The rapid evolution of the Internet of Underwater Things (IoUT) has led to the widespread adoption of autonomous underwater vehicle (AUV)-assisted underwater acoustic sensor networks (UASNs) for various applications such as marine environment monitoring and resource exploration. This article introduces an energy-balanced data collection scheme tailored for AUV-supported UASNs. The proposed scheme combines a node clustering method based on a social welfare function and an intelligent path planning strategy for the AUV. The node clustering approach integrates canopy and K-means algorithms for initial node clustering, followed by reclustering using an enhanced hierarchical clustering algorithm. To balance energy distribution, Atkinson's social welfare function is employed to select and rotate cluster heads (CHs) within each cluster. To address limited CH memory constraints, a lossless compression technique is introduced to reduce data storage requirements at the CHs. Moreover, the article introduces the use of the deep Q-network (DQN) technique for AUV path planning, considering multiple pertinent factors simultaneously. Simulation results demonstrate that the proposed data collection scheme effectively reduces energy consumption, prolongs network lifespan, and enhances data collection efficiency when compared to recent research endeavors.

The forthcoming 6G networks are expected to provide a vision of overlapping aerial-ground-underwater wireless networks. Meanwhile, the rapid development of the Internet of Underwater Things (IoUTs) brings forth many categories of Autonomous Underwater Vehicle (AUV)-assisted Underwater Wireless Networks (UWNs). In this paper, we argue that the AUV-assisted UWNs can be intelligently utilized to track underwater pollution. To perform smart underwater pollution tracking, we propose the paradigm of AUV flock-based networking system and Software-Defined Networking (SDN)-enabled AUV flock Networking System (SDN-AUVNS). We introduce the concept of Mobile Edge Computing (MEC) into the control of SDN-AUVNS and propose the upgrade of the control plane of the SDN-AUVNS to with the multi-tier edge computing ability. By the proposed system architecture, we adopt the artificial potential field theory to construct the network controlling model. And we present the underwater tracking model for SDN-AUVNS, especially for the underwater pollution equipotential line of a particular concentration. Furthermore, to provide accurate path planning for the equipotential line tracking, we utilize the linearizability mechanism to optimize and revise the control input for the SDN-AUVNS. Lastly, we give a fast united control algorithm that can intelligently schedule the SDN-AUVNS to track underwater pollution equipotential lines. In particular, we propose a smart approach with the name of ’Inverse Distance Weighting’ to optimize the detection sample of the SDN-AUVNS. Evaluation results indicate that our proposal is able to track/survey the equipotential lines within a satisfactory error.

The exploitation/utilization of marine resources and the rapid development of urbanization along coastal cities result in serious marine pollution, especially underwater diffusion pollution. It is a non-trivial task to detect the source of diffusion pollution, such that the disadvantageous effect of the pollution can be reduced. With the vision of 6G framework, we employ Autonomous Underwater Vehicle (AUV) flock and introduce the concept of AUV-based network. In particular, we utilize the Software-Defined Networking (SDN) technique to update the controllability of the AUV-based network, leading to the paradigm of SDN-enabled multi-AUVs network Intelligent Transportation Systems (SDNA-ITS). For SDNA-ITS, we utilize artificial potential field theories to model the control model. To optimize the system output, we introduce the graph-based Soft Actor-Critic (SAC) algorithm, i.e., a category of Multi-Agent Reinforcement Learning (MARL) mechanism where each AUV can be regarded as a node in a graph. In particular, we improve the optimization model based on Centralized Training Decentralized Execution (CTDE) architecture with the assistance of the SDN controller, by which each AUV can efficiently adjust its speed towards the diffusion source. Further, to achieve exact path planning for detecting the diffusion source, a dynamic detection scheme is proposed to output the united control policy to schedule the SDNA-ITS dynamically. Simulation results demonstrate that our approaches are available to detect the underwater diffusion source when the actual scenario is taken into account and perform better than some recent research products.

