**2. Sensor management and scheduling policies**

In redundantly deployed multimedia sensor networks a subset of cameras can perform continuous monitoring and provide information with a desired quality. This subset of active cameras can be changed over time, which enables balancing of the cameras energy consumption, while spreading the monitoring task among the cameras. In such a scenario the decision about the camera nodes activity and the duration of their activity is based on sensor management policies. *Sensor management policies* define the selection and scheduling (that determines the activity duration) of the camera nodes activity in such a way that the visual information from selected cameras satisfies the application specified requirements while the use of camera resources is minimized. Various quality metrics are used in the evaluation of sensor management policies, such as the energy-efficiency of the selection method or the quality of the gathered image data from the selected cameras. In addition, camera management policies are directed by the application; for example, target tracking usually requires selection of cameras that cover only a part of the scene that contains the non-occluded object, while monitoring of large areas requires the selection of cameras with the largest combined FoV. While energy-efficient organization of camera nodes is oftentimes addressed by camera management policies, the quality of the data produced by the network is the main concern of the application.

The problem of finding the best camera candidates is investigated in (Soro & Heinzelman, 2007). In this work, the authors propose several cost metrics for the selection of a set of camera nodes that provide images used for reconstructing a view from a user-specified view point. Two types of metrics are considered: coverage aware cost metrics and quality-aware cost metrics. The *coverage-aware cost metrics* consider the remaining energy of the camera nodes and the coverage of the indoor space, and favor the selection of the cameras with higher remaining energy and more redundant coverage. The *quality-aware cost metrics* favor the selection of the cameras that provide images from a similar view point as the user's view point. Thus, these camera selection methods provide a trade-off between network lifetime and the quality of the reconstructed images.

Monitoring of large areas (such as parking lots, public areas, large stores, etc.) requires complete coverage of the area at every point in time. Such an application is analyzed in (Dagher et al., 2006), where the authors provide an optimal strategy for allocating parts of the monitored region to the cameras while maximizing the lifetime of the camera nodes. The optimal fractions of regions covered by every camera are found in a centralized way at the base station. The cameras use JPEG2000 to encode the allocated region such that the cost per bit transmission is reduced according to the fraction received from the base station.

Oftentimes, the quality of a reconstructed view from a set of selected cameras is used as a criterion for the evaluation of camera selection policies. In the work (Park et al., 2006)

The chapter is organized as follows. In section 2 we present an overview of work related to sensor management and scheduling policies. A surveillance mechanism with its details in grouping, management and scheduling multimedia nodes to be energy efficient is explained

In redundantly deployed multimedia sensor networks a subset of cameras can perform continuous monitoring and provide information with a desired quality. This subset of active cameras can be changed over time, which enables balancing of the cameras energy consumption, while spreading the monitoring task among the cameras. In such a scenario the decision about the camera nodes activity and the duration of their activity is based on sensor management policies. *Sensor management policies* define the selection and scheduling (that determines the activity duration) of the camera nodes activity in such a way that the visual information from selected cameras satisfies the application specified requirements while the use of camera resources is minimized. Various quality metrics are used in the evaluation of sensor management policies, such as the energy-efficiency of the selection method or the quality of the gathered image data from the selected cameras. In addition, camera management policies are directed by the application; for example, target tracking usually requires selection of cameras that cover only a part of the scene that contains the non-occluded object, while monitoring of large areas requires the selection of cameras with the largest combined FoV. While energy-efficient organization of camera nodes is oftentimes addressed by camera management policies, the quality of the data produced by the network

The problem of finding the best camera candidates is investigated in (Soro & Heinzelman, 2007). In this work, the authors propose several cost metrics for the selection of a set of camera nodes that provide images used for reconstructing a view from a user-specified view point. Two types of metrics are considered: coverage aware cost metrics and quality-aware cost metrics. The *coverage-aware cost metrics* consider the remaining energy of the camera nodes and the coverage of the indoor space, and favor the selection of the cameras with higher remaining energy and more redundant coverage. The *quality-aware cost metrics* favor the selection of the cameras that provide images from a similar view point as the user's view point. Thus, these camera selection methods provide a trade-off between network lifetime

Monitoring of large areas (such as parking lots, public areas, large stores, etc.) requires complete coverage of the area at every point in time. Such an application is analyzed in (Dagher et al., 2006), where the authors provide an optimal strategy for allocating parts of the monitored region to the cameras while maximizing the lifetime of the camera nodes. The optimal fractions of regions covered by every camera are found in a centralized way at the base station. The cameras use JPEG2000 to encode the allocated region such that the cost per bit transmission is reduced according to the fraction received from the base

Oftentimes, the quality of a reconstructed view from a set of selected cameras is used as a criterion for the evaluation of camera selection policies. In the work (Park et al., 2006)

in section 3. Finally, the future work and conclusions are derived.

