Video understanding endeavors to generate descriptive texts by analyzing structured semantics from dynamic visual sequences, thus facilitating context-aware reasoning and interpretation. Recent advancements primarily rely on patch-level visual–textual alignment, bridging the gap between visual and textual modalities and therefore enabling more comprehensive reasoning. While promising, they often struggle to capture object-level semantics and temporal dependencies, resulting in limited interpretability and suboptimal compositional understanding. To address these issues, we propose a novel temporal relation framework for multi-modal video understanding, dubbed VideoU-MTR, which explicitly models object-level temporal relations to facilitate fine-grained and coherent representations of cross-frame object interactions. Specifically, we introduce a query-oriented frames identification mechanism that synergistically combines textual and visual attention, allowing the model to dynamically attend to semantically relevant video content across hierarchical levels while effectively filtering out irrelevant information. Furthermore, we employ an explicit temporal relation module to capture fine-grained temporal dependencies and inter-object dynamics by modeling object-centric sequences with time-aware attention and frame-level embeddings. Additionally, we propose a cross-modal alignment adapter that aligns temporally contextualized visual features with linguistic semantics at both object and frame levels. Extensive experiments on eight benchmarks across video question answering (VideoQA), long-term video understanding (LTVU), and video captioning (VideoCap) benchmarks demonstrate that VideoU-MTR achieves superior performance compared to state-of-the-art methods. Moreover, visualization analysis further validates the effectiveness of incorporating temporal information for enhancing video comprehension.

Exploring effective collaboration among multiple large language models (LLMs) represents an active research direction, with multiagent debate (MAD) emerging as a popular approach. MAD involves LLMs independently generating responses and refining their own responses by incorporating feedback from other agents in a debate manner. However,empirical experiments reveal the suboptimal performance of MAD in complex reasoning scenarios. We attribute this to the potential misleading caused by peer agents with limited individual capabilities. To address this, we propose a novel sequential collaboration framework named Explain-Analyze-Generate(EAG). By decomposing complex tasks into essential subtasks and employing a pipeline approach, EAG enable agents provide constructive assistance to peers, ultimately yielding higher performance. We conduct experiments on the comprehensive complex language reasoning benchmark: BIG-Bench-Hard (BBH). Our method achieves the highest performance on 19 out of 23 tasks, with an average improvement of 8% across all tasks, and incurs lower costs compared to MAD, demonstrating its effectiveness and efficiency.

Video question answering (VideoQA) has recently gained considerable attention in the field of computer vision, aiming to generate answers rely on both linguistic and visual reasoning. However, existing methods often align visual or textual features directly with large language models, which limits the deep semantic association between modalities and hinders a comprehensive understanding of the interactions within spatial and temporal contexts, ultimately leading to sub-optimal reasoning performance. To address this issue, we propose a novel temporal-aware framework for multi-modal video question answering, dubbed VideoQA-TA, which enhances reasoning ability and accuracy of VideoQA by aligning videos and questions at fine-grained levels. Specifically, an effective Spatial-Temporal Attention mechanism (STA) is designed for video aggregation, transforming video features into spatial and temporal representations while attending to information at different levels. Furthermore, a Temporal Object Injection strategy (TOI) is proposed to align object-level and frame-level information within videos, which further improves the accuracy by injecting explicit temporal information. Experimental results on MSVD-QA, MSRVTT-QA, and ActivityNet-QA datasets demonstrate the superior performance of our proposed method compared with the current SOTAs, meanwhile, visualization analysis further verifies the effectiveness of incorporating temporal information to videos.

The application of Large Language Models (LLMs) is rapidly expanding in fields such as natural language processing and computer vision. However, due to the enormous number of model parameters, while their emergent capabilities enhance performance, they also incur significant computational and storage costs, particularly during inference. This makes it particularly challenging to deploy large models in resource-constrained environments. To address this issue, we propose an innovative pruning method based on local entropy, aimed at improving model performance while reducing computational costs. Our approach leverages a small amount of calibration data and calculates the importance of weights based on the local entropy of weights and input activations, applying it to both unstructured and semi-structured pruning without the need for complex weight updates or retraining. Experimental results demonstrate that our method shows superior performance across models of various scales. Specifically, at a sparsity rate of 50%, our method significantly reduces model perplexity without the need for weight updates and outperforms existing pruning techniques such as SparseGPT and Wanda in zero-shot tasks. The code is available at: this https URL.

Multi-hop Reading Comprehension (RC) is a challenging task that requires models to integrate dispersed information and perform multi-step reasoning. In recent years, graph-based methods have shown promising performance on multi-hop RC tasks. However, they often overemphasize nodes and connection strengths, neglecting the rich semantic information carried by the node relations. To address these limitations, we propose a novel Hyper-Relational Semantic Knowledge Graphs (HR-SKGs) model, which constructs hyper-relational graphs by integrating node, relation, and topic information, capturing semantic features more precisely. Additionally, we introduce a topic-aware mechanism during paragraph retrieval to effectively filter paragraphs relevant to the question. In the graph reasoning phase, we propose a hyper-relational graph attention mechanism that integrates relation-aware and topic-aware approaches to optimize information flow and reasoning between nodes. Experiments demonstrate that our approach achieves significant performance improvements on the HotpotQA dataset, and outperforms other graph-based methods.

Multimodal emotion recognition in conversation (ERC) has attracted more attention due to its wide application in multiple fields. Most previous related works focused on directly fusing different modalities, resulting in the excessive introduction of irrelevant multimodal information, which interferes with the model’s prediction. In this paper, we propose a Mixture of Experts Multimodal Graph Attention Network Framework For Multimodal Emotion Recognition (MMGAT-EMO), which combines Multimodal Graph Attention Network (Multimodal GAT) with Mixture of Experts (MoE). According to the semantic relationship of the text modality, it selectively integrates audio and visual features from other nodes into the text modality of the current node and gives different weights to different experts, to handle complex multimodal scenarios. At the same time, we introduce cross-modal Contrastive Loss to shorten the distance between different modalities, for better cross-modal fusion. The MMGAT-EMO method has been evaluated on two widely used multimodal datasets, IEMOCAP and MELD. The results show that MMGAT-EMO is superior to all baseline models and has significantly improved F1-score. We release the code at https://github.com/tdfxlyh/MMGATEMO.

Text watermarking provides an effective solution for identifying synthetic text generated by large language models. However, existing techniques often focus on satisfying specific criteria while ignoring other key aspects, lacking a unified evaluation. To fill this gap, we propose the Comprehensive Evaluation Framework for Watermark (CEFW), a unified framework that comprehensively evaluates watermarking methods across five key dimensions: ease of detection, fidelity of text quality, minimal embedding cost, robustness to adversarial attacks, and imperceptibility to prevent imitation or forgery. By assessing watermarks according to all these key criteria, CEFW offers a thorough evaluation of their practicality and effectiveness. Moreover, we introduce a simple and effective watermarking method called Balanced Watermark (BW), which guarantees robustness and imperceptibility through balancing the way watermark information is added. Extensive experiments show that BW outperforms existing methods in overall performance across all evaluation dimensions. We release our code to the community for future research1.

Pruning has recently been widely adopted to reduce the parameter scale and improve the inference efficiency of Large Language Models (LLMs). Mainstream pruning techniques often rely on uniform layerwise pruning strategies, which can lead to severe performance degradation at high sparsity levels. Recognizing the varying contributions of different layers in LLMs, recent studies have shifted their focus toward non-uniform layerwise pruning. However, these approaches often rely on pre-defined values, which can result in suboptimal performance. To overcome these limitations, we propose a novel method called Dynamic Layerwise Pruning (DLP). This approach adaptively determines the relative importance of each layer by integrating model weights with input activation information, assigning pruning rates accordingly. Experimental results show that DLP effectively preserves model performance at high sparsity levels across multiple LLMs. Specifically, at 70% sparsity, DLP reduces the perplexity of LLaMA2-7B by 7.79 and improves the average accuracy by 2.7% compared to state-of-the-art methods. Moreover, DLP is compatible with various existing LLM compression techniques and can be seamlessly integrated into Parameter-Efficient Fine-Tuning (PEFT). We release the code\footnote{The code is available at: \url{https://github.com/ironartisan/DLP}.} to facilitate future research.

Network Function Virtualization (NFV) enables the realization of dedicated, proprietary network functions as software, which we can instantiate flexibly on commodity servers as Virtual Network Functions (VNFs). This approach facilitates significant cost reduction and operational flexibility. However, NFV also introduces new challenges, particularly regarding the availability of network services during the VNF deployment process, due to the inherently error-prone nature of software. The issue of ensuring high availability in VNF deployment has garnered considerable attention in the academic community, with redundancy provisioning commonly regarded as the standard solution. Additionally, the time-varying traffic in operator networks complicates the deployment process. Accurate traffic prediction enables operators to dynamically scale VNF instances based on demand, optimizing resource usage and reducing costs. Building on these considerations, we investigate the availabilityaware VNF deployment problem within data center networks. We incorporate a redundancy-sharing mechanism alongside traffic forecasting method to enhance resource utilization efficiency. We formally model the problem and propose an Availabilityguaranteed and Resource-efficient VNF Placement (ARVP) for mapping Service Function Chain Requests (SFCRs) in SDN/NFVenabled networks. We conduct a comprehensive numerical simulation to evaluate the performance of our proposed approach, comparing it against four alternative schemes from the existing literature. The results demonstrate that our algorithm outperforms the benchmarks regarding SFCR acceptance rate and activated nodes. Furthermore, it achieves up to 55 % resource savings when the availability requirement is six nines ($\mathbf{0. 9 9 9 9 9 9}$).

Measurement data obtained from “things” in the Internet of Things (IoT) faces challenges in efficient transmission due to the low-bandwidth data transmission link. We observe that measurement data are fixed in size and format, and low-entropy in the time domain, indicating that compression can be benefited. Rather than employing a single compression algorithm as advocated in existing literature, we argue that optimal transmission can be achieved by jointly considering compression overheads and network status, where software-defined networking (SDN) is employed to enforce network statistics and packet forwarding. This article presents a new paradigm that achieves optimal transmission of SDN-empowered compressed measurement data. We formulate the problem as an optimization problem and prove its nonpolynomial hardness time complexity. Due to this complexity, we introduceCompresso, a heuristic algorithm that efficiently solves the problem. We conduct rigorous simulations, and the results demonstrate the efficiency of the new paradigm andCompresso, i.e., attaining comparable performance to the optimal solution with 50% time usage reduction.

Generative diffusion models deliver remarkable synthesis quality but remain impractical for resource-limited Internet of Things (IoT) devices due to their substantial computational and memory demands. To bridge this critical gap, we present a comprehensive, multi-stage optimization framework that systematically reduces model size while meticulously preserving generative fidelity. The framework integrates an efficient backbone architecture designed for inherent lightness, a sensitivity-guided fine-grained pruning strategy that strategically removes redundant parameters to achieve high sparsity, and a novel distribution-aware quantization algorithm based on Gaussian Mixture Models (GMMs) to compress weights and activations with minimal quality degradation. Extensive validation across multiple diffusion architectures (DDPM, DDIM, SGM) and diverse datasets demonstrates the framework’s strong generalizability, achieving up to 79% model sparsity while preserving generative fidelity. To showcase practical utility, we demonstrate that our framework produces a compressed model compatible with standard mobile deployment toolchains, realizing a significant reduction in the on-device memory footprint required for inference. This work offers a robust and generalizable methodology for enabling advanced generative AI on a wide spectrum of edge and IoT platforms. Code is available at: https://github.com/mitchell-cheng/compress_diffusion.

The integration of vehicle edge computing (VEC) and microservice architectures improves real-time data processing and computational optimization in the Internet of Vehicles. Specifically, in high-traffic areas, the dynamic deployment and request routing of microservices with complex data dependencies within vehicle clusters can effectively reduce the computational load on edge devices. However, existing research has primarily focused on efficiently utilizing vehicular resources, overlooking the dynamic nature of vehicular cluster networks and the additional communication costs arising from data dependencies between microservices. Therefore, we propose a joint service deployment and request routing problem for vehicle collaboration. We first design a vehicle-road collaborative service framework assisted by temporary vehicle workers, expanding available resources and coverage by deploying microservice instances on selected temporary vehicle nodes. Second, recognizing the dependency between service deployment and request routing, we propose a dual-timescale service-deployment and request-routing policy. On a long timescale, a microservice-aware deployment method optimizes request selection and response time. On a short timescale, we propose a decentralized, swarm intelligence-based collaborative request routing method that constructs a response threshold model through agent interaction, thereby enhancing the collaborative optimization capability of the system. Finally, experimental results using real datasets show that our method outperforms other approaches in reducing request response time when communication costs are taken into account.

Traditional federated learning (FL) uploads local models to a central server for model aggregation and suffers from server centralization. While blockchain-based FL addresses the issue of centralization, new challenges arise, including limited scalability of a single chain, expensive overhead of blockchain consensus, and inconsistent quality of uploaded models. This paper proposes a new cross-chain-based FL (CBFL) framework. Specifically, we propose a three-layer cross-chain FL architecture consisting of a task-releasing chain, a relay chain, and local model uploading chains. The task-releasing chain is used for task issuers to release FL tasks and global model aggregation. The local model uploading chain manages local devices, stores local models and aggregates these local models. To verify the quality of local models, we propose a dual-criteria model quality inspection method based on cross entropy and cosine similarity to exclude substandard local models. We also propose hierarchical FL before global model aggregation to further reduce the communication overhead. Moreover, multi-signature is used to ensure the consistent transmission of models in the cross-chain process. Experiments corroborate that the proposed CBFL improves performance by about 50% compared to the existing BFL framework. Moreover, the proposed dual-criteria model quality inspection method has better robustness than Krum and Trimmed Mean.

Neural speech synthesis, or text-to-speech (TTS), aims to transform a signal from the text domain to the speech domain. While developing TTS architectures that train and test on the same set of speakers has seen significant improvements, out-of-domain speaker performance still faces enormous limitations. Domain adaptation on a new set of speakers can be achieved by fine-tuning the whole model for each new domain, thus making it parameter-inefficient. This problem can be solved by Adapters that provide a parameter-efficient alternative to domain adaptation. Although famous in NLP, speech synthesis has not seen much improvement from Adapters. In this work, we present HyperTTS, which comprises a small learnable network, “hypernetwork”, that generates parameters of the Adapter blocks, allowing us to condition Adapters on speaker representations and making them dynamic. Extensive evaluations of two domain adaptation settings demonstrate its effectiveness in achieving state-of-the-art performance in the parameter-efficient regime. We also compare different variants of , comparing them with baselines in different studies. Promising results on the dynamic adaptation of adapter parameters using hypernetworks open up new avenues for domain-generic multi-speaker TTS systems. The audio samples and code are available at https://github.com/declare-lab/HyperTTS.

Continual Few-shot Relation Extraction (CFRE) is a practical problem that requires the model to continuously learn novel relations while avoiding forgetting old ones with few labeled training data. The primary challenges are catastrophic forgetting and overfitting. This paper harnesses prompt learning to explore the implicit capabilities of pre-trained language models to address the above two challenges, thereby making language models better continual few-shot relation extractors. Specifically, we propose a Contrastive Prompt Learning framework, which designs prompt representation to acquire more generalized knowledge that can be easily adapted to old and new categories, and margin-based contrastive learning to focus more on hard samples, therefore alleviating catastrophic forgetting and overfitting issues. To further remedy overfitting in low-resource scenarios, we introduce an effective memory augmentation strategy that employs well-crafted prompts to guide ChatGPT in generating diverse samples. Extensive experiments demonstrate that our method outperforms state-of-the-art methods by a large margin and significantly mitigates catastrophic forgetting and overfitting in low-resource scenarios.

Multimodal Emotion Recognition in Conversation (ERC) has gained significant attention due to its wide-ranging applications in diverse areas. However, most previous approaches focused on modeling context at the semantic level, neglecting the context of dependency information at the emotional level. In this paper, we proposed a novel Reinforcement Learning framework for the multimodal EMOtion recognition task (RL-EMO), which combines a Multi-modal Graph Convolution Network (MMGCN) [1] module with a novel Reinforcement Learning (RL) module to model context at both the semantic and emotional levels respectively. The RL-EMO approach was evaluated on two widely used multi-modal datasets, IEMOCAP and MELD, and the results show that RL-EMO outperforms several baseline models, achieving significant improvements in F1-score. We release the code at https://github.com/zyh9929/RL-EMO.

In the multi-turn dialogue reasoning task, existing models conduct word-level interaction on the entire context to gather reasoning evidence, which aims to select the logically correct one from the candidate response options. Observing the fact that the salient reasoning evidence usually comes from certain snippets of the whole dialogue session, one promising study direction is to explicitly identify the candidate reasoning contexts correlated with the dialogue reasoning options, called option-related contexts, and then make logical inference among them. However, such option-related contexts are stained with noisy information. As a result, existing models may reason unfairly with biased context and select wrong options. To tackle the context bias problem, in this article, we propose a novel CounterFactual learning framework for Dialogue Reasoning, named CF-DialReas, which mitigates the bias information by subtracting the counterfactual representation from the total causal representation. Specifically, we consider two scenarios, i.e., factual dialogue reasoning where the whole context is available to estimate the total causal representation, and the counterfactual dialogue reasoning, which firstly utilizes three different types of utterance selectors to select option-unrelatedcontext, and then only the option-unrelatedcontext is available to guess the counterfactual representation. Experimental results on two public dialogue reasoning datasets show that the model with our mechanism can obtain higher ranking measures, validating the effectiveness of counterfactual learning of CF-DialReas. Further analysis on the generality of CF-DialReas shows that our counterfactual learning mechanism is generally effective to the widely-used models.

