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.

Deep learning shows great promise in providing more intelligence to the cross-platform web. However, insufficient infrastructure, heavy models, and intensive computation limit the use of deep learning with low-performing web browsers. We propose DeepAdapter, an integrated cloud-edge-device framework that ties the edge, the remote cloud, with the device by cross-platform web technology for adaptive deep learning services towards lower latency, lower mobile energy, and higher system throughput. DeepAdapter consists of context-aware pruning, service updating, and online scheduling. First, the offline pruning module provides a context-aware pruning algorithm that incorporates the latency, the network condition, and the device's computing capability to fit various contexts. Second, the service updating module optimizes branch model cache on the edge for massive mobile users and updates the new model pruning requirements. Third, the online scheduling module matches optimal branch models for mobile users. Also, a two-stage DRL-based online scheduling method named DeepScheduler can handle high concurrent requests between edge centers and remote cloud by designing the reward prediction model. Extensive experiments show that DeepAdapter can decrease average latency by 1.33x, reduce average mobile energy by 1.4x, and improve system throughput by 2.1x with considerable accuracy.

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.

The emergence of edge computing and 5G networks has fueled the growth of mobile Web AR. Although efforts have been made to improve the edge system efficiency for Web AR applications, efficient edge-assisted mobile Web AR services remain technically challenging. This paper presents EARNet, a distributed edge system orchestration approach for mobile Web AR in 5G networks. The design of EARNet makes three novel contributions. First, EARNet manages the edge network dynamics with respect to user mobility and their Web AR service requests by employing landmarks and grid index based edge node localization mechanisms. Second, EARNet takes into account both request serving performance and offloading cost in managing workload balance and quality of service and leverages dynamic hash and max heap mechanisms for efficient Web AR service lookup and AR computations. Third, EARNet designs the service migration schemes by optimizing several performance factors, such as message efficiency, scheduling latency, request density and locality of mobile users and edge nodes, and accuracy of Web AR services after migration. Experimental evaluations are conducted using the real base station deployment data in the Melbourne Central Business District (CBD) area. The results shows the effectiveness of the EARNet edge orchestration approach compared to several baseline approaches.

Heavy flow identification is essential for discovering potential adversarial activities in mobile edge networks. However, state-of-the-art falls short in accuracy with approximation algorithms and unbounded memory usages with precise measurement. To this end, we introduce BatchSketch, a "network - server" aligned solution to achieve both high accuracy and low memory usage. The intelligence behind is that BatchSketch first conducts coarse-grained filtering from the switch with bounded memory and computation resources, and it then sends the filtered flows to the RDMA-link attached server with plenty of memory for accurate measurement. Our primary experimental results indicate that the filter on the switch can filter 99% of non-heavy flows, remarkably reducing the memory usage for the measurement.

Multi-user mobile Augmented Reality (AR) has been successfully used in various fields as a novel visual interaction technology. But current mainstream wearable device-based and app-based solutions are still facing cross-platform, real-time communication, and intensive computing requirements. Mobile Web technology is envisioned to be a promising supporting technology for cross-platform application of mobile AR especially in 5G networks, which provide pervasive communication and computing resources thereby forming a formidable framework for the practical application of multi-user mobile Web AR. However, the problem of how to use these new techniques properly to achieve efficient communication and computing collaboration is obviously paramount in order for multi-user mobile Web AR to be realized in 5G networks. In this article, we propose the first edge-assisted multi-user collaborative framework for mobile Web AR in the 5G era. First, we propose a heuristic mechanism BA-CPP for efficient communication planning, which allows multi-user interaction synchronization to be achieved. Second, we introduce a motion-aware key frame selection mechanism called Mo-KFP to optimize the computational efficiency of the edge system, and simultaneously alleviate the initialization problem by collaborating with nearby mobile devices using the Device-to-Device (D2D) communication technique. Experiments are conducted in a real-world 5G network, and the results demonstrate the superiority of our proposed collaborative framework.

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.

