Underwater wireless sensor networks (UWSNs) have become highly efficient in conducting various operations in maritime environments. Compared to terrestrial wireless sensor networks, routing protocols in UWSNs are prone to high propagation delay, high energy consumption, low bandwidth, and low throughput. UWSNs are remotely located and operate without the need for human intervention. Most sensor batteries are energy restricted and very difficult to replace. One of the major challenges of UWSNs is the uneven utilization of energy resources, which reduces the network lifetime. Therefore, an energy-efficient routing mechanism is necessary to overcome the aforementioned problems. Many significant studies have attempted to realize this goal by designing energy-efficient routing protocols to provide efficient packet routing from source to destination. In this paper, we focus on discussing various energy-efficient routing protocols that are currently available for UWSNs, categorize them with a new taxonomy, and provide a comparative discussion. Finally, we present various research problems that remain open and challenges for future research.INDEX TERMS Acoustic communication, energy efficiency, maritime environment, network lifetime, routing protocol, underwater wireless sensor network.SHREYA KHISA received the B.S. degree in computer science and engineering from the University of Chittagong, Bangladesh, in 2017. She is currently pursuing the M.S. degree with the Mobile Computing Laboratory, Chosun University, South Korea. Her current research interests include wireless sensor networks, underwater wireless sensor networks, the Internet of Things, and unmanned aerial vehicle networks with a focus on network architectures and protocols.

The Internet of Things (IoT), which consists of a large number of small low-cost devices, has become a leading solution for smart cities, smart agriculture, smart buildings, smart grids, e-healthcare, etc. Integrating unmanned aerial vehicles (UAVs) with IoT can result in an airborne UAV-based IoT (UIoT) system and facilitate various value-added services from sky to ground. In addition to wireless sensors, various kinds of IoT devices are connected in UIoT, making the network more heterogeneous. In a UIoT system, for achieving high throughput in an energy-efficient manner, it is crucial to design an efficient medium access control (MAC) protocol because the MAC layer is responsible for coordinating access among the IoT devices in the shared wireless medium. Thus, various MAC protocols with different objectives have been reported for UIoT. However, to the best of the authors’ knowledge, no survey had been performed so far that dedicatedly covers MAC protocols for UIoT. Hence, in this study, state-of-the-art MAC protocols for UIoT are investigated. First, the communication architecture and important design considerations of MAC protocols for UIoT are examined. Subsequently, different MAC protocols for UIoT are classified, reviewed, and discussed with regard to the main ideas, innovative features, advantages, limitations, application domains, and potential future improvements. The reviewed MAC protocols are qualitatively compared with regard to various operational characteristics and system parameters. Additionally, important open research issues and challenges with recommended solutions are summarized and discussed.

Many hazardous industrial incidents can occur due to the inadequate and inefficient monitoring of the offshore plants. Manual inspections of the offshore plants on a regular basis is not only time consuming but also dangerous regarding to human safety. For considering the safety measurement and alleviating the burden of the manual inspection, unmanned aerial vehicles (UAVs) can be effectively utilized to collect data from the remote industrial environment. In an industrial scenario, less delay is required for emergency packets and high throughput is needed for monitoring packets. This paper proposes a priority-aware fast MAC (PF-MAC) protocol for UAV-assisted industrial Internet-of-things (IIoT) systems, ensuring fast and robust data delivery. At first, the IoT devices under the UAV communication range transmit a reservation frame to the UAV to catch transmission opportunities using CSMA/CA. The devices utilize static traffic priority and a novel adaptive backoff mechanism during CSMA/CA. After receiving the reservation frames from the IoT devices, the UAV calculates the dynamic device priority based on their static traffic priority, communication duration, sampling frequency, and remaining energy. Then, time slot is assigned by the UAV to each device for data transmission. To ensure fairness, if a device fails in contention during the CSMA/CA period, the static traffic priority is raised in the next retransmission. There is no prior work in the literature that considers both the traffic priority and the device priority to ensure Quality of Service in IIoT and related systems. According to our performance study, the proposed PF-MAC outperforms the conventional protocols in terms of delay and throughput.INDEX TERMS Internet of Things, unmanned aerial vehicle, medium access control, traffic priority, device priority, delay, quality of service.Shreya Khisa received the B.S. degree in computer science and engineering from University of Chittagong, Bangladesh in 2017. She is currently pursuing the M.S. degree with the Mobile Computing Laboratory, Chosun University, South Korea. Her current research interests include wireless sensor networks, Internet of things, and unmanned aerial vehicle networks with a focus on network architectures and protocols.

This paper investigates joint user pairing, power and time slot duration allocation in the uplink multiple-input single-output (MISO) multi-user cooperative rate-splitting multiple access (C-RSMA) networks in half-duplex (HD) mode. We assume two types of users: cell-center users (CCU) and celledge users (CEU); first, we propose a user pairing scheme utilizing a semi-orthogonal user selection (SUS) and a matchinggame (MG)-based approach where the SUS algorithm is used to select CCU in each pair which assists in reducing inter-pair interference (IPI). Afterward, the CEU in each pair is selected by considering the highest channel gain between CCU and CEU. After pairing is performed, the communication takes place in two phases: in the first phase, in a given pair, CEUs broadcast their signal, which is received by the base station (BS) and CCUs. In the second phase, in a given pair, the CCU decodes the signal from its paired CEU, superimposes its own signal, and transmits it to the BS. We formulate a joint optimization problem in order to maximize the sum rate subject to the constraints of the power budget of the user equipment (UE) and Quality of Service (QoS) requirements at each UE. Since the formulated optimization problem is non-convex, we adopt a bilevel optimization to make the problem tractable. We decompose the original problem into two sub-problems: the user pairing sub-problem and the resource allocation sub-problem where user pairing sub-problem is independent of resource allocation subproblem and once pairs are identified, resource allocation subproblem is solved for a given pair. Resource allocation subproblem is solved by invoking a successive convex approximation (SCA)-based approach. Simulation results demonstrate that the proposed SUS-MG-based algorithm with SCA outperforms other conventional schemes.

Emerging mobile edge computing (MEC) can be used in battery-constrained Internet of things (IoT). The execution latency of IoT applications can be improved by offloading computation-intensive tasks to an MEC server. Recently, the popularity of unmanned aerial vehicles (UAVs) has increased rapidly, and UAV-based MEC systems are receiving considerable attention. In this paper, we propose a dynamic computation offloading paradigm for UAV-based MEC systems, in which a UAV flies over an urban environment and provides edge services to IoT devices on the ground. Since most IoT devices are energy-constrained, we formulate our problem as a Markov decision process considering the energy level of the battery of each IoT device. We also use model-free Q-learning for time-critical tasks to maximize the system utility. According to our performance study, the proposed scheme can achieve desirable convergence properties and make intelligent offloading decisions.