## 8 Conclusions  
This paper addresses the reduction of the energy consumption of multihomed wireless devices using multiconnectivity via heterogeneous radio access technologies to receive MPTCP data packets. We propose an SDN-based approach supported by a DNN to select which wireless interface(s) a network node should use to reduce the overall network energy consumption. The scheme employs two algorithms. One initially selects the wireless interface(s) that should operate on each network node based on the achievable throughput. After this, the other algorithm fine-tunes the selection of the first by applying a DNN to select the interface(s), leading to the lowest overall energy consumption. Both algorithms run at the SDN controller, which implies that no change in the MPTCP implementation, either in the scheduler or the congestion control algorithm, is required. This preserves the features and throughput performance of the currently available MPTCP Linux kernel implementations while retaining operation compatibility. Two experimental setups mimicking realistic settings are utilized in this research. As the congestion control algorithm used by MPTCP manages the amount of data sent over a wireless interface, affecting its energy consumption, the first setup is used to conduct experimental testing and build a dataset for the energy consumption performance of different MPTCP congestion control algorithms available as Linux kernel implementations, namely LIA, OLIA, Cubic, BALIA, and wVegas. The experimental results show that Cubic and BALIA achieve the lowest energy consumption and the highest throughput among the other MPTCP congestion control algorithms available as Linux kernel implementations. Thus, the second setup tests the proposed scheme algorithms by implementing them on a Ryu-based SDN controller managing an OVS switch via the OpenFlow protocol when using either MPTCP BALIA or Cubic on the sender and receiving nodes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Nodes**} & **Proposed SDN** & \multicolumn{3}{c}{**Proposed SDN**} & \multirow{2}{*}{**MPTCP BALIA**} & **Proposed SDN** & \multirow{2}{*}{**MPTCP Cubic**} & \multirow{2}{*}{**LTE (SPTCP)**} & \multirow{2}{*}{**WiFi (SPTCP)**} \\  & **Scheme (with BALIA)** & & & & & \\  & & & **Cubic)** & & & \\ \hline
3 & 45.9 & 57 & 46.2 & 57.5 & 47.9 & 50.7 \\
4 & 46 & 57.2 & 46.3 & 57.8 & 48.1 & 50.9 \\
5 & 46.8 & 57.3 & 46.9 & 58 & 48.35 & 51.3 \\
6 & 47 & 57.5 & 47.25 & 58.2 & 48.7 & 51.6 \\
7 & 47.2 & 57.6 & 47.5 & 58.3 & 49.2 & 51.9 \\
8 & 47.3 & 57.9 & 47.5 & 58.5 & 49.35 & 52.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Energy consumption performance comparison (Joule). Extensive experimental results with a variable number of network nodes show that the proposed scheme is efficient in reducing network energy consumption by 19% compared with MPTCP BALIA or MPTCP Cubic while maintaining, on average, 8% higher throughput than these MPTCP variants. The future directions for this research include predicting the throughput values and energy consumption of fast-moving mobile network nodes using machine learning via the SDN controller. This prediction can be integrated with the proposed algorithms to cope with the fast-changing network topology and node connection status. [PERSON] contributed to building the testbed, developed the algorithms, and implemented them on the SDN controller. She conducted the experiments using the testbed. She also contributed to paper writing. [PERSON] designed the network architecture of the testbed and contributed to the design of the proposed algorithms. He also participated in writing the paper. [PERSON] and [PERSON] contributed to reviewing and writing the paper. All authors have read and agreed to the published version of the manuscript. This research was funded by UAE University, UPAR grant number 12N008/31N456. Not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article. The authors declare no conflict of interest.