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.

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.

Low-illumination image enhancement can provide more information than the original image in low-light scenarios, e.g., nighttime driving. Traditional deep-learning-based image enhancement algorithms struggle to balance the performance between the overall illumination enhancement and local edge details, due to limitations of time and computational cost. This paper proposes a histogram equalization–multiscale Retinex combination approach (HE-MSR-COM) that aims at solving the blur edge problem of HE and the uncertainty in selecting parameters for image illumination enhancement in MSR. The enhanced illumination information is extracted from the low-frequency component in the HE-enhanced image, and the enhanced edge information is obtained from the high-frequency component in the MSR-enhanced image. By designing adaptive fusion weights of HE and MSR, the proposed method effectively combines enhanced illumination and edge information. The experimental results show that HE-MSR-COM improves the image quality by 23.95% and 10.6% in two datasets, respectively, compared with HE, contrast-limited adaptive histogram equalization (CLAHE), MSR, and gamma correction (GC).

Unmanned surface vehicles (USVs) are becoming increasingly significant in fulfilling integrated sensing, computing, and communication with the emergence of bidirectional computation tasks. However, Quality-of-Service provisioning is still challenging since USVs are restricted with limited onboard resources and direct links between them and shore-based terrestrial base stations (TBSs) are frequently blocked. This article proposes a novel reconfigurable intelligent surface (RIS)-assisted cooperative unmanned aerial vehicle (UAV)–USV mobile-edge computing (MEC) network architecture, where RIS-mounted tethered UAV (TUAV) and rotary-wing UAVs (RUAVs) are collaboratively utilized to serve USVs. RUAVs energy minimization is formulated by jointly considering TUAV hovering altitude, RIS phase-shift vector, RUAV service selection indicator, and RUAVs turning points. A heuristic solution is proposed to tackle the formulated problem, where the original problem is first decoupled into three subproblems, e.g., the joint optimization of RIS phase-shift vector and TUAV hovering altitude subproblem, RUAVs service selection indicator subproblem, and RUAVs turning points subproblem, each of which is solved by the proposed modified alternative direction method of multiplier (ADMM) algorithm, the proposed enhanced simulated annealing (ESA) algorithm and the proposed successive convex approximation (SCA)-based algorithm. In this way, the challenging problem can be efficiently solved iteratively. The results show that the proposed solution can decrease RUAVs energy consumption by nearly 29% compared to numerous selected advanced algorithms. Moreover, the performance of the proposed solution regarding typical penalty coefficients and number of RIS reflecting elements is investigated.

Unmanned surface vehicles (USVs) in inland waterways have drawn increasing attention for their excellent capability to serve maritime time-consuming missions, such as autonomous navigation and intelligent monitoring. However, USVs struggle to accomplish emerging computation-intensive tasks (e.g., sensor, telemetry, etc) timely due to the limited on-board resources. This article proposes a novel reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) multiaccess edge computing (MEC) network architecture to support low-latency USVs data computation with time window. Aiming to enhance USVs task processing efficiency, the minimization of USVs task processing time is formulated by jointly considering UAVs flight route selection, USVs execution mode selection, UAVs hovering coordinates, and RIS phase shift vector. A heuristic solution is proposed to tackle the formulated challenging problem iteratively. The original problem is decoupled into three subproblems: 1) an enhanced deferred acceptance algorithm is proposed to solve UAVs flight route selection subproblem; 2) an enhanced Lagrangian relaxation method is proposed to solve USVs execution mode selection subproblem; and 3) a joint alternating direction method of multipliers (ADMMs)-successive convex approximation (SCA)-based algorithm is proposed to solve UAVs hovering coordinates subproblem. Experiment results demonstrate that the proposed solution can decrease task processing time by approximately 54% compared with numerous selected advanced algorithms. Moreover, the performance of the proposed solution under typical UAVs caching capability and the number of UAVs has been investigated.