ABSTRACT Transforming fashion design sketches into realistic garments remains a challenging task due to the reliance on labor‐intensive manual workflows that limit efficiency and scalability in traditional fashion pipelines. While recent advances in image generation and virtual try‐on technologies have introduced partial automation, existing methods still lack controllability and struggle to maintain semantic consistency in garment pose and structure, restricting their applicability in real‐world design scenarios. In this work, we present CG‐VTON, a controllable virtual try‐on framework designed to generate high‐quality try‐on images directly from clothing design sketches. The model integrates multi‐modal conditional inputs, including dense human pose maps and textual garment descriptions, to guide the generation process. A novel pose constraint module is introduced to enhance garment‐body alignment, while a structured diffusion‐based pipeline performs progressive generation through latent denoising and global‐context refinement. Extensive experiments conducted on benchmark datasets demonstrate that CG‐VTON significantly outperforms existing state‐of‐the‐art methods in terms of visual quality, pose consistency, and computational efficiency. By enabling high‐fidelity and controllable try‐on results from abstract sketches, CG‐VTON offers a practical and robust solution for bridging the gap between conceptual design and realistic garment visualization.

Multipath TCP leverages parallel data transmission across multiple paths to improve transmission rates, reliability, and resource utilization. However, Multipath TCP faces severe network security and communication reliability challenges when exposed to low-rate distributed denial-of-service (LDDoS) attacks. In this paper, we propose a robustness optimization security scheme against cascading failures in Multipath TCP (ROSE+) to tackle the challenges posed by Low-rate Distributed Denial of Service (LDDoS) attacks on network security and communication reliability. The scheme integrates the intricate network load-capacity cascading failures model and leverages the unique characteristics of multipath TCP to facilitate the redistribution of load traffic at ineffectiveness nodes, thereby alleviating the cascading failures induced by LDDoS attack streams. Additionally, we optimize the robustness of communication transmission systems by devising a load-capacity cascading failures model. The experimental results demonstrate that the scheme reduces the probability of cascading failures by 20.07%. This research provides new ideas and methods to improve the robustness and destruction resistance of multipath TCP transmission.

With the development of 5G technology, ultradense cellular networks are becoming a trend, while the deployment of large-area and high-density base stations (BSs) will bring new energy consumption problems. In this article, we explore the energy consumption of the edge demand response (EDR) from the two perspectives of edge users and edge facility providers. While guaranteeing the essential Quality of Experience (QoE), we try to improve the load balancing of edge servers and reduce system energy consumption. On the edge facility provider side, we mainly consider the physical machine turn-on problem and resource allocation of the two-phase EDR. On the user side, we start with user cost reduction and subchannel power allocation to emphasize the QoE. First, considering the density impacts of users and edge servers, we take inspiration from the classical PageRank algorithm and propose a method to calculate the edge nodes’ weights and, thus, determine the infrastructure’s state. Subsequently, combining distance factors, we design a subchannel power allocation method based on dynamic planning for 5G power-domain multiplexing nonorthogonal multiple access (PDM-NOMA). More importantly, based on the above work, we optimize the two-phase EDR process based on the upper confidence bound (UCB) algorithm of the multiarmed bandit (MAB) algorithm framework and dynamic planning. We compare the proposed QEL-UCB algorithm with two classical and five state-of-the-art algorithms on a real-world data set. The experimental results demonstrate that the proposed method improves by 18.52% in load balancing and reduces by 18.59% in energy consumption, which validates the method’s effectiveness.

