Neural-network model trading raises two unmet requirements for watermarking: exclusive ownership verification and updateability (transfer/revoke) without retraining. We present a training-time framework that jointly embeds a keydriven dynamic label-mapping and a proactive, self-defending trigger. The label-mapping encodes an owner-specific signature that authorized key holders can verify, update, or transfer by rotating keys; the defensive trigger actively hardens the model against unauthorized backdoor insertion and fakewatermark attempts. Our design yields three properties: (i) exclusivity—only the correct key decodes the watermark; (ii) updateability—ownership can be changed without retraining the backbone; and (iii) robustness—the watermark persists under common post-deployment changes. Across CIFAR-10/100, GTSRB, and Tiny ImageNet on five architectures, the method achieves (> 97%) watermark success rate while preserving original accuracy within 0.5 percentage points, retains (>85%) watermark strength after fine-tuning and (60%) pruning, and reduces fake-watermark success to (95%) on unprotected models). By coupling updateable encoding with proactive defense, our approach offers a practical, scalable path to secure, transferable ownership verification for neural-network marketplaces and model exchanges.

With the support of 5G technology, mobile edge computing has made the application of industrial IoT and power IoT more and more extensive. By deploying a certain number of edge servers at the edge of the network, network service delay may significantly reduce. For the IoT scenario where the content demand is unpredictable, there are multiple distributed cloud servers and the distributed cloud servers do not communicate directly, a feasible way to improve the network service quality is to dynamically optimize the storage of edge servers and formulate targeted caching strategies. This paper proposes an edge caching approach based on multi-agent deep deterministic policy gradient named MADDPG-C, which regards distributed cloud servers and edge servers as different types of agents and maximizes the efficiency of edge caching in cooperation and competition. Simulation experiments show that the proposed MADDPG-C can further improve the hit rate of the edge cache and reduce the waiting delay of terminal devices.

The rapid development of industrial IoT communication and the wide application of smart terminal devices, large-scale mobile data traffic and frequent data requests pose challenges to resource-constrained wireless communication networks. In order to reduce the network load, the introduction of content encoding caching to the network edge is considered an effective solution. Due to the heterogeneity of the resources of the mobile edge system, the variability of user requests, and the mobility of users, the coded caching strategy at the mobile edge needs to be continuously optimized. However, existing articles do not adequately investigate how to intelligently update the coded caching policy in dynamic environments. For this reason, this paper proposes a heterogeneous multi-agent MDS coded caching scheme (MACC) based on value decomposition, which considers the edge caching server with storage capacity and the mobile user as two different types of agents and improves the caching policy through interaction. In addition, considering the heterogeneous preference of heterogeneous multi-agent and the fact that an increase in the number may lead to a sharp increase in the training dimension, the idea of a value decomposition network is introduced in the training learning of the coded caching scheme. Simulation experiments verify that the proposed MACC can effectively reduce the load on the forward link and achieve a higher cache hit rate.

To meet the low delay requirements for data content access in Industrial Internet of Things, efficient content caching strategies need to be designed in mobile edge computing architectures. Most existing caching strategies reduce access delay by predicting content popularity and caching popular content earlier during off-peak traffic, but these strategies only focus on the temporal features of content popularity and ignore the spatial correlation of content popularity. Therefore, we propose a mobile edge cooperative caching strategy based on spatio-temporal graph convolutional model(STCC). In STCC, we integrate graph convolutional neural network and the gated recurrent unit to mine the spatio-temporal characteristics of content popularity and make effective prediction. Moreover, we divide the collaboration domain by hierarchical clustering and design a heuristic cache placement strategy to minimize the access delay. Simulation experiments show that the spatio-temporal graph convolution model proposed in STCC can predict content popularity better than existing time series model. Compared with existing caching strategies, STCC can significantly improve the cache hit ratio and reduce the average content access delay.

Serverless computing/Function as a Service (FaaS) has emerged as a new paradigm for running short-lived applications in the cloud. Serverless edge computing is recently adopting serverless computing at edge to run event-driven tasks and supporting application running of resource-constrained Internet of Things (IoT) devices by offloading their tasks to the edge. However, traditional task offloading methods are mainly based on heuristic algorithms for one-shot optimization, which leads to performance degradation in long-term operation. Fortunately, deep reinforcement learning techniques combining reinforcement learning and deep neural networks provide a promising alternative. Therefore, a function offloading algorithm DRLFO is proposed with a deep reinforcement learning algorithm based on actor-critic framework in this paper. The function offloading process in serverless edge computing environment is modeled as a Markov Decision Process. Finally, the experimental results show that the proposed algorithm can successfully converge and outperform the compared baseline algorithm in terms of function success rate and reduce the average latency by 4.6%-22.6%.