## 1 Introduction  
Wireless Sensor Networks (WSN) [1, 2] are considered as autonomous and self-organized systems consisting of a large number of small, inexpensive, battery-powered communication devices deployed throughout a physical space. These networks are mainly used for gathering information related to the surrounding environment (e.g., temperature, humidity, light, _etc._), and for transmission of the gathered data to a base station (_i.e._, sink), for further processing. In recent times there has been increased interest in video surveillance and environment monitoring applications. Visual information may be captured from the environment using CMOS cameras embedded in wireless sensor nodes. Wireless Multimedia Sensor Networks (WMSN) [3,4], should be able to process in real-time, retrieve or fuse multimedia data. Energy conservation and maximization of network lifetime are commonly recognized as a key challenge in the design and implementation of WSNs. One of the subjects that have been propounded for enhancement in efficiency of applications associated with WSNs is node clustering. Clustering in WSN pursues several objectives: (i) network scalability, (ii) energy conservation, (iii) network topology stabilization, (iv) routing overhead minimization, (v) optimized management strategies to prolong battery life and network lifetime and (vi) efficient data aggregation. Most of the time, distance from nodes to cluster-head or radio coverage (_i.e._, neighbourhood) are the main criterions for node clustering in WSN [5]. Nevertheless, the sensing region of multimedia nodes is very different from ordinary nodes in WSNs. Each multimedia node has a Field of View (FoV) and can only capture images from the objects within that region. In traditional WSNs, the sensor nodes collect information about different phenomena around them from the area determined by the sensing range of the node. However, video cameras capture images of objects of a region that is not necessarily in the camera's vicinity. The object covered by the camera can be distant from the camera and the captured images will depend on the relative positions and orientation of the cameras towards the observed object [6-8]. Therefore, because of non-coincidence between radio neighbourhood and sensed region by multimedia nodes, node clustering and coverage techniques in WSN do not satisfy WMSN requirements. As a result of developments in low cost, low power, low resolution camera sensors, having a dense network consisting of multimedia sensors has become applicable. This kind of deployment has more performance than sparse networks of high power, high resolution cameras. However, overlapping FoVs in dense deployments yield wasting of power in the network because of redundant sensing of the area [7]. In this paper, we first propose an approach for multimedia node clustering that satisfies FoV constraints. In this approach, the overlapping area between FoV of multimedia nodes is the main criterion for node clustering in contrast to radio or distance neighbourhood. If the overlapped area between FoV of two nodes is relatively wide, they act similarly from the coverage point of view and thus we select them as members of a cluster. Thus, nodes belonging to the same cluster may not necessarily be neighbours. The main aim of this clustering method is energy conservation and network lifetime prolongation through the creation of potential of cooperation among nodes belonging to the same cluster and avoiding redundant sensing and processing. Applications may range from multiple-perspective monitoring and localization to collaborative object detection or distributed video coding. Second, we present a cooperative scheduling scheme for object detection as an example of an application which can be developed based on the proposed clustering method. The environment sensing task is divided in clusters of overlapping FoV areas.