Cloud Manufacturing (CMfg) has revolutionized traditional manufacturing by enabling resource sharing across factory boundaries. As industry increasingly adopts cloud-edge-device collaborative ecosystems, effective authentication and key agreement (AKA) mechanisms play a critical role in safeguarding security across these complex multi-factory environments. Existing schemes, however, focus primarily on isolated binary security relationships (e.g., device-edge, device-cloud, or edge-cloud pairs individually), neglecting the integrated cloud-edge-device collaborative security demands inherent in multi-factory environments, while often relying on trusted authorities and high-overhead cryptographic mechanisms. This leads to redundant authentication processes, increased latency, and security vulnerabilities as devices must separately establish connections with each entity. To bridge these gaps, this paper introduces OMEGA: a comprehensive Cloud-Edge-Device Authentication and Key Agreement scheme for collaborative multi-factory manufacturing that pioneers an integrated security architecture. OMEGA’s distinctive advantage lies in its ability to establish all necessary secure connections (device-edge, device-cloud, and edge-cloud) through a single authentication request from the smart manufacturing device (SMD), significantly reducing authentication overhead. By leveraging lightweight hash operation, OMEGA creates a cohesive security fabric that enables SMDs to concurrently access specialized capabilities from multiple clouds while leveraging edge computing for time-sensitive operations. Security analysis using both Real-Or-Random (ROR) model and ProVerif formal verification tool demonstrates OMEGA achieves robust security while performance evaluation confirms its superior efficiency in industrial environments.

Traditional Chinese medicine(TCM), as a treasure of Chinese culture, still has considerable vitality today. In the treatment of certain diseases such as diabetes mellitus and hypertension, TCM has significant advantages. In practical applications, however, TCM faces certain challenges. For example, the mechanism of action of herbal formulas remains unclear, and prescription issuance is highly dependent on the personal experience of doctors, making it difficult to achieve precise medication targeting specific conditions. This has, to some extent, impacted its credibility. To address these issues, we have developed an AIbased TCM diagnostic assistant called DMease, designed to assist doctors in diagnosing diabetes mellitus. We utilize large language models (LLM) to process multi-source TCM data, constructing SYMPTOM-TARGET-HERB triple knowledge graphs. In addition, we use the Kolmogorov-Arnold Network (KAN) to support prescription formulation. Preliminary experimental results on test data suggest that DMease shows potential to support doctors' diagnostic judgment. Furthermore, our case study illustrates its capability for reducing prescription complexity, warranting further clinical investigation.

With the intelligent multimedia development, it is very common to use artificial intelligence methods to process multimedia data, but the problem of protecting the privacy of data is very critical. This paper focuses on privacy-protected split federated learning in processing images and we propose a framework called MPCSFL, which avoids the transmission of data labels and makes use of secure multi-party computation to enhance privacy protection. Compared with traditional privacy protection methods based on differential privacy, MPCSFL can keep the gradient matching loss above 0.4 in iDLG attack. In the model inversion attack scenario, MPCSFL will not expose the effective information of the raw data when the traditional protection method is cracked, which means that MPCSFL can protect the privacy more effectively.

Network-layer security threats have become increasingly sophisticated, exposing significant vulnerabilities in the current Internet architecture. Despite various proposed solutions, the field faces fundamental challenges in balancing user privacy with network security and achieving practical deployment. This paper presents a novel approach called PIPE (Privacy-preserving Identity and Path Enhancement), which leverages a distributed infrastructure of Key Distribution Servers (KDS) to integrate Decentralized Identity (DID) with source and path verification. Our solution binds user identity, address, path, and data while maintaining privacy through encryption and per-hop address transformation. By embedding DID information in address and implementing encrypted path verification, we achieve enhanced network accountability without compromising privacy. Experimental results demonstrate PIPE’s practicality and advantages over existing approaches in terms of deployment flexibility and security guarantees. Our work contributes to the evolution of secure network architectures by balancing accountability requirements with privacy protection while ensuring practical deployability.

Accurate detection of strawberry diseases is critical for ensuring yield stability and fruit quality, yet existing detection methods struggle with fine-grained symptom variations, complex field conditions. To address these challenges, we propose StrawDet-YOLO, a lightweight yet high-precision detection model based on YOLOv8, tailored for strawberry disease recognition in real-world agricultural environments. First, we construct Strawberry-12, a new benchmark dataset containing 3,906 images and 16,107 annotations across 12 categories, including diseases, pests, and nutrient deficiencies. Second, we introduce the BFSA (Bilinear Fused Synergic Attention) module for refined spatial-channel attention fusion and the R-SCConv (Residual Spatial and Channel Convolution) module for robust multi-scale feature reconstruction, improving the model’s sensitivity to subtle lesions. Finally, we design a gradient-aware loss function GWIoU (Gradient-Weighted IoU loss) to dynamically emphasize hard samples and enhance localization performance. Extensive experiments demonstrate that StrawDet-YOLO achieves a 7.7% precision improvement and superior mAP compared to YOLOv8n, while maintaining real-time inference at 0.4 ms per image. These results validate its practicality for deployment in smart farming and disease monitoring applications.

