Compressing the bi-level image after preprocessing and segmentation proves to be a decent alternative for achieving a general architecture for some applications of WVSN [12].
We determined in [13] that JBIG2 [14], CCITT Group 4 [15] and Gzip_Pack [16] are appropriate binary image compression standards for inaccessible applications of WVSN.
WVSNs are in need of a compression process with acceptable compression rate, low computational complexity, low power consumption, and low dynamic memory usage to reduce the number of bits used for representing the image, which will also reduce the communication cost of the bits to be sent to the destination.
It reduces the data in hand on the initial stage itself, which will surely reduce the computational complexity and storage requirement burdens evolving in the WVSNs scenario.
Based on the previous discussion, the design problem in WVSN is more challenging than in WSN.
In the WVSN, when an event occurs, neighboring sensors that are geographically close will sense the event and capture the images.
Due to these power consumption features of the communication part, we discuss a Wi-Fi power management scheme, which includes buffer resizing for less energy consumption of the WVSN node.
In this paper, we propose a WVSN communication system with optimal buffer size to address the communication energy minimization problem.
There is a great potential for the multicore systems to be used for WVSN as the raw performance increases come from the increasing number of cores instead of the frequency.
In WVSN, the application requirement is often to capture the data for a particular time and then switch the node to a sleep mode so as to conserve the energy.
The development in microelectronics, low-power multi-functional sensors, embedded OS technology has caused the progress in wireless video sensor network (
WVSN) which is a network of wirelessly interconnected devices that are able to share and retrieve multimedia contents such as still images, video and audio data and scalar sensor data from the environment.
Figures 3(a) and 3(b) successfully support that the inference tree method is effective for initializing an optimal data transmission path in
WVSN.