Enabling deep learning technology on the mobile web can improve the user’s experience for achieving web artificial intelligence in various fields. However, heavy DNN models and limited computing resources of the mobile web are now unable to support executing computationally intensive DNNs when deploying in a cloud computing platform. With the help of promising edge computing, we propose a lightweight collaborative deep neural network for the mobile web, named LcDNN, which contributes to three aspects: (1) We design a composite collaborative DNN that reduces the model size, accelerates inference, and reduces mobile energy cost by executing a lightweight binary neural network (BNN) branch on the mobile web. (2) We provide a jointly training method for LcDNN and implement an energy-efficient inference library for executing the BNN branch on the mobile web. (3) To further promote the resource utilization of the edge cloud, we develop a DRL-based online scheduling scheme to obtain an optimal allocation for LcDNN. The experimental results show that LcDNN outperforms existing approaches for reducing the model size by about 16x to 29x. It also reduces the end-to-end latency and mobile energy cost with acceptable accuracy and improves the throughput and resource utilization of the edge cloud.

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.

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.

Combining image segmentation with Web technology lays a good foundation for lightweight, cross-platform, and pervasive Web artificial intelligence applications, and further improves the capability of Web-of-Things (WoT) applications. However, no matter whether we use a Web real-time communication media server for advanced processing that views camera inputs as a video stream, or transfer continuous camera frames to the remote cloud for processing, we are unable to obtain a satisfactory real-time experience due to high resource consumption and unacceptable latency. In this article, we present EdgeBooster, a computational-efficient architecture that leverages a common edge server to minimize the communication costs, accelerates the camera frame segmentation, and guarantees an acceptable segmentation accuracy with the prior knowledge. EdgeBooster provides real-time segmentation by developing parallel technology that enables segmentation on slices of a camera frame and using presegmentation based on superpixels to accelerate the graph-based segmentation. It also introduces recent DNN-based segmentation results as the prior knowledge to improve the performance of the graph-based segmentation, especially in nonideal scenes, such as dark light and weak contrast. Finally, it creates a pure frontend segmentation that can provide continuous and stable services for mobile users in unstable networks, such as a weak network or with an unstable edge server. The experimental results show that EdgeBooster is able to achieve a considerable accuracy for the mobile Web, running at no less than 30 frames per second in real scenes.

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.

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.

In Internet of Things (IoT)scenarios, the coordination of physical systems is often complex and rigid. Work to date has not comprehensively explored how to flexibly construct distributed IoT services to satisfy the coordination requirement. In our work, we propose a declarative approach to construct an event-driven IoT service system, where physical devices and systems are explicitly modeled as a service architecture foundation, the service behavior is flexibly declared based on distributed events, and it is step-by-step refined to have rigid service properties while allowing runtime adaptation. As an example, the controllability property of physical systems is discussed. Finally, we establish a distributed event-driven IoT service platform to test our method. Some experiments are performed to concept-prove our work.

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.

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.

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.

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.

Deep learning shows great promise in providing more intelligence to the mobile web, but insufficient infrastructure, heavy models, and intensive computation limit the use of deep learning in mobile web applications. In this paper, we present DeepAdapter, a collaborative framework that ties the mobile web with an edge server and a remote cloud server to allow executing deep learning on the mobile web with lower processing latency, lower mobile energy, and higher system throughput. DeepAdapter provides a context-aware pruning algorithm that incorporates the latency, the network condition and the computing capability of the mobile device to fit the resource constraints of the mobile web better. It also provides a model cache update mechanism improving the model request hit rate for mobile web users. At runtime, it matches an appropriate model with the mobile web user and provides a collaborative mechanism to ensure accuracy. Our results show that DeepAdapter decreases average latency by 1.33x, reduces average mobile energy consumption by 1.4x, and improves system throughput by 2.1x with a considerable accuracy. Its contextaware pruning algorithm also improves inference accuracy by up to 0.3% with a smaller and faster model.

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.

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.

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.

Internet of Things (IoT) services play an increasingly important role in modern society with the more deployment in critical infrastructures. Ensuring IoT systems operating in a secure and safe environment is becoming a mandatory and urgent requirement. We have proposed a non-intrusive runtime monitoring mechanism in our previous work, but only runtime of C applications is considered in it. With the widely use of Java applications, the runtime monitoring of Java services is quite essential and important. However, the existing memory forensics on user processes are mainly focused on native stack of C or Java heap, which cannot meet the requirements of runtime monitoring of IoT services. In this paper, We propose an approach to dump and semantically reconstruct a Java stack, where a history of executed methods is obtained. We show the algorithms to extract methods in calling and the associated parameters, which are further encapsulated to be IoT events in XML format for runtime monitoring of IoT services, from Java stacks. The challenge of determining whether a stack frame is interpreted or compiled when reconstruct frames from the bottom to the top of the stack is solved. Experiments and evaluations show that the reconstruction is feasible and effective, with low resource occupation and real-time responses.

