2025/10/10 by Mohammud Junaid Bocus, Bocus, Mohammud J., Qiu, Senhui +4
Computer Science · Engineering · #Bluetooth and Wireless Communication Technologies #FOS: Computer and information sciences #Green IT and Sustainability #Hardware Architecture (cs.AR) #IoT and Edge/Fog Computing #Networking and Internet Architecture (cs.NI) #Performance (cs.PF)
paper · pdf · doi:10.48550/arxiv.2510.09842
openalex publication_date 2025/10/10 · openalex created_date 2025/10/15 · openalex updated_date 2026/07/28
Energy efficiency has emerged as a defining constraint in the evolution of sustainable Internet of Things (IoT) networks. This work moves beyond simulation-based or device-centric studies to deliver measurement-driven, network-level smart energy analysis. The proposed system enables end-to-end visibility of energy flows across distributed IoT infrastructures, uniting Bluetooth Low Energy (BLE) and Visible Light Communication (VLC) modes with environmental sensing and E-ink display subsystems under a unified profiling and prediction platform. Through automated, time-synchronized instrumentation, the framework captures fine-grained energy dynamics across both node and gateway layers. We developed a suite of tools that generate energy datasets for IoT ecosystems, addressing the scarcity of such data and enabling AI-based predictive and adaptive energy optimization. Validated within a network-level IoT testbed, the approach demonstrates robust performance under real operating conditions.