Orchestrating the intertwined computing and network resources at the foundational fabric level of Computing Power Networks (CPNs) remains a critical challenge. Current network architectures often lead to resource fragmentation and performance bottlenecks because routing decisions are decoupled from the dynamic, intrinsic processing loads of network nodes. This fundamental disconnect creates inefficiencies and security risks that undermine the entire CPN substrate. To address this, we introduce GAT-GRPO, a Deep Reinforcement Learning (DRL) framework designed for the fine-grained Joint Routing and in-Network Processing Placement (JR-PP) task. GAT-GRPO features a novel dual-path Graph Attention Network (GAT) that explicitly models the distinct heterogeneity of link states and nodal capacity. To master the complex combinatorial action space, we are the first to leverage Group Relative Policy Optimization (GRPO)—a stable, critic-free algorithm—for network resource orchestration. Extensive evaluations show GAT-GRPO boosts network revenue by 3.7%-5.3% over latest baselines. Mininet emulations further validate its superiority, with a 6.7% reduction in peak queue length and a 6% decrease in peak link utilization against the latest DRL agent. Our work delivers a practical, high-performance solution for foundational resource orchestration in CPNs, enabling a more intelligent and resilient networking substrate for higher-level services.

Attribute-Based Access Control (ABAC) has been chosen to replace the traditional access control model due to its dynamics, flexibility and scalability recently. However, during the migration and deployment process of ABAC policies, the key issue is how to mine accurate access control policies and quickly evaluate the policies when an access request arrives. Previous approaches typically addressed these two aspects separately, and the emergence of integrated solutions has provided new insights for addressing this issue. However, despite the high accuracy of the integrated solution in policy evaluation, it still falls short in terms of interpretability and performance. Therefore, this paper proposes a decision tree based ABAC policy mining and policy evaluation (DTAME) scheme, which employs different policy mining and evaluation algorithms based on various datasets. For access control lists, we utilize decision tree algorithms to mine ABAC policies and conduct policy evaluations based on policy trees. For access control logs, we adopt a decision tree to achieve performance approximating that of XGBoost. Experimental results show that the scheme offers superior interpretability and policy evaluation performance at the expense of a certain degree of accuracy.

The widespread adoption of intelligent Internet of Things (IoT) has sparked increased efforts to foster extensive data interaction and collaboration across diverse fields, leading to a trust crisis in cross-domain scenarios. Moreover, cross-domain collaboration increases the complexity of key management, especially in resource-constrained IoT environments where high computational costs are impractical. This situation poses risks of key leakage and inefficient key updates. This paper introduces DKGAuth, a blockchain-based method for distributed key generation and authentication tailored for resource-constrained cross-domain intelligent IoT systems. Initially, we propose a lightweight cross-domain authentication architecture based on blockchain to address the trust crisis effectively among different domains in the intelligent IoT. Secondly, building upon this architecture, we introduce a distributed key generation method that revolutionizes the key infrastructure to address key management concerns. Additionally, we design an algorithm to combine key factors, minimizing costs associated with both key generation and updates. Finally, we establish a simulation environment to assess the computational, storage, and read/write overheads of our approach. In the same configuration, compared to other solutions, the efficiency of key updates improves by 83% when updated 100 times.

With the extensive development of SDN technology, the scalability and reliability of SDN multi-control have gradually attracted people's attention. This paper designs the two-layer trusted multi-controller architecture based on multi-chain, and relies on the decentralization, decision-making credibility, and high-performance guarantee provided by the architecture as the core foundation to design the multi-controller scalability mechanism and reliability mechanism. As the core component of the distributed system, multi-chain provides the basic control layer with the CFT blockchain as a trusted distributed coordination component, and provides the dynamic decision layer with the BFT blockchain to ensure the multi-party participation and availability of decision-making processes such as abnormal detection and recovery. It ensures the decentralization and high performance of the cluster by dynamically adjusting the members of the decision-making layer. The scalability mechanism includes the initial load balancing mechanism(ILBM) and fine-grained dynamic load balancing mechanism, the former is responsible for the prevention of uneven load, and the latter is responsible for the detection and recovery of partial overload and global overload. The reliability solution is responsible for the detection and recovery of controller crush and link failure exceptions. Both types of mechanisms rely on the exception detection mechanism based on switch reporting(EDSR) to achieve efficient and reliable multitype exception detection. Finally, the simulation results show that the ILBM can prevent the overload of the controller, and the two-layer trusted multi-controller architecture can ensure high performance while providing credibility and decentralization for decision-making.

Nowadays, by integrating the smart devices carried by users with existing communication infrastructures to provide large-scale, fine-grained and complex sensing services, crowdsensing as a novel sensing paradigm has significantly enriched the applications of smart city and promoted the development of Internet of Things (IoT). However, privacy has become skyrocketing concern for crowdsensing and gravely affected the deployment of crowdsensing. In this article, we present a framework to make the tradeoff between minimizing data aggregation error and guaranteeing system stability by jointly considering the privacy of participants, the randomness of sensing task arrival and the cost of platform. We propose an online control mechanism by exploiting Lyapunov stochastic optimization technique. Additionally, considering that, in reality, it always takes different time for different tasks to make sensing decisions, we extend standard Lyapunov stochastic optimization technique to make separate decisions for different types of sensing tasks in consecutive time. Through rigorous theoretical analysis, we prove that our time-average data aggregation error is approximately optimal while still maintaining system stability. By carrying out extensive simulations, we demonstrate the superiority of our proposed mechanisms.

