Hybrid big data optimization based energy-efficient and AI-powered green architecture toward smart cities and 5G-IoT applications

Abstract

The convergence of Internet of things (IoT) and 5G holds immense potential for transforming industries by enabling real-time, massive-scale connectivity and automation. However, the growing number of devices connected to the IoT systems demands a communication network capable of handling vast amounts of data with minimal delay. These generated enormous complex, high-dimensional, high-volume, and high-speed data also brings challenges on its storage, transmission, processing, and energy cost, due to the limited computing capabilities, battery capacity, memory, and energy utilization of current IoT networks. In this paper, a seamless architecture by combining mobile and cloud computing is proposed. It can agilely bargain with 5G-IoT devices, sensor nodes, and mobile computing in a distributed manner, enabling minimized energy cost, high interoperability, and high scalability as well as overcoming the memory constraints. An artificial intelligence (AI)-powered green and energy-efficient architecture is then proposed for 5G-IoT systems and sustainable smart cities. The experimental results reveal that the proposed approach dramatically reduces the transmitted data volume and power consumption and yields superior results regarding interoperability, compression ratio, and energy saving. This is especially critical in enabling the deployment of 5G and even 6G wireless systems for smart cities.

Keywords

Big data / Compression ratio / Energy-efficient / Internet of things / Mobile cloud computing / Smart cities

CRediT authorship contribution statement

Ihab Nassra: Writing–original draft, Visualization, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization, Validation. Juan V. Capella: Writing–review & editing, Supervision, Investigation, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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