Distributed Multi-Objective Optimization for Edge Computing in Resource-Constrained Social IoT Networks

The integration of the Social Internet of Things (SIoT) with wireless sensor networks (WSNs) significantly enhances the efficiency, scalability, and intelligence of distributed sensing systems. However, these networks often encounter severe resource constraints, limiting the control and management traffic that can be introduced. WSNs, typically composed of battery-powered edge sensor nodes with limited computational capabilities, memory, and communication bandwidth, face challenges in optimizing performance. In this work, we address the multiobjective optimization problem within the context of resource-constrained SIoT, aiming to reduce the energy consumption of edge nodes while simultaneously enhancing the quality of the received data based on channel conditions. We propose a Pareto optimization framework to jointly optimize the compression factor and coding rate (CR) in a WSN scenario utilizing Long-Range (LoRa) technology for communication. This framework explores the tradeoffs between energy consumption and data reconstruction quality, leveraging compressive sensing (CS) for efficient data compression to alleviate the transmission load on edge nodes. Furthermore, we present a distributed optimization solution to minimize energy consumption while maximizing data quality, thereby reducing signaling and control overhead. This study contributes to the development of energy-efficient, scalable, and sustainable SIoT systems by providing a foundation for optimizing data transmission in LoRa networks.