Network traffic classification has been highly concerned by academia and industry for decades. In recent years, deep learning has attracted many scholars to use it in network traffic classification due to its excellent performance in the fields of computer vision and natural language processing. However, the performance of the neural network depends on its structure in the same dataset. When looking for the neural network to classify network traffic, it is necessary to constantly adjust the structure of the neural network to achieve better results, which is very time-consuming and experience-dependent. To solve the above problem, this paper proposes an Adaptive Convolutional Neural Network Structure for Network Traffic Classification (ACNNS-NTC) algorithm. The proposed algorithm first pre-processes the network traffic data used for training and testing, and then uses particle swarm optimization algorithm to optimize the network structure of the convolutional neural network, to generate convolutional neural network structure for network traffic classification, and verify the classification results. Experimental results show that the accuracies of the ACNNS-NTC algorithm on public datasets (ISCX-IDS2012, USTC-TFC2016, CIC-IDS2017) are above 99%. At the same time, the generated convolutional neural network has a more succinct structure and fewer model parameters compared with the existing methods.

With the rise of the Internet of Things (IoT), the number of mobile devices with sensing and computing capabilities increases dramatically, paving the way toward an emerging paradigm, i.e., crowdsensing that facilitates the interactions between humans and the surrounding physical world. Despite its superiority, particular attention is paid to be able to submit sensing data to the platform wherever possible to avoid leaking the sensitive information of participants and to incentivize them to improve sensing quality. In this article, we propose an incentive mechanism for participants, aiming to protect them from privacy leakage, ensure the availability of sensing data, and maximize the utilities of both platforms and participants by means of distributing different sensing tasks to different participants. More specifically, we formulate the interactions between platforms and participants as a multileader-multifollower Stackelberg game and derive the Stackelberg equilibrium (SE) of the game. Due to the difficulty to obtain the optimal strategy, a reinforcement learning algorithm, i.e., Q-learning is adopted to obtain the optimal sensing contributions of participants. In order to accelerate learning speed and reduce overestimation, a deep learning algorithm combined with Q-learning in a dueling network architecture, i.e., double deep Q network with dueling architecture (DDDQN) is proposed to obtain the optimal payment strategies of platforms. To evaluate the performance of our proposed mechanism, extensive simulations are conducted to show the superiority of our proposed mechanism compared with state-of-the-art approaches.

Community detection for Location-based Social Networks (LBSNs) has been received great attention mainly in the field of large-scale Wireless Communication Networks. In this paper, we present a Differential Privacy Latent Trajectory cOmmunity Discovering (DP-LTOD) scheme, which obfuscates original trajectory sequences into differential privacy-guaranteed trajectory sequences for trajectory privacy-preserving, and discovers latent trajectory communities through clustering the uploaded trajectory sequences. Different with traditional trajectory privacy-preserving methods, we first partition original trajectory sequence into different segments. Then, the suitable locations and segments are selected to constitute obfuscated trajectory sequence. Specifically, we formulate the trajectory obfuscation problem to select an optimal trajectory sequence which has the smallest difference with original trajectory sequence. In order to prevent privacy leakage, we add Laplace noise and exponential noise to the outputs during the stages of location obfuscation matrix generation and trajectory sequence function generation, respectively. Through formal privacy analysis, we prove that DP-LTOD scheme can guarantee ϵ-differential private. Moreover, we develop a trajectory clustering algorithm to classify the trajectories into different kinds of clusters according to semantic distance and geographical distance. Extensive experiments on two real-world datasets illustrate that our DP-LTOD scheme can not only discover latent trajectory communities, but also protect user privacy from leaking.

The Internet of Things has such a profound impact that we have witnessed crowdsensing has emerged as the most popular sensing paradigm where participants sense and aggregate data to the platform by smart devices. However, the participants may not be willing to involve in data sensing and aggregation if they are not sufficiently compensated or their personalized private information are disclosed. In order to overcome the above issues, this article proposes a payment-privacy protection level (PPL) game, where each participant submits his sensing data with a specified PPL while the platform chooses a corresponding payment to the participant. Additionally, we derive the Nash equilibrium point of the game. Considering that the payment-PPL model is unknown in practice, we employ a reinforcement learning technique, i.e., Q-learning to obtain the payment-PPL strategy in a dynamic payment-PPL game. We further use the deep Q network (DQN), which combines a deep-learning technique with Q-learning to accelerate the learning speed. Through extensive simulations, we verify that our proposed algorithm using DQN achieves superior performance in terms of utilities of both platform and participants and data aggregation accuracy compared with the one using Q-learning.

An identifier network (IN), as one of the promising network architectures to solve the IP dual properties problems, has been applied in many areas, including Internet of Things (IoT) for smart cities scenario. The separation mechanisms of an access/core network and identifier/location can benefit IoT in terms of trust management, access control, and privacy protection. The core network in IN is independent of the access network by introducing two namespaces, which makes the core network difficult to be attacked but easy for trust management. However, access network, such as access wireless sensor network (WSN), is facing serious trust and security challenge. Therefore, this article addresses this problem by using network address shuffling. We present an optimization framework of defense cost for IoT security and formulate it as a stochastic cost optimization problem by considering the impacts of network address shuffling control, network autoimmunity control, and defense cost. To improve its generality, we adopt a Lyapunov optimization theory and transform the formulated optimization problem into a queue stability problem, and further decompose the queue stability problem into two subproblems to solve the initial optimization problem. Finally, a novel stochastic cost minimization mechanism (SCMM) consisting of two algorithms for the derived subproblems is proposed. Through logical theoretical analyses, it is proved that our proposed mechanism can achieve the optimized results while maintaining the security of each access WSN and guaranteeing the limited network resources. The extensive simulation results verify that the tradeoff between defense strategy and limited network resources can be well tackled by the balancing factor.