The integration of external knowledge graphs into dialogue systems effectively mitigates the generation of generic and uninteresting responses. This approach, particularly the explicit modeling of conversation flows from related concept entities, facilitates the generation of semantically rich and informative responses. However, recent models guided by concept entity flows present two primary limitations: (1) a limited semantic understanding of the post message, which complicates the selection of highly relevant 1-hop concept entities, and (2) an inability to extract dynamic and diverse semantic relations between the post message and 2-hop concept entities. To address these issues, we introduce FluGCF, a novel model that fluently generates dialogues with coherent guidance from concept entity flows. FluGCF employs a ternary fusion to explicitly model multi-hop concept entity flows using a post-aware knowledge encoding mechanism. This mechanism learns semantic concept entity features from both word and sentence-level text features. Additionally, we design a corresponding ternary decoding mechanism that dynamically selects concept entities or words from the vocabulary to enhance fluency and diversity in dialogue generation. FluGCF, implemented in PyTorch, was extensively evaluated on a large-scale dataset, revealing that it surpasses baseline models, including the state-of-the-art knowledge-aware model ConceptFlow, by nearly 15% in terms of fluency. Furthermore, it demonstrated notable enhancements in coherence, diversity and informativeness.

Fine-tuning is widely used as the default algorithm for transfer learning from pre-trained models. Parameter inefficiency can however arise when, during transfer learning, all the parameters of a large pre-trained model need to be updated for individual downstream tasks. As the number of parameters grows, fine-tuning is prone to overfitting and catastrophic forgetting. In addition, full fine-tuning can become prohibitively expensive when the model is used for many tasks. To mitigate this issue, parameter-efficient transfer learning algorithms, such as adapters and prefix tuning, have been proposed as a way to introduce a few trainable parameters that can be plugged into large pre-trained language models such as BERT, HuBERT. In this paper, we introduce the Speech UndeRstanding Evaluation (SURE) benchmark for parameter-efficient learning for various speech processing tasks. Additionally, we introduce a new adapter, ConvAdapter, based on 1D convolution. We show that ConvAdapter outperforms the standard adapters while showing comparable performance against prefix tuning and Low-Rank Adaptation with only 0.94% of trainable parameters.

With the 5G commercial deployments rapidly proceeding, operating mobile networks efficiently has become a great challenge. Self-organizing networks have been proposed to focus on automatically monitoring, analyzing and optimizing networks. Regarding the growing scale and complexity, 5G self-organizing networks confront the challenge to handle the massive data. Therefore, AI models are urgently needed to enable end-to-end network automation. In this demonstration, benefiting from the open data interfaces of the standardized NWDAF within the 5GC network, we implement a containerized network data analytic framework embedding Docker-based AI model containers into 5G networks. Furthermore, we simulate a use case automatically monitoring and optimizing user-level QoE in real time and simulation results are presented.

We are going to enter the most intelligent era than ever before. Intelligent electronics network is infiltrating into our life and making it more convenient. Nonetheless, users always want smart home be more intelligent and complete more features. users’ issues are endless. Modular packaging device services and effective choreography algorithms can flexible fit different issues. Many organizations have been proving, implementing and managing business solutions for many specific individual industries. However, when comes to smart home for end users, there are numerous limitations in process, tooling, and skills. In the paper, we provide a lightweight visualized service creating tool and an AI-driven service flow construction model. It helps end users to create services though drag-and-drop, and then deploy new services automatically. And in the end a case study will be introduced.

Intelligent service robots have become an indispensable aspect of modern‐day society, playing a crucial role in various domains ranging from healthcare to hospitality. Among these robotic systems, human‐machine dialogue systems are particularly noteworthy as they deliver both auditory and visual services to users, effectively bridging the communication gap between humans and machines. Despite their utility, the majority of existing approaches to these systems primarily concentrate on augmenting the logical coherence of the system’s responses, inadvertently neglecting the significance of user emotions in shaping a comprehensive communication experience. To tackle this shortcoming, we propose the development of an innovative human‐machine dialogue system that is both intelligent and emotionally sensitive, employing multimodal generation techniques. This system is architecturally comprised of three components: (1) data collection and processing, responsible for gathering and preparing relevant information, (2) a dialogue engine, which generates contextually appropriate responses, and (3) an interaction module, responsible for facilitating the communication interface between users and the system. To validate our proposed approach, we have constructed a prototype system and conducted an evaluation of the performance of the core dialogue engine by utilizing an open dataset. The results of our study indicate that our system demonstrates a remarkable level of multimodal generation response, ultimately offering a more human‐like dialogue experience.

The local parts of the target are vitally important for robust object tracking. Nevertheless, existing excellent context regression methods involving siamese networks and discrimination correlation filters mostly represent the target appearance from the holistic model, showing high sensitivity in scenarios with partial occlusion and drastic appearance changes. In this paper, we address this issue by proposing a novel part-aware framework based on context regression, which simultaneously considers the global and local parts of the target and fully exploits their relationship to be collaboratively aware of the target state online. To this end, the spatial-temporal measure among context regressors corresponding to multiple parts is designed to evaluate the tracking quality of each part regressor by solving the imbalance among global and local parts. The coarse target locations provided by part regressors are further aggregated by treating their measures as weights to refine the final target location. Furthermore, the divergence of multiple part regressors in each frame reveals the interference degree of background noise, which is quantified to control the proposed combination window functions in part regressors to adaptively filter redundant noise. Besides, the spatial-temporal information among part regressors is also leveraged to assist in accurately estimating the target scale. Extensive evaluations demonstrate that the proposed framework help many context regression trackers achieve performance improvements and perform favorably against state-of-the-art methods on the popular benchmarks: OTB, TC128, UAV, UAVDT, VOT, TrackingNet, GOT-10k, LaSOT.

The fine-grained prediction of traffic anomalies is crucial for Traffic Management Bureau to alleviate congestion and avoid public safety incidents. While in practice, the fine-grained prediction is very challenging due to two issues. 1)Data sparsity. At the fine-grained setting, missing data is inevitable and widespread on spatial and temporal dimension. Existing methods have weak performance as they do not handle missing data properly. 2)Data distribution mutation. At the fine-grained setting, the traffic conditions of adjacent road segments are sometimes completely different, invalidating existing spatiotemporal smoothing-based methods. This paper proposes GMAT-DU, a novel model that aims to predict traffic anomaly from sparse data in fine-grained manner. To solve the first issue, we propose a Decay Unrolling (DU) mechanism to make the model applicable to sparse datasets. The performance will be progressively enhanced by the spatiotemporal unrolling of high-impact neighbors. For the second issue, we combine the meta-features of roads with correlations between roads, which are learnt from road semantic information and historical spatiotemporal data, and make the model focusing on the high-impact neighbors by a Graph Meta-features based ATtention (GMAT) mechanism. Extensive experiments on two real-world datasets validate the effectiveness of our method. The experiment results show the significant advantages against the state-of-the-art models.

Few-shot relation classification (FSRC) focuses on recognizing novel relations by learning with merely a handful of annotated instances. Meta-learning has been widely adopted for such a task, which trains on randomly generated few-shot tasks to learn generic data representations. Despite impressive results achieved, existing models still perform suboptimally when handling hard FSRC tasks with similar categories that confuse the model to distinguish correctly. We argue this is largely due to two reasons, 1) ignoring pivotal and discriminate information that is crucial to distinguish confusing classes, and 2) training indiscriminately via randomly sampled tasks of varying difficulty. In this article, we introduce a novel prototypical network approach with contrastive learning that learns more informative and discriminative representations by exploiting relation label information. We further design two strategies that increase the difficulty of training tasks and allow the model to adaptively learn to focus on hard tasks. By doing so, our model can better represent subtle inter-relation variance and grow up through task difficulty. Extensive experiments on three standard benchmarks demonstrate the effectiveness of our method.

Fine-grained urban crime prediction is of great significance to urban management and public safety. Previous crime prediction work has been done at a relatively coarse time granularity, which may suffer from two issues for fine-grained crime prediction. 1)The zero-inflation problemassociated with fine-grained granularity. Crime occurrence is sparse, and when the time granularity becomes finer, it leads to a more sparse prediction label for this problem resulting in the zero inflation problem. 2)Insufficient amount of informationinvolved in crime datasets. When the spatio-temporal granularity becomes smaller, more information from related fields needs to be introduced to extract spatio-temporal features to assist the analysis. To address the first issue, we introduce a classification-labeled continuousization strategy and a weighted loss function for sparse classification problem, making the model more likely to focus on non-zero elements in zero-inflated datasets. For the second issue, we propose a novel deep learning based model, termed attention-based spatio-temporal multi-domain fusion network, which fuses features from multiple datasets in related domains. We evaluate our method on six real-world datasets collected in New York City and experiments on our model show the advantages beyond many competitive baselines.

With the explosive growth of various intelligent device and the rapid development of wireless network communication technology, most people prefer to use video applications on smart devices. However, the main challenges when using video codec technology on mobile devices are: 1) The explosive growth of multimedia applications has caused the allocation of computing resources to become an important issue; 2) high power consumption and limited battery power; 3) high cpu utilization causes the system to be unresponsive. In this paper, aGPU basedHigh DefinitionParallelVideoCodec (GHPVC) is proposed, which is a low energy consumption and high efficient video codec on mobile devices. First, Frame Data Management model and Prediction Model Selector model are proposed in order to get higher data transmission efficiency and parallel execution efficiency. Second, a GPU based Parallel ME module is proposed because the ME module is the most power-consuming and computationally intensive module in video codec. The GHPVC is proposed on the basis of conforming to the H.264 standard. Moreover and experimentally evaluated for different GPU devices on different mobile devices. Experimental results show that compared with the existing H.264 scheme, the proposed GHPVC not only has significant improvement in codec performance, but also effectively reduces energy consumption and CPU utilization.

Security is critical to networks, but TCP/IP-basedlegacynetworks are difficult to advance new security functions due to the use of costly inflexible hardware devices and error-prone network configurations. Recent literature explores the paradigm of consolidating security services with the forwarding functionality using Software-defined Networking (SDN). Existingfull SDNdeployment, replacing all legacy network devices with SDN devices, is cost-prohibitive. Whereas thehybrid SDNthat only upgrades partial legacy devices to SDN switches is considered practical. However, the challenge is to minimize threats and deployment expenses simultaneously under heterogeneous end-host businesses that have various importance. In this paper, we study the challenge and propose theEnd-host-importance-Aware secure service-enabled hybrid Sdn deplOymeNt (EASON)problem. We mathematically formulate the EASON problem as an integer programming problem, prove its non-polynomial time complexity, and propose a heuristic algorithm called BonSèc. We conduct rigorous simulations on real-world topologies and traces. Experimental results show that BonSèc achieves comparable security and cost performances to the optimal solution on small topologies. Meanwhile, it is scalable on larger topologies.

It is important to minimize the energy consumption of large-scale, geographically distributed edge data centers (EDCs). While modern processing units (PUs) have energy-saving features like Dynamic Voltage and Frequency Scaling (DVFS) and Per-Core Power Gating (PCPG), optimization is still complex and requires a holistic approach. This article presents a new decentralized, three-timescale, online optimization approach that enables multicore micro data centers (MDCs) to optimize their per-PU power states, per-enabled-PU voltage-frequency levels and offloading schedules at three different timescales. The key idea is that we employ multi-timescale Lyapunov optimization to decouple the energy minimization between workload scheduling and result delivery at a small timescale and PU configuration at large timescales. Another important aspect is that we apply the primal decomposition to decouple the PU configuration between a per-enabled-PU voltage-frequency level at an intermediate timescale and a per-PU power state at a large timescale. Experiments demonstrate that the proposed approach improves energy efficiency significantly by up to 4.5 times in our considered lightly loaded situations where DVFS alone does not work effectively, compared to existing benchmarks.

Few-shot relation learning refers to infer facts for relations with a few observed triples. Existing metric-learning methods mostly neglect entity interactions within and between triples. In this paper, we explore this kind of fine-grained semantic meaning and propose our model TransAM. Specifically, we serialize reference entities and query entities into sequence and apply transformer structure with local-global attention to capture intra- and inter-triple entity interactions. Experiments on two public datasets with 1-shot setting prove the effectiveness of TransAM.

Long-term traffic prediction is essential for pre-control of traffic departments, which allows traffic dispatchers to make earlier decisions than short-term traffic prediction. This task is extremely challenging mainly due to the difficulty of obtaining accurate spatial dependency at different time periods and weak correlation between predicted values and historical data for largest time step. Existing methods either use the same adjacency matrix at every moment or recompute a different adjacency matrix at every moment, which may introduce incorrect neighbors to the target node. Moreover, many previous methods either obtain the predicted values step by step, which causes error propagation problem, or obtain the predicted values for each step independently, which loses the correlation information between the multi-step predicted values. In this paper, a Time-varying Adjacency Mask is proposed to correct the spatial dependence which makes spatial dependence different but highly similar at each moment. Besides, a Self-Smoothing Regularization is proposed to establish the relationship between the predicted values of adjacent time slices and to restrict their differential values. Extensive experiments including traditional long-term traffic speed prediction, time-phased speed prediction and rush hour speed prediction are conducted on two real-world datasets, experimental results show the superior performance of our proposed model.

Devamanyu Hazarika, Yingting Li, Bo Cheng, Shuai Zhao, Roger Zimmermann, Soujanya Poria. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Most of siamese network and correlation filter (CF) based trackers usually employ the context regression scheme to achieve appealing performance in both accuracy and efficiency. However, they are prone to drifting in challenging situations exhibiting occlusion, out-of-view and large-scale variations due to the lack of failure correction ability in these regressors. Particle filter based trackers can help to recover from tracking failures since several particles of high confidence about the target can be remained for the probability estimation of next frames, but need the large numbers of particles for each frame. In this paper, we propose a generic dynamic particle filter framework, which can reasonably control the number of particles in different scenarios, to improve the robustness of siamese and CF trackers by jointing the target classifier to relieve drifting. Our fundamental insight is that general scenarios are processed efficiently by the context regressor with few particles, while special scenarios are handled effectively by the target classifier with many particles. We propose a novel measure to determine whether to adopt the regressor and few particles to estimate target states with high measure scores, or increase the number of particles to prevent drifting with the proposed multi-template matching strategy in the classifier. In extensive experiments on eight large-scale benchmarks including OTB, UAV, TC128, VOT2017, VOT2019, LaSOT, TrackingNet and Got-10k, the proposed framework enables many basic siamese and CF trackers to operate at least over 24 frames per second and achieve superior tracking performance than themself, as well as the comparable accuracy with the state-of-the-art trackers. Furthermore, our framework is flexible and still has great potential for improvement and generalization.

Both siamese network and correlation filter (CF)-based trackers have exhibited superior performance by formulating tracking as a similarity measure problem, where a similarity map is learned by the correlation between a target template and a region of interest (ROI) with a cosine window. Nevertheless, this window function is usually fixed for various targets and not changed, undergoing significant noise variations during tracking, which easily makes model drift. In this article, we focus on the study of a noise-aware (NA) framework for robust visual tracking. To this end, the impact of various window functions is first investigated in visual tracking. We identify that the low signal-to-noise ratio (SNR) of windowed ROIs makes the above trackers degenerate. At the prediction phase, a novel NA window customized for visual tracking is introduced to improve the SNR of windowed ROIs by adaptively suppressing the variable noise according to the observation of similarity maps. In addition, to further optimize the SNR of windowed pyramid ROIs for scale estimation, we propose to use the particle filter to dynamically sample several windowed ROIs with more favorable signals in temporal domains instead of this pyramid ROIs extracted in spatial domains. Extensive experiments on the popular OTB-2013, OTB-50, OTB-2015, VOT2017, TC128, UAV123, UAV123@10fps, UAV20L, and LaSOT datasets show that our NA framework can be extended to many siamese and CF trackers and our variants obtain superior performance than baseline trackers with a modest impact on efficiency.

Internet of Things (IoT) is playing an increasingly important role in Intelligent Transportation Systems (ITS) for real-time sensing and communication. In ITS, vehicle types, volume and speeds provide important information for road traffic management. However, the present methods for on-road traffic monitoring are lacking in providing cost-effective means to meet the demands. In this paper, we propose MagMonitor, a novel method for on-road traffic surveillance through a single small and easy-to-install magnetic sensor. The developed magnetic sensor system is wireless-connected, cost-effective, and environmental-friendly. First, a magnetic model of a moving vehicle is presented. The model employs multiple magnetic dipoles for modelling moving vehicle and varies depending on the on-road vehicle types. Through modelling of local magnetic field perturbations caused by moving vehicles, we extract the characteristics of magnetic waveforms for vehicle identification and speed estimation. The proposed model and estimation technique are validated with real field experimental data. Furthermore, we analyze and compare the performance of the proposed estimation technique with other speed estimation algorithms, which shows the superior accuracy of the proposed technique.

Network function virtualization (NFV) is a promising technology that decouples network functions from hardware. Connecting virtual network functions (VNFs) in series to form a service function chain (SFC) can flexibly orchestrate and expand network functions. However, there are higher availability requirements for SFCs. This paper aims to solve the SFC placement problem under availability and resource constraints. This paper proposes the sideway cross (SC) backup model, which considers the availability of both VNFs and physical machines (PMs) in a data center. The SC model cross-arranges the backup instances of VNFs to guarantee availability and optimize resource consumption. Then, this paper proposes the heuristic meteor shower optimization (MSO) algorithm to place SFCs. Compared to traditional heuristic algorithms, MSO can improve the execution time by approximately 200%. Combined with the SC backup model, MSO can effectively improve the availability and resource overhead. The evaluation results show that the proposed approach can guarantee higher availability and consumes fewer resources. The proposed approach only needs 75% of the resources to achieve the same availability as the state-of-the-art models.

