## 7 Application Layer in WMSN
### Multimedia Source Coding Techniques
Each description alone provides acceptable low quality version of the original and combining all descriptions together gives higher resolution. This technique can be used in conjunction with multi path transport approach to achieve load balancing and meet the available bandwidth as shown in [37] and [36]. * Distributed Video Coding (DVC) [70], used for low complexity encoding by shifting the complexity to the sink side, incorporates concepts from source coding with decoder side information for creating an Intra-coded frame along with a side information frame. Therefore, in this technique, multimedia content can be partitioned into multiple streams consisting both intra-coded and decoder side information frames by using simple and low power encoder while the decoder at the destination side can be complex exploiting the availability of the resource such as energy and processing power capability. Two practical DVC encoders are proposed in the literature, Wyner-Ziv (WZ) [71] and PRIZM [72]. DVC coding has been used for WMSN in [73] and [68]. It is shown in [74] through practical implementation of DVC in WMSN that there is a tradeoff between computation and transmission power consumption depending on the encoding schemes used in implementing DVC codec. While a computational intensive scheme, such as discrete cosine transform (DCT), consumes more computational power, it achieves significant compression hence less needed transmission power. On the other hand a less computational intensive scheme, such as a pixel based codec needs less computational power but more transmission power. Therefore, the choice of either scheme (DCT or pixel based) to implement the DVC codec for multimedia content in WMSN depends on the tolerable distortion (quality) and power consumption. As another methodology for real time video compression and communication over WMSN, Address-Event Representation (AER) is demonstrated in [75]. AER overcomes the limitations of video transmitting over the network in terms of data rate and bandwidth. It is shown in [75] that for streaming video of size 320 \(\times\) 240 differencing, Crossbow MICAz will be hardly able to deliver the video content at about 2 fps at 250 Kbps data rate which it will be hard to understand for the end-user and consume all the communication capabilities of the sensor nodes. Therefore, the proposed algorithm in [75] encodes the video using AE representation, by using custom image sensors capable of detecting intensity-differencing information, and perform a zero-computation compression of the frame-difference video. This compression technique enables sensor nodes to stream temporal frame-difference video over the network at high rates where frame-differencing video can be obtained by subtracting each pixel intensity value in the previous frame from the corresponding pixel intensity value of the current frame. By this way, each pixel value now in the processed video can be presented by only 2 bits (comparing to grayscale video that has a size of 8 bits for each pixel) requiring less bandwidth and also preserving the privacy of the users due to the reduction of intensity information, but it still reveals the motion information. Then frame-difference video can be further compressed by using Address-Event Representation (AER), where events are signaled when changes in pixel intensity reach a certain threshold. In a frame-difference AER image, the pixels that experience large intensity changes will generate events first and more frequently than other events and thus it will be available at receiver side immediately. In order to further compress AER video, it is only required to read less events outputted by the AER algorithm without any computation. It is shown that compression of a 320 \(\times\) 240-pixel frame-difference images using AER is resulting in 50 times less bandwidth requirement than non-AER scanning video.