As a novel sensing paradigm, crowdsensing has gained great attention due to large-scale user participation, low cost and wide data source, replacing traditional sensor based sensing in intelligent transportation, environmental monitoring, urban public management, etc. In crowdsensing, however, user privacy leakage is a common but fatal problem, where the participants in crowdsensing might not provide their data if their sensing data expose their personal private information or even lead to malicious attacks. Additionally, it is still challenging for the platform to consider the randomness of sensing task arrival, the dynamic participation of participants and the complexity of task allocation. To this end, an online control mechanism is presented to maximize the profit of platform while guaranteeing system stability and providing personalized location privacy protection. By exploiting Lyapunov optimization theory, we transform the optimization problem into a queue stability problem, decomposing it into three subproblems further. Through rigorous theoretical analysis, we prove that our time-averaged profit is approximately optimal. We also carry out extensive simulations to verify the superiority of our proposed mechanism.

With the growing popularity and prosperity of living streaming applications, it is naturally confronting users' quality of experience (QoE) degradation issues especially under dynamic environments arised from nonnegligible factors such as high latency and intermittent bitrate. In this paper, we propose an efficient adaptive bitrate (ABR) algorithm called BitLat to achieve both bitrate-control and latency-control. BitLat is based on reinforcement learning to get strong adaptability for dealing with the complex and changing network conditions. More specifically, in our work, we determine the specific value of latency threshold with the help of current advanced algorithm, and design the structure of the neural network in reinforcement learning, the features used in the training process, and the corresponding reward function. Additional, we use the Dynamic Reward Method to further enhance the performance. Comprehensive experiments are conducted to demonstrate BitLat outperforms the state-of-the-art ABR algorithms, with improvements in average QoE of 20%-62%.

The latest increasing popularity of high-speed railways (HSR) has stimulated growing demands for wireless Internet services in HSR networks, especially for video streaming. However, due to the high variability and unpredictability of wireless communications in HSR networks, it is still difficult for the existing solutions to provide high-quality video streaming services to HSR passengers. This paper addresses this crucial problem first by reporting on field experiments performed to investigate the characteristics of HSR networks. Then the paper formulates an intractable optimization problem for dynamic adaptive streaming over HTTP (DASH)-enabling service in HSR networks considering various factors, including packet loss, energy consumption, video service quality, etc. By leveraging Lyapunov optimization approaches, the formulated optimization problem is transformed into a queue stability problem which is of high scalability and generality. Moreover, in order to overcome the intractability of the initial optimization problem, the queue stability problem is further decomposed into three subproblems which can be easily solved individually. Finally, a novel joint stochastic DASH optimization (JSDO) mechanism consisting of three algorithms for the derived subproblems is proposed. Rigorous theoretical analyses and realistic dataset-based simulations demonstrate the effectiveness of the proposed JSDO mechanism.

The recent proliferation of public hotspots has given rise to Wi-Fi advertising where venue owners promote their business by pushing advertisers' advertisements on their hotspots. However, a small business usually has insufficient budget to make a purchase for a whole webpage. Therefore, in this paper, we propose GAWA, a Group-buying based Auction mechanism for Wi-Fi Advertising among a venue owner, group leaders and advertisers, which is composed of three phases. More specifically, in the first phase, we propose an algorithm to decide a group bid for each group leader and winning advertisers for each group. In the second phase, the venue owner assigns venues to group leaders by a novel winning group leader determination algorithm. In the third phase, the mechanism determines how much each winning group leader should charge each advertiser in the winning group. We prove that GAWA is computationally efficient, and possesses excellent economic properties such as individual rationality, budget balance, and truthfulness. We evaluate the proposed algorithms using large-scale simulations, and demonstrate the effectiveness and efficiency of our design when comparing with the state-of-the-art approaches.

Widespread deployment of High-Speed Railway (HSR) in recent years brings strong demand for high-quality onboard Internet services. However, the wireless link between the train and Base Station (BS) suffers from numerous problems, including frequent handover, severe Doppler shift, and great penetration loss, etc. It is still a challenge to provide HSR passengers with high-quality Internet services. In this paper, the Packet Loss Rate (PLR) of HSR networks is measured along Beijing-Shanghai railway line. Measurement results indicate that PLR remains at a high level for a long period and changes frequently in a large interval. To address this problem, we focus on frame loss problem of train-to-BS wireless link and propose a novel Loss-Aware Adaptive Scalable Transmission mechanism (LAAST) to fit for HSR networks. In LAAST, variable number of frame copies are transmitted according to Frame Loss Probability (FLP) to improve the scalability and efficiency of transmission over train-to-BS link. The optimal relationship between frame duplication number and FLP is derived through nonlinear programming model. Simulations demonstrate the effectiveness and fitness of LAAST for HSR networks.

