With the evolutional development of maritime networks, the explosive growth of maritime data has put forward elevated demands for the computing capabilities of maritime devices (MDs). Unmanned aerial vehicle (UAV) is able to alleviate the computing pressure of MDs by forwarding the computing tasks to the edge server on the coast. However, UAV relaying introduces a significant security challenge due to the vulnerability of line-of-sight (LoS) communication channels, which can be exploited for eavesdropping on computing tasks. In this paper, an efficient secure communication scheme is proposed for UAV-relay-assisted maritime mobile edge computing (MEC) with a flying eavesdropper. The secure computing capacity of MDs is maximized by jointly optimizing the transmit power, time slot allocation factor, computation optimization and UAV trajectory. Due to multi-variable coupling, the formulated optimization problem (OP) is non-convex. We first transform OP by introducing auxiliary variables. Then, the transformed OP is decomposed and solved in an iterative manner by applying block coordinate descent (BCD) and successive convex approximation (SCA). Numerical results show that the secure computing capability of the UAV-relay-assisted maritime MEC system of proposed secure communication scheme can be effectively improved compared with benchmarks.

The vigorous development of maritime networks and the explosive growth of maritime sampling data put forward more and more high demands on the computing and communication capability of maritime equipment. Unmanned aerial vehicle (UAV), as the mobile relay device guarantees the capability by transferring part of the computing tasks of maritime equipment to carrying mobile edge computing (MEC) servers on land. However, the transmitting data of the UAV's communication channel can be easily intercepted due to the line of sight (LoS) feature, which brings the secure data transmission issue. To solve this issue, we propose a secure data transmission scheme in the UAV-relay assisted maritime MEC system. Specifically, a malicious UAV attempts to intercept the transmission data while another UAV helps forward the offloading computational data to the maritime surface users. A ground jammer transmits jamming signals with the object to block data intercepting. To maximize the users' minimum secure calculation capacity, we jointly optimize the transmit power of users and the relay UAV, the time slot allocation factor, and the UAV flight trajectory with block coordinate descent (BCD) and successive convex approximation (SCA) techniques. Numerical findings demonstrate that the proposed scheme can effectively improve the secure calculation capability of the system compared with four benchmark schemes.

In this paper, we propose a SWIPT-based distributed node sleep scheduling method combining with clustering technology. The information transmission layer adopts a distributed topology structure. Active nodes are divided into cluster head nodes and cluster member nodes. The cluster head node acts as a relay of cluster member nodes, which can reduce the single-hop communication distance and greatly reduce the energy consumption of information transmission. The energy-carrying communication process is divided into three time slots: the first time slot, the sensor node collects energy, according to the residual energy of the node, the energy collected in the first time slot, and the requirements of network connectivity, the sleep scheduling optimization algorithm will divide the nodes into sleeping nodes and active nodes, and the active nodes are clustered according to the relative position of the information sending rate of the node; in the second time slot, the cluster head node receives the information sent by the cluster member node, and the sleeping node continues to collect energy; In three time slots, the cluster head node processes the collected data and sends it to the sink node, and the cluster member nodes and sleeping nodes continue to collect energy. Finally, according to the user's requirements for energy utilization and network coverage, an appropriate time slot division factor is selected. Compared with the centralized sleep scheduling method, the energy utilization and coverage of the network are improved.