In recent years, the rise of location-based service applications such as cashier-less shopping, mobile advertisement targeting, and geo-based augmented reality (AR) has been remarkable. These applications offer convenient and interactive experiences by utilizing indoor localization technology. One popular research area in indoor localization is passive fingerprinting localization based on Channel State Information (CSI), which uses general-purpose Wi-Fi platforms and “unconscious cooperative sensing” to achieve device-free localization. However, existing studies face challenges related to inadequate fingerprint richness, limited distinguishability, and inconsistent fingerprint features in real-world dynamic environments. To address these challenges, we prpose MFFLoc in this paper. MFFLoc extracts and processes amplitude and phase information from CSI in a 2D manner. It then fuses the amplitude and phase information using multimodal fusion representation, resulting in rich and distinguishable fused fingerprint features. This approach allows MFFLoc to achieve satisfactory accuracy with just one communication link, reducing deployment costs. To overcome the issue of inconsistent fingerprint features in dynamic environments, MFFLoc proposes an unsupervised domain adaptation method. It employs a dual-flow structure, with one flow operating in the source domain and the other in the target domain. The adaptation layer, with correlated weights, remains unshared between the two flows. Meta-learning is also used to automatically determine the most suitable adaptation layer. Through extensive 6-day experiments conducted in a dynamic indoor environment, MFFLoc showcases superior performance compared to state-of-the-art systems. It demonstrates higher localization accuracy and robustness, making it a promising solution for indoor localization applications.

In recent years, indoor localization has attracted a lot of interest and has become one of the key topics of Internet of Things (IoT) research, presenting a wide range of application scenarios. With the advantages of ubiquitous universal Wi-Fi platforms and the “unconscious collaborative sensing” in the monitored target, Channel State Information (CSI)-based device-free passive indoor fingerprinting localization has become a popular research topic. However, most existing studies have encountered the difficult issues of high deployment labor costs and degradation of localization accuracy due to fingerprint variations in real-world dynamic environments. In this paper, we propose BSWCLoc, a device-free passive fingerprint localization scheme based on the beyond-sharing-weights approach. BSWCLoc uses the calibrated CSI phases, which are more sensitive to the target location, as localization features and performs feature processing from a two-dimensional perspective to ultimately obtain rich fingerprint information. This allows BSWLoc to achieve satisfactory accuracy with only one communication link, significantly reducing deployment consumption. In addition, a beyond-sharing-weights (BSW) method for domain adaptation is developed in BSWCLoc to address the problem of changing CSI in dynamic environments, which results in reduced localization performance. The BSW method proposes a dual-flow structure, where one flow runs in the source domain and the other in the target domain, with correlated but not shared weights in the adaptation layer. BSWCLoc greatly exceeds the state-of-the-art in terms of positioning accuracy and robustness, according to an extensive study in the dynamic indoor environment over 6 days.

APIs have intricate relations that can be described in text and represented as knowledge graphs to aid software engineering tasks. Existing relation extraction methods have limitations, such as limited API text corpus, and are affected by the characteristics of the input text. To address these limitations, we propose utilizing large language models (LLMs) (e.g., GPT-3.5) as a neural knowledge base for API relation inference. This approach leverages the entire Web used to pre-train LLMs as a knowledge base and is insensitive to the context and complexity of input texts. To ensure accurate inference, we design an AI chain consisting of three AI modules: API Fully Qualified Name (FQN) Parser, API Knowledge Extractor, and API Relation Decider. The accuracy of the API FQN Parser and API Relation Decider is 0.81 and 0.83, respectively. Using the generative capacity of the LLM and our approach’s inference capability, we achieve an average F1 value of 0.76 under the three datasets, significantly higher than the state-of-the-art method’s average F1 value of 0.40. Compared to the original CoT and modularized CoT methods, our AI chain design has improved the performance of API relation inference by 71% and 49%, respectively. Meanwhile, the prompt ensembling strategy enhances the performance of our approach by 32%. The API relations inferred by our method can be further organized into structured forms to provide support for other software engineering tasks.

TCP is currently the most widely used Internet transmission protocol, which is extensively applied to applications on the Internet to enable reliable data transmission. The TCP congestion control algorithm has a significant performance impact on all applications that use the TCP. However, traditional TCP congestion control algorithms rely on fixed feedback mechanisms, which can be challenging to adapt to complex and changing network environments and application scenarios, resulting in network performance bottlenecks. To address this issue, we design a congestion control windowing solution, DRLFcc, which is based on deep reinforcement learning and the TCP fast recovery mechanism. DRLFcc has demonstrated the ability to facilitate real-time adaptation of the congestion window to dynamic changes in network conditions while incorporating fast recovery mechanisms, thereby effectively enhancing network throughput and improving data transmission capacity recovery in high-loss wireless networks. The DRLFcc algorithm is validated in NS-3, and experimental results show an average improvement of 196% in effective throughput and a 22.4% reduction in round trip time. Compared to traditional TCP congestion control algorithms, the DRLFcc algorithm demonstrates superior performance and robustness.