Information-Centric Networking (ICN) focuses on data dissemination instead of host communication, and it is a promising solution for video streaming in Vehicular Ad-hoc Networks (VANETs). In Information-Based VANETs, content copies can be cached by arbitrary mobile nodes in the networks. Thus, it is required to optimize the routing of content requests (referred to as Interest packets) in order to quickly locate potential content providers as soon as possible. This article proposes a Preference-aware Fast Interest Forwarding for video streaming in ICN-based VANETs (PaFF). In PaFF, each mobile user creates a Highly Preferred Content Table (HPCT) to maintain the content caching status of nodes who have similar mobility patterns and video playback behavior. Based on HPCT, a preference-aware forwarder selection mechanism is proposed to select the next hop of Interest packets in order to minimize latencies and maximize reliability. Simulation results show that PaFF achieves a considerable improvement in terms of start-up delay and cache hit ratio while incurring almost the same overhead with respect to state-of-art solutions.

Cognitive radio networks enable the unlicensed users to share the spectrum with licensed users, while it makes dynamic environment. The use of the channel by unlicensed users should be sensible of the licensed users in advance whether acceptable interference occurs. Mostly the routing protocol considered the previous state prediction. However, the routing probe and react strategy by unlicensed users sometimes is not only determined by the last one node state. In this paper we introduce a predictive node mobility model which is monitoring a licensed user's mobility, capable of reducing the delay.

This paper proposed a mobility-aware multimedia data transfer mechanism using Multipath TCP in Vehicular Network. Since high transmission rate and low latency are the two key factors for multimedia data transmission that could provide stable video streaming services, therefore, we first adopted Multipath Transport Control Protocol which can transfer data concurrently for improving transmission rate and designed Quality-aware Data Distribution to dynamically allocate the data to different subflows. Moreover, in Vehicular Network, the mobile terminal, which means the vehicle, can communicate with remote server through roadside unit (RSU). However, the communication link between terminals and remote server will disrupt while the vehicle exceeding the communication range of roadside unit; and there also exists the situation that the vehicle is in the communication range of several RSU. Accordingly, we exploited a mobility-aware distance measurement for checking whether the vehicle has moved out of the communication range of any RSU or it is in multi-RSU's communication range. Afterwards, we designed a handover mechanism which transfer data to connected path (4G) for stable transmission using MPTCP when the vehicle exceeded the communication range of RSU; while one mobile can communicate with more than one RSU, we exploited a mechanism which can trigger new path for multipath data transmission and non-corporation Nash Equilibria was employed for solving the problem of fairness in the same kind of network technology multipath transmission. Simulation results show how mobility-aware multimedia data transfer mechanism improve the performance of transmission comparison with state-of-art solution.

Nowadays, Wireless Sensor Networks (WSNs) have been widely deployed and applied. Due to energy consumption is one of the critical problems existing in WSNs, many energy-optimization related routing mechanisms are proposed. However, most of existing routing mechanisms rarely focus on both energy consumption and data transmission delay in high-loss WSN environments. Therefore, in this paper, we formulate the routing problem in high-loss WSNs with the considerations of energy efficiency improvement and data transmission delay reduction. Through exploiting the stochastic Lyapunov optimization approaches, the formulated problem is converted to a queue stability problem with virtual queues and is further decomposed into three subproblems, including transmission control, admission control and utility maximization. By solving the three decomposed problems, Stochastic Optimal Routing Algorithm (SORA) is established and proposed. Finally, the performance of SORA is evaluated by comprehensive simulations.

We study the distributed Kalman filter in sensor networks where multiple sensors collaborate to achieve a common objective. Diffusion Kalman filtering algorithms have been a popular topic in linear dynamic system estimation problems, In there algorithms, nodes cooperate with their direct neighbors and diffuse the information across the entire network through a sequence of Kalman iterations and data-aggregation. In this article, we propose the diffusion Kalman filtering with quantized global (DKFQFI)algorithm because of the limited sources in the wireless environment, where nodes exchange their quantized states with neighbors to reduce the consumption of resources. To prove the convergence of the DKFQFI algorithm, we derive the theoretical expressions of the mean and mean-square performance. From the expressions, we show that the mean performance and mean-square performance of the proposed algorithm are unbiased and stable. Therefore, the feasibility of the algorithm is verified. Moreover, the proposed algorithm achieves an outperform by simulation.

The newly rising of dynamic adaptive streaming over HTTP (DASH) enables consumers access diverse bit rate encoded video content according to the link situation, which improves the quality of experience (QoE) of consumers in mobile environment. While Content-Centric Mobile Networks (CCMNs) bring content centric design to mobile environment and has already become a promising solution for the content-based applications. The major challenges of DASH in CCMNs are how to determine proper video quality in a mobile environment and to reduce broadcasting storm. To address these problems, we design a novel adaptive video streaming solution in CCMNs (DAS-CCMN). We first analyze the load degree of content carrier, and define an interest satisfied potential (ISP) concept to reflect the ability that a content carrier can satisfy the interest request from a consumer. In DAS-CCMN, each mobile node shares its ISP information with neighbors. All nodes store this information in their interest satisfied potential table (ISPT). Based on information recorded in ISPT, a self-learning based rate determination strategy is presented to choose video content with proper bit rate. Moreover, an interest flooding control is presented to solve the broadcast storming problem for adaptive video streaming in CCMNs. Simulation results show our solution improves the performance of current DASH service in CCMNs.