**2. Sensor management and scheduling policies** 

is the main concern of the application.

and the quality of the reconstructed images.

station.

distributed look-up tables are used to rank the cameras according to how well they image a specific location, and based on this, they choose the best candidates that provide images of the desired location. Their selection criterion is based on the fact that the error in the captured image increases as the object gets further away from the center of the viewing frustum. Thus, they divide the frustum of each camera into smaller unit volumes (subfrustums). Then, based on the Euclidian distance of each 3D point to the centers of subfrustums that contain this 3D point, they sort the cameras and find the most favorable camera that contains this point in its field of view. The look-up table entries for each 3D location are propagated through the network in order to build a sorted list of favorable cameras. Thus, camera selection is based exclusively on the quality of the image data provided by the selected cameras, while the resource constraints are not considered.

In order to reduce the energy consumption of cameras, the work (Zamora & Marculescu, 2007) explores distributed power management of camera nodes based on coordinated node wake-ups. The proposed policy assumes that each camera node is awake for a certain period of time, after which the camera node decides whether it should enter the low-power state based on the timeout statuses of its neighboring nodes. Alternatively, camera nodes can decide whether to enter the low-power state based on voting from other neighboring cameras.

Selection of the best cameras for target tracking has been discussed often (Pahalawatta et al., 2004; Ercan et al., 2006). Pahalawatta et al. present a camera selection method for target tracking applications used in energy-constrained visual sensor networks. The camera nodes are selected by minimizing an information utility function (obtained as the uncertainty of the estimated posterior distribution of a target) subject to energy constraints. However, the information obtained from the selected cameras can be lost in the case of object occlusions. This occlusion problem is further discussed by Ercan et al. where they propose a method for camera selection in the case when the tracked object becomes occluded by static or moving occluders. Finding the best camera set for object tracking involves minimizing the MSE of the object position's estimates. Such a greedy heuristic for camera selection shows results close to optimal and outperforms naive heuristics, such as selection of the closest set of cameras to the target, or uniformly spaced cameras. The authors here assume that some information about the scene is known in advance, such as the positions of static occluders, and the object and dynamic occluders prior probabilities for location estimates.

As a conclusion, in multimedia sensor networks, sensor management policies are needed to assure balance between the opposite requirements imposed by the wireless networking and vision processing tasks. While reducing energy consumption by limiting data transmissions is the primary challenge of energy-constrained visual sensor networks, the quality of the image data and application, QoS, improve as the network provides more data. In such an environment, the optimization methods for sensor management developed for wireless sensor networks are hard to directly apply to multimedia sensor networks. Such sensor management policies usually do not consider the event-driven nature of multimedia sensor networks, nor do they consider the unpredictability of data traffic caused by a monitoring procedure. Thus, more research is needed to further explore sensor management for multimedia sensor networks. Since sensor management policies depend on the underlying networking policies and vision processing, future research lies in the intersection of finding the best trade-offs between these two aspects of visual sensor networks. Additional work is needed to compare the performance of different camera node scheduling sensor policies, including asynchronous (where every camera follows its own on-off schedule) and synchronous (where cameras are divided into different sets, so that in each moment one set of cameras is active) policies. From an application perspective, it would be interesting to explore sensor management policies for supporting multiple applications utilizing a single visual sensor network.

The presented mechanism in the following section groups multimedia nodes in clusters based on their common sensing region of the whole deployment region. The clusters monitor the environment independently but in each cluster the members collaborate in data acquisition in an intermittent manner. The scheduling and activity times in each cluster are determined based on the cluster population and the scale of overlapping between FoV of cluster members. So, the data transmissions are not limited in this kind of sensor management but the volume of sensed data is reduced by management in only sensing subsystem and applying coordination among cluster members to optimize capturing image times and to avoid redundant sensing of the same data in the overlapped FoVs. On the other hand, the sensing region is divided between clusters and each cluster monitors its domain with its exclusive frequency and member scheduling. Thus, clusters are not synchronized for sensing the region whiles each point of the sensing region is monitored frequently according to the number of nodes that cover that point by their sensing subsystem.
