## 1 Introduction  
The wireless sensor networks (WSNs) represent a blooming technology where they can probe and collect environmental information, such as temperature, atmospheric pressure and irradiation to provide ubiquitous sensing, computing and communication capabilities. Besides collecting these scalar data (e.g., temperature) from the environment, a newer trend in WSNs is to deploy sensor nodes with cameras to capture and transmit visual data (i.e., images and video data) back to the sink node. Thanks to the rapid advancement of sensor technology, equipping sensors with cameras is possible [1]. In this way, sensor nodes can send the captured visual data to provide richer sensing and monitoring information, which enables more applications in areas such as wide-life observation and security surveillance. These kinds of camera-equipped sensor networks are known as Wireless Visual Sensor Networks (WVSNs). The hardware components of a WVSN consist of tiny camera sensor nodes, embedded processors and wireless transceivers [1]. The WVSN is totally different from a traditional WSN in five ways:  
1. _Field of View coverage requirement for data source nodes_: In traditional WSNs, when an event occurs, the nodes within the sensing range of the event will sense the event and become data source nodes to transmit the sensed data back to the sink. However, in WVSNs, besides the sensing range, another more important criterion, Field of View (FoV), should be considered. The FoV comes from the fact that the camera captures visual data of the event from a certain direction or angle. In other words, besides the sensing range coverage, the FoV of the sensor nodes should also cover the event so as to capture the visual data of event. In Figure 1, we illustrate an example to illustrate the idea of FoV. In Figure 1a, the event is inside the sensing range of the sensor nodes (i.e., nodes \(A\), \(B\), \(C\), \(D\), _E_) so they can sense the event and then transmit the sensed data back to the sink in the WSN. In Figure 1b, we can see that due to the angle of the camera on the sensors, not every sensor node inside the sensing range can capture the event. In Figure 1b, nodes \(A\), \(B\) and \(C\) are the only data source nodes that can capture the visual data. In other words, even though nodes \(D\) and \(E\) are within the sensing range, the FoV of the cameras do not cover the event so that nodes \(D\) and \(E\) are not data source nodes [2]. 2. _Network bandwidth consumption_: The size of visual data is much larger than scalar data so more network bandwidth resource are required for its transmission. As a result the bandwidth-demanding visual data transmission posts a challenging routing decision in limited frequency spectrum WVSNs. In addition, most of the applications require QoS (e.g., delay, jitter and loss) support so that the visual data could playback at the sink nodes successfully. These QoS requirements make the visual data transmission in WVSNs even more challenging than in WSNs. 3. _Collision in transmitting data_: When an event occurs, neighboring sensors that detect the event will transmit the visual data back to the sink node at the same time. Because these neighboring sensors node are often geographically close to each other, collisions will happen when transmitting the data at the same time. In addition, due to the large visual data size, the sensors have to transmit a sequence of packets to be able to transmit the whole captured visual data. This will further increase the probability of collisions. When collisions or interference happens,the garbled packets need to be retransmitted. Such retransmissions will increase the possibility of collisions even more. In order to tackle this issue, new MAC aware transmission and routing schemes should be devised for WVSN. 4. _Multimedia data processing_: As compared to the scalar data processing in WSNs, the visual data processing in WVSNs is more complex and it demands more hardware resources such as CPU power and memory buffer.