## 5 Cooperative Scheduling for Object Detection in Cluster-Based WMSN.  
For object detection applications, sensor nodes are usually programmed to work in one of these two ways: (i) lower tier nodes (e.g., scalar sensors) detect movement and awake the camera sensors. (ii) The camera nodes sense the environment periodically.  
In the first case, scalar nodes detecting movement have to be aware of which camera nodes have to be awakened. Awakening all nodes surrounding the scalar sensor does not assure detecting the object; camera sensors with their FoV targeting the object are not necessarily radio neighbours or even may not be in the surroundings of the scalar sensor. Knowing the area sensed by tier of cameras is not scalable, since the scalar sensor should keep information from all cameras that sense large areas covered by the scalar sensor. Finally, a possible solution is sending the information towards a sink that takes the responsibility of awaking nodes in the tier of cameras. However, these solutions do not seem good in terms of performance parameters such as delay and energy conservation.  
In the second case, cameras should perform duty-cycled monitoring over the area that they sense. That means that every T seconds all sensors in the monitored area will awake and will perform object detection, see Figure 9.a. This is the situation for a planned network in which every sensor is placed in such a position that there is no overlapping among sensors. Nevertheless, this duty-cycle scheduling will produce high power consumption in those situations in which there are overlapping sensors, since camera nodes with overlapping areas do not cooperate to sense the area and thus they redundantly monitor the area.  
In this section, we propose a cooperative mechanism based on the proposed clustering method that coordinates nodes belonging to the same cluster to work in a collaborative manner to detect objects. The objective of this mechanism is to increase power conservation by avoiding similar sensing and redundant processing at the same time. Also, collaborative sensing by nodes that have FoVs intersecting each other yields to more reliability: cluster members will monitor the region sequentially  
Figure 8: Percentage of the covered area with respect to the whole deployment area.  
and if a moving object is not detected in one image capturing, it will be in the vicinal FoVs at the next capturing times. Thus, the other members in the same cluster may detect the object.  
Let us divide the environment in domains covered by clusters of nodes (MCCD, Section 3.3) with certain degree of overlapping between members. All clusters concurrently sense their domains: in each cluster, members are awakened sequentially in an intermittent manner by the first member with a time interval (_i.e._, Tinterval is the time between awakening two consecutive members) related to the cluster-size and the scale of overlapping, see Figure 9(b). In this way, each node of a given cluster periodically participates in capturing an image from its unique perspective and surveying the presence of an object and finally sleeps again with a cluster-based period called T\({}_{\text{p}}\). Formulas for these periods are derived in Section 5.1. Table 2 gives the pseudo-code for this algorithm.