Multipath TCP (MP-TCP) algorithms are supposed to be fair to single-path TCP and utilize multiple paths to support high quality end-to-end services. Existing MP-TCP algorithms in literature are confronted with the problems: 1) MP-TCP can be excessively aggressive towards single-path TCP in their coexisting environments; 2) they sometimes fail to balance congestion on multiple paths; 3)their performance is prone to degradation under the circumstances of path heterogeneity, wireless network environments and bandwidth-intensive applications. In this paper, we propose a fluid-based MP-TCP algorithm to address the above problems. Our algorithm has a unique stable equilibrium and serves the design goals of fairness and congestion-balancing. Simulation results show that our algorithm achieves improvement of TCP-friendliness and window fluctuation performance in different scenarios.

Multipath congestion control algorithms are supposed to be friendly to traditional single-path TCP (SP-TCP). Existing multipath Additive Increase Multiplicative Decrease algorithms (MP-AIMD) for multipath TCP (MP-TCP) are confronted with the problems: 1) they are unfair to SP-TCP when the multiple available paths have heterogeneity, e.g. different RTT; 2) they all use packet losses as congestion signals and induce spurious backoffs on a wireless link with high error rate; 3) congestion window fluctuation reduces their potential to support smooth high-quality service such as multimedia applications. This paper proposes a path quality based multipath AIMD (AIMD-PQ) to tackle the above problems. We utilize the round trip time (RTT) on each sub-path to formulate the path quality estimation. AIMD-PQ balances the loads among its sub-paths, moves traffic off the most congested sub-path, and triggers Multiplicative Decrease by both packet loss and path quality signals. Simulation results show that AIMD-PQ improves both the TCP-friendliness and window fluctuation performance of MP-TCP in a heterogeneous wireless network environment.

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.

As a promising mobility protocol, Proxy Mobile IPv6 (PMIPv6) can provide mobility support without the involvement of the mobile nodes (MNs). It depends on the Local Mobility Anchor (LMA) and Mobility Access Gateway (MAG) to emulate the home network for the attached MNs. Based on this design principle, PMIPv6 works well only for small scale networks. However, with the rapid increase of the MNs as well as the huge traffic loads, PMIPv6 as a centralized mobility management protocol in which both the location management and dada forwarding are performed by the LMA, will induce lots of limitations, such as non-optimal routing, single point of failure, scalability and so on. Therefore, a trend to distributed mobility management (DMM) is popular, and several distributed solutions for PMIPv6 have been proposed until now. In this paper, we survey the different schemes of DMM and focus on the related solutions for PMIPv6 by qualitative analysis, and conclude the remaining challenges and open issues for future development.

Recent work has shown that Proxy Mobile IPv6 (PMIPv6) is a promising mobility management protocol for its salient features, such as supporting unmodified Mobile Node (MN). However, PMIPv6 does not consider the multicast routing support. Moreover, current research mainly concerns on the multicast receiver mobility, but it is a critical and challenging issue to ensure service connectivity for mobile multicast senders, which has not been addressed well. In this paper, we propose an efficient multicast sender mobility scheme for PMIPv6 (PMIP-BT), in which the multicast data can be transmitted through the PMIPv6 tunnel and the multicast sender mobility is transparently enabled in the PMIPv6 networks. Numerical results show that the proposed scheme outperforms the current scheme in terms of signaling cost.

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.

Online content propagation gives rise to tremendous data explosion and requires efficient management for a large amount of network resources, after next generation network service predomination. Especially in the age of social network, the way of content propagation and consumption has significantly changed from requesting to sharing. Since massive users are tending to influenced by the trends in social community and mainstream media, content caching becomes an effective method for providing better quality of service for such social relationships. A key challenge is traditional caching strategies cannot meet the dynamic variation in geo-social environments. In this paper, we propose a dynamic social content caching scheme with social user mobility. We employ a combination of cooperative filtering recommendation and cache update algorithm to effectively predict and manage cache updates. Simulation results show that our caching method over performs than classical caching methods that rely on the historical popularity prediction.

Routing in mobile ad hoc networks (MANETs) is an extremely challenging issue due to the features of MANETs. In this paper, we present a novel bio-inspired hybrid routing protocol (B-iHRP) supporting multimedia delivery based on zone routing framework, ant colony optimization (ACO) and physarum autonomic optimization (PAO). B-iHRP divides network topology into a series of zones subjectively. Within a zone, the route table of central node is proactively maintained by perceptive ants which can sense link status metrics through cross-layer perception to assess the discovered routes. Among zones, perceptive ants are sent to reactively find routes to destinations as well as assess the discovered routes with the metrics by source nodes. Afterwards, B-iHRP uses PAO to select the optimal one from the found routes and optimize autonomically the local routes during the course of multi-zone communication sessions. Simulation results show how B-iHRP can achieve the effective performance compared to existing state-of-the-art algorithms.

Content-Centric Networking (CCN) is an entirely novel networking paradigm, in which packet forwarding relies upon lookup operations on content names directly instead of fixed-length host addresses. The unique features of CCN names, i.e., variable length, huge cardinality, and hierarchical structure, introduce new challenges that could hinder the deployment of such a new architecture at the Internet scale. In this paper, we make an in-depth study of characteristics of large-scale CCN names, and propose a simple yet efficient CCN-customized name lookup engine (named by TB2F), which capitalizes the strengths of Tree-Bitmap (TB) and Bloom-Filter (BF) mechanisms, while counteracts their main limitations. To this end, TB2F splits CCN prefix into a constant size T-segment and a variable length B-segment with a relative short length, which are treated using TB and BF, respectively. Furthermore, an optimal length of the T-segment is found to improve the lookup efficiency. Experimental comparisons with respect to the reference Name Prefix-Trie and Bloom-Hash have been also carried out. The results show that TB2F properly configured has good scalability and efficiency by (i) speeding up lookup operations and reducing the false positive rate with respect to Bloom-Hash; (ii) requiring less memory than Name Prefix-Trie; (iii) achieving a low overhead in updating operations in the large scale case.

