To realize fully functioning inland waterway communications, portable and affordable unmanned surface vehicles (USVs) have become increasingly significant in wireless inland ship networks. However, USVs are struggling in handling resource-intensive services due to the limited battery and CPU resources. In addition, the power of signal transmitted between USVs and the shore base stations suffers from considerable attenuation. In this paper, an intelligent reflecting surface (IRS)-aided wireless inland ship mobile edge computing (MEC) network architecture is proposed to support USVs’ communication and computation. The minimization of network energy consumption is formulated under a number of QoS requirements. To achieve an efficient solution to the formulated challenging problem, a low-complexity enhanced block coordinate descent algorithm is proposed and thus the original optimization problem can be divided into a list of sub-problems. The extensive results verify the effectiveness of jointly considering USVs’ offloading decisions, USVs’ transmission power, unmanned aerial vehicle (UAV) trajectory and IRS phase shift-vector design for energy efficiency in a spare wireless inland ship MEC network and show that the proposed algorithm can decrease energy consumption by approximately 75% compared to a baseline algorithm without optimization design. Also, the network performance under different IRS elements and accuracy parameters are investigated.

This paper establishes a new unmanned aerial vehicle (UAV) swarm-enabled wireless inland ship mobile edge computing (MEC) network with time windows, where UAVs are deployed to support time-constrained and low-resource unmanned surface vehicles (USVs) communication and computation. We aim to minimize UAV swarm energy consumption subject to numerous constraints. To tackle the challenging formulated problem, we decompose the original challenging problem into a list of sub-problems according to block coordinate descent (BCD) method and propose a heuristic algorithm to solve subproblems in an iterative manner. Specifically, the optimization of USVs offloading decisions is formulated as mixed-integer non-linear programming and then a modified deferred acceptance algorithm is proposed. The joint consideration of UAVs flight speed and UAV swarm stability is converted into a single-objective optimization problem after determining UAV control system’s latency requirements, and then Lagrangian multiplier method with Karush-Kuhn-Tucker (KKT) conditions is proposed. Successive convex approximations (SCA)-based algorithm is proposed to convert the non-convex constraints to convex ones, and then UAVs hovering coordinates can be obtained by solving the convex approximation problem. Numerical results verify the effectiveness of jointly considering USVs offloading decisions, UAVs flight speed, UAVs horizontal hovering coordinates and the number of UAVs for energy efficiency in comparison with several advanced baseline algorithms without optimization design.

Unmanned surface vehicles (USVs) play a key role in inland waterway transport through intelligent IoT sensing and analytics. The embedded USV platforms, however, suffer limited computation resources. This could be tackled by offloading computation tasks from USVs to unmanned aerial vehicles (UAVs), which perform as relays and forward data to multi-access edge computing (MEC) servers. A major challenge for UAVs is that the process consumes a great deal of battery power, limiting their operational time. This paper proposes a novel cooperative UAV-USV MEC platform, where UAVs are deployed to provide communication and computation backhaul for USVs. A UAV energy consumption minimization problem is formulated by jointly considering USVs task execution mode, UAVs trajectories, UAVs arrival times and UAVs hovering coordinates. This problem is divided into three subproblems: 1) optimizing UAVs trajectories using enhanced ant colony optimization; 2) identifying the optimal USVs task execution mode selection indicators based on a surrogate LR algorithm; and 3) reducing the time between UAV arrivals and hovering coordinates by solving the joint ADMM and successive convex approximation subproblems. The results show that the proposed solution significantly reduces the energy consumption of UAVs by up to 55% compared to some baseline energy-saving algorithms.

The increasing amount of user equipment (UE) and the rapid advances in wireless body area networks (WBANs) bring revolutionary changes in healthcare systems. However, due to the strict requirements on size, reliability and battery lifetime of UE devices, it is difficult for them to execute latency sensitive or computation intensive tasks effectively. In this paper, we aim to enhance the UE computation capacity by utilizing small size coordinator-based mobile edge computing (C-MEC) servers. In this way, the system complexity, computation resources and energy consumption are considerably transferred from the UE to the C-MEC, which is a practical approach since C-MEC is power charged, in contrast to the UE. Firstly, the system architecture and the mobility model are presented. Secondly, several transmission mechanisms are analyzed along with the proposed mobility-aware cooperative task offloading scheme. Numerous selected performance metrics are investigated regarding the number of executed tasks, the percentage of failed tasks, average service time and the energy consumption of each MEC. The results validate the advantage of task offloading schemes compared to traditional relay-based technique regarding the number of executed tasks. Moreover, one can obtain that the proposed scheme archives noteworthy benefits such as low latency and efficiently balance the energy consumption of C-MECs.

The joint optimization problem of a 5G-inspired IIoT-MEC interactive network aims to maximize revenue of MNOs and minimize IIoT operators’ economic cost is formulated, which is challenging to be solved. This letter proposes a dynamic pricing strategy for IIoT-MEC network to maximize MNOs’ revenue while providing acceptable service prices for IIoT mobile devices (MDs). The dynamic pricing problem is first modeled as a discrete finite Markov decision process (MDP). Then Q-learning algorithm is utilized to solve this problem. The results show that the proposed dynamic pricing strategy can significantly enhance MNOs’ revenue and decrease IIoT operators’ economic cost.

Network lifetime maximization of wireless biomedical implant systems is one of the major research challenges of wireless body area networks (WBANs). In this paper, a mutual information (MI)-based incremental relaying communication protocol is presented where several on-body relay nodes and one coordinator are attached to the clothes of a patient. Firstly, a comprehensive analysis of a system model is investigated in terms of channel path loss, energy consumption, and the outage probability from the network perspective. Secondly, only when the MI value becomes smaller than the predetermined threshold is data transmission allowed. The communication path selection can be either from the implanted sensor to the on-body relay then forwards to the coordinator or from the implanted sensor to the coordinator directly, depending on the communication distance. Moreover, mathematical models of quality of service (QoS) metrics are derived along with the related subjective functions. The results show that the MI-based incremental relaying technique achieves better performance in comparison to our previous proposed protocol techniques regarding several selected performance metrics. The outcome of this paper can be applied to intra-body continuous physiological signal monitoring, artificial biofeedback-oriented WBANs, and telemedicine system design.