Lightweight and precise recognition is a key component of web-based augmented reality (Web AR) applications. Although edge-based distributed deep learning approach is now possible to achieve satisfactory recognition for Web AR applications, it puts significant pressure on the computation and energy consumption of the mobile web browser, especially the app-based embedded browser. Thus, reducing the model size and accelerating the inference are regarded as the two fundamental challenges to enable this edge-based collaborative recognition system efficiently. In this paper, we propose a lightweight collaborative recognition system (LCRS) for Web AR applications. LCRS contributes to three aspects: (1) we design a composite deep neural network for reducing the model size and inference latency by introducing binary convolutional neural network; (2) we provide a joint training method to co-train the general branch and the binary branch; (3) we develop a JavaScript library for the mobile web browser to execute and accelerate inference of the binary branch, which also provides a collaborative mechanism between the mobile web browser and the edge server. We have conducted extensive experiments using several well-known networks and datasets. The experimental results have shown that the proposed system outperforms the existing approaches in terms of reducing the model size by about 16x to 29x, and it also reduces end-to-end latency and outpaces the existing state-of-the-art approaches by over 3x to 60x when applying it in practical Web AR cases.

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.

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.

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.

The ever changing traffic and Quality‐of‐Service (QoS) requirements of different traffic classes in multimedia applications impose tremendous challenges to the routing algorithm design. To address these challenges, a novel priority‐based dynamic multi‐path routing algorithm (PDMR) over Software Defined Network (SDN) is proposed in this paper. PDMR comprehensively considers the priorities of multimedia streams and the real‐time status of the link. The goal of PDMR is to determine what resources to be allocated to a set of flows keeping their priority, improving the utilization of network resources for satisfying stringent QoS requirements of the traffic flow. According to different QoS requirements in multimedia applications, traffic is classified into two classes and allocated their respective priorities. In addition, we derive a classification of the link cost function by considering the real‐time parameters such as link bandwidth, link delay, and link load to allocate the resources flexibly for transmission and to reduce the influence of between traffic classes. Furthermore, our proposed communication model between application services and the controller, which can guarantee the reliable transmission of the designated traffic. By conducting extensive simulations, we demonstrate that our proposed PDMR algorithm significantly outperforms other existing non‐priority‐based schemes in improving network performance.

Mobile augmented reality (Mobile AR) is gaining increasing attention from both academia and industry. Hardware-based Mobile AR and App-based Mobile AR are the two dominant platforms for Mobile AR applications. However, hardware-based Mobile AR implementation is known to be costly and lacks flexibility, while the App-based one requires additional downloading and installation in advance and is inconvenient for cross-platform deployment. In comparison, Web-based AR (Web AR) implementation can provide a pervasive Mobile AR experience to users thanks to the many successful deployments of the Web as a lightweight and cross-platform service provisioning platform. Furthermore, the emergence of 5G mobile communication networks has the potential to enhance the communication efficiency of Mobile AR dense computing in the Web-based approach. We conjecture that Web AR will deliver an innovative technology to enrich our ways of interacting with the physical (and cyber) world around us. This paper reviews the state-of-the-art technology and existing implementations of Mobile AR, as well as enabling technologies and challenges when AR meets the Web. Furthermore, we elaborate on the different potential Web AR provisioning approaches, especially the adaptive and scalable collaborative distributed solution which adopts the osmotic computing paradigm to provide Web AR services. We conclude this paper with the discussions of open challenges and research directions under current 3G/4G networks and the future 5G networks. We hope that this paper will help researchers and developers to gain a better understanding of the state of the research and development in Web AR and at the same time stimulate more research interest and effort on delivering life-enriching Web AR experiences to the fast-growing mobile and wireless business and consumer industry of the 21st century.