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.

Concurrent Multipath Transfer (CMT), enabled by Stream Control Transmission Protocol (SCTP), is considered as one preferred data-transport mode due to increased available bandwidth. However, CMT performance degrades seriously in terms of data reordering due to path dissimilarity and frequent packet loss from wireless unreliability. Most relevant solutions follow the packet sequence numbers and thereby focus on strict in-order reception and packet-specific retransmission. Passively adapting to the network conditions, those approaches are not general and well enough responding to the dynamicity of wireless environment. By applying Network Coding (NC) to CMT, this paper proposes a progressive end-to-end solution (CMT-NC) to those problems in heterogeneous wireless networks. CMT-NC is capable of avoiding reordering and compensating lost packets. Further, an innovative group-based transmission management mechanism enhances the robustness and reliability of data transfer. Simulation results show how by using CMT-NC significant improvements in comparison to another state-of-the-art solution are obtained.

In this paper, we present a novel bio-inspired trusted routing framework (B-iTRF) which composed of trust mechanism and routing strategy. For trust mechanism, B-iTRF monitors neighbors' behavior in real time and then assesses neighbors' trust value based on the priori knowledge. For routing strategy, each node finds routes to the Sink based on ant colony optimization. In the process of path finding, B-iTRF senses and calculates the metrics of the found routes to support the route selection. Moreover, B-iTRF also assesses the availability of route based on Physarum autonomic optimization to maintain the route table. Simulation results show that B-iTRF can achieve the effective performance compared to existing state-of-the-art algorithms.

High-quality multimedia streaming services in Vehicular Ad-hoc Networks (VANETs) are severely hindered by intermittent host connectivity issues. The Information Centric Networking (ICN) paradigm could help solving this issue thanks to its new networking primitives driven by content names rather than host addresses. This unique feature, in fact, enables native support to mobility, in-network caching, nomadic networking, multicast, and efficient content dissemination. In this paper, we focus on exploring the potential social cooperation among vehicles in highways. An ICN-based COoperative Caching solution, namely ICoC, is proposed to improve the quality of experience (QoE) of multimedia streaming services. In particular, ICoC leverages two novel social cooperation schemes, namely partner-assisted and courier-assisted, to enhance information-centric caching. To validate its effectiveness, extensive ns-3 simulations have been executed, showing that ICoC achieves a considerable improvement in terms of start-up delay and playback freezing with respect to a state-of-the-art solution based on probabilistic caching.

The original architecture of content-centric network CCN may suffer from interest flooding attacks. In this paper, we focus on one type of interest flooding attacks called denial of service against content source DACS attack. To damage CCN, it floods a large number of malicious interests requesting content that does not exist, which guarantees that no cache hit can occur at routers until these malicious interests reach the target content source. Thus, it can directly exhaust the resource of the victim. To counter it, we propose a threshold-based detecting and mitigating TDM scheme. The basic idea is to detect DACS attack on the basis of the frequency that pending interest table items in CCN routers expire recording this frequency by introducing two counters with their corresponding thresholds and one indicator for counter mode and to mitigate it by implementing the rate limiter in each router. From the viewpoint of a CCN router, we analyze the performance of TDM in terms of detection ability and effect on mitigating malicious traffic. In addition, we briefly analyze the overhead of TDM. The results show that TDM achieves high detection ability and good effect on mitigating malicious traffic while bringing in small overhead on countering DACS attack. To the best of our knowledge, this is the first attempt to design a detailed scheme embedded with corresponding algorithms on countering this attack. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, we introduce a deployment strategy to support large scale mobility management based on Proxy Mobile IPv6 (PMIPv6). As a centralized mobility management protocol, PMIPv6 employs a single-handed mobility anchor, Local Mobility Anchor (LMA), to maintain the mobility context and routing status of all registered mobile nodes in a localized domain. However, this single-handed mobility anchor will suffer with ever-increasing mobile Internet traffic. We propose a distributed local mobility anchor architecture with new prefix and address management schemes and then present a mobility management procedure with operational aspects. In the proposed architecture, the registration message is always routed to the closest LMA thanks to the anycast routing. The other LMAs create the direct host route to the serving Mobility Anchor Gateway (MAG) for the optimized packet transmission. We also analyze the proposed architecture's performance in terms of scalability, robustness, and efficiency. The results demonstrate that the state synchronization of the proposed architecture induces extra signaling cost during the handover, however, the optimized route established by it can save more cost when the PMIPv6 service is adopted in the large-scale environment.

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.