The rapid development of intelligent underwater devices promotes marine exploitation activities, including marine resource exploitation, marine target tracking, etc. This work will present how to utilize the Autonomous Underwater Vehicle (AUV) swarm or multi-AUVs system to track the underwater diffusion pollution, especially the equipotential line of particular concentration. Different from most of the current research, in this work, we take the AUV swam as a network system and utilize the Software-Defined Networking (SDN) technique to optimize the network architecture, constructing an SDN-enabled AUV network Intelligent Transportation Systems (ITS). With the centralized management ability of the SDN technique, we propose the software-defined Centralized Training Decentralized Execution (CTDE) architecture based on the graph-based Soft Actor-Critic (SAC) algorithm to optimize the system control and management. To improve the computing and training efficiency, we embed the self-attention mechanism into the critic network construction, leading to a self-attention-based SAC algorithm. Evaluation results demonstrate that our proposed approach is able to exactly track the equipotential lines of a particular concentration in many categories (with different types of equipotential lines (including the shape, noise, and diffusion value)) of underwater diffusion fields. Meanwhile, our proposed approaches outperform some classical schemes in system awards, tracking errors, etc.

The implementation of a variety of complex and energy-intensive mobile applications by resource-limited mobile devices (MDs) is a huge challenge. Fortunately, mobile edge computing (MEC) as a new computing paragon can offer rich resources to perform all or part of the MD’s task, which greatly reduces the energy consumption of the MD and improves the quality of service (QoS) for applications. However, offloading tasks to the edge server is vulnerable to attacks such as tampering and snooping, resulting in a deep learning (DL) security feature developed by major cloud service providers. An effective security strategy method to minimize ongoing attacks in the MEC setting is proposed. The algorithm is based on the synthetic principle of a special set of strategies, and it can quickly construct suboptimal solutions even if the number of targets achieves hundreds of millions. In addition, for a given structure and a given number of patrollers, the upper bound of the protection level can be obtained, and the lower bound required for a given protection level can also be inferred. These bounds apply to universal strategies. By comparing with the previous three basic experiments, it can be proved that our algorithm is better than the previous ones in terms of security and running time.

Dynamic opportunistic channel access with software‐defined radio at a network layer in distributed cognitive IoT introduces a concurrent channel selection along with end‐to‐end route selection for application data transmission. State‐of‐the‐art cognitive IoT big data‐based routing protocols are not explored in terms of how the spectrum management is being coordinated with the network layer for concurrent channel route selection during end‐to‐end channel route discovery for data transmission of IoT and big data applications. In this paper, a reactive big data‐based “cognitive dynamic source routing protocol” is proposed for cognitive‐based IoT networks to concurrently select the channel route at the network layer from source to destination. Experimental results show that the proposed protocol cognitive DSR with concurrent channel route selection criteria is outperformed. This will happen when it is compared with the existing distributed cognitive DSR with independent channel route application data transmission.

In the 21st century, the power quality and availability with customer demands to the society is the main challenging factor right now. Therefore, the gird monitoring system become a vital issue to monitor power grid system. The current smart grid system mainly focuses on smart metering system and improving the customer utility communication system. On customer management side, although those advancement provides an extra benefit, in spite of, the management of a grid system is one of the major dominating field in the era of Internet of Thing (IoT). From the field of industry and academia researches, Wireless sensor networks (WSNs) is getting to much popularity for monitoring power grid. Moreover, saving node energy enhance the lifespan of whole monitoring network. Due to lack of energy making policy, unnecessary activating all participate nodes consume node energy drastically, which is the main reason for shortening the lifetime of monitoring system. To solve this issue, maintenance technology provides the best opportunity to preserve node energy. This study investigates the issues that are associated with energy consumption using maintenance protocols in prospect of Rampal, Bangladesh power plant data. The modelling data has been collected through literature survey. Extensive simulation work has done for monitoring Rampal power using WSN. Finally, a comparative study of maintenance protocols were performed to maintain optimal network correction and thereafter extending the lifetime of monitoring network.