## 1 Introduction  
The massive increase in the data traffic volume and the user demand for high-speed data rates and high-quality services has created a requirement for innovative and adaptable network implementations in the existing wireless technologies. The machine-to-machine (M2M) and the Internet of Things (IoT) paradigms [1] presume the connectivity of billions of wireless devices in diverse application domains, such as smart grids, vehicular networks, and many others, which demands low latency, high reliability, and high throughput. The fifth-generation (5G) wireless network standard came with radio enhancements (new radio) to provide users/devices with communication services such as ultra-reliable and low-latency (URLLC) communication, high-throughput mobile broadband (eMBB), and the massive support of machine-type communication (mMTC). However, the increasing demand for wireless bandwidth may negatively impact the performance of these services. Thus, the 3 GPP introduced a method known as dual connectivity/multiconnectivity, in which a user device can send and receive data concurrently to and from two or multiple nodes using the same (e.g., macro-cell and small cells) or different technologies (e.g., cellular base stations and WiFi access points) [2; 3]. This can boost the amount of bandwidth available to a user's device, help to manage its mobility more effectively, and increase reliability. At the same time, the emergence of the multipath transmission control protocol (MPTCP) [4] can aid the realization of multiconnectivity by allowing a user to simultaneously transmit through multiple radio interfaces, such as WiFi and LTE or 5G, while providing the reliability of single-path TCP. MPTCP provides concurrent and uninterrupted data transmissions through multiple paths via multiple subflows. Each one is connected to one wireless network. It overcomes the limitations in the traditional TCP by maximizing throughput and redundancy, in addition to providing a resilient reaction to failure [5]. Using devices with dual or multiconnectivity can be challenging due to energy consumption, especially with battery-powered wireless devices used in emerging technologies like IoT. It is crucial for wireless devices to be energy-efficient while providing optimal performance. Higher energy consumption leads to increased battery capacity and a larger device size and weight. Therefore, minimizing energy consumption is crucial to extend the battery life (for the same form factor) and maintain a small carbon footprint. When using two wireless interfaces simultaneously, it is essential to ensure that the overall energy consumed by both interfaces does not significantly exceed that of a single interface. The operation of the MPTCP protocol relies on two main components, namely a scheduler and a congestion control algorithm. The scheduler selects the traffic path (the wireless interface) for each packet, whereas the congestion control algorithm estimates the amount of data to be sent over each path in order to avoid congestion. The execution of both components controls the traffic share of each interface, which directly affects the energy consumed by each interface, as the more data sent, the longer the time that the interface will be used and hence more energy is consumed. For the first component (the scheduler), the MPTCP Linux kernel implementation uses the default scheduler, which splits the traffic on each path based on the round-trip time (RTT) experienced over that path. However, for the second component (the congestion control algorithm), there are five different algorithms available as Linux kernel implementations, namely OLIA, LIA, wVegas, Cubic, and BALIA [6; 7; 8; 9; 10]. To transfer some amount of data (e.g., a file), each algorithm assigns the traffic load differently to the operating wireless interfaces (i.e., LTE/5G and WiFi) and hence it achieves different MPTCP throughput performance and, in turn, consumes a different amount of energy.