deep fade environments. The CS theory model has been proposed to reduce energy consumption by taking fewer measurements of the original signal or information and reconstructing it with acceptable image quality at the receiver side. The transmission and processing energy can be reduced by transmitting fewer measurements from the sensor side itself. The MIMO model and CS algorithm have been simulated, and results show that CS performs well on images.