In recent years, applying smart contract to Blockchain-based Service Networks (BSNs) has been considered as one of the most promising solution to boost the integration and adoption of Blockchain in big businesses. However, smart contract are especially vulnerable to attack due to poor coding. Although many existing vulnerability detection tools are restricted by rigorous rules that are defined by the experts in advance, these tools are observed to have a high false positive rate in practice. Thus we propose a vulnerability detection framework for smart contract based on the attention mechanism and data flow. The code of smart contract is transformed to a data flow according to the abstract syntax tree that is built from the code. The data flow we built with smart contract code could represent the relationships of code semantic logic. Source code, data flow, and the tags of smart contract code are used as datasets to mask processing. Then, we construct a bidirectional multi-layer transformer architecture based on the attention mechanism to train our dataset. After training, we can get the label of whether there is a vulnerability in the final smart contract. Finally, the model we proposed reaches state-of-the-art results in the practical experiments of smart contract vulnerability detection with 92.54%, 81.79%, and 86.84% in the results Accuracy, Recall, and F1score, respectively.

With the advent of the 5G era, high-speed and secure network access services have become a common pursuit. The QUIC (Quick UDP Internet Connection) protocol proposed by Google has been studied by many scholars due to its high speed, robustness, and low latency. However, the research on the security of the QUIC protocol by domestic and foreign scholars is insufficient. Therefore, based on the self-similarity of QUIC network traffic, combined with traffic characteristics and signal processing methods, a QUIC-based network traffic anomaly detection model is proposed in this paper. The model decomposes and reconstructs the collected QUIC network traffic data through the Empirical Mode Decomposition (EMD) method. In order to judge the occurrence of abnormality, this paper also intercepts overlapping traffic segments through sliding windows to calculate Hurst parameters and analyzes the obtained parameters to check abnormal traffic. The simulation results show that in the network environment based on the QUIC protocol, the Hurst parameter after being attacked fluctuates violently and exceeds the normal range. It also shows that the anomaly detection of QUIC network traffic can use the EMD method.

In recent years, as the demand for senior care services has further increased, it has become more difficult to obtain matching services from the vast amount of data. Therefore, this paper proposes a service recommendation framework PCE-CF based on an embedded user portrait model. The framework accurately describes the elderly users through four dimensions—population, society, consumption, and health—and constructs the user portrait model by embedding tags. The embedded vector of each older man is learned through the deep learning model, and different feature groups are meaningfully expressed in the transformation space. In addition, location context and dynamic interest model are introduced to process embedded vectors, and users' service preferences are predicted according to their dynamic behaviors. The experiment results show that the PCE-CF framework proposed in this paper can improve the recommendation algorithm's efficiency and have higher feasibility in personalized service recommendations.

With various industrial wireless networks greeting booming development, modern industrial devices configured with several network interfaces increasingly become the norm. Such multihomed industrial devices can increase application throughput by making use of multiple network paths, enabled by the multipath transmission control protocol (MTCP) (MPTCP). However, MPTCP might be challenged in the heterogeneous industrial networks because concurrent transmitting industrial application data over asymmetric network paths with different delays is almost bound to the receive buffer blocking problem, which is caused by out-of-order packet arrival and is harmful to the performance of the multipath transmission. The existing MPTCP solutions generally use static mathematical models to evaluate path quality and prohibit transmission on paths with poor quality, which are unable to perform efficiently under highly dynamic and complex network environments. Therefore, in this article, we propose a learning-driven latency-aware MPTCP variant, called${l}\,^2$-MPTCP, which seeks to possibly mitigate the out-of-order packet arrival and receive buffer blocking problems associated with the network heterogeneity in the industrial Internet.${l}\,^2$-MPTCP accurately computes each MPTCP path’s forward delay and assigns application data to multiple paths according to their calculated forward delay differences by using a novel multiexpert learning-enabled forward delay estimator.${l}\,^2$-MPTCP dynamically manages path usage and chooses the optimal path collection for bandwidth aggregation and multipath transmission by using a promising reinforcement learning-empowered multipath manager. Experimental results demonstrate that${l}\,^2$-MPTCP outperforms the current MPTCP solutions in terms of multipathing service quality.

