## 2 Wireless Multimedia Sensor Networks
### Large Volume of Multimedia Data
#### 2.3.1 Reducing Data Redundancy  
With limited bandwidth available, having WMSNs to deliver all of the captured multimedia data is certainly not cost-effective. As a result, it is mandatory to introduce ways to reduce the volume of data to be transmitted. For example excessive redundant data can be removed by applying onboard local processing algorithms, or by implementing distributed source coding and multimedia in-network processing amongst neighboring nodes. Meanwhile, possible in-network data storage solutions can also be used to limit the necessity of data transmissions. * Local Processing: Local processing refers to using onboard image analysis techniques to extract useful imagery components for the description of events of interest [59; 60; 61]. Depending on the intelligence of algorithms, local processing can be categorized into different levels [60], in which the required bandwidth decreases with the increase of algorithm intelligence and level of inference, as shown in Figure 2. One example is the large vehicle detection scenario described in [61]. Instead of streaming raw video data, a camera sensor in its basic setting transmits a whole frame to the sink whenever motion is detected. However when more local processing is allowed, camera sensors are able to perform background subtraction to detect the moving object. Only if the detected object is larger than a threshold then the portion of image containing the detected object will be transmitted. With more intelligence and coupling with multimedia in-network processing, camera sensor nodes can cooperate, and a portion of the image with the detected object will only be sent when two camera sensors have determined the object is of interest following their own individual criteria. Furthermore, as demonstrated by [PERSON] et al. [62], camera sensors can collaborate and produce a textual only description for the events. Although local processing is a promising way to reduce the amount of data to be transmitted, it is worth to note that however, accompanying local processing are that more hardware resources will also be needed. * Multimedia In-Network Processing: Multimedia in-network processing is defined as intermediate network computing amongst local nodes to promote network scalability through energy savings [111]. Under the current literature, there exists mainly two kinds of multimedia in-networkprocessing techniques as given in Figure 3. The first is multimedia data fusion where typically a cluster head gathers data from its cluster members (normally after a certain level of local processing has been executed) and combines them to create a summarized report on the event of interest so as to reduce data redundancy and also to improve level of inference. Due to its sensor heterogeneity, multimedia data fusion in WMSNs usually is multimodal, i.e., involving fusion of different types of data such as images, videos, and non-imagery data [63; 64]. Online multi-view video summarization [65] is another in-network processing technique. For a group of camera sensors with overlapped field of views (FoVs), an online local processing algorithm is used to select the important frames at each sensor and only features of the selected frames together with their foreground mask sizes are broadcast. By comparing the received features and foreground mask sizes with its own, a sensor then decides whether its selected frames will be delivered to the sink or not. From the received frames, the sink does the analysis to obtain the event information. * Distributed Source Coding: In typical multimedia source coding problems, all of the source information is available for compression at a centralized place. However, in WMSNs the correlated source information usually resides in multiple camera sensors [66]. Due to resource limitations, transferring all source information to a centralized location is certainly not feasible.