Two frameworks for joint compressing and recovering destructed signals in wireless multimedia sensor networks

The WSNs are developed to sense, gather, process and transmit the real-world information and so in recent years, solving numerous challenges of wireless sensor networking are considered intensely. In this paper based on compressive sensing two frameworks denoted as IndM and ECM are proposed to compress and reconstruct the signals of the sensors even for networks which the data transmission is imperfect. In addition, in Wireless Multimedia Sensor Networks intra- and inter-signal correlations can be exploited in the theory of distributed source coding and similarly in distributed compressive sensing to compress signals as much as possible. These cases may be occurred in applications and services work with smart spaces and context aware networks. While IndM reconstructs the signals of sensors separately, ECM method uses the concepts of distributed compressive sensing and the shared information between sensor´s signals to compress the signals more. Compressing the sensors´ signals based on proposed methods in WMSN applications brings lower amount of transmitted data and therefore lower bandwidth usage. In addition, because of exploiting compressive sensing sampling method to compress the signals and this sampling scheme is too simple, using proposed methods brings lower computational cost and also more lifetime in sensor side. Furthermore, all of these achievements can be attain in imperfect transmission system.