# An Intelligent Cluster-Based Routing Scheme in 5G Flying Ad Hoc Networks
## 2 Related Work
\begin{cases}\text{long-lifetime data},&\text{ if }t_{p}\geq\tau\\ \text{short-lifetime data},&\text{ otherwise}\end{cases} \]

where \(t_{p}\) and \(\tau\) represent data lifetime and its threshold, respectively. Long-lifetime data have a lifetime greater than a predefined threshold \(t_{p}\geq\tau\). Therefore, it does not vary frequently and can be updated at least or more than every \(\tau\) time period. The long-lifetime data resides in the CC; thus, it is also known as the global data available to other UAVs and BSs in the network. In this work, the long-lifetime data is the residual energy of a UAV and the network plane in which a UAV resides. Short-lifetime data has a lifetime shorter than a predefined threshold \(t_{p}<\tau\). So, it varies frequently and changes within every time period \(\tau\). The short-lifetime data resides in the DCs in each network plane; therefore, it is also known as the local data available to other UAVs and BSs in the same network plane only. In this work, the short-lifetime data is the mobility rate of UAVs. ### Hybrid Framework

We propose a hybrid framework comprised of CC and DCs. The CC has _three_ main functions. It (a) gathers long-lifetime data and serves as a central data repository to provide a global view of the network, (b) processes global data with high processing capability, and (c) determines network-wide policies and decisions (e.g., policies related to the initialization of the clustering process and vertical routing). The CC provides these functions even when network partitions occur. The DC has _three_ main functions. It (a) gathers short-lifetime data and serves as a local data repository to provide a local view of the network, as well as sends long-lifetime data to the CC; (b) processes local data; and (c) manages the underlying cluster structure. The DCs provide these functions even when CC failure occurs. The hybrid framework provides _three_ main advantages according to the CAP theorem of distributed computing [42], which is important due to the highly dynamic FANET. First, _consistency_ in which the same short-lifetime data is available to all UAVs and BSs in each network plane, and the same long-lifetime data is available to all UAVs and BSs in the network. Second, _availability_ in which both CC and DCs provide unexpired data to UAVs and BSs in the network. Third, _partition tolerance_ in which the network continues to operate despite the failure of some of the controllers, namely CC and DCs. In addition, load distribution can be achieved by offloading traffic from CC to DCs, which helps to reduce congestion level at the CC, hence reducing end-to-end delay. ## 4 System Model and Functions

This section presents the system model and functions, which are based on the traditional clustering and routing mechanisms. Traditional clustering schemes are implemented in a single network plane only without communication across different network planes, and there is lack of a framework to handle long- and short-lifetime data while ensuring the availability of fresh data. However, the vertical clustering scheme forms inter-plane clusters, which are local tasks performed by DCs. DCs use long-lifetime (i.e., transmission range) and short-lifetime (i.e., geographical location) data for vertical clustering in order to enhance cluster stability and network scalability. Clusters are formed by segregating nodes in the network into groups of nodes with similar nature. For simplicity, only two clusters are shown in Figure 2. Cluster \(C_{1}\) has a cluster head \(CH_{1}\), two cluster members \(CM_{1,1}\) and \(CM_{1,2}\), and a cluster gateway \(CG_{1,1}\), and cluster \(C_{2}\) has a cluster head \(CH_{2}\), two cluster members \(CM_{2,1}\) and \(CM_{2,2}\), and a cluster gateway \(CG_{2,1}\).