At present, Underwater Wireless Sensor Networks (UWSNs) have been widely used in enormous applications, and it simultaneously faces many security threats. The trust mechanism plays an important role in protecting network security. Many theories, e.g., subjective logic, Bayesian, cloud model, entropy theory, evidence theory, etc., have been adopted to evaluate the node trust of underwater wireless sensor networks. However, due to the unique characteristics of the underwater dynamic environment, the existing trust mechanisms used in traditional networks (such as P2P networks, Ad-hoc networks, etc.) cannot be directly used in underwater wireless sensor networks. Therefore, this paper proposes a new trust evaluation and update mechanism for underwater wireless sensor networks based on the C4.5 decision tree algorithm (TEUC). In the TEUC, trust features are first defined and selected. Based on the selected trust features, the threshold of trust feature fluctuation is set combining the IF-THEN rules. In addition, two types of trigger mechanisms are designed to update trust. In the sliding window, multiple exclusion factors and a support factor are used to calculate the trust features. Moreover, the C4.5 algorithm is used to classify and update trust of nodes taking into account the mobility and the unique characteristics of UWSNs. Finally, simulation experiments show that the proposed algorithm outperforms the traditional ones in dynamic network environment.

Marine environmental monitoring provides crucial information and support for the exploitation, utilization, and protection of marine resources. With the rapid development of information technology, the development of three-dimensional underwater acoustic sensor networks (3D UASNs) provides a novel strategy to acquire marine environment information conveniently, efficiently and accurately. However, the specific propagation effects of acoustic communication channel lead to decreased successful information delivery probability with increased distance. Therefore, we investigate two probabilistic neighborhood-based data collection algorithms for 3D UASNs which are based on a probabilistic acoustic communication model instead of the traditional deterministic acoustic communication model. An autonomous underwater vehicle (AUV) is employed to traverse along the designed path to collect data from neighborhoods. For 3D UASNs without prior deployment knowledge, partitioning the network into grids can allow the AUV to visit the central location of each grid for data collection. For 3D UASNs in which the deployment knowledge is known in advance, the AUV only needs to visit several selected locations by constructing a minimum probabilistic neighborhood covering set to reduce data latency. Otherwise, by increasing the transmission rounds, our proposed algorithms can provide a tradeoff between data collection latency and information gain. These algorithms are compared with basic Nearest-neighbor Heuristic algorithm via simulations. Simulation analyses show that our proposed algorithms can efficiently reduce the average data collection completion time, corresponding to a decrease of data latency.

Mobile sink is widely used for data collection in wireless sensor networks. It can avoid ‘hot spot’ problems but energy consumption caused by multihop transmission is still inefficient in real-time application scenarios. In this paper, a greedy scanning data collection strategy (GSDCS) is proposed, and we focus on how to reduce routing energy consumption by shortening total length of routing paths. We propose that the mobile sink adjusts its trajectory dynamically according to the changes of network, instead of predetermined trajectory or random walk. Next, the mobile sink determines which area has more source nodes, then it moves toward this area. The benefit of GSDCS is that most source nodes are no longer needed to upload sensory data for long distances. Especially in event-driven application scenarios, when event area changes, the mobile sink could arrive at the new event area where most source nodes are located currently. Hence energy can be saved. Analytical and simulation results show that compared with existing work, our GSDCS has a better performance in specific application scenarios.

In underwater acoustic networks (UANs), communication between nodes is susceptible to long propagation delays, limited energy, and channel conflicts, and traditional multi-access control (MAC) protocols cannot easily cope with these challenges. To enhance network throughput and balance channel allocation fairness and energy efficiency, this paper proposes a multi-objective optimization MAC protocol (MOMA-MAC) based on multi-agent reinforcement learning. MOMA-MAC utilizes a delay reward mechanism combined with the Multi-agent Proximal Policy Optimization Algorithm (MAPPO) to design a dual reward mechanism, which enables agents to adaptively collaborate and compete to optimize the use of network resources. According to experimental results, MOMA-MAC performs noticeably better than traditional MAC protocols and deep reinforcement learning-based methods in terms of throughput, energy efficiency, and fairness in multi-agent scenarios, showing great potential for improving communication efficiency and energy utilization.

Currently, the Internet of Underwater Things (IoUT) plays an important role in ocean exploration, monitoring, and protection. However, it faces many security threats due to resource constraints, such as denial-of-service attacks. To overcome these challenges, a novel underwater network architecture that incorporates cloud computing and edge computing technologies, namely the Ocean-Oriented Cloud Edge Collaboration Networks (O-OCECNs), is designed. O-OCECNs, while enhancing computational capabilities, still faces network threats such as energy depletion attacks and data pollution attacks, etc. On this basis, a trust management mechanism called ETrust is proposed in this paper, which uses ensemble learning to ensure network security. In the ETrust mechanism, nodes collect trust evidences by monitoring the behavior and communication results of other nodes and then deliver the evidences to the cluster head node. The cluster head node uploads the collected evidences to the edge server in its region to complete the trust value computation. Then, the edge server uploads the trust values to the cloud server, which utilizes the built-in learning to complete the trust evaluation. Finally, the cloud server outputs the trust evaluation value to the cluster head node and it adjusts the network topology. The experimental result demonstrates that the proposed scheme can detect malicious nodes with a trust evaluation accuracy of up to 98.55%. It also performs well in capturing selective forwarding attacks occurring in the network. Furthermore, the mechanism enhances network lifetime more effectively compared to existing schemes. Experimental result shows that our scheme maintains the average residual energy of devices at approximately 63%, compared to 50% with other methods.

Edge computing brings data storage closer to the location where it is needed. Therefore, the edge devices, especially smart industrial edge devices, require higher storage systems. NAND flash memory has the advantages of small size, high speed, and strong shock resistance, which is widely used in various storage systems, providing a good choice for edge devices. NAND flash has unique physical characteristics, such as "out-of-place updates" and "pre-write erasure"; therefore, the traditional address mapping methods require improvement. This paper presents a novel demand-based page-level address mapping algorithm called DPAM. The goal of DPAM is to provide efficient address translation by using a smaller address mapping table. Due to the high service cost of block-level address mapping and hybrid address mapping, a page-level address mapping scheme is proposed. The algorithm is implemented and tested on the flash simulation platform FlashSim. The results indicate that our algorithm provides improvements of 7.11% for the hit ratio and 7% for the number of block erasures compared with other approaches.