Designing a Communication-efficient and Privacy-preserving Collaborative Image Processing Framework for Distributed Smart Sensor Networks under Bandwidth and Energy Constraints
Keywords:
Cloud computing, Differential privacy, Edge AI, ESP32-CAM, Federated learning, Raspberry PiAbstract
This work investigates the challenges associated with distributed smart sensor networks for large-scale image acquisition and analysis, particularly in applications such as environmental monitoring, smart cities, and surveillance. Despite their increasing deployment, these systems face significant constraints, including limited communication bandwidth, restricted energy resources, and critical privacy concerns due to the transmission of raw image data to centralized servers. To address these challenges, this work proposes a novel communication-efficient and privacy-preserving collaborative image processing framework. The proposed approach leverages edge-based feature extraction to process images locally at sensor nodes, thereby reducing the need to transmit high-volume raw data. In addition, adaptive compression techniques are employed to further minimize communication costs without substantially degrading the quality of extracted features. The framework also incorporates federated learning, enabling distributed model training across multiple nodes while keeping sensitive data localized. To enhance security, secure aggregation mechanisms are integrated to ensure that shared model updates remains confidential and resistant to potential adversarial attacks. Experimental results demonstrate that the proposed framework achieves significant performance improvements, including up to a 65% reduction in communication overhead and up to a 40% decrease in energy consumption compared to conventional centralized approaches. Importantly, these efficiency gains are achieved while maintaining competitive accuracy in image processing and analysis tasks. Overall, this work presents a scalable, energy-efficient, and privacy-aware solution for next-generation distributed sensing systems, making it highly suitable for deployment in resource-constrained and privacy-sensitive environments.
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