Sensors in wireless sensor network (WSN) are usually deployed in the wild or even hostile circumstance. What is worse, most of the sensors have limited communication bandwidth, computation resources and energy. Therefore, it is challenging to ensure the security of WSN without decreasing its network performance. Network coding (NC) is a promising way for improving communication capability in WSN, e.g., high throughput, robustness and low-energy. Nevertheless, network coding is vulnerable to malicious attacks. Presently, many secure detections, such as information theoretic-based or cryptographic-based techniques, have been proposed to deal with a single type of attack, but are incapable of resisting the joint attacks, e.g., the union of pollution attacks and replay attacks. In this paper, a secure detection service is presented. It is deployed on every node of WSN to monitor, manage and control the messages passing through them in real-time. In the service, a lightweight timestamp-based message authentication code, namely TMAC, is designed with Exclusive OR network coding. Based on TMAC and time synchronization technique, a joint detection is implemented to resist pollution attacks and replay attacks synchronously. The correctness of the detection service is proved. Finally, the performance evaluation shows that the detection scheme brings negligible extra-expense in communication bandwidth and computational complexity compared to MAC-based schemes, and consumes energy lowly compared to other joint detection schemes.

Few-shot relation extraction (FSRE) aims to predict the relation for a pair of entities in a sentence by exploring a few labeled instances for each relation type. Current methods mainly rely on meta-learning to learn generalized representations by optimizing the network parameters based on various collections of tasks sampled from training data. However, these methods may suffer from two main issues. 1) Insufficient supervision of meta-learning to learn discriminative representations on very few training instances, which are sampled from a large amount of base class data. 2) Spurious correlations between entities and relation types due to the biased training procedure that focuses more on entity pair rather than context. To learn more discriminative and unbiased representations for FSRE, this paper proposes a two-stage approach via supervised contrastive learning and sentence- and entity-level prototypical networks. In the first (pre-training) stage, we introduce a supervised contrastive pre-training method, which is able to yield more discriminative representations by learning from the entire training instances, such that the semantically related representations are close to each other, and far away otherwise. In the second (meta-learning) stage, we propose a novel sentence- and entity-level prototypical network equipped with fine-grained feature-wise fusion strategy to learn unbiased representations, where the networks are initialized with the parameters trained in the first stage. Specifically, the proposed network consists of a sentence branch and an entity branch, taking entire sentences and entity mentions as inputs, respectively. The entity branch explicitly captures the correlation between entity pairs and relations, and then dynamically adjusts the sentence branch's prediction distributions. By doing so, the spurious correlations issue caused by biased training samples can be properly mitigated. Extensive experiments on two FSRE benchmarks demonstrate the effectiveness of our approach.

Few-shot relation extraction (FSRE) focuses on recognizing novel relations by learning with merely a handful of annotated instances.Meta-learning has been widely adopted for such a task, which trains on randomly generated few-shot tasks to learn generic data representations.Despite impressive results achieved, existing models still perform suboptimally when handling hard FSRE tasks, where the relations are fine-grained and similar to each other.We argue this is largely because existing models do not distinguish hard tasks from easy ones in the learning process.In this paper, we introduce a novel approach based on contrastive learning that learns better representations by exploiting relation label information.We further design a method that allows the model to adaptively learn how to focus on hard tasks.Experiments on two standard datasets demonstrate the effectiveness of our method.

Question Answering (QA) models over Knowledge Bases (KBs) are capable of providing more precise answers by utilizing relation information among entities. Although effective, most of these models solely rely on fixed relation representations to obtain answers for different question-related KB subgraphs. Hence, the rich structured information of these subgraphs may be overlooked by the relation representation vectors. Meanwhile, the direction information of reasoning, which has been proven effective for the answer prediction on graphs, has not been fully explored in existing work. To address these challenges, we propose a novel neural model, Relation-updated Direction-guided Answer Selector (RDAS), which converts relations in each subgraph to additional nodes to learn structure information. Additionally, we utilize direction information to enhance the reasoning ability. Experimental results show that our model yields substantial improvements on two widely used datasets.

Mobile Edge Computing (MEC) supports ultra-low latency and high-bandwidth services as an emerging network architecture by deploying servers at the edge of the network to provide computing and storage resources. Along with the MEC technology, Network Function Virtualization (NFV) provisions Service Function Chains (SFC) on MEC servers to improve user service experience and achieve fast access to the mobile user. However, users are constantly moving in the edge network, and different users usually have different delay requirements for service requests. To guarantee the QoS of mobile users, it is necessary to migrate SFCs to an advisable edge server when users move across Base Stations (BS). This paper focuses on the SFCs reconfiguration scheme with resource capacity constraints in the MEC network to support the seamless migration of mobile user services. We first formalize the SFCs reconfiguration problem of the edge network as a mathematical model, which aims to minimize the end-to-end delay and operating costs of user services. Then, we convert the problem into an equivalent shortest path problem and design a Dynamic Programmingbased SFC Migration algorithm (DPSM). Finally, we conduct simulation experiments to evaluate the performance of the algorithm based on a real-world dataset. The experiment results show the effectiveness and efficiency of our algorithm.

In the mobile and pervasive computing environment, automatic service composition faces the challenges of the highly dynamic network structure and limited computation resources. Existing approaches create service flows under the assumption that network context changes are slower than the time required to plan the composite service which is no longer set up in the mobile and pervasive computing environment. In this paper, we propose an online automatic service composition approach to dynamically construct service flows and interleave the planning and executing processes. Particularly, it introduces a novel distributed heuristic searching algorithm to determine solutions with limited knowledge bases and execute them in real time. Simulation results show that our proposed online approach reduces the composition time and performs faster composition time and higher composition success rates than state-of-the-art approaches in mobile computing environments.

Network Function Virtualization (NFV) is a promising paradigm that separates network functions from proprietary devices. Network service in NFV-enabled networks is achieved as a Service Function Chain (SFC), consisting of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within dynamic networks is a key challenge. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF Instance (VNFI). In this paper, we assume that each deployed VNFI is used by multiple SFCs and deal with the optimal location allocation for the concurrent migration of VNFIs based on the actual network situation. We first formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the end-to-end delay for all affected services and to guarantee network load balancing after the migration simultaneously. To this end, we prove the NP-hardness of this problem and propose the Improved Hybrid Genetic Evolution (IHGE) algorithm to address it. Besides, to reduce the computation overhead of IHGE for large-scale networks, a multi-stage heuristic algorithm based on optimal order (MSH-OR) is designed. Finally, we perform a side-by-side comparison with prior algorithms. Extensive evaluation shows that the proposed approaches can effectively reduce the average delay for different scale networks while ensuring network load balancing.

For a cloud service provider, it necessitates an emerging cloud ecosystem to consolidate the existing users and attract more potential users, further gaining its market share. Therefore, in this article, we design a price-incentive resource auction mechanism in cloud environment. In response to the cloud resource price, each user synthesizes her bidding budget and QoS requirement, and purchases cloud resources according to her resource demand in a strategic manner. The cloud service provider, meanwhile, can regulate the resource demands of users through conducting a market-based pricing strategy, against too low prices to cover the operational costs (i.e., energy costs) or too high prices resulting in user churn. In virtue of an elaborate market-based pricing strategy, the interests of users and the cloud service provider are balanced. Our price-incentive resource auction mechanism targets to stimulate maximum users willing to purchase resources and perform their applications at the cloud, on the premise of a minimum profit rate guaranteed for the cloud service provider. It is also able to provide budge balance and truthfulness guarantee, and satisfy the envy-freeness. In order to carry out the above objectives, we carefully design the user utility function reflecting the complicated user interest, and formulate our resource pricing and auction problem as a bin packing problem, which has non-polynomial computational complexity. Regarding the NP-hardness of optimization problem and the concavity of user utility, we present a computational-efficient ($1+\epsilon $)-approximate algorithm namely PIRA. Finally, we conduct simulations based on the real-world dataset to validate the effectiveness of our proposed approach.

With more users outsourcing their applications to the cloud, resource pricing becomes an important issue for IaaS cloud management. Jointly considering her own bidding budget and the price of cloud resources, each user is self-motivated to purchase cloud resources according to her resource demand which maximizes her own utility. Meanwhile, the cloud service provider (CSP) regulates the price of cloud resources with a certain profitability objective achieved. With an elaborate resource pricing strategy, the goals from users and the CSP are balanced and respectively satisfied to some extent. This article provides an insight into the market-oriented cloud pricing strategy. In specific, we propose an auction market in the IaaS cloud, where multiple users with heterogeneous bidding budgets and QoS requirements subscribe cloud resources according to their resource demands. The resource pricing and demand allocation scheme targeting revenue maximization also satisfies essential properties including budget feasibility, incentive compatibility and envy-freeness. To attack the NP-hardness and non-convexity of revenue maximization problem, we design a price-incentive resource auction mechanism namely RARM, which preserves an ( 1+α) approximation ratio on revenue maximization. Finally, we evaluate our RARM mechanism based on the real-world dataset to certify the efficacy of our proposed approach.

Network Function Virtualization (NFV) brings flexible provisioning and great convenience for enterprises outsource their network functions to the Data Center Networks (DCNs). Network service in NFV is deployed as a Service Function Chain (SFC), which includes an ordered set of Virtual Network Functions (VNFs). However, in one SFC, the SFC delay increases linearly as the length of SFC increases. SFC parallelism can achieve high performance of SFC. In this article, we focus on the parallelized SFC placement problem in DCN considering availability guarantee and resource optimization. Firstly, we define the parallelized SFC and propose a multi-flow backup model. The parallelized SFC consists of multiple parallelized sub-SFCs. We split large data flow into multiple small sub-flows, each of them can be transmitted in one sub-SFC. The backup model provides backup sub-SFCs for working sub-SFCs to improve availability. Finally, we design three placement strategies and a Hybrid Placement Algorithm (HPA) aimed at mapping SFCs to DCN. Evaluation results show that our proposed solutions outperform the related work. We can reduce SFC delay (30%) and optimize link consumption (reduce 40%) while guaranteeing availability (99.999%).

With the evolution of Web technologies, various services have become available in a pervasive network environment. Combining simple atomic services into sophisticated applications with a quality-of-service (QoS) guarantee has become a widely studied problem. Given the increasing number of QoS attributes to be considered, conventional service composition approaches with manual workflows and massive computational burdens are no longer effective. Therefore, this article first suggests an efficient many-objective (with four or more objectives) automatic service composition approach named MaSC. In particular, this article introduces a temporal goal decomposition mechanism based on a temporal model to divide an unwieldy problem into several fine-grained subproblems. Viewing this model as an individual representation, we employ an evolutionary process with a novel fitness function to explore the composition solution. The experimental results on the benchmarks show that our approach can simultaneously optimize up to six objectives and achieve a better trade-off between the computation cost and the QoS than two recently proposed automatic composition approaches.

Since the advent of network function virtualization (NFV), cloud service providers (CSPs) can implement traditional dedicated network devices as software and flexibly instantiate network functions (NFs) on common off-the-shelf servers. NFV technology enables CSPs to deploy their NFs to a cloud data center in the form of virtual network functions (VNFs) without costly capital expenditures and operating expenses. However, it is an essential but intractable issue for CSPs to devise a suitable VNF placement scheme to optimize network resource consumption and improve network performance. In this article, we focus on the VNF placement problem for mapping users’ service function chain requests (SFCRs) in cloud networks. To enhance network resource utilization, we consider the fundamental resource overheads and implementation method of VNFs. The VNF placement problem is formulated as an integer linear programming model with the aim of minimizing the total network resource consumption while guaranteeing the delay requirements of SFCRs. We devise a two-phase optimization solution (TPOS) to solve the problem. TPOS contains a mapping phase to map SFCRs on servers and an adjustment phase to optimize the placement of VNFs and VNF requests. Evaluation results demonstrate that TPOS can derive near-optimal server resource consumption and significantly enhance network resource utilization. TPOS can guarantee the delay requirements of SFCRs and outperform contrastive schemes in terms of activated servers, SFCR acceptance ratio, and average VNF utilization.

Nowadays, network softwareization is an emerging techno-economic transformation trend that significantly impacts how enterprises deploy their network services. As an essential technology in this trend, Network Function Virtualization (NFV) enables scalable and inexpensive network services by flexibly instantiating Virtualized Network Functions (VNFs) on commercial-off-the-shelf devices. In this paper, we focus on the VNF placement problem in NFV-enabled networks, aiming to maximize the number of accepted Service Function Chain Requests (SFCRs) while guaranteeing their delay requirements. To improve resource utilization efficiency, we take account of Fundamental Resource Overheads (FROs) and the shareability of VNF instances. We mathematically formulate the VNF placement problem and propose the Throughput Optimization and Delay Guarantee (TO-DG) heuristic solution, consisting of an affinity-based SFCR mapping algorithm and a VNF request adjustment algorithm. The evaluation results show that the performance of TO-DG is near to results derived by ILP solver for small scale problems. Moreover, TO-DG obtains higher network throughput than contrasting schemes in different scenarios and significantly improves network resource utilization.

Context-aware QoS management is a new research field dealing with methods by which traditional QoS management algorithms in mobile and fixed networking systems can have more intelligent decision-making mechanisms by fully exploiting all context information being available in their network environment. Motivated by addressing the critical problem, we propose a novel context-aware cognitive QoS management approach, which contains three main steps for context discretization, context reduction and QoS guarantee, which can provide the quantitative end-to-end QoS guarantee and management. We developed a context-aware systematic end-to-end QoS management module in a real-life multimedia conferencing system and compared its performance with existing approaches. Experimental results show that our proposed approach outperforms the existing approaches in improving the mobile and fixed networking video transmission quality.

Web services, as an effective realization technology of SOA, have been deployed largely on the Internet. Comparing with the traditional Web pages, the information island problem existing in Web services field is more serious. Web services with specific functions not only submerge in the Web services library, but also submerge in the traditional Web pages library. As a result, finding web service rapidly and accurately becomes a problem needing solving, and this promots the research on the field of web services discovery. In this paper, we propose a semantics mining&indexing-based rapid Web services discovery framework. First, focus on the Web services formalizing model with the basic information of Web service or composition. Then propose a Web services matching engine with high accurency, and there is no semantics ontology dependence in matching engine, also, we propose a Web services discovery framework basing index library, which can help providing a low processing time in request searching. When creating the index library, we apply semantics mining to overcome the shortcoming of low searching accurancy in traditional index framework. Finally, the experiments are tested that the Web service discovery framework shows high precision rate and recall rate, which can provides a solution to the problem of persuiting both low searching processing time and high search accurancy in Web service discovery field.

The emerging, overclocking signal‐based acoustic covert communication technique allows smart devices to communicate (without users’ consent) utilizing their microphones and speakers in ultrasonic side channels, which offers users imperceptible and convenient personalized services, e.g., cross‐device authentication and media tracking. However, microphones and speakers could be maliciously used and pose severe privacy threats to users. In this paper, we propose a novel high‐frequency filtering‐ (HFF‐) based protection model, named UltraFilter, which protects user privacy by enabling users to selectively filter out high‐frequency signals from the metadata received by the device. We also analyze the feasibility of using audio frequencies (i.e., ≤18 kHz) to the acoustic covert communication and carry out the acoustic covert communication system by introducing the auditory masking effect. Experiments show that UltraFilter can prevent users’ private information from leaking and reduce system load and that the audio frequencies can pose threats to user privacy.

Cloud computing is expanding to distributed edge computing(or known as fog computing). Connecting edge and cloud open great potential for real-time and mobility support workflow applications. However, scheduling workflow on a hybrid edge-cloud environment is an NP-hard problem. This paper proposes the Geo-Aware Multi-Agents-System-Based Workflow Allocation Approach(GMAS). Leveraging a novel geo-aware negotiation mechanism, GMAS addresses resource location caused transmission delays, which are the primary sources of workflow bottlenecks. In Multi-Agents-System(MAS), this paper proposes a geo-aware cost model and a dynamic workflow re-structuring strategy that decrease the impact of resource locations on workflow cost. Finally, this paper evaluates GMAS on Cloudsim, and the result shows that GMAS decreases the workflow makespan and traffic overheads.

The next-generation network system is envisioned to be a multi-service network supporting different applications with multiple requirements. In this vision, Network SLicing (NSL) is considered a key mechanism to create multiple virtual networks over the same physical infrastructure. However, it is a challenging problem to deploy NSL with great flexibility and full automation. In this paper, we propose a novel lightweight SOA-based NSL creation system, which includes the design domain, execution domain, and infrastructure domain. The design domain provides an easily-operating service design environment. Besides, we define a detailed description of NSL named Network SLicing Descriptor (NSLD), illustrating the resource and requirements for designing network services. The execution domain is a service execution environment with multi-tenancy support. This domain based on micro service architecture is distributed and self-organized. The infrastructure domain contains multiple network infrastructure resource domains that enable micro services. With these components, tenants can create NSL freely via an intuitive and easy-to-use UI on a web browser. We also implement an IoT NSL scenario to solve the multi-flow transmission problem. After adding sub-services and QoS policy into the design domain, the scenario can run automatically at the execution domain.

Network Function Virtualization (NFV) enables cloud service providers (CSPs) to flexibly place their network functions on common off-the-shelf servers in the form of virtual network functions (VNF), without incurring costly capital and operating expenses (CAPEX/OPEX). In the NFV-enabled network, service function chains (SFCs) are responsible for accomplishing users' service requests by steering traffic through a set of VNFs in a specified order. Therefore, it is an important but intractable issue for CSPs to devise an optimal VNF placement scheme to enhance network performance and profits. In this paper, we focus on the VNF placement problem for mapping SFC requests (SFCRs) in NFV-enabled networks, considering the delay requirement of SFCRs. To improve resource utilization, we consider the basic resource overheads and sharability of VNF instances. Then we formulate the problem as an integer linear programming (ILP) model, with the purpose of total resource consumption minimization. Afterward, the novel SFCR mapping algorithm (SMA) and VNF request adjustment algorithm (VAA) are proposed to map SFCRs and optimize the placement of VNF requests. Simulation results show that our approach is near-optimal in terms of node resource consumption. Besides, it provides higher performance in terms of node resource consumption, average VNF utilization and the number of activated servers compared with the benchmarks.

