**Preface to "Energy-Efficient Computing and Communication"**

Improving the energy efficiency in communications and computing systems has become one of the most important issues to realize green ICT. Even though a number of studies have been conducted, most of them focused on one aspect—either communications or computing systems. However, salient features in communications and computing systems should be jointly considered, and novel holistic approaches across communications and computing systems are required to implement energy-efficient systems. In this regard, this Special Issue aimed to gather recent advances in energy-efficient communications and computing technologies.

Park et al. [1] propose a novel scheme that improves the energy efficiency and network throughputs by controlling the topology of the multi-unmanned aerial vehicle (UAV) network. The use of UAVs has been researched in various industrial fields, and a number of studies on operating multiple autonomous networking UAVs suggest a potential use of UAVs in largescale environments. However, achieving efficient performance in multi-UAV operations remains challenging in terms of energy efficiency, network overhead, and so on. The proposed network topology control scheme functions between the data link layer (L3) and the network layer (L2), and the proposed methodology includes swarm intelligence, meaning that whole topology control can be achieved with lower cost and effort, and without a centralized controller. The experimental results confirm the improvement in performance of the proposed method compared to previous approaches.

Tran et al. [2] implemented a novel one-bit coding metasurface that is capable of focusing and steering beams for enhancing power transfer efficiency of electromagnetic (EM) wavebased wireless power transfer systems. The proposed metasurface includes 16 × 16 unit cells that were designed with a fractal structure and the operating frequency of 5.8 GHz. By appropriately handling the on/off states of the coding metasurface, the reflected EM wave impinged on the metasurface can be controlled. To verify the working ability of the coding metasurface, a prototype metasurface with a control board was fabricated and measured. The experimental results demonstrate that the coding metasurface is capable of focusing a beam to a desired direction. In addition, for practical scenarios, the authors propose an adaptive optimal phase control scheme for focusing the beam to a mobile target and proved that the proposed adaptive optimal phase control scheme outperforms the random phase control and beam synthesis schemes.

Mobile edge computing (MEC) technology was developed to mitigate the overload problem in networks and cloud systems. An MEC system computes the offloading computation tasks from resource-constrained Internet of Things (IoT) devices. Several convergence technologies with renewable energy resources (RERs) such as photovoltaics have been proposed to improve the survivability of IoT systems. Parck et al. [3] propose an MEC integrated with RER system, denoted energy-harvesting (EH) MEC. Since the energy supply of RERs is unstable forvarious reasons, EH MEC needs to consider the state-of-charge (SoC) of the battery to ensure system stability. Therefore, the authors devised an offloading scheduling algorithm considering the EH MEC battery as well as the service quality of experience (QoE). In the first stage of the scheduling algorithm, a non-convex optimization problem was formulated and a greedy algorithm was constructed to obtain approximate optimal solutions. In the second stage, based on Lyapunov optimization, a low-complexity algorithm is proposed that considers both the workload queue and battery stability.

Ko et al. [4] propose an energy efficient cooperative computation algorithm (EE-CCA), where a pair of IoT devices decides whether to offload some parts of the task to the opponent by considering their energy levels and the task deadline. To minimize the energy outage probability while completing most tasks before their deadlines, a constraint Markov decision process (CMDP) problem is formulated and the optimal offloading strategy is obtained by linear programming (LP). The evaluation results demonstrate that the EE-CCA can reduce the energy outage probability up to 78% compared with the random offloading scheme while completing tasks before their deadlines with high probability.

Ko et al. [4] propose an energy efficient cooperative computation algorithm (EE-CCA), where a pair of IoT devices decides whether to offload some parts of the task to the opponent by considering their energy levels and the task deadline. To minimize the energy outage probability while completing most tasks before their deadlines, a constraint Markov decision process (CMDP) problem is formulated and the optimal offloading strategy is obtained by linear programming (LP). The evaluation results demonstrate that the EE-CCA can reduce the energy outage probability up to 78% compared with the random offloading scheme while completing tasks before their deadlines with high probability.

For energy-neutral operation (ENO) of wireless sensor networks (WSNs), Choi and Lee [6] applied a wireless-powered communication network (WPCN) to a WSN with a hierarchical structure. In this hierarchical WPSN, sensor nodes with high harvesting energies and good link budgets have energy remaining after sending their data to the cluster head (CH), whereas the CH suffers from energy scarcity. The authors applied the simultaneous wireless information and power transfer (SWIPT) technique to the considered WPSN so that the sensor nodes can transfer their remaining energy to the CH while transmitting data in a cooperative manner. To maximize the achievable rate of sensing data while guaranteeing ENO, a novel ENO framework is presented that provides a frame structure for SWIPT operation, rate improvement subject to ENO, SWIPT ratio optimization, as well as clustering and CH selection algorithm.

Joung et al. [6] propose a power control method for a buffer-aided relay node (RN) to enhance the energy efficiency of the RN system. By virtue of a buffer, the RN can reserve the data at the buffer when the the channel gain between an RN and a destination node (DN) is weaker than that between an SN and RN. The RN then opportunistically forwards the reserved data in the buffer according to channel condition between the RN and the DN. By exploiting the buffer, the RN reduces transmit power when it reduces the transmission data rate and reserves the data in the buffer. Therefore, without any total throughput reduction, the power consumption of RN can be reduced, resulting in the energy efficiency (EE) improvement of the RN system. For power control, a simple power control method was devised based on a twodimensional surface fitting model of an optimal transmit power of RN.

These papers offer a broad view of the relevant, diversified, and challenging problems arising in energy-efficient communications and computing. I would like to express my sincere thanks to all the authors, reviewers, and the staff at MDPI.

> **Sangheon Pack**  *Guest Editors*
