ENERGY EFFICIENCY ANALYSIS OF EDGE IOT UMUDIGI A9 PRO ON CROSS-TIER NETWORKS: IMPACT OF DISTANCE AND USER-FRIENDLY APPLICATIONS

Eleazar Mfonobong; Longinus Ezema; Chukwuchekwa Nkwachukwu

doi:10.4314/njt.v43i3.18

Authors

E. B. Mfonobong Department of Electrical and Electronic Engineering, Federal University of Technology, Owerri, Imo State, Nigeria
L. S. Ezema Department of Electrical and Electronic Engineering, Federal University of Technology, Owerri, Imo State, Nigeria
C. Nkwachukwu Department of Electrical and Electronic Engineering, Federal University of Technology, Owerri, Imo State, Nigeria

DOI:

https://doi.org/10.4314/njt.v43i3.18

Keywords:

Edge IoT, 3G and 4G network, Energy utilization, Distance impact, User-friendly applications, Mobile device efficiency

Abstract

This paper investigated the energy utilisation of edge IoT mobile devices on the 3G and 4G networks, specifically focusing on the impact of the distance from the base station and user-friendly applications. Measurements were taken, and data was collected on a live network during active and passive sessions using enabling software on the test device. The study found that energy consumption varies based on distance, service type, and network usage. With IoT-matched software applications, the Umudigi A9 Pro device showed increased power consumption as distance from the base station increased and as a function of the network utilised. The study also examined video streaming, voice calls, and video playback services, focusing on their energy profiles. The analysis of both real-time and non-real-time services was carried out. Real-time services exhibited an average maximum power consumption of 243.2mW, while non-real-time usage averaged 209.1mW. The internal energy utilisation levels ranged from 370.1mW to 446.9mW for online idle radio operations and from 322.5mW to 458.3mW for offline idle radio operations, varying from the cell centre to the cell edge within a base station sector. It was shown that real-time applications on edge mobile user equipment (UEs) are more energy-intensive than other applications. Their energy requirements increased as these nodes moved towards the cell edge of the network coverage area, possibly due to Signal-to-Interference-plus-Noise Ratio (SINR) limitations. Real-time services require more energy than non-real-time services and understanding the relationship between signal strength and test device energy requirements about the service deployed can serve as an advisory tool for energy management.

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