Data processing is the basis and kernel for implementing terminal applications in Internet of Things(IoTs) services. As a large amount of sensing devices need to be connected to the IoTs service, it will bring an enormous number of heterogeneous data, and the data is difficult to be directly used by terminal applications in upper layer. So we need a data processing framework to achieve data integration and processing in IoTs services. In this paper, we introduce a data processing framework, LambDP, which includes a protocol stack system, a publish/subscribe system and a data processing platform. Protocol stack system offers dynamical protocol adaptation. Publish/subscribe center is a message queue, which can provide transfer and distribution for data. Data processing platform is based on lambda theory to implement parallel computing streaming mode and batch mode of data. In addition, we use the workflow pattern to encapsulate the business logic in LambDP into components, which can provide data processing services to upper layers applications. As for the performance of the data processing framework based on Lambda, we will deploy the data processing platform to give the verification.

Graph convolutional networks (GCN) have been applied in knowledge base question answering (KBQA) task. However, the pairwise connection between nodes of GCN limits the representation capability of high-order data correlation. Furthermore, most previous work does not fully utilize the semantic relation information, which is vital to reasoning. In this paper, we propose a novel multi-hop KBQA model based on hypergraph convolutional network. By constructing a hypergraph, the form of pairwise connection between nodes and nodes is converted to the high-level connection between nodes and edges, which effectively encodes complex related data. To better exploit the semantic information of relations, we apply co-attention method to learn similarity between relation and query, and assign weights to different relations. Experimental results demonstrate the effectivity of the model.

This paper investigates the neural dynamics and gamma oscillation on a complex network with excitatory and inhibitory neurons (E-I network), as such network is ubiquitous in the brain. The system consists of a small-world network of neurons, which are emulated by Izhikevich model. Moreover, mixed Regular Spiking (RS) and Chattering (CH) neurons are considered to imitate excitatory neurons, and Fast Spiking (FS) neurons are used to mimic inhibitory neurons. Besides, the relationship between synchronization and gamma rhythm is explored by adjusting the critical parameters of our model. Experiments visually demonstrate that the gamma oscillations are generated by synchronous behaviors of our neural network. We also discover that the Chattering(CH) excitatory neurons can make the system easier to synchronize.

Multi-hop question answering models based on knowledge graph have been extensively studied. Most existing models predict a single answer with the highest probability by ranking candidate answers. However, they are stuck in predicting all the right answers caused by the ranking method. In this paper, we propose a novel model that converts the ranking of candidate answers into individual predictions for each candidate, named heterogeneous knowledge graph based multi-hop and multi-answer model (HGMAN). HGMAN is capable of capturing more informative representations for relations assisted by our heterogeneous graph, which consists of multiple entity nodes and relation nodes. We rely on graph convolutional network for multi-hop reasoning and then binary classification for each node to get multiple answers. Experimental results on MetaQA dataset show the performance of our proposed model over all baselines.

Multiple domain fusion has been widely used for urban anomalies forecasting problem, as urban anomalies such as traffic accidents or illegal assembly are usually caused by many complex factors and they would affect many fields. Although many efforts have been devoted to fusing multiple datasets for anomalies detection, most of the work is to extract the spatio-temporal features one by one from multiple datasets and then fuse to get the result or anomaly score. However, the correlation between data from multiple domains at each moment is ignored, which is especially important when detecting anomalies by analyzing the impacts from multiple datasets. In this paper, we propose a novel end-to-end deep learning based framework, namely deep spatio-temporal multiple domain fusion network to collect the impacts of urban anomalies on multiple datasets and detect anomalies in each region of the city at next time interval in turn. We formulate the problem on a weighted graph and obtain spatiotemporal features with adaptive graph convolution and temporal convolution. In addition, a cross-domain convolution network is applied to fully obtain connection between multiple domains. We evaluate our method with real-world dataset collected in New York City and experiments on our model show the advantages nearly 10% beyond the state-of-the-art urban anomalies detection methods.

The structural information of Knowledge Bases (KBs) has proven effective to Question Answering (QA).Previous studies rely on deep graph neural networks (GNNs) to capture rich structural information, which may not model node relations in particularly long distance due to oversmoothing issue.To address this challenge, we propose a novel framework GlobalGraph, which models long-distance node relations from two views: 1) Node type similarity: GlobalGraph assigns each node a global type label and models long-distance node relations through the global type label similarity; 2) Correlation between nodes and questions: we learn similarity scores between nodes and the question, and model long-distance node relations through the sum score of two nodes.We conduct extensive experiments on two widely used multi-hop KBQA datasets to prove the effectiveness of our method.

Urban anomaly prediction is of great importance for urban management and public safety. Accurate anomaly prediction can avoid much unnecessary loss. Urban anomalies are usually caused by many complex factors, such as festivals, demonstrations and market promotions. It is not possible to predict anomalies from the perspective of reason, thus, most of the previous work analyzes the impacts of anomalies from multiple crowd flow datasets and observes the shift to ordinary distribution when they occur. Most existing models use observation-based methods to extract relevant spatiotemporal features, which are difficult to fully extract hidden relationships and eventually lead to low accuracy and low recall. In this paper, we propose an end-to-end deep learning based approach, called spatiotemporal multi-modal fusion model to collect the impacts of urban anomalies on multiple crowd flow datasets and predict anomalies in each region of the city for next time interval in turn. More specifically, we model the city into a graph and regard each region as a node. We use graph convolution network to obtain its spatial features and use gate recurrent units to obtain its temporal features. The features of those multiple modalities are further aggregated with points of interest in a two-stage-fusion method for assigning different weights to different functional regions. We evaluate our method using five datasets associated with New York City: 311 complaints, taxicab data, bike rental data, points of interest and road network dataset. Results show the advantages nearly 10% beyond the-state-of-the-art urban anomalies prediction methods.

Software-Defined Networking (SDN), providing flexible controlling and monitoring mechanisms that simplifies network management, is becoming prevalent in recent years. However, replacing all legacy network devices with SDN-capable devices is cost-prohibitive. One practical approach for the SDN deployment is to incrementally upgrade a few legacy devices to SDN devices. The network, which consists of legacy and SDN devices, is called a hybrid SDN. Existing hybrid SDN deployment schemes do not consider the security impact of device deployment. They use the same type of devices to upgrade, and upgraded devices could be compromised if an attacker controls one SDN device by leveraging its vulnerabilities.In this paper, we consider this security issue in the hybrid SDN deployment and present the Secure and Cost-effective Switch Upgrade (SCESU) problem. The SCESU problem aims to upgrade a few network devices to satisfy the security requirement by using multiple SDN switch types with a minimal upgrade cost. The complexity of the SCESU problem comes from common vulnerabilities shared among different types of SDN devices and attack propagations among network nodes. To efficiently solve the problem, we propose the BAGUETTE algorithm to judiciously choose and upgrade critical legacy switches with selected SDN devices. Simulation results show that BAGUETTE achieves up to about 92.1% security enhancement compared with legacy network and reduces to 11.1% cost of the securest deployment.

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within the dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF Instance (VNFI). In this paper, we assume that each deployed VNFI is used by multiple SFCs and deal with the optimal location allocation for the contemporaneous migration of VNFIs based on the actual network situation. We first formalize the VNFI migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the end-to-end delay for all affected SFCs and to guarantee network load balancing after the migration simultaneously. Then, we prove the NP-hardness of this problem and propose a multi-stage heuristic algorithm based on optimal order (MSH-OR) to solve it. Extensive evaluation shows that the proposed approach can reduce the average delay by about 16% - 25% for different scale networks while ensuring network load balancing compared with the previous algorithms.

Network Function Virtualization (NFV) brings flexible provisioning and great convenience for enterprises outsource their network functions to the Data Center Networks (DCNs). Network service in NFV is deployed as a service chain, also known as Service Function Chain (SFC), which includes an ordered set of Virtual Network Functions (VNFs). However, there might be some small size flows being queued behind the large flows in one SFC, resulting in network congestion and high SFC delay. In this paper, we focus on the parallelized SFC placement problem in DCN considering availability guarantee and resource optimization. Firstly, we define the parallelized SFC and propose a multi-flow backup model. Then, we design three placement strategies and a Hybrid Placement Algorithm (HPA) aiming at mapping SFCs to DCN. Compared with the existing approaches, our proposed solutions can reduce SFC delay and optimize link consumption while guaranteeing availability.

Ensuring an information fabric safe is critical and mandatory. For its related Internet of Things (IoT) service system running on the open Internet, existing monitoring methods may fail due to only inspecting softwares, and the physical system may not be able to be protected. In this paper, a protection framework is provided to protect the physical system with the IoT services as a controlling means, which involves: multiple observation sources to get the inconsistent and untrusted observation knowledge, extracting true traces from the former, and checking properties for the physical system with considering adversarial environments. A novel certainty measure scheme is proposed to evaluate the service trace by fusing different observations.

Multi-hop knowledge base question answering (KBQA) aims at finding the answers to a factoid question by reasoning across multiple triples. Note that when human performs multi-hop reasoning, one tends to concentrate on specific relation at different hops and pinpoint a group of entities connected by the relation. Hypergraph convolutional networks (HGCN) can simulate this behavior by leveraging hyperedges to connect more than two nodes more than pairwise connection. However, HGCN is for undirected graphs and does not consider the direction of information transmission. We introduce the directed-HGCN (DHGCN) to adapt to the knowledge graph with directionality. Inspired by human's hop-by-hop reasoning, we propose an interpretable KBQA model based on DHGCN, namely two-phase hypergraph based reasoning with dynamic relations, which explicitly updates relation information and dynamically pays attention to different relations at different hops. Moreover, the model predicts relations hop-by-hop to generate an intermediate relation path. We conduct extensive experiments on two widely used multi-hop KBQA datasets to prove the effectiveness of our model.

Knowledge-guided conversation models, whose inputs are current input sentence with its background knowledge, make the generation of responses more informative and meaningful. Existing methods assume that words in responses come from the vocabulary of the whole corpus. However, for specific input and knowledge, only a small vocabulary is useful in prediction and other words lead to uncorrelated noise. In this paper, we propose a Dynamic Vocabulary based Knowledge-guided Conversation Model (DVKCM). Inspired by dynamic vocabulary mechanism, DVKCM adopts the vocabulary construction module to allocate the sentence-level vocabulary which relates to the input sentence and background knowledge, and then only uses the small vocabulary to execute the decoding part. Through the sentence-level vocabulary mechanism, we reduce the generation of noise effectively. Experiments on both automatic and human evaluation verify the performance of our model compared with previous models. Moreover, we find that dynamic vocabulary can be applied to other conversation models to improve their performance.

Mobile Edge Computing (MEC) is an emerging architecture that supports ultra-low latency and high-bandwidth services by deploying servers at the edge of the network to provide computing and storage resources. Recent studies tend to combine (Network Function Virtualization) NFV with MEC and deploy (Virtualized Network Functions) VNFs on MEC servers to achieve fast access to the edge user equipment (UE). However, to guarantee the QoS requirements of mobile users, it is necessary to migrate VNFs to an advisable edge server when users move across Base Stations (BS). How to choose the target BS for VNFs migration? How to select the path for VNF data migration? How to ensure the QoS of user services during the migration process? To solve these issues, we study the seamless VNFs migration problem in mobile edge networks and formulate it as an ILP model, which aims to minimize the migration delay and cost. Then we propose a migration algorithm based on Dijkstra (MBD) to obtain the migration destination BS and migration paths. We implement the mathematical model in Gurobi and design a Greedy algorithm to compare the performance with the MBD algorithm. The experiment results show the effectiveness and efficiency of our algorithm.

Network function virtualization (NFV) plays a vital role in 5G mobile networks. Concatenating virtual network functions (VNFs) into service function chains (SFCs) provides flexible and diverse network support for intelligent applications. However, the mobile network connection is very unreliable. A reasonable SFC composition mechanism is essential for stable service providing. This paper proposes a high availability service function chain composition approach, TSFCC. TSFCC includes real-time road marking strategy, bi-composition mechanism, and VNF reallocation mechanism. Evaluation results prove that TSFCC can adapt to the mobile network environment and provide users with efficient and highly available SFC service.

Network function virtualization (NFV) and mobile edge computing (MEC) enable internet service providers (ISPs) to deploy service function chains (SFCs) to achieve the convenience and performance benefit without incurring high service delay, capital expenditures, and operating expenses. In MEC-NFV networks, network services are deployed in the form of service function chains (SFCs), each consisting of an ordered set of virtual network functions (VNFs). In this paper, we focus on the VNF placement problem in MEC-NFV enabled networks, aiming to optimize the throughput of SFC requests (SFCRs). First, we involve the sharing mechanism of VNF instances in the problem formulations, which can improve network resource utilization and save more node resources. Then we formulate the problem mathematically and propose a correlation-based mapping algorithm to map SFCRs in the network. Moreover, we design an adjustment algorithm to optimize the mapped SFCRs. Evaluation results show that our proposed solution efficiently improves the throughput of SFCRs compared with the benchmarks.

With the development of the internet technology, a large amount of softwares have emerged to meet users' increasing needs. At the mean time, software systems have been faced with a problem that they must adapt to the dynamic network environment. It is obvious that a variety of software development models have been proposed in the past few decades. However, the majority of these methods are gradually unadaptable to new circumstances. In this paper, we proposed a new software development model integrating collective intelligence, service and ecosystem. On the one hand, we have introduced the model in detail. On the other hand, We took a practical example to demonstrate the effectiveness of the proposed model.

Spiking Neural Networks(SNN) stands out as a promising solution to perform complex computations or solve pattern recognition tasks, which is based on cerebral cortical dynamics of neuroscience. However, it is challenging for SNN to accurately capture the biological properties, since most SNN algorithms depend on the different variants of Integrate-and-Fire (IF) neuron model, which produces less biophysical properties of neural networks. Learning the mathematical foundations on how mammalian neocortex mechanism is performing in information processing and artificial intelligence is particularly important. This paper investigates the neural dynamics and gamma oscillations in a complex network with balanced excitatory and inhibitory neurons (E-I network), as such networks are ubiquitous in the brain. The network consisting of hybrid regular spiking (RS) and chattering (CH) excitatory neurons and fast spiking (FS) inhibitory neurons emulated by the Izhikevich model, is designed to simulate the cortical regions of the human brain. Besides, the relationship between synchronization and gamma rhythm is explored by adjusting the critical parameters of our method. Experiments visually demonstrate that gamma oscillations are generated by synchronous behaviors of our neural network. We also discover that the CH excitatory neurons can make the system easier to synchronize. These findings shed some light on further enhancements of the human brain and facilitate the development of artificial intelligence.

Mobile Edge Computing (MEC) is an emerging network architecture that provides computing capabilities at the edge of the mobile network. Recent approaches tend to deploy MEC applications in the Network Function Virtualization (NFV) network. In the MEC-NFV environment, mobile network services are deployed as service chains, also known as Service Function Chains (SFCs). In this paper, we focus on the SFC placement problem in the MEC-NFV environment while guaranteeing availability. We design a backup model to improve SFC availability. Besides, we propose a Dynamic Programming (DP)-based algorithm to place SFC. Evaluation results show that our proposed solutions outperform the existing approaches in terms of availability guarantee and resource optimization.

An important challenge for the development of the IoT is the growing number of connected devices. These devices and the information they generate are necessary resources in IoT solutions. However, the different interfaces and protocols of these devices and the data in different formats generated by devices make it difficult for us to manually connect these resources to the Internet of Things environment. In this paper, we propose a service-oriented IoT resources access and provisioning framework which can automatically adapt and access all resources and provide resources to the IoT context-aware environment in a service manner. Unified resource access system is responsible for connecting heterogeneous devices and adapting various interfaces and protocols dynamically using the protocol stack. The description model models the underlying resources and upper-level entities respectively, which realizes the sharing and reuse of resources. Based on the access system and description model, the service provider layer implements data interpretation that interprets the sensed data into contextual scene information, and resource mapping that maps resource operations to service interfaces which is opened to the IoTs Context-Aware Environment. Finally, we developed a framebased protocol stack system and used the system to complete an application in the Internet of things, which sufficiently proved the framework's ability to uniformly access of heterogeneous resources. In the mean-time, we test the performance of the protocol stack system and find that it can satisfy the requirement of resource consumption in the process of resource access in most application development.

Both siamese network and correlation filter (CF) based trackers have recently achieved superior performance in tracking scenarios with various challenging factors. For the challenging scale variations, most of these state-of-the-art trackers usually employ multiple patches with different bounding boxes to estimate the target size. However, these patches are fixedly generated by the hand-crafted bounding boxes in spatial domains, which may be suboptimal to cope with scale changes due to the lack of temporal scale information. In this letter, we tackle the problem of efficient scale estimation by presenting a generic scheme that allows the adaptive generation of bounding boxes in temporal domains and improves the tracking accuracy. Specifically, we introduce the novel particle scale space by refining the conventional particle filter and extend this space to many siamese and CF trackers for robust tracking. Extensive experiments are performed on the OTB2013, OTB50, OTB100 and UAVDT datasets. The proposed variants maintain at least almost identical frame-rates with baseline trackers and perform favorably against them, as well as other state-of-the-art trackers.

Particle filter algorithms are a very important branch for visual object tracking in the past decades, showing strong robustness to challenging scenarios with partial occlusion and large-scale variations. However, since a large number of particles need to be extracted for the accurate target state estimation, their tracking efficiency typically suffers especially when meeting deep convolutional features, which have been developed for handling significant variations of the target appearance in the visual tracking community. In this paper, we propose to elegantly exploit deep convolutional features with few particles in a novel hierarchical particle filter, which formulates correlation filters as observation models and breaks the standard particle filter framework down into two constituent particle layers, namely, particle translation layer and particle scale layer. The particle translation layer focuses on the object location with the deep convolutional features capturing semantics but failing to precisely estimate the object scale, while the particle scale layer pays attention to large-scale variations with the lightweight hand-crafted features handling spatial details of the object size. Moreover, an efficient ensemble method is proposed to help explore deeper convolutional features with more semantics in the particle translation layer. Extensive experiments on four challenging tracking datasets, including OTB-2013, OTB-2015, VOT2014, and VOT2015 demonstrate that the proposed method performs favorably against a number of state-of-the-art trackers.