Providing IP multicast to Mobile Nodes has drawn significant attention and so far many approaches have been proposed. However, current mobile multicast schemes mainly focus on the multicast receiver mobility support based on the host-based mobility management protocol. Recent work has shown that ensuring service connectivity for mobile multicast sources is a challenging issue and is still largely open. With the rapid development of the network-based mobility solution, multicast source mobility support schemes in Proxy Mobile IPv6 (PMIPv6) networks are needed urgently. In this paper, a base solution (BS) and a direct multicast routing scheme (DMRS) are proposed for multicast source mobility in PMIPv6 networks. In order to transmit multicast data through the PMIPv6 tunnel, we adopt the Multicast Listener Discover (MLD) Proxy function in the BS. In the DMRS, locally optimized routing for traffic flows can be provided. The performance of the proposed schemes is evaluated by theoretical analysis and implementation. These results show that our proposed schemes outperform the previous solutions in terms of the signaling cost and the BS has lower multicast handover delay than the DMRS.

There is a trade-off between routing efficiency and energy equilibrium for sensor nodes in wireless sensor networks (WSNs). Inspired by the large and single-celled amoeboid organism-slime mold physarum polycephalum, this paper presents a novel physarum-inspired routing protocol (P-iRP) for WSNs to address the above issue. In P-iRP, a sensor node selects its proper next hop by using a proposed physarum-inspired selecting next hop model (PSN), which considers comprehensively the distance, energy residue and location of the next hop. We introduce the PSN's routing selecting strategy and detail PiRP's algorithms. Simulation results show how P-iRP can achieve the effective trade-off between routing efficiency and energy equilibrium compared to existing classical algorithms.

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.

Information-Centric Networking (ICN) is a clean-slate networking architecture that puts information is focus instead of addressed hosts. Construction of content retrieval model to estimate the delivery performance is challenging in this ICN-based Mobile Ad hoc Networks (MANETs). In this paper, we propose a novel content retrieval model (PRCRM) for Information-Centric MANETs (ICMs) in 2-dimensional case. By investigating the distribution of content popularity, receiver-driven mechanism, content caching and replacement mechanism and generalized mobility model in 2-dimensional space, PRCRM constructs a novel content retrieval model based on the content hit/miss probability to estimate the content retrieval-related performance. We evaluate PRCRM by comparing its performance with another state of the art solution in terms of RTT and throughput. Simulation results demonstrate PRCRM's rationality and validity and it is shown that PRCRM is available to analyze content retrieval in ICMs.

Mobile devices equipped with multiple network interfaces can increase their throughput by making use of parallel transmissions over multiple paths and bandwidth aggregation, enabled by the stream control transport protocol (SCTP). However, the different bandwidth and delay of the multiple paths will determine data to be received out of order and in the absence of related mechanisms to correct this, serious application-level performance degradations will occur. This paper proposes a novel quality-aware adaptive concurrent multipath transfer solution (CMT-QA) that utilizes SCTP for FTP-like data transmission and real-time video delivery in wireless heterogeneous networks. CMT-QA monitors and analyses regularly each path's data handling capability and makes data delivery adaptation decisions to select the qualified paths for concurrent data transfer. CMT-QA includes a series of mechanisms to distribute data chunks over multiple paths intelligently and control the data traffic rate of each path independently. CMT-QA's goal is to mitigate the out-of-order data reception by reducing the reordering delay and unnecessary fast retransmissions. CMT-QA can effectively differentiate between different types of packet loss to avoid unreasonable congestion window adjustments for retransmissions. Simulations show how CMT-QA outperforms existing solutions in terms of performance and quality of service.

Abstract Mobile IPv6 (MIPv6) is a host‐based mobility support specification which has been approved by the IETF as the standardized solution of global mobility management in IPv6 network for Mobile Node (MN). However, MIPv6 handover procedure results in a large handover delay. To improve its performance, some MIPv6 variants such as Fast Handover for MIPv6 (FMIPv6) and Hierarchical MIPv6 (HMIPv6) were proposed. However, they all require the MN to support the mobility management protocols which increase the difficulty of management and deployment. Recently, IETF NETLMM workgroup published the Proxy Mobile IPv6 (PMIPv6) which is a network‐based localized mobility management to provide the mobility support for mobile host without the involvement of the mobility signaling. In this paper, we analyze the singling cost of PMIPv6 and implement it in our test‐bed to evaluate its performance. The results show that the PMIPv6 has lower signaling cost and packet delivery cost, and it can improve the handover performance of UDP and TCP than the other mobility management protocols under low‐delay networks, as for the wide area networks, it needs to introduce some mechanism like fast handover to further improve its performance. Copyright © 2009 John Wiley & Sons, Ltd.

Mobile multicast becomes a research hotspot with the development of wireless and mobile technologies, and it is based on the traditional multicast protocols and mobility management protocols to provide the multicast services for mobile subscribers. Several mobile multicast methods were proposed in the past few years, but most of them are based on the mobile IPv6 and its alternatives which require the mobile hosts to support the mobility function. Recently, the proxy mobile IPv6 (PMIPv6) was proposed to provide the mobility support for mobile node with or without mobility involvement, and the previous studies have shown that the PMIPv6 can improve the handover performance. However, the PMIPv6 mainly concerns on the unicast routing support and little considers the multicast routing. In this paper, we study the multicast support in PMIPv6 and propose two multicast methods called the MAG (mobile access gateway)-based method and LMA (local mobility anchor)-based method, and analyze their performance under different scenarios. The analytical results show that the LMA-based method is suitable for the bigger PMIPv6 domain, and larger network topology scenarios (more than 105), whereas the MAG-based method is used for the smaller PMIPv6 domain.