Energy-Efficient Data Dissemination Approach Using Multiple-Criteria Decision Modeling for Internet of Things Environments

The modern internet infrastructure has enabled numerous applications by providing a seamless connectivity experience across each mode of connectivity. Infrastructure-based connectivity and device-to-device (D2D) are well-known connectivity modes for internet-based applications. The selection of the underlying communication medium significantly affects energy consumption during data transfer. This study proposes an Energy-Efficient Data Dissemination Approach (EEDDA) that integrates encounter prediction with a multi-criteria decision-making (MCDM) framework to reduce infrastructure-based energy consumption in IoT mobility environments. Unlike traditional optimization approaches that focus on single-objective routing or static network models, the proposed framework dynamically selects between Device-to-Device (D2D) and Internet-based transmission based on delay tolerance, encounter probability, data size, and energy consumption metrics. Real mobility traces from the publicly available University of Southern California (USC) dataset were used for validation. Simulation results demonstrate that under high delay tolerance scenarios, the proposed approach achieves up to 70%–80% reduction in energy consumption compared to conventional Internet-based transmission while maintaining Quality of Service (QoS).