With greater numbers of cloud applications being deployed in virtual clusters (VCs) and running in datacenters, the energy consumption of datacenters is experiencing rapidly cumulative growth. Thus, it is a critical issue that how to allocate virtual machines (VMs) of the VC in physical machines (PMs) for energy savings as much as possible. Considering each PM/switch has a certain failure rate, VMs may not be executed when they meet with any PM/switch fault. So, a compact allocation scheme may benefit in low energy consumption but increase the risk of violating the availability of the VC. In this paper, we consider four optimization objectives about the VC and the datacenter, i.e., availability, energy consumption, average resource utilization, and resource load balance. Then we propose a multi-objective optimization model and raise an evolution algorithm to trade-off among these four optimization objectives. Finally, experimental results show the effectiveness and efficiency of our algorithm.

As more and more services are deployed in the cloud datacenter, network traffic is growing exponentially. Virtual machines (VMs) of a virtual cluster (VC) must be allocated on physical machines (PMs) in the datacenter with a certain topology. Each VM needs some resources to run various services. Apparently, allocating VMs in the datacenter as compactly as possible can reduce traffic consumption and avoid bandwidth-related bottlenecks. However, loose allocation scheme can reduce the loss expectation of VMs due to failure possibilities of PMs and switches, and the availability of the VC is increased thereby. To enhance availability and reduce network bandwidth usage, it is significant to determine the scheme of allocating VMs of the VC. In this paper, we first introduce four typical datacenter architectures with network topologies and corresponding cost matrices, and extend to generality. Then we propose a joint optimization function to measure the risk of VC and the core bandwidth usage with a global availability constraint. Subsequently, an evolutionary algorithm is raised to minimize the value of the constrained optimization function. Finally, the evaluation results show the effectiveness of the proposed approach and performance advancement over the existing approaches.

Scientific workflow scheduling is one of the most challenging problems in cloud computing because of the large-scale computing tasks and massive data volumes involved. A cloud system is a distributed system that follows the on-demand resource provisioning and pay-per-use billing model. Therefore, practical scheduling approaches are essential for good workflow performance and low overheads. This paper proposes a novel workflow allocation approach, the Geo-aware Multiagent Task Allocation Approach (GMTA), which aims to optimize large-scale scientific workflow execution in container-based clouds. GMTA is an agent-based workflow allocation method that includes a market-like agent negotiation mechanism and a dynamic workflow restructuring strategy. It decreases workflow makespans and traffic overheads by reasonable task replications. Furthermore, the performance of GMTA is verified on real scientific workflows in the CloudSim environment.

Network Function Virtualization (NFV) is a promising technology that decouples network functions from the physical device on which they deployed. Network service in NFV is deployed as Service Function Chain (SFC) that consists of an ordered set of Virtual Network Functions (VNFs). In this paper, we focus on the VNF placement problem in data center networks considering availability guarantee and resource optimization. Firstly, we define an availability model that takes both physical device failures and VNF failures into consideration when evaluating the availability of SFC. Secondly, we propose a novel Joint Path-VNF (JPV) backup model that combines path backup and VNF backup in a joint way. In the JPV backup model, resource consumption can be effectively reduced. Finally, we design an Affinity-Based Algorithm (ABA) to reduce physical link consumption when map VNFs. The evaluation results show that ABA and JPV can achieve better availability improvement (99.99%) with less resource (reduce 40% PL consumption).

Mobile Edge Computing (MEC) is a promising network architecture that pushes network control and mobile computing to the network edge. Recent studies propose to deploy MEC applications in the Network Function Virtualization (NFV) environment. The mobile network service in NFV is deployed as a Service Function Chaining (SFC). In the dynamic and resource-limited mobile network, SFC placement aiming at optimizing resource utilization is a challenging problem. In this article, we solve the SFC placement problem in the MEC-NFV environment. We formulate the SFC placement problem as a weighted graph matching problem, including two sub-problems: a graph matching problem and an SFC mapping problem. To efficiently solve the graph matching problem, we propose a Linear Programming–(LP) based approach to calculate the similarity between VNFs and physical nodes. Based on the similarity, we design a Hungarian-based algorithm to solve the SFC mapping problem. Evaluation results show that our proposed LP-based solutions outperform the heuristic algorithms in terms of execution time and resource utilization.

Nowadays Telecom service providers are seeking new paradigms of service creation and execution platform to reduce new services' time to market and increase profitability. However, the existing static services orchestration approaches cannot meet the dynamic complicated business demands. This paper proposes a hybrid service orchestration platform for Internet-Telephony networks. Firstly, designs a hybrid service orchestration language for developers to achieve dynamic and rapid orchestration of new hybrid services over the Internet-Telephony networks. Secondly, proposes an event-driven and component-based hybrid service orchestration container to meet the asynchronous dynamic interactions between hybrid services. Thirdly, proposes a cost-aware auto-scaling approach, including the pre-scaling and real-time scaling stages, to dynamically scale the required resources at different levels. Finally, illustrates the hybrid voice chatting services orchestration scenario, and also the effectiveness and practicability of the proposed platform are validated through extensive experiments.

With the increasing scale of cloud datacenters and rapid development of virtualization technologies, many cloud-based services have been deployed to meet requirements. Virtual machines (VMs) are placed on physical servers, and often provide virtual environment for cloud services. Therefore, virtual machines placement (VMP) problem has gradually attracted many attentions. It is meaningful that how to effectively and efficiently place VMs on servers to guarantee the service reliability and reduce the bandwidth consumption. In this paper, we first formulate VMP with a reliability model and a bandwidth consumption model, and analyse its complexity. Then we propose a VMP optimization approach to solve the problem and prove its effectiveness and efficiency. The core algorithm of our approach is an approximation algorithm to get VM partitions under the constraint of a specified reliability parameter. Then placement problem is transformed into matching problem between VM partitions with physical servers. Finally, the evaluation results show the effectiveness of the proposed approach and performance advancement over the existing approaches.

Mobile Edge Computing (MEC) is a promising network architecture that pushes network control and mobile computing to the network edge. Recent studies propose to deploy MEC applications in the Network Function Virtualization (NFV) environment. The mobile network service in NFV is deployed as a Service Function Chains (SFC). In this paper, we solve the SFC placement problem in a MEC-NFV environment. We formulate the SFC placement problem as a weighted graph matching problem, including two sub-problems: a graph matching problem and a SFC mapping problem. To efficiently solve the graph matching problem, we propose a linear programming-based approach to calculate the similarity between physical nodes and VNFs. Based on the similarity, we design a Hungarian-based placement algorithm to solve the SFC mapping problem. Evaluation results show that our proposed solutions outperform the greedy algorithm in terms of execution time and resource utilization.

Network Function Virtualization (NFV) brings great flexibility and scalability to the deployment of network services by decoupling network functions from dedicated devices, which has attracted more attention from both academia and industry. Network services in NFV are deployed in the form of Service Function Chain (SFC), which consists of multiple ordered Virtual Network Functions (VNFs). However, how to effectively place VNFs remains a problem to be solved. In this paper, we investigate joint correlation-aware VNF selection and placement problem. We first formulate the problem as an Integer Linear Programming (ILP) problem and propose a method based on self-learning matrix to partition VNF correlation. Then, we design a Joint Correlation-aware VNF Placement (JCVP) algorithm based on Dynamic Programming to transform the problem into several VNF mapping subproblems. Extensive simulation results show that compared with the previous algorithms our approach has better performance in link occupancy, SFC acceptance, and VNF utilization rate.

Although network function virtualization (NFV) is a promising approach for providing elastic network functions, it faces several challenges. A critical but difficult issue for the service and network providers is deciding where to instantiate a list of virtual network functions (VNFs), namely VNF placement problem. In this paper, we investigate the VNF placement, for the purpose of resource and network traffic consumption minimization. Moreover, we consider the arrival rates of users' requests for different types of service function chains (SFCs). This allows the placement scheme to adapt to users' time-varying requests and improve the network resource utilization. Then we formulate the VNF placement problem as a jointly constrained optimization problem. Afterwards, we propose an approach called joint optimization resource and traffic consumption (JORTC) with enhanced biogeography-based (EBBO) optimization algorithm to resolve the VNF placement problem. Finally, the evaluation results show the effectiveness of J-ORTC approach and performance advancement over the benchmarks.

The increasing momentum of service-oriented architecture has led to the emergence of divergent delivered services, where service selection is meritedly required to obtain the target service fulfilling the requirements from both users and service providers. Despite many existing works have extensively handled the issue of service selection, it remains an open question in the case where requests from multiple users are performed simultaneously by a certain set of shared candidate services. Meanwhile, there exist some constraints enforced on the context of service selection, e.g. service placement location and contracts between users and service providers. In this paper, we focus on the QoS-aware service selection with constraints from a fairness aspect, with the objective of achieving max-min fairness across multiple service requests sharing candidate service sets. To be more specific, we formulate this problem as a lexicographical maximization problem, which is far from trivial to deal with practically due to its inherently multi-objective and discrete nature. A fairness-aware algorithm for concurrent service selection (FASS) is proposed, whose basic idea is to iteratively solve the single-objective subproblems by transforming them into linear programming problems. Experimental results based on real-world datasets also validate the effectiveness and practicality of our proposed approach.

As greater numbers of distributed cloud applications are required to deploy in virtual clusters (VCs) in the datacenter, the VC allocation problem is facing various QoS requirements and different resource optimizations. In this paper, we propose an SOA-based Optimizer, name as VCA-optimizer, for solving customizable VC allocation problems with different constraint conditions and different optimization objectives in the datacenter.

The cloud computing simplifies application development into the orchestration of virtual-services workflow. However, network latency between geographically distributed hosts would slow down the workflow's makespan time. This paper proposes a geo-aware task allocation approach (GTAA). GTAA partitions the workflow for geo-distributed data centers(DCs) and reduces sub-workflows across DCs. GTAA aims to optimize overall workflow makespan time and improves the efficiency of workflow.

Network Function Virtualization (NFV) is an emerging technology decouples network functions from hardware. Network service in NFV is deployed as a service chain, also known as Service Function Chain (SFC). SFC consists of an ordered set of Virtual Network Functions (VNFs). However, VNFs bring new challenges in providing network services with availability guarantee. In addition, in a customizable and dynamic NFV-enabled network, the composition and mapping of service chain are different from that of a traditional network. In this paper, we define an availability model that takes both hardware and VNF failures into consideration. Then we propose Joint Path-VNF backup model to combine path and VNF backup in a joint way. And a priority-based algorithm is designed for service chain composition and mapping. Simulation results show that our proposed solutions can reduce resource consumption while guaranteeing availability.

Sensitive data protection is vital for mobile users. An effective way is to store sensitive data in the hidden volume of mobile devices with Plausibly Deniable Encryption (PDE) systems. Typically, PDE creates a hidden volume inside the outer volume. However, existing PDE systems could lose data, due to overriding the hidden volume, and significantly waste physical storage, because of the fixedly reserved area for the hidden volume. In this paper, we present MobiGyges to solve the above problems. MobiGyges leverages the Thin Pool to coordinate the storage allocation for both the outer volume and the hidden volume which avoids data override and prevents data loss. Moreover, it improves the storage efficiency by virtualizing the total physical storage into small storage blocks and utilizing the blocks for the outer volume and the hidden volume, which eliminates the reserved area. We implement MobiGyges prototype on Google Nexus 6P with LineageOS 13 operating system. Experimental results show that MobiGyges avoids data loss and improves storage utilization up to 31% compared with existing works.

Video surveillance is an important application of Internet of Thing (IoT) that provides convenience remote monitoring service to end users. How to reduce the power consumption of mobile devices with limited energy has become a research hotspot. We propose an energy-efficient architecture that includes smartphones' various power saving solutions for video transmission over wireless networks. Results demonstrate that each of our proposed solutions is significantly outperforms than the standard scheme.

Mobile edge networks benefit cloud applications in particularly providing a shorter response latency for mobile terminals. However, the cumulative increase of mobile terminals and emerging cloud applications, poses new challenges for edge networks. To combat this issue, the promising idea of mobile micro-clouds (MMCs) is proposed to enhance edges and the cloud. In this paper, we investigate the problem of virtual cluster (VC) placement in MMCs, to minimize the average response latency with various requests among multiple cloud applications. Then a hybrid swarm intelligence approach is proposed to optimize VC placement scheme, for a trade-off between the average response latency and the overall VC placement cost. The preliminary evaluation results show the effectiveness and efficiency of our approach.

Mobile Edge Computing (MEC) is a promising architecture where network services are deployed to the network edge. Recent studies tend to deploy Network Function Virtualization (NFV) services to MEC. Network services in NFV are deployed as Service Function Chains (SFCs). In this paper, we mainly focus on the SFC placement problem in a MEC-NFV environment, which is different from the data center network. Firstly, we formulate this problem as a weighted graph matching problem consisting of graph matching and SFC mapping. Then, we propose a linear programming-based approach to match the edge network and SFC. Finally, we design a Hungarian-based placement algorithm to map SFC in the edge network. A heuristic-based greedy algorithm is also designed to compare the performance. Evaluation results show that our proposed solutions outperform the greedy algorithm in terms of execution time.

With the explosive growth of large scale services, traditional mobile networks have become increasingly unable to guarantee the efficient operation of services. However in the fifth generation (5G), supported by Software Defined Network (SDN) and Network Function Virtualization (NFV), network slicing technology [1] makes mobile networks more intelligent and flexible. 5G network slicing allows a set of logically independent virtual networks to be created on a common physical infrastructure and provides appropriate monitoring, management and resource allocation for a variety of different types of communication services [2].

Network Function Virtualization (NFV) is a new network paradigm that decouples network functions from dedicated hardware. Network services in NFV are deployed as service chains, also known as Service Function Chains (SFCs). SFC consists of an ordered set of Virtual Network Functions (VNFs). One of the main challenge when deploying SFC is to efficiently make use of the resource. In this paper, we focus on the SFC composition and mapping considering resource optimization. We formulate the SFC composition and mapping problem as a weighted graph matching problem. Then we propose a Hungarian based algorithm to solve the SFC composition and mapping problem in a coordinated way.

Network Function Virtualization (NFV) has emerged as an innovative and promising network architecture which can migrate Network Functions (NFs) from costly physical equipment to dynamically allocated virtualized instances. Using these Virtual Network Functions (VNFs), many end-users can chain VNFs together to create network services which are commonly referred to as Service Function Chains (SFCs). An SFC involves multiple VNFs and describes how they interact, as incorrect dependencies between any of these VNFs may cause packets forwarding errors. However, few orchestration tools are equipped with a verification mechanism to check SFC before deployment. In addition, most existing NFV orchestration tools are over complicated, making it difficult for end-users to learn and use. In this paper, we introduce a model called SFC-Verifier (SFC-V) which targets on automatically detecting the constraints between VNFs, helping end-users compose and verify SFCs in service design phase. Built on SFC-V, we present EasyOrchestrator which facilitates end-users to create and deploy network service. Users can develop customizable network services via a UI-friendly environment on a web browser. The evaluation results demonstrate that the SFC verification time and response time of EasyOrchestrator are much smaller than the benchmarks. Besides, it effectively reduces the end-users' service development time and improves service development accuracy.

Advances in mobile Internet technology have enabled the clients of Web services to be able to keep their service sessions alive while they are on the move. Since the services consumed by a mobile client may be different over time due to client location changes, a multi-dimensional spatiotemporal model is necessary for analyzing the service consumption relations. Moreover, competitive Web service recommenders for the mobile clients must be able to predict unknown quality-of-service (QoS) values well by taking into account the target client's service requesting time and location, e.g., performing the prediction via a set of multi-dimensional QoS measures. Most contemporary QoS prediction methods exploit the QoS characteristics for one specific dimension, e.g., time or location, and do not exploit the structural relationships among the multi-dimensional QoS data. This paper proposes an integrated QoS prediction approach which unifies the modeling of multi-dimensional QoS data via multi-linear-algebra based concepts of tensor and enables efficient Web service recommendation for mobile clients via tensor decomposition and reconstruction optimization algorithms. In light of the unavailability of measured multi-dimensional QoS datasets in the public domain, this paper also presents a transformational approach to creating a credible multi-dimensional QoS dataset from a measured taxi usage dataset which contains high dimensional time and space information. Comparative experimental evaluation results show that the proposed QoS prediction approach can result in much better accuracy in recommending Web services than several other representative ones.

Central heating is a sophisticated process, and many factors influence the heating loading for boilers, without comprehensively monitoring and analysis, it is difficult to find out potential hazards of heating supplying electrical equipment. This paper presents the architectural model for OLE process control Web service based real time remote monitoring for central heating electrical equipment, and focus on the development of OPC XML Web service interface, data types, structures and XML message interaction, and the Publish-Subscribe based real time messages dispatching, and the service component architecture based visual configuration software. We also illustrated remote monitoring and control scenarios for central heating electrical equipment.

With the fast development of Internet of Things (IoT), a large amount of services are being generated continuously by different business process applications hosted on edge devices. In order to facilitate seamless access and service life cycle management of large, distributed and heterogeneous IoT services, service computing and fog computing have been widely used as the promising technologies. However, an execution environment integrating IoT services into these two technologies is still an open research challenge. In this paper, we proposed a novel service-based fog execution environment to make the business process applications fit in the dynamic IoT service environment. The proposed IoT execution environment promises a full-life cycle management of the IoT services, a low latency response of the edge devices and a distributed execution of the business process applications. An actual running intelligent medical case is given to validate our proposed IoT execution environment.

