Adaptive video compression and transmission algorithms for smart surveillance in IOT networks

Efficient compression and the reliable transmission of video data represent significant challenges within surveillance systems operating on Internet-of-Things (IoT) architectures. To address these issues, adaptive algorithms designed for video compression and transmission are introduced, aiming to achieve optimal performance in IoT networks. The expansion of IoT technologies has facilitated the deployment of large-scale intelligent video surveillance infrastructures, which produce substantial volumes of video information. Nevertheless, constraints such as restricted network bandwidth, limited energy availability, and the necessity for real-time processing present considerable obstacles to effective video transmission in these contexts. This paper proposes adaptive algorithms for video compression and transmission, specifically tailored for intelligent video surveillance in IoT networks. The presented methodology dynamically modifies parameters related to compression and transmission rates in response to real-time network conditions, the complexity of the visual content, and priority levels derived from scene analysis. By incorporating streamlined machine learning techniques and leveraging contemporary computational resources, the system attains an equilibrium among video quality, latency, and resource utilization. Experimental evaluations conducted on an IoT video surveillance testbed indicate that the proposed techniques markedly decrease bandwidth consumption and transmission delays, all while preserving adequate video quality necessary for successful object detection and activity recognition. These results suggest that adaptive video optimization techniques are essential for scalable and reliable intelligent video surveillance in the IoT future.

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