With the increase of network complexity, the flexibility of network control and management becomes a nontrivial problem. Both Software Defined Network (SDN) and Autonomic Network technologies are sophisticated technologies for the network control and management. These two technologies could be combined together to construct a software defined self-managing solution for the future network. An autonomic QoS management mechanism in Software Defined Network (AQSDN) is proposed in this paper. In AQSDN, the various QoS features can be configured autonomically in an OpenFlow switch through extending the OpenFlow and OF-Config protocols. Based on AQSDN, a novel packet context-aware QoS model (PCaQoS) is also introduced for improving the network QoS. PCaQoS takes packet context into account when packet is marked and managed into forwarding queues. The implementation of a video application's prototype which evaluates the self-configuration feature of the AQSDN and the enhancement ability of the PCaQoS is presented in order to validate this design.

Federated learning (FL) strikes a balance between privacy preservation and collaborative model training. However, the periodic transmission of model updates or parameters from each client to the federated server incurs substantial communication overhead, especially for participants with limited network bandwidth. This overhead significantly hampers the practical applicability of FL in real-world scenarios. To address this challenge, we propose FedSparse, an innovative sparse communication framework designed to enhance communication efficiency. The core idea behind FedSparse is to introduce a communication overhead regularization term into the client’s objective function, thereby reducing the number of parameters that need to be transmitted. FedSparse incorporates a Resource Optimization Proximal (ROP) term and an Importance-based Regularization Weighting (IRW) mechanism into the client update objective function. The local update process optimizes both the empirical risk and communication overhead by applying a sparse regularization weighted by update importance. By making minimal modifications to traditional FL approaches, FedSparse effectively reduces the number of parameters transmitted, thereby decreasing the communication overhead. We evaluate the effectiveness of FedSparse through experiments on various datasets under non-independent and identically distributed (non-IID) conditions, demonstrating its flexibility in resource-constrained environments. On the MNIST, Fashion-MNIST, and CIFAR datasets, FedSparse reduces the communication overhead by 24%, 17%, and 5%, respectively, compared to the baseline algorithm, while maintaining similar model performance. Additionally, on simulated non-IID datasets, FedSparse achieves a 6% to 8% reduction in communication resource consumption. By adjusting the sparsity intensity hyperparameter, we demonstrate that FedSparse can be tailored to different FL applications with varying communication resource constraints. Finally, ablation studies highlight the individual contributions of the ROP and IRW modules to the overall improvement in communication efficiency.

Mobile multimedia services have been increasingly popular. However, radio resources are limited. The quality of experience of all users in wireless networks can be enriched by resource allocation, which is affected by many factors in current wireless networks. In this paper, we focus on a hybrid unicast-multicast (HUM) video streaming scheme, and develop an adaptive resource allocation framework for HUM by combining adaptive cooperative transmission and base station handoff. The optimization problem is formulated as a multi-dimensional, complexity-constrained nonlinear NP-hard problem. To solve the problem, a greedy algorithm and a genetic algorithm are proposed. Simulation results demonstrate that our scheme has superior performance than other transmission methods in the literature. The genetic algorithm has better performance, but the greedy algorithm can maintain better balance between performance and resource consumption.

Graph-based approaches are successful for histology image classification tasks but still face many challenges, such as: 1) the lack of nuclei-level labels and the significant variations between histology images make it extremely difficult to extract discriminative high-level nuclei features like nuclei type, texture and micro-environment; 2) graph-based approaches cannot handle large-scale cell graph nodes typically contained in histology images; and 3) graph neural networks (GNNs) struggle to learn the long-range dependency of cell graphs. To address the above challenges, we propose a scalable graph-based framework for multi-organ histology image classification. We develop a two-step MNP supervised training approach to extract discriminative high-level nuclei features for histology images without nuclei-level labels. Additionally, we introduce a nuclei sampling strategy to make our graph-based framework scalable for large-scale cell graphs. Furthermore, we propose HierArchical Transformer Graph Neural Network (HAT-Net+) for cell graph classifications. HAT-Net+ adopts Transformer to model the long-range dependency of cell graphs and a parameter-free approach to adaptively fuse different hierarchical graph representations of each layer. We achieved the state-of-the-art results on four public histology image classification datasets: CRC dataset (100%), Extended CRC dataset (98%), UZH dataset (96.9%) and BACH dataset (88%). Unlike other methods, our approach can be used in various histology image classification tasks, even for images without nuclei-level labels, indicating its potential in cancer diagnosis.

There are few network resources in wireless multimedia sensor networks (WMSNs). Compressing media data can reduce the reliance of user’s Quality of Experience (QoE) on network resources. Existing video coding software, such as H.264 and H.265, focuses only on spatial and short-term information redundancy. However, video usually contains redundancy over a long period of time. Therefore, compressing video information redundancy with a long period of time without compromising the user experience and adaptive delivery is a challenge in WMSNs. In this paper, a semantic-aware super-resolution transmission for adaptive video streaming system (SASRT) for WMSNs is presented. In the SASRT, some deep learning algorithms are used to extract video semantic information and enrich the video quality. On the multimedia sensor, different bit-rate semantic information and video data are encoded and uploaded to user. Semantic information can also be identified on the user side, further reducing the amount of data that needs to be transferred. However, identifying semantic information on the user side may increase the computational cost of the user side. On the user side, video quality is enriched with super-resolution technologies. The major challenges faced by SASRT include where the semantic information is identified, how to choose the bit rates of semantic and video information, and how network resources should be allocated to video and semantic information. The optimization problem is formulated as a complexity-constrained nonlinear NP-hard problem. Three adaptive strategies and a heuristic algorithm are proposed to solve the optimization problem. Simulation results demonstrate that SASRT can compress video information redundancy with a long period of time effectively and enrich the user experience with limited network resources while simultaneously improving the utilization of these network resources.