Network Function Virtualization (NFV) is an emerging technology which can migrate Network Functions (NFs) from costly hardware to dynamically allocated virtualized instances. Using these Virtual Network Functions (VNFs), many nonprofessional people can chain VNFs together to create network services. However, most existing NFV orchestration tools are not equipped with a verification mechanism to check service function chain (SFC) before deployment. In addition, most of orchestration tools are complicated, making it difficult for end-users to learn and use. In this paper, we present a network service creation platform with automatic verification mechanism, called EasyOrchestrator. It is designed to automatically detect the dependencies and conflicts between VNFs, thus to help end-users compose and verify SFCs in design phase. It also provides end-users with a design-as-development network service creation environment.

With the advances in wireless communication and the growing popularity of mobile devices, it has become rather normal to watch videos using mobile devices. However, there are severely challenges to using video codec on mobile devices, because: 1) insufficient computing resources, there is poor performance using mobile device ; 2) the limited battery capacity on mobile device; 3) CPU utilization is too high when using traditional video codec. In this paper, we proposed a GPU based High Definition Parallel Video Codec on mobile devices, which is an efficient video codec with the cooperation of CPU and GPU. The video codec system is fully compliant with the video codec h264 standard. Compared with the scheme using existing X264, the presented experimental results evaluated the GPU based video codec achieves appreciable improvements in FPS(frames per second), the energy consumption and utilization of CPU are reduced properly at the same time.

With the emergency of IoT, there are many IoT network slices with different network requirements. Most of the current IoT system are specific and non-programmable and therefore their slices are difficult to reuse. It is difficult to meet different QoS requirements especially in IoT system because there are plenty of IoT sensors in IoT system. In this paper, we propose a novel IoT network slicing creation system which based on two emerging SDN and NFV technologies. It provides an easily-operating service creation environment and a service execution environment based on micro service architecture. We implement an IoT muti-flow transmission scenario. After adding subservices and QoS policies into a business process at the design plane, the IoT scenario can run automatically at the execution plane. Experiment results on the scenario show that the numbers of packets per second of different flows are changing gradually depend on QoS policies.

Network coding has become a promising approach to improve the communication capability for WSN, which is vulnerable to malicious attacks. There are some solutions, including cryptographic and information-theory schemes, just can thwart data pollution attacks but are not able to detect replay attacks. In the paper, we present a lightweight timestamp-based message authentication code method, called as TMAC. Based on TMAC and the time synchronization technique, the proposed detection scheme can not only resist pollution attacks but also defend replay attacks simultaneously. Finally

No abstract available.

Correlation-filter-based trackers, showing strong discrimination ability in challenging situations, have recently achieved superior performance in visual tracking. However, because the model treats the tracker's predictions in new frames as training data, the filter can be contaminated by small incorrect predictions, which cause model drift. Particle-filter-based trackers usually produce more accurate results due to the richer image representations used in prediction, but suffer when the environments are complex throughout an image sequence. In this letter, we propose an innovative real-time algorithm, which combines the particle filter with correlation filters in the prediction stage, enabling accurate predictions by the particle filter and alleviating model drift. Moreover, an effective decision fusion strategy is proposed to get more precise object predictions, thus further enhancing the overall tracking performance. Extensive evaluations on the OTB-2013 benchmark demonstrate that the proposed tracker is very promising compared with the state-of-the-art trackers, while operating over 85 frames/s.

Video surveillance has become an important Internet multimedia application to provide live streaming services. Energy efficiency is critical to guarantee the service time and quality of power-intensive video streaming on wireless surveillance systems. In particular, video coding and data communication constitute the majority of power dissipation in embedded multimedia devices driven by capacity-limited batteries. However, the complex power characteristics and time-varying channel status pose crucial challenges on enabling low-power high-quality video streaming. To address these critical problems, this paper presents a power-efficient and network-adaptive (PENA) framework for source- forward error correction (FEC) coding in embedded systems. First, an analytical framework is developed to characterize the rate-distortion-power tradeoff for video encoding and data transfer. Second, a joint rate control and FEC coding solution is proposed to maximize video quality under power constraint. Distinct from the existing source-FEC coding algorithms, PENA is able to effectively leverage the video rate-distortion model and system power features. We conduct the system implementation and performance evaluation with real embedded surveillance devices over wireless networks. Experimental results demonstrate PENA achieves appreciable improvements over the reference source-FEC coding schemes in terms of power conservation, perceived video quality, and end-to-end delay.

The advancements in networking technologies and hand-held devices enable mobile users to concurrently receive real-time multimedia streaming (e.g., high-definition video) with different radio interfaces (e.g., Wi-Fi and LTE networks). Stream control transmission protocol (SCTP) is an important transport-layer solution to implement concurrent multipath transfer (CMT) over heterogeneous wireless networks with multihomed terminals. However, it is challenging to distribute multimedia content to resource-limited mobile devices because of the contradiction between energy consumption and streaming quality. To deliver the energy-efficient and quality-aware multimedia streaming over multiple wireless networks, this paper presents an Energy and goodPut Optimized CMT (EPOC) solution. First, we develop an analytical framework to model the relationship between energy consumption and goodput performance for real-time multimedia transmission to multihomed mobile devices. Second, we propose a joint forward error correction coding and rate allocation scheme to minimize energy consumption while satisfying goodput constraint. EPOC effectively leverages the energy-goodput tradeoff and multipath diversity to optimize mobile multimedia transmission in heterogeneous networking environment. We conduct the performance evaluation through extensive semi-physical emulations in Exata involving H.264 video streaming. Compared with the reference CMT schemes using SCTP, EPOC achieves appreciable improvements in energy conservation, goodput, and video peak signal-to-noise ratio.

Surveillance video has become an important multi-media application in recent years, with development trends toward wide viewing angles and high definition. At the same time, transmission of surveillance video over the Internet and storage in the cloud are becoming increasingly popular. However, it is extremely challenging to transfer surveillance video with both a wide viewing angle and a high resolution over the Internet because of 1) the tradeoff between a wide viewing angle and high-surveillance object resolution for a single camera, 2) the large throughput requirement, and 3) the fluctuating delay and bandwidth of the Internet. In this paper, we present a reference-frame-cache-based surveillance video transmission system (RSVTS), which delivers wide-viewing-angle and high-definition surveillance video over the Internet in real time using multiple rotatable cameras. First, we develop an efficient video mosaicing method for merging two (or more) surveillance videos together. Second, novel reference frame selection and update algorithms are proposed for the RSVTS encoder to reduce the amount of encoded data. Third, we implement a reference frame cache on both the sender and receiver sides to increase video quality by increasing the probability of video decoding. We conduct a performance evaluation using a simulation system that integrates RSVTS and ns-3. Compared with the H.264/SVC and H.264/AVC schemes, RSVTS achieves appreciable improvements in enhancing the video peak signal-to-noise ratio and bandwidth conservation.

Supported by the latest technical innovations mobile users are able to simultaneously receive real-time streaming services with different radio access technologies (e.g. LTE and Wi-Fi). The stream control transmission protocol (SCTP) is a vitally important transport protocol to enable concurrent multipath transfer (CMT) in heterogeneous wireless networks with multihomed terminals. However enabling CMT of real-time video streaming to multihomed mobiles is challenged with key technical dilemmas: 1) high-quality real-time video transmission is constrained by stringent requirements in delay and throughput; 2) wireless networks are bandwidth-limited and error-prone; and 3) the congestion control and packet retransmission modules in the SCTP may incur frequent deadline violations and throughput fluctuations. Motivated by addressing these challenging problems this research proposes a Video and Raptor code aware CMT (CMT-VR) solution. First we develop a mathematical model to formulate the utility maximization problem of multipath real-time video delivery over parallel wireless networks. Second we present a transmission framework that includes online packet scheduling Raptor coding adaptation and retransmission control algorithms. CMT-VR is distinct from the existing SCTPs in leveraging the video frame priority and rateless Raptor coding. The performance verification is conducted by means of system evaluations over real wireless networks and extensive semiphysical emulations in the Exata platform. Evaluation results demonstrate that CMT-VR achieves appreciable improvements over the reference schemes in perceived video quality goodput and end-to-end delay.

Internet of Things (IoT) can provide new value-added service by connecting the physical devices to virtual environments association with their context, and there is also a huge demand in ad hoc services by the end users for IoT applications. By extending mashup concept into IoT applications, we can achieve a novel and more lightweight services creation approach. This paper proposes a lightweight IoT service mashup middleware based on REST-style architecture for IoT applications, and design an uniform sensor devices access and dynamically protocol stack management framework, propose a distributed publish/subscribe based messages distribution service, and situational IoT services mashup approach, which can be integrated easily to create new composite and situational applications, and also apply the REST principles to define an extensible interface to build comprehensive and situational mashup applications. Based on proposed service mashup middleware, the end user can integrate applications and services in a more lightweight manner. We also illustrated the scenarios for RESTful Web service mashups representing for coal mine safety monitoring and control automation. In the experiments, the end-user evaluation has been conducted to evaluate the middleware, and also the performance has been measured and analyzed.

Cloud-assisted video streaming has emerged as a new paradigm to optimize multimedia content distribution over the Internet. This article investigates the problem of streaming cloud-assisted real-time video to multiple destinations (e.g., cloud video conferencing, multi-player cloud gaming, etc.) over lossy communication networks. The user diversity and network dynamics result in the delay differences among multiple destinations. This research proposes Differentiated cloud-Assisted VIdeo Streaming (DAVIS) framework, which proactively leverages such delay differences in video coding and transmission optimization. First, we analytically formulate the optimization problem of joint coding and transmission to maximize received video quality. Second, we develop a quality optimization framework that integrates the video representation selection and FEC (Forward Error Correction) packet interleaving. The proposed DAVIS is able to effectively perform differentiated quality optimization for multiple destinations by taking advantage of the delay differences in cloud-assisted video streaming system. We conduct the performance evaluation through extensive experiments with the Amazon EC2 instances and Exata emulation platform. Evaluation results show that DAVIS outperforms the reference cloud-assisted streaming solutions in video quality and delay performance.

The rapid development of Internet of Things (IoT) attracts growing attention from both industry and academia. IoT seamlessly connects the physical world and cyberspace via various sensors. It is more worth for us to pay attention to the mechanism of the events to work collaboratively rather than those standalone sensors. In this paper, we present a Distributed Event-centric Collaborative Workflows development system for IoT application, called DECW. It supports loosely coupled event-based interaction between processes, which enables real-time response to events from the physical world. Unlike traditional centralized control flow mode, the interaction between processes in DECW is constrained by the event interface. Users could dynamically adjust the interface between processes without modifying the internal logic of the process. In addition, DECW system provides a full lifecycle for the development and operation of the IoT application, including graphical creation of processes, dynamic definition of the process interaction interfaces, logical validation, distributed packaging and deployment, parallel execution, and real-time monitoring and managing the running status of the IoT application.

In some special circumstances, e.g. tsunamis, floods, battlefields, earthquakes, etc., communication infrastructures are damaged or non-existent, as well as unmanned aerial vehicle (UAV) cluster. For the communication between people or UAVs, UAVs or mobile smart devices (MSDs) can be used to construct Mobile Ad Hoc Networks (MANETs), and Multipath TCP (MPTCP) can be used to simultaneously transmit in one TCP connection via multiple interfaces of MSDs. However the original MPTCP subpaths creating algorithm can establish multiple subpaths between two adjacent nodes, thus cannot achieve true concurrent data transmission. To solve this issue, we research and improve both the algorithm of adding routing table entries and the algorithm of establishing subpaths to offer more efficient use of multiple subpaths and better network traffic load balancing. The main works are as follows: (1) improve multi-hop routing protocol; (2) run MPTCP on UAVs or MSDs; (3) improve MPTCP subpaths establishment algorithm. The results show that our algorithms have better performance than the original MPTCP in achieving higher data throughput.

In some special circumstances, e.g. tsunamis, floods, battlefields, earthquakes, etc., communication infrastructures are damaged or non-existent, as well as unmanned aerial vehicle (UAV) cluster. For the communication between people or UAVs, UAVs or mobile smart devices (MSDs) can be used to construct Mobile Ad Hoc Networks (MANETs), and Multipath TCP (MPTCP) can be used to simultaneously transmit in one TCP connection via multiple interfaces of MSDs. However the original MPTCP subpaths creating algorithm can establish multiple subpaths between two adjacent nodes, thus cannot achieve true concurrent data transmission. To solve this issue, we research and improve both the algorithm of adding routing table entries and the algorithm of establishing subpaths to offer more efficient use of multiple subpaths and better network traffic load balancing. The main works are as follows: (1) improve multi-hop routing protocol; (2) run MPTCP on UAVs or MSDs; (3) improve MPTCP subpaths establishment algorithm. The results show that our algorithms have better performance than the original MPTCP in achieving higher data throughput.

With the IoT technology increasing and aging social have coming, personalized service assisted elder and patient living is a critical application in IoT-Based Healthcare application. However, the scale and complexity of personalized service is increasing with wildly applied to our life, which cause response time decrease and resource waste in large-scale IoT-Based Healthcare application. Therefore, it is necessary of studying on dealing with the large-scale and complexity of personalized services in large-scale IoT-Based Healthcare application. In this paper, we propose proactive personalized service leveraging Complex Event Processing (CEP) to deal with a large number and complexity of personalized services. Firstly, personalized service defined as complex event pattern that expresses in the form of Directed Acyclic Graph (DAG). Secondly, we propose a complex event pattern partitioning and clustering algorithms to optimize the processing of dealing with personalized services. Finally, we realize a prototype system based on proposed our approach named BCEPCare. Experiment result shows that BCEPCare is superior to the traditional ESPER in large-scale IoT-Based healthcare application.

The publish/subscribe paradigm can be used to build IoT service communication infrastructure owing to its loose coupling and scalability. Its features of decoupling among event producers and event consumers make IoT services collaborations more real-time and flexible, and allow indirect, anonymous and multicast IoT service interactions. However, in this environment, the IoT service cannot directly control the access to the events. This paper proposes a cross-layer security solution to address the above issues. The design principle of our security solution is to embed security policies into events as well as allow the network to route events according to publishers' policies and requirements. This solution helps to improve the system's performance, while keeping features of IoT service interactions and minimizing the event visibility at the same time. Experimental results show that our approach is effective.

With the rapid growth of cloud computing and mobile computing, it is commonplace for users to request cloud services from mobile devices. Mobile edge clouds (MECs) allow the users to access the cloud services seamlessly. Although cloudlets provide a promising technique to reduce the service access latency, how to place the data-intensive service in MECs to reduce the communication overhead between different services is yet to be addressed. To attack this challenge, this paper proposes an approach for mining interacting services and placing the services on the cloudlets by optimizing the communication overhead. In this approach, a frequent itemsets mining algorithm is proposed to obtain the fine-grained frequent 2-itemsets by analyzing the cookies. This algorithm determines the minimum support threshold automatically, based on which FP-tree with FP-matrix is constructed to avoid traversing the FP-tree during the process of frequent 2-itemsets discovery, then a searching algorithm is presented to mine the discriminative frequent 2-itemsets with interestingness measure. Furthermore, the communication overhead is optimized with the capacity constraints of cloudlets. Finally, we validate the efficacy of our approach by real-world data based simulations. The results show our approach can reduce the communication overhead for service placement in MECs.

As the dominant mobile operating system in the markets of smartphones, Android platform is increasingly targeted by attackers. Besides, attackers often produce novel malware to bypass the conventional detection approaches, which are largely reliant on expert analysis to design the discriminative features manually. Therefore, more effective and easy-to-use approaches for detection of Android malware are in demand. In this paper, we design and implement EasyDefense, a lightweight defense system that is integrated with Android OS for easy and effective detection of Android malware utilizing machine learning methods and the ensemble of them. Besides universal static features such as permissions and API calls, EasyDefense also employs the N-gram features of operation codes (opcodes). These N-gram features are extracted and learnt automatically from raw data of applications. Experimental results on 204,650 applications show that users can easily and effectively protect the privacy and security on their smartphones through this system.

The rapid development of mobile internet attracts end-users to creating mobile applications. The traditional development process cannot meet their needs. In this paper, we present a cross-platform mobile development environment, EasyApp. It provides a highly-integrated, UI-friendly and easily-operating environment. The architecture of this environment is based on OSGi framework. Users could create mobile applications with draggable widgets and package applications for multiple platforms. Native APIs could be invoked with native API plugins.

Customizable mobile services are usually expressed with complex services composed of different atomic services. Fine-grained atomic mobile services are not so convenient for end users to reuse. Considering that in identical or similar service domains, a great deal of the business logics and functions are reusable within the scope. So we present a template-based framework to allow reuse of services and to achieve rapid mobile application development. The reusable fine-grained service logics and functions are encapsulated into comparatively coarse-grained templates, from which the designers can create the personalized composite services and edit the templates efficiently.

Internet of Things(IoT) was first coined in 1999 by Kevin Ashton. However with the technologies advancement, it seems that intelligent devices will invisibly be embedded in our life in few years. Enormous amounts of data need be exchanged every seconds. It calls for a seamless effect and easily interpretable communicating architecture. The research proposal will try to address some challenges and possible path in IoT enabled smarthome. It focuses primarily on two categories. Firstly, devices in IoT field is always distributed. So, there is a distributed IoT services architecture instead of traditional control-center solution. However, those devices are also limited in a certain area (such as a home local network). In order to reduce delay and burden, those distributed devices collect context information though a self-organizing broadcast network, and determine their next action based on that context data. Secondly, using Docker (a lightweight hardware-agnostic and platform-agnostic container) to package services. In our smarthome network, IoT services are distributed across different home electronics. Docker shields all the differences, so that we can quickly deploy and update module in the smarthome network after purchasing new electronics.

