Name
Abstract | ||
|---|---|---|
Dealing with visual data is the key for environmental monitoring tasks in Wireless Multimedia Sensor Networks (WMSNs). Tasks such as object detection, recognition, and/or tracking do require extracting and using the right information from the inherently large amount of visual data. The widely accepted solution of legacy WSNs, transmitting the acquired data to a central base station for further processing, would render a WMSN totally useless because of the unacceptable use of bandwidth and energy. Therefore, we consider the in situ processing as a viable solution for WMSNs. However, processing power and memory capacity restrictions of existing multimedia sensor nodes along with their power consumption are the limiting factors for wide-spread use of in situ processing. Nevertheless, recent technological improvements and introduction of the new ARM cores encourage us to evaluate the image processing capabilities of ARM7/ARM9/ARM11 based micro-controllers for in situ processing in WMSNs. In this work, we first discussed the architectural design differences among the various ARM cores. Then we classified image processing algorithms into three categories. Then, we evaluated the performance of each microcontroller by running a set of basic image processing algorithms necessary for object detection, recognition, and/or tracking. The test results show that ARM11 runs up to 6---30 times faster than ARM9 and ARM7, respectively. Besides, ARM11 consumes up to 5---7 times less energy than ARM9 and ARM7 for the same type of operations. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s11554-012-0288-z | J. Real-Time Image Processing |
Keywords | Field | DocType |
visual data,arm11 run,arm11 consumes,image processing capability,situ processing,basic image processing,situ image processing capability,object detection,arm-based micro-controllers,classified image processing algorithm,acquired data,processing power,image processing algorithms,cpu time | ARM9,Computer science,Image processing,Real-time computing,Microcontroller,Artificial intelligence,Computer hardware,Computer vision,Object detection,CPU time,Bandwidth (signal processing),Digital image processing,Energy consumption | Journal |
Volume | Issue | ISSN |
9 | 1 | 1861-8219 |
Citations | PageRank | References |
2 | 0.39 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Z. Cihan Taysi | 1 | 46 | 6.78 |
| A. Gokhan Yavuz | 2 | 123 | 7.69 |
| M. Amac Guvensan | 3 | 134 | 7.62 |
| M. Elif Karsligil | 4 | 73 | 13.69 |