With the development of social networks, more and more mobile social network devices have multiple interfaces. Multipath TCP (MPTCP), as an emerging transmission protocol, can fit multiple link bandwidths to improve data transmission performance and improve user experience quality. At the same time, due to the large-scale deployment and application of emerging technologies such as the Internet of Things and cloud computing, cyber attacks against MPTCP have gradually increased. More and more network security research studies point out that low-rate distributed denial of service (LDDoS) attacks are relatively popular and difficult to detect and are recognized as one of the most severe threats to network services. This article introduces six classic queue management algorithms: DropTail, RED, FRED, REM, BLUE, and FQ. In a multihomed network environment, we perform the performance evaluation of MPTCP under LDDoS attacks in terms of throughput, delay, and packet loss rate when using the six algorithms, respectively, by simulations. The results show that in an MPTCP-enabled multihomed network, different queue management algorithms have different throughput, delay, and packet loss rate performance when subjected to LDDoS attacks. Considering these three performance indicators comprehensively, the FRED algorithm has better performance. By adopting an effective active queue management (AQM) algorithm, the MPTCP transmission system can enhance its robustness capability, thus improving transmission performance. We suggest that when designing and improving the queue management algorithm, the antiattack performance of the algorithm should be considered: (1) it can adjust the traffic speed by optimizing the congestion control mechanism; (2) the fairness of different types of data streams sharing bandwidth is taken into consideration; and (3) it has the ability to adjust the parameters of the queue management algorithm in a timely and accurate manner.

The multipath TCP (MPTCP) enables multihomed mobile devices to realize multipath parallel transmission, which greatly improves the transmission performance of the mobile communication network. With the rapid development of all kinds of emerging technologies, network attacks have shown a trend of development with many types and rapid updates. Among them, low‐rate distributed denial of service (LDDoS) attacks are considered to be one of the most threatening issues in the field of network security. In view of the current research status, by using the network simulation software NS2, this paper first compares and analyzes the throughput and delay performance of the MPTCP transmission system under LDDoS attacks and, further, conducts simulation experiments and analysis on the queue occupancy rate of the LDDoS attack flow to extract the basic attack characteristics of the LDDoS attacks. The experimental results show that the LDDoS attacks will have a major destructive effect on the throughput performance and delay performance of the MPTCP transmission system, resulting in a decrease in the robustness of the transmission system. By analyzing and comparing the occupancy rate of the LDDoS attack flow in the MPTCP transmission system, it can be concluded that (1) the occupancy rate of the LDDoS scattered pulse traffic sent by each puppet machine changes slightly, and (2) the occupancy rate of LDDoS attack data flow is much greater than that of ordinary TCP data flow.

With the rapid growth of 6G communication and smart sensor technology, the Internet of Things (IoT) has attracted much attention now. In the 6G‐based IoT applications on the multiprocessor platform, the partitioned scheduling has been widely applied. However, these partitioned scheduling approaches could cause system resource waste and uneven workload among processors. In this paper, a smart semipartitioned scheduling strategy (SSPS) was proposed for mixed‐criticality systems (MCS) in 6G‐based edge computing. Besides tasks’ acceptance rate and weighted schedulability, QoS is considered in SSPS to improve the service quality of the system. The SSPS allocates tasks into each processor, and some tasks can migrate to other processors as soon as possible. By comparing with the several existing algorithms, the experimental results show that the SSPS achieves the best in the schedulability and QoS of the system.

The automatic generation of chest X-ray report is a hot research topic at present. Considering the lack of research on Chinese report generation, we propose a method suitable for lung description in Chinese reports-a model that combines semantic labels and hierarchical LSTM. The model analyzes the anomaly report, extracts high-frequency keywords as semantic labels, and adds the abnormal binary classification module in the encoder to correct the results of the semantic labels for the templated characteristics of the Chinese report. In the design of the decoder, to address the problem of lack of correlation between semantic Labels, a two-layer LSTM model that fuses semantic tags and image features is proposed. The comparison with the baseline experiment shows that the proposed model can effectively improve the quality of report generation.