The spectrum limitation of single wireless networks prompts the bandwidth aggregation of heterogeneous access medium (e.g., LTE and Wi-Fi) to support high-quality real-time video services. Energy consumption of mobile devices is of vital significance to provide user-satisfied multimedia streaming applications. However, it is challenging to develop an energy-efficient bandwidth aggregation scheme with regard to the stringent delay and quality constraints imposed by wireless video transmission. To address the critical problem, this paper presents an Energy-quaLity aware Bandwidth Aggregation (ELBA) scheme. First, we develop an analytical framework to model the delay-constrained energy-quality tradeoff for multipath video transmission over heterogeneous wireless networks. Second, we propose a bandwidth aggregation framework that integrates energy-minimized rate adaptation, delay-constrained unequal protection, and quality-aware packet distribution. The proposed ELBA scheme is able to effectively leverage the wireless channel diversity and video frame priority for enabling energy-minimized quality-guaranteed streaming to multihomed devices within imposed deadline. We conduct the performance evaluation through both experiments over real wireless networks and extensive emulations in Exata platform. Experimental results demonstrate the performance advantages of ELBA over existing bandwidth aggregation schemes in energy conservation, video quality, and end-to-end delay.

Recently, the frequent coal mine safety accidents have caused serious casualties and huge economic losses. It is urgent for the global mining industry to increase operational efficiency and improve overall mining safety. This paper proposes a lightweight mashup middleware to achieve remote monitoring and control automation of underground physical sensor devices. First, the cluster tree based on ZigBee Wireless Sensor Network (WSN) is deployed in an underground coal mine, and propose an Open Service Gateway initiative (OSGi)-based uniform devices access framework. Then, propose a uniform message space and data distribution model, and also, a lightweight services mashup approach is implemented. With the help of visualization technology, the graphical user interface of different underground physical sensor devices could be created, which allows the sensors to combine with other resources easily. Besides, four types of coal mine safety monitoring and control automation scenarios are illustrated, and the performance has also been measured and analyzed. It has been proved that our lightweight mashup middleware can reduce the costs efficiently to create coal mine safety monitoring and control automation applications.

In recent years, Web service discovery has been a hot research topic. In this paper, we propose a novel Web services discovery approach, which can mine the underlying semantic structures of interaction interface parameters to help users find and employ Web services, and can match interfaces with high precision when the parameters of those interfaces contain meaningful synonyms, abbreviations, and combinations of disordered fragments. Our approach is based on mining the underlying semantics. First, we propose a conceptual Web services description model in which we include the type path for the interaction interface parameters in addition to the traditional text description. Then, based on this description model, we mine the underlying semantics of the interaction interface to create index libraries by clustering interaction interface names and fragments under the supervision of co-occurrence probability. This index library can help provide a high-efficiency interface that can match not only synonyms but also abbreviations and fragment combinations. Finally, we propose a Web service Operations Discovery algorithm (OpD). The OpD discovery results include two types of Web services: services with “Single” operations and services with “Composite” operations. The experimental evaluation shows that our approach performs better than other Web service discovery methods in terms of both discovery time and precision/ recall rate.

High-Definition (HD) wireless video has already dominated popular multimedia applications to provide content-rich mobile streaming services. Transmission Control Protocol (TCP) is pervasively employed as the transport-layer solution in video communication systems to achieve firewall traversal and network-friendliness. However, it is severely challenging to effectively deliver HD mobile video using TCP over wireless networks: 1) HD live video streaming is featured by high transmission rate and stringent delay constraint; 2) wireless networks are bandwidth-limited and error-prone; and 3) the congestion control and data retransmission mechanisms in TCP may result in frequent throughput fluctuations and deadline violations. To address these critical issues, this research presents a Forward Error Correction (FEC) coding scheme dubbed PATON (Priority-Aware and TCP-Oriented codiNg). First, we develop an analytical model of FEC coding-based real-time video communication with TCP over wireless networks. Second, we propose a heuristic solution for prioritized frame selection, FEC redundancy adaptation, and packet size adjustment to maximize real-time video quality. The proposed PATON is able to effectively leverage the frame priority and TCP connection state to minimize the video distortion. We conduct the performance evaluation through extensive semi-physical emulations in Exata involving HD video streaming encoded with H.264 codec. Compared with the existing FEC coding schemes, PATON achieves appreciable improvements in terms of video Peak Signal-to-Noise Ratio (PSNR), end-to-end delay, ratio of lost frames, and goodput.

Modern Internet of Things (IoT)-aware business processes include various geographically dispersed sensor devices. Large amounts of raw data acquired from sensors need to be regularly transmitted to the targeted processes in enterprise data centers, resulting in a significant increase in network load and latency. It is necessary to execute such processes in a distributed way. The existing work has proposed different algorithms to partition a given process for distributed execution; however, they cannot satisfy the decentralized nature of IoT-aware business processes. Moreover, up to now, there is few work that studies uncertain optimal deployment problems in which traffic data for guiding subsequent deployment derives from experts' empirical knowledge. This paper proposes a novel location-based fragmentation algorithm and α-optimal deployment solution to deal with the mentioned problems, where α is the given confidence level. A hardware-in-the-loop simulation platform based on NS-3 was built. Based on this platform, an integrated monitoring process was deployed that ran on different virtual computers, and process fragments communicated with each other via a simulated network. The experimental results show that the proposed approach can reduce network traffic and round-trip time.

The Internet of Things (IoT) infrastructure with numerous diverse physical devices are growing up rapidly, which need a dynamic services coordination approach that can integrate those heterogeneous physical devices into the context-aware IoT infrastructure. This paper proposes a situation-aware dynamic IoT services coordination approach. First, focusing on the definition of formal situation event pattern with event selection and consumption strategy, an automaton-based situational event detection algorithm is proposed. Second, the enhanced event-condition-action is used to coordinate the IoT services effectively, and also the collaboration process decomposing algorithm and the rule mismatch detection algorithms are proposed. Third, the typical scenarios of IoT services coordination for smart surgery process are also illustrated and the measurement and analysis of the platform's performance are reported. Finally, the conclusions and future works are given.

The advancements in wireless communication technologies prompt the bandwidth aggregation for mobile video delivery over heterogeneous access networks. Multipath TCP (MPTCP) is the transport protocol recommended by IETF for concurrent data transmission to multihomed terminals. However, it still remains challenging to deliver user-satisfied video services with the existing MPTCP schemes because of the contradiction between energy consumption and received video quality in mobile devices. To enable the energy-efficient and quality-guaranteed video streaming, this paper presents an energy-distortion-aware MPTCP (EDAM) solution. First, we develop an analytical framework to characterize the energy-distortion tradeoff for multipath video transmission over heterogeneous wireless networks. Second, we propose a video flow rate allocation algorithm to minimize the energy consumption while achieving target video quality based on utility maximization theory. The performance of the proposed EDAM is evaluated through both experiments in real wireless networks and extensive emulations in exata. Experimental results show that EDAM exhibits performance advantages over existing MPTCP schemes in energy conservation and video quality.

Recently, collaborative filtering-based methods are widely used for service recommendation. QoS attribute value-based collaborative filtering service recommendation mainly includes two important steps. One is the similarity computation, and the other is the prediction for QoS attribute value, which the user has not experienced. In previous studies, the performances of some methods need to be improved. In this paper, we propose a ratio-based method to calculate the similarity. We can get the similarity between users or between items by comparing the attribute values directly. Based on our similarity computation method, we propose a new method to predict the unknown value. By comparing the values of a similar service and the current service that are invoked by common users, we can obtain the final prediction result. The performance of the proposed method is evaluated through a large data set of real web services. Experimental results show that our method obtains better prediction precision, lower mean absolute error ($MAE$) and faster computation time than various reference schemes considered.

With rapid adoption of the cloud computing model, many enterprises have begun deploying cloud-based services. Failures of virtual machines (VMs) in clouds have caused serious quality assurance issues for those services. VM replication is a commonly used technique for enhancing the reliability of cloud services. However, when determining the VM redundancy strategy for a specific service, many state-of-the-art methods ignore the huge network resource consumption issue that could be experienced when the service is in failure recovery mode. This paper proposes a redundant VM placement optimization approach to enhancing the reliability of cloud services. The approach employs three algorithms. The first algorithm selects an appropriate set of VM-hosting servers from a potentially large set of candidate host servers based upon the network topology. The second algorithm determines an optimal strategy to place the primary and backup VMs on the selected host servers with k-fault-tolerance assurance. Lastly, a heuristic is used to address the task-to-VM reassignment optimization problem, which is formulated as finding a maximum weight matching in bipartite graphs. The evaluation results show that the proposed approach outperforms four other representative methods in network resource consumption in the service recovery stage.

This paper proposes a Semantic Web-based context ontological reasoning service for multimedia conferencing process management. When the chairman enters the basic conference information, the process will automatically select the appropriate means of notifications based on the conference time and the participant contact details. In this system, all types of multimedia conferencing processes are encapsulated in Web services and deployed on an enterprise service bus. A large multimedia conference ontology is created that contains business rules. In addition, with the objective of improving and management for data redundancy in ontological reasoning service, we propose an extensible breadth-first-search-based graph search algorithm that can successfully eliminate the redundant portion of the ontology, which can ensure validity of context ontology reasoning. Finally, the proposed multimedia conferencing process management can obtain the desired results, and some corresponding experimental analysis are reported.

The advancements in wireless infrastructures and communication technologies prompt the bandwidth aggregation for mobile video delivery over heterogeneous access networks. Multipath TCP (MPTCP) is the transport protocol recommended by IETF to enable concurrent data transmissions over multiple communication paths. However, it still remains problematic to deliver user-satisfied video services with the existing MPTCP schemes due to the contradiction between mobile device energy and video distortion. To enable the energy-efficient and quality-guaranteed video streaming, this paper presents an Energy-Distortion Aware MPTCP (EDAM) solution. First, we develop an analytical model to capture the energy-distortion tradeoff for multipath video transmission over heterogeneous wireless networks. Second, we propose a video flow rate allocation algorithm to minimize the energy consumption while achieving target video quality based on utility maximization theory. The performance of the proposed EDAM is evaluated through extensive emulations in Exata involving real-time H.264 video streaming. Evaluation results demonstrate that EDAM outperforms the reference MPTCP schemes in reducing energy consumption, as well as in improving video PSNR (Peak Signal-to-Noise Ratio).

This paper proposes a novel Web service discovery approach that depend on the mining the underlying semantic structures of interaction interface parameters, which can match interfaces with high precision when the parameters of those interfaces contain meaningful synonyms, abbreviations, and combinations of disordered fragments. Especially, we propose a conceptual Web services description model in which we include the type path for the interaction interface parameters in addition to the traditional text description. Then, based on this description model, we mine the underlying semantics of the interaction interface to create index libraries by clustering interaction interface names and fragments under the supervision of co-occurrence probability. Finally, we propose a Web service Operations Discovery algorithm (OpD) that support the “Single” operations and services with “Composite” operations discovery. The experimental shows that our approach performs better than other approaches in terms of both discovery time and precision.

In this paper, we show an end-user oriented tool suite for mobile application development. The advantages of this tool suite are that the graphical user interface (GUI), as well as the application logic can both be developed in a rapid and simple way, and web-based services on the Internet can be integrated into our platform by end-users. This tool suite involves three sub-systems, namely ServiceAccess, EasyApp and LSCE. ServiceAccess takes charge of the registration and management of heterogeneous services, and can export different form of services according to the requirements of the other sub-systems. EasyApp is responsible for developing GUI in the form of mobile app. LSCE takes charge of creating the application logic that can be invoked by mobile app directly. Finally, a development case is presented to illustrate the development process using this tool suite. The URL of demo video: https://youtu.be/mM2WkU1_k-w

Applications in Android often have the ability of accessing sensitive resources on mobile devices. These resources have different levels of security and usage constraint in scenarios which have different requirements for privacy. Therefore, it is in demand for users to have fine-grained privacy protection and resources usage constraint that taking the context information into account, which is not supported by inherent access control mechanism of Android. To address these issues, we designed and implemented an enhanced context-aware adaptive access control system (EasyGuard) in Android to provide adaptive access control automatically when the specific context is detected according to the pre-configured policies. In addition, we developed an application to facilitate users that have little domain knowledge in android to configure policies reasonably. Experimental results show that users can easily protect their privacy, security and save energy of mobile devices through this system.

In some special circumstances, such as earthquakes, tsunamis and floods, etc. Infrastructure communication facilities are damaged, all communications are interrupted. For the communication between peoples, Android smartphones can be used to construct Mobile Ad Hoc Networks (MANETs). To improve the work efficiency, it is necessary to run the Information Systems (Android Applications) in MANETs, therefore the distribution of information becomes convenient. However, it is very hard to get a real MANET environment to test Android Applications, and so far, we have not found any MANETs simulation environments which can be used to test actual Android Applications. Therefore, we propose a Simulation Experimental Environment for Android Applications in MANETs (SEEM). The test results show that the SEEM is practicable to test Android Applications in MANETs. We believe that the SEEM will be beneficial to the researchers and developers who need to develop and test actual Android Applications in MANETs.

*** The rapid development of mobile internet abstracts many non-professional persons to creating mobile applications. Traditional development process cannot meet their needs. In this paper, we present a cross-platform mobile development environment based on OSGi framework, EasyApp. It provides a highly-integrated, UI-friendly and easily-operating environment. Applications are comprehensively developed with web techniques. Users could create mobile applications with draggable widgets. Native APIs of mobile phone can be invoked with abundant plugins. After designing, users could package and download applications of multiple platforms. ***

The technological evolutions in wireless communication systems prompt the bandwidth aggregation (e.g., Wi-Fi and LTE radio interfaces) for concurrent video transmission to hand-held devices. However, multipath video transport to the battery-limited mobile terminals is confronted with challenging technical problems: 1) high-quality real-time video streaming is throughput-demanding and delay-sensitive; 2) mobile device energy and video quality are not adequately considered in conventional multipath protocols; and 3) wireless networks are error-prone and bandwidth-limited. To enable the energy-efficient and quality-guaranteed live video streaming over heterogeneous wireless access networks, this paper proposes an energy-video aware multipath transport protocol (EVIS). First, we present a mathematical framework to analyze the frame-level energy-quality tradeoff for delay-constrained multihomed video communication over multiple communication paths. Second, we develop scheduling algorithms for prioritized frame scheduling and unequal loss protection to achieve target video quality with minimum device energy consumption. EVIS is able to effectively leverage video frame priority and rateless Raptor coding to jointly optimize energy efficiency and perceived quality. We conduct performance evaluation through extensive emulations in Exata involving real-time H.264 video streaming. Emulation results demonstrate that EVIS advances the state-of-the-art with remarkable improvements in energy conservation, video peak signal-to-noise ratio (PSNR), end-to-end delay, and goodput.

High-frame rate (HFR) video streaming is emerging as a new paradigm in popular multimedia applications (e.g., mobile cloud gaming) to achieve smooth viewing experience perceived by end users. However, it is severely challenging to guarantee the delivery quality of HFR video over wireless platforms with regard to the high transmission rate and limited network resources. Multihoming capability enables mobile devices to concurrently receive video data with different radio interfaces (e.g., cellular and Wi-Fi). To effectively deliver mobile HFR video over multiple wireless access networks, this paper develops an application-layer transmission scheme dubbed joint FRAme Scheduling and Error Resilience (FRASER). First, we propose an unequal frame scheduling approach by taking advantage of interlaced forward error correction coding and reliability-aware data allocation to minimize the total distortion. Second, we introduce an error resilience scheme at the receiver side to proactively leverage the out-of-order and overdue video packets to mitigate the error propagations. The proposed FRASER is able to substantially reduce the probability of I (Intra) frame loss and consecutive P (Predicted) frame drops. We conduct the performance evaluation through extensive emulations in Exata involving HFR video streaming encoded with H.264 codec. Evaluation results show that FRASER outperforms the reference transmission schemes in terms of video peak signal-to-noise ratio, end-to-end delay, and received frame rate. Thus, FRASER is recommended for delivering HFR video streaming to multihomed mobile devices in heterogeneous wireless networks.

Recent technological advancements in wireless infrastructures and handheld devices enable mobile users to concurrently receive multimedia contents with different radio interfaces (e.g., cellular and Wi-Fi). However, multipath video transport over the resource-limited and error-prone wireless networks is challenged with key technical issues: 1) conventional multipath protocols are throughput-oriented, and video data are scheduled in a content-agnostic fashion and 2) high-quality real-time video is bandwidth-intensive and delay-sensitive. To address these critical problems, this paper proposes a bandwidth-efficient multipath streaming (BEMA) protocol featured by the priority-aware data scheduling and forward error correction-based reliable transmission. First, we present a mathematical framework to formulate the delay-constrained distortion minimization problem for concurrent video transmission over multiple wireless access networks. Second, we develop a joint Raptor coding and data distribution framework to achieve target video quality with minimum bandwidth consumption. The proposed BEMA is able to effectively mitigate packet reordering and path asymmetry to improve network utilization. We conduct performance evaluation through extensive emulations in Exata involving real-time H.264 video streaming. Compared with the existing multipath protocols, BEMA achieves appreciable improvements in terms of video peak signal-to-noise ratio, end-to-end delay, bandwidth utilization, and goodput. Therefore, BEMA is recommended for streaming high-quality real-time video to multihomed terminals in heterogeneous wireless networks.

We study the problem of one-way video communication in a single-source, multiple-destination scenario over the lossy Internet. Forward error correction (FEC) coding is commonly adopted for data protection in implementing loss-resilient video transmission systems. However, the burst packet losses over the Internet frequently degrade FEC performance and induce video quality deteriorations. To address the challenging problem, we propose a novel transmission scheme dubbed trading delay for distortion (TELFORD) that includes three components: 1) adaptive multidestination status estimation; 2) delay-constrained transmission rate assignment; and 3) differentiated FEC packet spreading. We analytically formulate and solve the problem of FEC packet allocation and scheduling to minimize the end-to-end video distortion. The proposed TELFORD is able to cope with multiple-destination scheduling separately. We conduct performance evaluation through semiphysical emulations in Exata using real-time H.264 video streaming. Experimental results show that TELFORD outperforms existing error control transmission schemes in improving the video peak signal-to-noise ratio and mitigating packet transmission impairments.

