On hybrid network coding for visual traffic surveillance
A large volume of data is generated by traffic surveillance devices such as cameras and sensors integrated into an intelligent transportation system (ITS), a subfield of the Internet of Things (IoT). We argue that network coding can be applied to leverage on an emerging fog architecture that relies...
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| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/150612 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | A large volume of data is generated by traffic surveillance devices such as cameras and sensors integrated into an intelligent transportation system (ITS), a subfield of the Internet of Things (IoT). We argue that network coding can be applied to leverage on an emerging fog architecture that relies on edge resources, to achieve higher throughput, saving up network bandwidth, and provide resilience to link failures, while also achieving simple obfuscation against wire-tapping attacks by linearly combining the source packets. There are two broad linear network coding paradigms in the literature — deterministic and random network coding, each with their own strengths and limitations. With the aid of software-defined network (SDN), we rethink about the possibility of applying a hybrid approach to deal with networks at different scales. Under network conditions that reflect expected network properties of an ITS, our simulation results show that the proposed hybrid approach performs better than other alternates. |
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