Applying Multipath TCP (MPTCP) towards transport-layer parallel multipath transmission to increase the throughput performance of mobile devices has attracted considerable attention. Significant results in this area have resulted in many highly promising solutions; however most of the existing solutions follow the conventional sender-centric design and the strict layering principle, without considering the fact that the receiver's intelligence and cross-layer activities can result in distinct performance advantages. In this paper, we propose MPTCP-RC, a receiver-centric cross-layer solution to aid MPTCP towards efficient parallel multipath data transmission in a wireless environment. In MPTCP-RC, the receiver performs path usage decision and cross-layer activity, by making use of its acquired first-hand knowledge of both transport layer and MAC layer, rather than only giving feedback to the sender then waiting for the decision. We evaluate and demonstrate the benefits of our proposal by simulations.

Summary Multipath TCP (MPTCP) is a promising transport protocol that allows a multihomed device to simultaneously use multiple network interfaces to send application data over multiple paths. However, although applying MPTCP to data delivery introduces many and attractive benefits, the MPTCP is vulnerable to network attacks. When a path within the MPTCP connection suffers from some types of attacks (eg, a denial‐of‐service attack) and becomes underperforming, it will undoubtedly cause transmission interruption in the stable paths and thus degrade the application‐level performance. Unfortunately, the MPTCP path management mechanism is very simple and cannot timely prevent the usage of underperforming paths in multipath transmission. In this paper, we introduce a new “potentially underperforming” (PU) concept to MPTCP and propose a novel PU‐aware path usage management mechanism ((PU)2M2) for MPTCP aiming to (1) detect and declare an underperforming path and prevent the usage of underperforming paths in multipath transmission, (2) provide a finite‐state‐machine model to change per‐path's state accordingly and effectively manage multiple paths for data transmission, and (3) alleviate the packet reordering problem and make MPTCP avoid throughput performance degradation during network underperforming. We demonstrate the benefits of applying (PU)2M2 to MPTCP.

Wireless body sensor networks (WBSNs) mostly consist of low‐cost sensor nodes and implanted devices which generally have extremely limited capability of computations and energy capabilities. Hence, traditional security protocols and privacy enhancing technologies are not applicable to the WBSNs since their computations and cryptographic primitives are normally exceedingly complicated. Nowadays, mobile wearable and wireless muscle‐computer interfaces have been integrated with the WBSN sensors for various applications such as rehabilitation, sports, entertainment, and healthcare. In this paper, we propose MGRNG, a novel muscle activity‐driven green‐oriented random number generation mechanism which uses the human muscle activity as green energy resource to generate random numbers (RNs). The RNs can be used to enhance the privacy of wearable device communications and secure WBSNs for rehabilitation purposes. The method was tested on 10 healthy subjects as well as 5 amputee subjects with 105 segments of simultaneously recorded surface electromyography signals from their forearm muscles. The proposed MGRNG requires only one second to generate a 128‐bit RN, which is much more efficient when compared to the electrocardiography‐based RN generation algorithms. Experimental results show that the RNs generated from human muscle activity signals can pass the entropy test and the NIST random test and thus can be used to secure the WBSN nodes.

Time‐slotted channel hopping (TSCH), which can enable highly reliable and low‐power wireless mesh networks, is the cornerstone of current industrial wireless standards. In a TSCH network, all nodes must maintain high‐precision synchronization. If an adversary launches a time‐synchronization attack on a TSCH network, the entire network communication system can be paralyzed. Thus, time‐synchronization security is a key problem in this network. In this article, time synchronization is divided into single‐hop pairwise, clusterwise, and three‐level multihop according to the network scope. We deeply analyze their security vulnerabilities due to the TSCH technology itself and its high‐precision synchronization requirements and identify the specific attacks; then, we propose corresponding security countermeasures. Finally, we built a test bed using 16 OpenMoteSTM nodes and the OpenWSN software to evaluate the performance of the proposed scheme. The experimental results showed that serious security vulnerabilities exist in time‐synchronization protocols, and the proposed countermeasures can successfully defend against the attacks.