The proliferating wireless infrastructures with complementary characteristics prompt the bandwidth aggregation for concurrent video transmission in heterogeneous access networks. Multipath TCP (MPTCP) is an important transport-layer protocol recommended by IETF to integrate different access medium (e.g., Cellular and Wi-Fi). This paper investigates the problem of mobile video delivery using MPTCP in heterogeneous wireless networks with multihomed terminals. To achieve the optimal quality of real-time video streaming, we have to seriously consider the path asymmetry in different access networks and the disadvantages of the data retransmission mechanism in MPTCP. Motivated by addressing these critical issues, this study presents a novel quAlity-Driven MultIpath TCP (ADMIT) scheme that integrates the utility maximization based Forward Error Correction (FEC) coding and rate allocation. We develop an analytical framework to model the MPTCP-based video delivery quality over multiple communication paths. ADMIT is able to effectively integrate the most reliable access networks with FEC coding to minimize the end-to-end video distortion. The performance of ADMIT is evaluated through extensive semi-physical emulations in Exata involving H.264 video streaming. Experimental results show that ADMIToutperforms the reference transport protocols in terms of video PSNR (Peak Signal-to-Noise Ratio), end-to-end delay, and goodput. Thus, we recommend ADMIT for streaming high-quality mobile video in heterogeneous wireless networks with multihomed terminals.

Internet of Things (IoT) technology demands a complex, lightweight distributed architecture with numerous diverse components, including end devices and applications adapted for specific contexts. This paper proposes a situation-aware IoT services coordination platform based on the event-driven service-oriented architecture (SOA) paradigm. Focus is placed on the design of an event-driven, service-oriented IoT services coordination platform, for which we present a situational event definition language (SEDL), an automaton-based situational event detection algorithm, and a situational event-driven service coordination behavior model, which is based on an extended event-condition-action trigger mechanism. Moreover, we propose a reliable real-time data distribution model to support the effective dispatching sensory data between information providers and consumers, which is based on the grid quorum mechanism to organize those brokers into a grid overlay network to facilitate the asynchronous communication in a large-scale, distributed, and loosely coupled IoT applications environment. We also illustrate the various illustrations for IoT services coordination and alarming disposal process of coal mine safety monitoring and control automation scenarios, and also report the measurement and analysis of the platform's performance.

The communication systems in existing smart grids mainly take the request/reply interaction model, in which data access is under the direct control of data producers. This tightly controlled interaction model is not scalable to support complex interactions among smart grid services. On the contrary, the publish/subscribe system features a loose coupling communication infrastructure and allows indirect, anonymous and multicast interactions among smart grid services. The publish/subscribe system can thus support scalable and flexible collaboration among smart grid services. However, the access is not under the direct control of data producers, it might not be easy to implement an access control scheme for a publish/subscribe system. In this article, we propose a Data-Centric Access Control Framework (DCACF) to support secure access control in a publish/subscribe model. This framework helps to build scalable smart grid services, while keeping features of service interactions and data confidentiality at the same time. The data published in our DCACF is encrypted with a fully homomorphic encryption scheme, which allows in-grid homomorphic aggregation of the encrypted data. The encrypted data is accompanied by bloom-filter encoded control policies and access credentials to enable indirect access control. We have analyzed the correctness and security of our DCACF and evaluated its performance in a distributed environment.

This paper focuses on matching ontologies created for similar domains through different sources. Different solutions use lexical, structural or logical processing and analysis to match ontologies. However, an important aspect is also interpreting concepts that entities are presented with and using them in relation to semantics in an ontology. The paper demonstrates analyzing and extending the concepts used to define entities in an ontology, discusses establishing and filtering matching candidates by reasoners, and then describes constructing correspondences between entities from different ontologies using lexical and semantic analysis. The experiments show that our prototype, called OMI-DL, is among the top group over many ontologies adopted from the OAEI benchmark data set. We also provide an evaluation of the OMI-DL method for matching the DOLCE+DnS Ultralite ontology and the domain ontology developed in the m:Ciudad project.

Nowadays, with the rapid development of hardware and network technologies, various types of smart devices are released by different vendors, resulting in the emergence of Internet of Things (IoT). However, most of the existing approaches for IoT device management are designed in a centralized way, whose efficiency meets challenges recently because of the large scale of heterogeneous device modules and highly dynamic essence of the networks. To tackle this challenge, we propose a distributed approach for IoT device management and service composition from a social network point of view. Specifically, we introduce Restful web service to encapsulate heterogeneous IoT device modules, providing uniform interfaces for IoT service invocation. According to the relationships between IoT devices, social network theory is applied to model IoT services in different dimensions. After fully considering the social properties, a flexible and scalable schemes for IoT service composition is designed to satisfactorily meet users' requirements from several aspects. Finally, simulation experiments based on dataset from reality are conducted to validate the effectiveness of our approach.

Summary With the development of Internet of Things (IoT), many middleware solutions have been proposed for the integration of physical world with the Web. However, most leading middleware solutions provide only limited resource matching and selection functionality. When significant amounts of resources are available, selecting the appropriate resources becomes challenging and time‐consuming. This paper addresses the growing issues of resource matching and selection in IoT solutions. In this paper, we propose a novel resource model to describe the IoT resources in a multidimensional manner. Based on the resource model, a resource matching algorithm that selects the well‐matched resources by matching the similarity between resources is proposed. Moreover, we constructed a combined resource set and used it and OWLS‐TC to evaluate our work comprehensively. Experimental results show that the proposed resource matching approach is more effective and efficient than existing approaches. Copyright © 2013 John Wiley & Sons, Ltd.

Inter destination video synchronisation (IDVS) is a key technology in emerging interactive multimedia applications. It is essential to ensure the synchronous experiences of users in such applications. However, one inevitable barrier for IDVS is the packet transfer delay differences (PTDDs) among different destinations. Existing researches have tried to eliminate the side effects of PTDD passively and schedule the video packets in a ‘back‐to‐back’ fashion. In this paper, the authors propose to proactively leverage such differences to design a transmission scheme that enhances video quality while ensuring the synchronous arrival of packets. Based on the network measurements from a real multimedia conferencing system and the Planetlab, they find that the PTDD is between the ranges of 100–250 ms. Motivated by this observation, they propose a scheme named asynchronous departure for synchronous arrival (ADSA), which inserts intervals between consecutive packets according to the synchronisation reference. To prove the superiority of ADSA, they carry out analysis based on Gilbert loss model and continuous time Markov chain. They conduct performance evaluation through emulations in Exata and experimental results show that ADSA improves the video peak signal‐to‐noise ratio by up to 9.1 and 6.9 dB compared with existing latest and earliest schemes, respectively.

Video may pass through various types of heterogeneous networks during the process of transmission, which has adverse impacts on the real-time video quality. Traditional methods focus on how to compress videos based on the video flow without considering the real-time network information. This paper presents an adaptive method that combines video encoding and the video transmission control system over heterogeneous networks. This method includes the following steps: first, to collect and standardize the real-time information describing the network and the video, then to assess the video quality and calculate the video coding rate based on the standardized information, and then to process the encoded compression of the video according to the calculated coding rate and transfer the compressed video. The experiments show that there is a significant improvement for the quality of real-time videos transmission without changing the existing network, particularly the core equipment. Our solution is easy to deploy and implement quickly and may help to extensively ensure video quality for normal users.

The massive proliferation of wireless infrastructures with complementary characteristics prompts the bandwidth aggregation for Concurrent Multipath Transfer (CMT) over heterogeneous access networks. Stream Control Transmission Protocol (SCTP) is the standard transport-layer solution to enable CMT in multihomed communication environments. However, delivering high-quality streaming video with the existing CMT solutions still remains problematic due to the stringent quality of service (QoS) requirements and path asymmetry in heterogeneous wireless networks. In this paper, we advance the state of the art by introducing video distortion into the decision process of multipath data transfer. The proposed distortion-aware concurrent multipath transfer (CMT-DA) solution includes three phases: 1) per-path status estimation and congestion control; 2) quality-optimal video flow rate allocation; 3) delay and loss controlled data retransmission. The term `flow rate allocation' indicates dynamically picking appropriate access networks and assigning the transmission rates. We analytically formulate the data distribution over multiple communication paths to minimize the end-to-end video distortion and derive the solution based on the utility maximization theory. The performance of the proposed CMT-DA is evaluated through extensive semi-physical emulations in Exata involving H.264 video streaming. Experimental results show that CMT-DA outperforms the reference schemes in terms of video peak signal-to-noise ratio (PSNR), goodput, and inter-packet delay.

Load distribution is a key research issue in deploying the limited network resources available to support traffic transmissions. Developing an effective solution is critical for enhancing traffic performance and network utilization. In this paper, we investigate the problem of load distribution for real-time traffic over multipath networks. Due to the path diversity and unreliability in heterogeneous overlay networks, large end-to-end delay and consecutive packet losses can significantly degrade the traffic flow's goodput, whereas existing studies mainlyfocus on the delay or throughput performance. To address the challenging problems, we propose a Goodput-Aware Load distribuTiON (GALTON) model that includes three phases: (1) path status estimation to accurately sense the quality of each transport link, (2) flow rate assignment to optimize the aggregate goodput of input traffic, and (3) deadline-constrained packet interleaving to mitigate consecutive losses. We present a mathematical formulation for multipath load distribution and derive the solution based on utility theory. The performance of the proposed model is evaluated through semi-physical emulations in Exata involving both real Internet traffic traces and H.264 video streaming. Experimental results show that GALTON outperforms existing traffic distribution models in terms of goodput, video Peak Signal-to-Noise Ratio (PSNR), end-to-end delay, and aggregate loss rate.

To solve the problem of low information integration for heating industry, a framework of information service platform is proposed. The framework realizes information sharing and integration control of central heating. Four core components are designed to implement process development, service collaboration, publish/subscribe and event rule. A heating management system has been designed based on these components, which realizes intelligent and security of the production management. And three application subsystems have been developed to realize heating maintenance service, multi-level alarm service and heating charging service. The information service platform effectively achieves information integration and rapid service development.

In order to build a low-latency lightweight publish/subscribe (pub/sub) system for IOT services, we propose an efficient and scalable broker architecture, called Grid Quorum-based pub/sub system (GQPS). As a core component in the event-driven SOA framework for IOT services, this architecture organizes multiple pub/sub brokers into a quorum-based peer-to-peer topology for efficient topic searching. It also leverages a topic searching algorithm and a caching strategy to achieve a small and constant search latency. Lightweight RESTful interfaces make our GQPS more suitable for IOT services. Cost analysis and experiment study demonstrate that GQPS achieves a significant performance gain in search satisfaction without compromising search cost. We applied GQPS in the District Heating Control and Information Service System in Beijing, China, which validates the feasibility and availability of our architecture.

The safety production in coal mine has attracted considerable research attentions due to the frequently occurred mining accidents. In order to ensure the safety production in coal mine, technology of the Internet of Things (IoT) is widely used to detect the situation in coal mine. Here the situation is composed of several elements. Existing solutions for such situation detection are mainly based on the directed graph or automatic machine. These methods are only effective when few situation element change simultaneously or the change(s) can be determined clearly. However, when the situation comprises a lot of elements or the element's change is ambiguous, these methods cannot effectively determine the situation. In this paper, we propose a situation detection method based on neural network. Trained neural network can detect the situation well, especially when multiple situation elements change at the same time or changes are ambiguous.

Due to complex environment of the coal mine, it's necessary to monitor the information of underground environment, device and miner instantly in order to ensure the safety of coal mine production. However, the exiting coal mine can not meet the requirements of coverage without blind spots as it is developed by the wired network. This paper proposes a RESTful Web services mashup augmented coal mine safety monitoring and control automation using ZigBee wireless sensor network, which can collect the underground temperature, humidity methane values and personal position through sensor nodes in the coal mine, and also collects the personnel position information inside the mine, and then implement a RESTful Application Programming Interface (API) on sensor nodes to provide access to sensors and actuators, allowing for them to be easily combined with other enterprise information resources based on the success of mashup applications. We also illustrated three different of scenarios for RESTful Web service mashups representing for coal mine safety monitoring and control automation. Finally, we give the conclusions.

The mashup is a hot research topic in recent years. In this paper, we propose an efficient Mashup Resource Discovery(MRD) approach to facilitating mashup searching. Basing on an index library, MRD acts high performance in response time. By an external ontology base and a synonym base, we apply a semantics discovery, avoiding semantics annotation in mashup description files. By evaluating the prestige of mashups(especially for mashup api), we rank the results and make mashups with high prestige get higher ranking. The experimental evaluation shows that our MRD act high performance both in time consuming and r-p curve statistics.

Web service evaluation is one of the key problems in web service discovery and selection research fields. In this paper, we propose an approach to evaluate the web service performance. Different from traditional evaluation methods, our approach is based on users experience, and we apply the idea and result in common webs evaluation fields to help construct our evaluation system. We import the Alexa ranking to help evaluate the information providing performance of web services, we import the idea of Page Rank to help designing our evaluation method for the function sharing performance of web services.

We propose a Discovery approach to find web services composition flows sorted by similarity. The approach extracts information from BPEL files. When creating new web services composition, the discovery result can be reused directly or provide reference. We import the lexical semantic in matching keywords. By analysis and proof, we show that our keyword splitting method can extend the application of lexical semantics matching to a large extent and our approach can provide higher precision ratio than traditional search engine.

As the convergence between telephony systems and data systems at all levels of the stack, provision of hybrid services that span multiple networks have attracted much more attentions from both academic and industry communities. The Internet of Things (IoT) is a novel paradigm that is rapidly gaining ground which needs to pay special attention to. In this paper, we present the Flexible Hybrid Service Supporting Platform (FHSSP) leverages on Service-Oriented Architecture (SOA) and Web services technologies. Aiming at the dynamic and flexible characteristics of the heterogeneous network environment, we introduced the template reuse based service creation, flexible service execution as well as resource adaptor methodologies, which ensure FHSSP to deliver “User-Centric” hybrid services in a rapid, cost-effective and highly scalable way. Specifically a typical communication Web services based multimedia conferencing system (MMCS) is presented as an illustrative example of applying FHSSP. Finally we conclude the paper with future work.

The merging of telecommunication and Internet domains is a real challenge for supporting the evolution towards the next generation of Web services. Enterprise service bus (ESB) has been regarded as a promising way to support dynamic and agile service integration in distributed heterogeneous environments. As communication services have strong real-time requirements and are based on asynchronous interactions, generic ESB based service integration is no longer suitable. This paper proposes an enhanced ESB based execution platform in which a low-latency service engine has been seamlessly integrated to support flexible communication Web services over heterogeneous networks. The design and implementation of the enhancements, a novel communication Web services based multimedia conference system, the prototype, and some preliminary performance measurements are presented. The performance results are encouraging.

Recent advances in Web services have made it practical to provide communication Web services to enable communication through SOA and package communication capability as services. This paper provides an appropriate implementation to deliver the multimedia conferencing communication components as Web services in order to be used simply by Web service clients in converged applications.

We have witnessed the convergence of the telecom technologies and the Internet technologies recent years. Hybrid services combined with Internet service functionalities and telecommunication service functionalities have attracted much more attentions form both academic and industry communities. In this paper, we have proposed novel service creation architecture leverage on the so-called "web 2.0"'s methodology "mashup", to let the end-users or service developers conduct the service creation procedure in a lightweight and rapid way. Recommender System theory and related technologies are also introduced to assist the mashup creator to build higher quality mashups in less time. We have also made some preliminary evaluations about the system and point out some further directions.

Context-aware paradigm is intended to make decisions proactively for users to adapt to contexts changes in a pervasive environment so as to improve users' work efficiency. However, this commitment deduces because of the intrinsic uncertainty i.e. incompleteness, inaccuracy and inconsistency of context-aware paradigm. We bring forward a situation-aware approach, supported by Bayesian Networks, ontology and Domain Specific Language techniques, for dealing with uncertain context-aware paradigm. Situation, a description of logically combined contexts, is high-level abstract contexts; hence, it can shield the trivialness and inconstancy of low-level contexts. BN mapping contexts to situations is good at dealing with incomplete, inaccurate and erroneous low-level contexts; ontology is referred to eliminate inconsistencies among situations and contexts. Besides, DSL can offer flexibilities for its easy readability and good reusability. Also, interruption ratio, precision and efficiency are evaluated to validate the effectiveness of our approach w.r.t. a situation-aware multimedia conference application.

In this paper, we introduce the session initiation protocol (SIP) based multimedia conferencing on Internet, and mainly focus on the design and implementation for conferencing communication services model, such as SIP connection, session management, media control conferencing management, also we provide a prototype. Finally, we give the conclusions.

Service-oriented communication (SOC) is a new trend in the industry to enable communication through a service-oriented architecture (SOA) and thereby encapsulate communication capabilities as services. In this paper, we design the session initiation protocol (SIP) based multimedia conferencing communication services model, And mainly focus on formal analysis for BPEL based multimedia conferencing communication services orchestration and to guarantee the process correctness for such applications, and also providing an automated support for the formal analysis model of their behavior. Finally, we give the conclusions.

Integrated service creation environment is a service oriented platform that we have developed which spans the fixed telephone networks, mobile networks and Internet networks to leverage on the existing communication infrastructures. The user requirements elicitation which reflects the focus on user needs and perceptions is a vitally important factor for user interaction design for the personalized goal to gain. In this paper, we emphasize on the adaptive mobile user requirement elicitation framework. We concentrate on ontology driven user requirements elicitation and reflection based user requirements evolution framework with context awareness computing, which can dynamically analyse user requirements for a personalized and adaptive services in integrated service creation environment.