Mobility Management-Based Autonomous Energy-Aware Framework Using Machine Learning Approach in Dense Mobile Networks

A paramount challenge of prohibiting increased CO2 emissions for network densification is to deliver the Fifth Generation (5G) cellular capacity and connectivity demands, while maintaining a greener, healthier and prosperous environment. Energy consumption is a demanding consideration in the 5G era to combat several challenges such as reactive mode of operation, high latency wake up times, incorrect user association with the cells, multiple cross-functional operation of Self-Organising Networks (SON), etc. To address this challenge, we propose a novel Mobility Management-Based Autonomous Energy-Aware Framework for analysing bus passengers ridership through statistical Machine Learning (ML) and proactive energy savings coupled with CO2 emissions in Heterogeneous Network (HetNet) architecture using Reinforcement Learning (RL). Furthermore, we compare and report various ML algorithms using bus passengers ridership obtained from London Overground (LO) dataset. Extensive spatiotemporal simulations show that our proposed framework can achieve up to 98.82% prediction accuracy and CO2 reduction gains of up to 31.83%.

[1]  Xianfu Chen,et al.  Energy-Efficiency Oriented Traffic Offloading in Wireless Networks: A Brief Survey and a Learning Approach for Heterogeneous Cellular Networks , 2015, IEEE Journal on Selected Areas in Communications.

[2]  Gerhard Fettweis,et al.  Small-Cell Self-Organizing Wireless Networks , 2014, Proceedings of the IEEE.

[3]  Jie Wu,et al.  An Efficient Prediction-Based Routing in Disruption-Tolerant Networks , 2012, IEEE Transactions on Parallel and Distributed Systems.

[4]  Xuemin Shen,et al.  Energy-Aware Traffic Offloading for Green Heterogeneous Networks , 2016, IEEE Journal on Selected Areas in Communications.

[5]  Ying Jun Zhang,et al.  Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks , 2018, IEEE Transactions on Mobile Computing.

[6]  Jennifer C. Hou,et al.  Modeling steady-state and transient behaviors of user mobility: formulation, analysis, and application , 2006, MobiHoc '06.

[7]  Dag Lunden,et al.  Life Cycle Assessment of ICT , 2014 .

[8]  Ali Imran,et al.  Mobility Prediction-Based Autonomous Proactive Energy Saving (AURORA) Framework for Emerging Ultra-Dense Networks , 2018, IEEE Transactions on Green Communications and Networking.

[9]  Muhammad Ali Imran,et al.  Control-Data Separation Architecture for Cellular Radio Access Networks: A Survey and Outlook , 2016, IEEE Communications Surveys & Tutorials.

[10]  Zhisheng Niu,et al.  Toward dynamic energy-efficient operation of cellular network infrastructure , 2011, IEEE Communications Magazine.

[11]  Muhammad Tayyab Asif,et al.  Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction , 2014, IEEE Transactions on Intelligent Transportation Systems.

[12]  Phone Lin,et al.  SES: A Novel Yet Simple Energy Saving Scheme for Small Cells , 2017, IEEE Transactions on Vehicular Technology.

[13]  Muhammad Ali Imran,et al.  Challenges in 5G: how to empower SON with big data for enabling 5G , 2014, IEEE Network.

[14]  Zhifeng Zhao,et al.  Human Mobility Patterns in Cellular Networks , 2013, IEEE Communications Letters.

[15]  Ursula Challita,et al.  Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial , 2017, IEEE Communications Surveys & Tutorials.

[16]  Syed Aziz Shah,et al.  Seizure episodes detection via smart medical sensing system , 2018, J. Ambient Intell. Humaniz. Comput..

[17]  Injong Rhee,et al.  SLAW: Self-Similar Least-Action Human Walk , 2012, IEEE/ACM Transactions on Networking.

[18]  Metin Öztürk,et al.  Energy-Aware Smart Connectivity for IoT Networks: Enabling Smart Ports , 2018, Wirel. Commun. Mob. Comput..

[19]  Muhammad Ali Imran,et al.  How much energy is needed to run a wireless network? , 2011, IEEE Wireless Communications.

[20]  Muhammad Ali Imran,et al.  Leveraging Intelligence from Network CDR Data for Interference Aware Energy Consumption Minimization , 2018, IEEE Transactions on Mobile Computing.

[21]  Ahmed Zoha,et al.  Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning , 2020, Sensors.