The growing popularity of multihoming techniques has motivated the rise of multipath protocols. Multipath TCP (MPTCP), as an extension of TCP, can use multiple paths to deliver data in parallel without influencing any functionality of TCP. Although applying MPTCP to mobile terminals for data transmission can provide many and attractive benefits, including performance improvement and latency reduction, there is still significant ongoing effort addressing many remaining concerns. One major concern is related to handling buffer blocking. Many researchers have attempted to optimize data scheduling way to mitigate this problem. In this paper, we propose a multiple attribute-aware data scheduling strategy for MPTCP (MPTCP-MA2). The proposed MPTCP-MA2solution is invoked to undergo three phases: (i) monitor each path's status information constantly, (ii) use an optimized path sorting algorithm to compare and sort all available paths, (iii) adaptively transmit data packets over multiple paths according to the path sorting result. Furthermore, MPTCP-MA2focuses on mitigating buffer blocking as well as boosting the robustness of the data transfer. The simulation result shed light on how MPTCP-MA2achieves a little higher throughput than existing solutions.

One major concern when applying Multipath TCP (MPTCP) to the real-time multimedia applications is related to MPTCP's fully-reliable and fully-ordered service nature, which will inevitably degrade users' Quality of Experience (QoE) for multimedia streaming services in a heterogeneous wireless network environment because asymmetric wireless links are commonly with different transmission characteristics and sensitive to variations. In this paper, we first discuss the design considerations of partially reliable-MPTCP associated with the real-time constraint of multimedia streaming. Then we propose a context-aware QoE-oriented MPTCP Partial Reliability extension (PR-MPTCP+) for providing partially reliable multimedia streaming service to an upper layer protocol. Finally, we evaluate the proposed PR-MPTCP+solution using a wide range of multimedia quality metrics.

One major concern of concurrent multipath transfer (CMT) in multi-homed Stream Control Transport Protocol (SCTP)-based heterogeneous wireless networks is that the utilization of different paths with diverse QoS-related networking parameters may cause packet reordering and buffer blocking. Although many efforts have been devoted to addressing the packet reordering issue, their sender-dependent-only scheduler does not consider balancing overhead and sharing load between the SCTP sender and receiver. This paper proposes a novel Receiver-driven Multipath Data Scheduling strategy for CMT (CMT-RMDS) necessitating the following aims: (1) alleviating the packet reordering problem, (2) improving the CMT performance, and (3) balancing overhead and sharing load between the sender and receiver. Simulation results show that the proposed CMT-RMDS solution outperforms the existing CMT solutions in terms of data delivery performance in heterogeneous wireless networks.

Stream Control Transport Protocol (SCTP)-based multimedia streaming has gained variety of attentions and resulted in many peer-reviewed publications. However, there is no MAC-SCTP cross-layer path switching strategy appropriate for wireless networking, where wireless error tends to occur frequently due to the intrinsic wireless link characteristics. As a remedy, we in this paper propose a novel Cross-layer Cognitive SCTP (SCTP-C2) for efficient multimedia data delivery by jointly considering the characteristics of MAC layer and transport layer. A Cross-layer Path Switching Trigger (CPST) is designed in SCTP-C2to improve the efficiency of the path switching mechanism and further provide an optimal congestion window (cwnd) fast recovery scheme after path switching. A Congestion-aware Multimedia Data Distributor (CMDD) is introduced in SCTP-C2to overcome a "hot-potato" congestion problem and enable an optimal transmission behavior by identifying network congestion. The results gained by a close realistic simulation topology show that how SCTP-C2outperforms existing SCTP protocol in terms of consumers' experience of quality for multimedia streaming service.

The packet loss and handover tend to occur often in burst in heterogeneous wireless network. The Sender-based transport control mechanisms make current SCTP cannot provide an expected adaptive transmission rate adjustment and recovery strategy to ensure the users' quality of experience for multimedia streaming service due to the abrupt and frequent transmission rate fluctuation. Moreover, current SCTP solutions seldom consider balancing the overhead and sharing the load between the sender and receiver. In this paper, we propose a novel receiver-driven SCTP-based multimedia delivery solution which runs some important functions at receiver including: 1) appropriate sending rate estimation and advertisement, supported by a designed receiver-based sending rate estimator; and 2) primary path selection and fast recovery, enabled by a developed receiver-assisted path switch trigger. The simulation results show that how the proposed solution outperforms existing SCTP protocol in terms of multimedia delivery performance.

