Multi-Objective Optimization of Green Small Cell Allocation for IoT Applications in Smart City

Abstract

Small cells (SCs) have proven their marvelous capability, especially dealing with high density of user equipment (UE) in the configuration of Internet of Things (IoT). It is widely accepted that the SCs concept is one of the prospective solutions for 5G networks, specifically targeting IoT, to attain the high demand in the future smart city. SC deployment has attracted the attention of multiple research efforts, due to its importance and potential towards enhancing future networking for IoT. However, there is still a lack of works providing SC allocation with comprehensive optimization modeling, which provides essential infrastructural platform to SC functionalities and services (e.g. offloading). The analysis of the number and positions of SCs, towards the simultaneous optimization of both energy efficiency and data rates, is a highly required research direction. SC allocation facilitates the sustainability and reliability, to support exponentially increasing number of users, as well as yielding feasibility of achieving high traffic demand and low latency performance, in addition to longer sustainability based on enhanced energy efficiency. In this paper, a smart small cell allocation scheme is proposed for IoT in smart city. Integer programming multi-objective optimization problem is formulated, and a new algorithm, based on the fusion of Branch-and-Bound (BnB) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), is developed. Energy efficiency optimization and data rate maximization are formulated, as mutual objectives to fulfill the key demands of IoT in smart city, based on the estimation of users' behaviors. Simulations are conducted, whose results show that the proposed Smart-SC allocation outperforms the different considered benchmarks, resulting in higher offered data rates, while achieving better energy efficiency.