## 7 Application Layer in WMSN
### Collaborative In-network Multimedia Processing  
The use of densely deployed sensor nodes in the wireless sensor network provides an inherent protection against normal and provoked system faults. The redundancy of information gathered by neighboring nodes can be exploited for more accurate and robust observation results through effective data fusion and aggregation. In WMSN, the redundancy of information can also be found as overlapping of FoVs of camera sensors located in the same cluster. In addition, the redundancy can be exploited for networking to avoid single-points of communication failure. Therefore, it is necessary to develop efficient and distributed filtering and in-network cooperative processing mechanisms to enable real time retrieval of useful information. For example, in data aggregation, two cameras may collectively fuse their information to obtain a lower bandwidth aggregated result, which is then routed (and possibly fused with other sensor node readings along the way or within the cluster) to the cluster head or the sink. A distributed filtering architecture is presented in [77] for authoring wide-area sensor-enriched services that supports scalable data collection from high bit-rate multimedia sensors by greatly reducing the bandwidth demands. This architecture (Internet- scale Resource-Intensive Sensor Network, IrisNet) enables the use of application-specific filtering of sensor data near their sources and provides interfaces that simplify collecting and manipulating these data. Also it reduces the processing and bandwidth requirements by detecting repeated computations among the services and eliminating as much of the redundancy as possible. In order to reduce the bandwidth consumed, IrisNet uses distributed filtering in which each service processes its desired sensor feeds on the CPU of the sensor nodes where the data are gathered instead of transferring the raw data across the network. In addition, in order to reduce the computation demands of this approach, IrisNet includes a mechanism for sharing results between sensing services running on the same node. A collaborative hybrid classifier learning algorithm is proposed in [78] to achieve online vector machine learning for target classification in WMSN. The collaborative hybrid learning algorithm uses progressive distributed computing paradigm for in-network multimedia processing in each cluster, and peer-to-peer paradigm between the cluster heads. The authors show that this algorithm overcomes the disadvantages of using centralized learning paradigm or distributed mobile agent learning paradigm, as consuming too much energy and bandwidth as the case of centralized learning paradigm, and imbalance energy consumption and the need for centralized processing center as the case of distributed mobile agent learning paradigm. An Artificial Immune System (AIS) based recognition scheme is presented in [79]. The proposed scheme can be used to construct high resolution images from multiple low resolution images captured by multiple sensor nodes in order to improve pattern recognition success rate and image communication energy efficiency. Principal Component Analysis (PCA) is used here for image dimension reduction that extracts only its major or principal components in order to be transmitted to the base station for pattern recognition. In order to reduce the information redundancy and extra number of captured image frames for energy efficient image processing and communication, a sleep/awake schedule algorithm is performed. A node's sleep/awake status depends on the minimum needed number of image frames to be acquired by the sensor node for successful pattern detection and on the residual energy of the sensor node within a cluster group. In [80], A compacted probabilistic binary visual classification for human targets in WMSN is presented, where a Gaussian process classifier (GPC) is used for classifier learning instead of support vector machine (SVM).