Underwater wireless optical communication (UWOC) enables high-rate data offloading for underwater sensing systems, but its strong directionality makes multi-node networking vulnerable to misalignment, occlusion, and dynamic link disruptions. Existing control-plane-driven recovery is often too slow for such transient failures. We present A-SCAN, a data-plane self-healing mechanism that maintains neighbor-angle mappings and performs lightweight angle-guided recovery without triggering global routing updates. Based on the locally recovered topology, Q-SHARP performs quality-aware multi-hop path selection and backup optimization in the control plane. Together, they separate fast local link recovery from slow global routing optimization, enabling more stable self-healing communication in directional UWOC networks.

Efficient task offloading is vital for latency-sensitive Industrial IoT (IIoT) systems. Existing deep learning-based approaches, however, face long training time, poor adaptability, and heavy reliance on labeled data. We propose SRCO, a Semi supervised Reservoir Computing-based Offloading framework that uses a fixed dynamic reservoir and trains only the readout layer, enabling fast model updates with minimal overhead. A semi-supervised strategy further exploits unlabeled data to reduce labeling cost. Experiments show that SRCO improves of floading accuracy by up to 15.6% and reduces training time by up to 59.6% compared with state-of-the-art methods, demonstrating strong efficiency and adaptivity for real-time IIoT applications.

Underwater networking is vital for enabling collaboration between divers, vehicles, and sensors in marine exploration, monitoring, and emergency response. Yet achieving reliable communication in such dynamic, bandwidth constrained environments remains challenging. Acoustic and radio frequency technologies suffer from attenuation, latency, and hardware overhead, while optical wireless systems typically require specialized transceivers or strict alignment, limiting practicality in mobile underwater networks. To address these limitations, we present AquaLink, a QR code–driven Optical Camera Communication (OCC) framework that enables robust underwater messaging using commodity smartphones and tablets. At its core, AquaQR employs blue–green 2-bit color encoding, Low-Density Parity-Check (LDPC) error correction, and geometric augmentations tailored for optical stability in turbid waters. An auto-configuration module adapts parameters before transmission, and a lightweight enhancement pipeline ensures real-time robustness under diverse conditions. Field trials in pool, lake, and coastal environments achieve over 90% decoding success at 5 m and up to 2× higher throughput than prior QR-based systems. By eliminating specialized hardware, AquaLink provides a scalable, low cost foundation for underwater visual networking, supporting message exchange, peer interaction, and localized link formation.

The Industrial Internet of Things (IIoT) integrates diverse wireless and heterogeneous devices to enable time-sensitive applications. Multi-access edge computing (MEC) offers computing services for nearby tasks to meet their time requirements. However, offloading a large number of tasks to servers with minimal time is a challenging issue. Existing approaches typically allocate tasks into equal-length timeslots for offloading based on optimization or heuristic methods, overlooking the time-varying nature of task arrival density. This neglect significantly increases task execution time. To address this problem, we propose an Adaptive Task Offloading scheme with two-stage hybrid Matching (ATOM). In ATOM, a global buffer with an adjustable threshold is employed to store task information, enabling it to adapt to the time-varying arrival density and execute different offloading stages accordingly. In the online matching stage, if the threshold is not reached, tasks in the buffer are promptly offloaded to the most suitable server. In the offline matching stage, when the threshold is exceeded, all tasks in the buffer are optimally matched with servers and offloaded in batches. Experimental results demonstrate that ATOM outperforms state-of-the-art schemes in terms of average execution time and timeout rate, achieving reductions of 23.3% and 10.4%, respectively.

In the Industrial Internet of Things (IIoT), Multi-access Edge Computing (MEC) emerges as a transformative paradigm for managing computation-intensive tasks, where task offloading plays an important role. However, due to the complex environment of IIoT, existing deep reinforcement learning-based schemes suffer from significant shortcomings in accuracy and convergence speed during model training when addressing the issue of task offloading. In this paper, to solve this problem, we propose an online task offloading scheme based on reinforcement learning, leveraging the double deep Q network (DQN) and dueling DQN with a prioritized experience replay mechanism, called thePrioritized experience-basedDoubleDuelingDQNtask offloading scheme (P-D3QN). P-D3QN enhances action selection accuracy using double DQN and mitigates Q-value overestimation by decomposing state and advantage using dueling DQN. Additionally, we adopt the prioritized experience replay mechanism to enhance the convergence speed of model training by selecting transitions that induce a higher training error between the evaluation network and the target network. Experimental results demonstrate that P-D3QN outperforms several state-of-the-art schemes, achieving a reduction of 21.0% in the average cost of the task and improving the completion rate of the task by 19.5%.

Frequently task offloading in the Multi-access Edge Computing (MEC) system takes up a lot of network resources, which leads to serious network congestion problems. To deal with such problems, applying data pricing strategies in task offloading to schedule traffic data is perceived as a promising solution for Internet service providers (ISPs). However, the traditional application-oriented data pricing strategies do not consider user satisfaction of the task executing process, resulting in lower ISP profit. In this paper, a novel scheme named App-set Usage Patterns-Aware task offloading scheme (AUPA) is proposed to alleviate the tension between traffic data supply and user satisfaction. We mine the sequential patterns in frequent application sets to extract temporal association rules. Then these association rules are used to design a smart data pricing strategy to guide the task offloading decision. Finally, we formulate the ISP’s profit maximization problem as a nonlinear programming (NLP) problem based on partial offloading, and we simulate the scheduling processuse using the Stackelberg game model. The performance of our solution is evaluated in terms of ISP’s profit, consumers’ surplus, capacity utilization, and traffic efficiency. The results show that our scheme significantly improves the ISP’s profit by about 20% while ensuring capacity constraints compared with other baseline schemes.

Protocol parsing is to discern and analyze packets' transmission fields, which plays an essential role in industrial security monitoring. The existing schemes parsing industrial protocols universally have problems, such as the limited parsing protocols, poor scalability, and high preliminary information requirements. This paper proposes a text similarity-based protocol parsing scheme (TPP) to identify and parse protocols for Industrial Internet of Things. TPP works in two stages, template generation and protocol parsing. In the template generation stage, TPP extracts protocol templates from protocol data packets by the cluster center extraction algorithm. The protocol templates will update continuously with the increase of the parsing packets' protocol types and quantities. In the protocol parsing phase, the protocol data packet will match the template according to the similarity measurement rules to identify and parse the fields of protocols. The similarity measurement method comprehensively measures the similarity between messages in terms of character position, sequence, and continuity to improve protocol parsing accuracy. We have implemented TPP in a smart industrial gateway and parsed more than 30 industrial protocols, including POWERLINK, DNP3, S7comm, Modbus-TCP, etc. We evaluate the performance of TPP by comparing it with the popular protocol analysis tool Netzob. The experimental results show that the accuracy of TPP is more than 20% higher than Netzob on average in industrial protocol identification and parsing.