# Optimal User Association Strategy for Large-Scale IoT Sensor Networks with Mobility on Cloud RANs
## 1 Introduction
This can be a critical hurdle to maintain real-time, mission critical applications. Figure 1: The three-layer architecture of C-RAN. The situation can become worse in the cases when the IoT devices have mobility. Due to the small coverage of RRHs, even a small change of IoT device's location can cause a handover (Handover is a procedure of terminating the current association with RRH \(i\) and then switching to another RRH \(j\) where \(i\
eq j\). Data communication between a user device and an RRH becomes possible only after making an association.). Since we consider the case of large-scale IoT devices being deployed and operating, the entire network may be significantly influenced in a negative way from frequent handovers and the resulting control message flood. In this paper, we propose an optimal user association strategy between mobile IoT devices and RRHs to effectively balance the traffic load between RRHs on a dense C-RAN. Employing such strategy, the number of congested RRHs can be minimized, therefore reducing the network delay. In addition, we propose a novel way of reducing the occurrences of handovers during the communication process. The reduced number of handovers not only allows diminishing the amount of control messages, but also eliminating the amount of time needed to perform unnecessary handover procedures. The specific contributions we make in this work are summarized as follows:  
* We propose an optimization problem formulation for the user association, which will allow distributing the traffic load evenly among short-range RRHs to prevent them from being congested. * We suggest a revised optimal user association strategy, which will allow minimizing the occurrences of handovers to prevent control message flooding. The optimal solution to the revised problem formulation simultaneously enhances the load-balancing performance and minimizes the number of handovers. Such a joint consideration has not been made in previous works yet, which is one of the major contributions we make in this paper. * We propose a way to reduce the computational load for operating the network with the usage of the proposed optimal user association scheme. In a nutshell, if there is no change in the network topology, the previous association can be re-used without necessity to find an optimal solution. * We implement a discrete-time network simulator and evaluate the performance of the proposed methods. In addition, we compare the performance of the proposed strategies against the conventional association strategies. * We monitor and analyze the time complexity of each algorithm to evaluate whether they are applicable for large-scale, real-time applications. The remainder of this paper is organized as follows. Section 2 summarizes several important related research works. In Section 3, we first introduce the network parameters, and then, we propose the optimal user association problem formulations. Section 4 presents the network parameters and the evaluation results. A brief discussion on the time complexity of the proposed schemes is given in Section 5. Finally, Section 6 concludes this paper. The abbreviations used in this paper are outlined in Table 1. \begin{table}
\begin{tabular}{c l} \hline \hline
**Abbreviation** & **Meaning** \\ \hline BBU & BaseBand processing Unit \\ BS & Base Station \\ C-RAN & Cloud Radio Access Network \\ CAPEX & Capital Expenditure \\ IoT & Internet of Things \\ LPBS & Low-Power Base Station \\ LTE & Long Term Evolution \\ MBS & Macro Base Station \\ OPEX & Operating Expenses \\ QoS & Quality of Service \\ RRH & Remote Radio Head \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of abbreviations.