As a promising solution to the scalability issue of the current routing infrastructure, locator/identifier separation has gained variety of attentions and resulted in thousands of peer-reviewed publications. However, there is still significant ongoing work addressing many challenges of the secure Locator/Identifier Separation Context (LISC). In this paper, we propose a novel Cognitive Adaptive Access-Control solution (CAAC) for a secure LISC with three modules, which are Tag-aware Access-Control module (TAC) that devotes to generate user tag (UTag) and service tag (STag) by cognizing their natural and dynamic attributes, Adaptive Policy Generation paradigm (APG) that serves to select proper policy instance for adaptive and intelligent access control, and Cooperative Decision Making module (CDM) that contributes to provide efficient decision-making by multi-peer parallel cooperation. We implement the designed CAAC in our identifier-based network platform to verify its advantages.

In this paper, we propose TCP-friendly CMT, a novel TCP-friendly Stream Control Transmission Protocol (SCTP)-based Concurrent Multipath Transfer (CMT) solution necessitating the following aims: (i) fairness to TCP flows, (ii) load sharing, and (iii) improve multimedia delivery performance. To satisfy the first requirement, a Weighted Moving congestion window (WM-cwnd) based Additive Increase and Multiplicative Decrease (AIMD)-enhanced congestion control mechanism is designed to make TCP-friendly CMT preserve fairness to TCP flows. A newly WM-cwnd-based data distribution algorithm is further introduced in TCP-friendly CMT to make proper load sharing and improve multimedia delivery performance. Finally, a proposal for saving energy is introduced. The simulation results show how the proposed TCP-friendly CMT solution improves the data delivery performance, as well as users' quality of experience for multimedia streaming service while still remaining fair to the competing TCP flows.

Extension for the multi-homing feature of Stream Control Transport Protocol (SCTP), Concurrent Multipath Transfer (CMT) can achieve bandwidth aggregation by making use of parallel transmisson over selected paths. However, if CMT-based path selection depended solely upon the information provided by transport layer, it cannot really make the desired bandwidth aggregation. Motivated by the urgent needs of cross-layer bandwidth aggregation and the advances of Ant Colony Optimization (ACO) in network selection, this paper proposes a novel ACO based cross-layer bandwidth aggregation scheme for efficient Concurrent Multipath data Transfer (CMT-ACO) in wireless transmission. CMT-ACO provides an efficient data delivery with two modules, which are ACO-based Efficiency Aware model (ACO-EA) that devotes to sense paths' transmission efficiency(supported by a cross-layer factor) and reduce ``ping-pongquot; path switching (enabled by a stabilization factor), and ACO-based Bandwidth Aggregation scheme (ACO-BA) that contributes to provide a cross-layer optimal bandwidth aggregation scheme. The results gained by a close realistic simulation topology show that how CMT-ACO outperforms existing CMT protocol in terms of performance and quality of service in multi-homed SCTP-based wireless networks.

With feature of flows across multiple interfaces based on the multi-homing feature of Stream Control Transport Protocol (SCTP), Concurrent Multipath Transfer (CMT) has been regarded as a promising protocol for content-rich multimedia data delivery under stringent bandwidth, delay, and loss wireless environment. However, current CMT researches mostly pay attention to improve CMT protocol itself depended solely upon the information provided by transport layer. In this paper, we propose a novel MAC-SCTP based Cross-layer Cognitive CMT (CMT-CC) for efficient multimedia distribution in varying wireless transmission. A Cross-layer Quality Sense Model (CQSM) is designed in the CMT-CC to cognize the paths' quality and select candidate paths for multimedia content delivery. By condition-aware cognitive ability to distinguish the causes of transmission condition change, a further proposed Intelligent Multimedia Content Distributor (IMCD) makes adaptive multimedia delivery behaviors in compliance with real-time wireless condition. Results obtained by a close realistic simulation topology show how the CMT-CC outperforms existing CMT approach in terms of users' of quality of experience for multimedia streaming services.