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.

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.

No abstract available.

In order to ensure the safety of ships during the voyage, we need to use the AIS data to find outlying ship trajectories and remind other ships to take the necessary avoidance actions. In the process of ship trajectory outlier detection, on the one hand, the ship trajectory outlier detection model trained on historical data is needed, on the other hand, the requirement for real-time detection should be met. Therefore, this paper designs ship trajectory outlier detection service system based on collaborative computing. The service system can combine the advantages of batch computing framework and stream computing framework. Trajectory data services, real-time annotation service are implemented in stream computing framework, F-DBSCAN outlier detection service, model training service, and model-based outlier detection service are implemented in batch computing framework. Memory database is used to complete data interaction between the two frameworks. The experiment shows that the service system can detect the outlying ship trajectories according to the real-time AIS data while using the outlier detection model.

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.

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.

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.

With the rapid development of location-based services in mobile Internet, spatial keyword queries have been widely employed in various real-life applications in recent years. To realize the great flexibility and cost savings, more and more data owners are motivated to outsource their spatio-textual data services to the cloud. However, directly outsourcing such services to the untrusted cloud may arise serious privacy concerns. In this paper, we study the privacy-preserving top-$k$spatial keyword query problem in untrusted cloud environments. Existing studies primarily focus on the design of privacy-preserving schemes for either spatial or keyword queries, and they cannot be applied to solve the privacy-preserving spatial keyword query problem. To address this problem, we present a novel privacy-preserving top-$k$spatial keyword query scheme. In particular, we build an encrypted tree index to facilitate privacy-preserving top-$k$spatial keyword queries, where spatial and textual data are encrypted in a unified way. To search with the encrypted tree index, we propose two effective techniques for the similarity computations between queries and tree nodes under encryption. To improve query performance on large-scale spatio-textual data, we further propose a keyword-based secure pruning method. Thorough analysis shows the validity and security of our scheme. Extensive experimental results on real datasets demonstrate our scheme achieves high efficiency and good scalability.

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.

Software-defined networking (SDN) is a promising future network architecture which introduces new dimensionsin flexibility and adaptability to cope with different Quality of service (QoS) metrics, such as latency constraint, loss rate, etc. In this paper, we provide differentiated Services for flows of a topic-oriented publish/subscribe system to address some of the QoS guarantee issues, by leveraging multi-priority queue functions of SDN. Flows are bifurcated into multi-priority queues in terms of packet properties such as delay and reliability requirements. An active feedback-based queue management mechanism is responsible for ensuring bounded queueing delays for those higher priority queues, and a mapping method accounts for mapping time-constrained flows to different priority queues. The Experiments verify the effectiveness of the queue mechanism and flow scheduling.

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.

Currently, the smartphone has become an essential communication and amusement tool, which has strong computing power and a variety of functions. Especially, the market share of smartphone with android system account for 84% in 2016[1]. Under android system, a large of privacy data (e.g. photos or videos) are stored in external storage (emulated Sdcard storage), which can be accessed by installed apps. This not only results in privacy leakage but also incurs ransomware attack[2] (e.g. simplocker). Therefore, we present Sdguard, an app, can implement fine-grain permission control based on Linux DAC mechanism and detect ransomware which encrypts content of file stored in external storage or lock user screen. To install Sdguard app, we need to ensure that the smartphone has been rooted and use FUSE filesystem on external storage. During installing, sdcard daemon of android (i.e. FUSE daemon) is replaced by our customized sdcard daemon. After rebooting system, the customized daemon is loaded, and each component of Sdguard is running.

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.

Cloud gaming has emerged as a promising application to enable high-end game playing with thin clients. Transmission control protocol (TCP) is pervasively adopted as the transport-layer protocol in the mainstream cloud gaming systems for video communication. However, streaming mobile cloud gaming video using the TCP is challenged with several key technical barriers: (1) the performance limitations of wireless networks in bandwidth and reliability; (2) the high throughput demand and stringent delay constraint imposed by high-quality gaming video transmission; and (3) the deadline violations and throughput fluctuations caused by the packet retransmission and congestion control mechanisms in the TCP. To address these critical problems, this paper proposes an application-layer source-forward error correction (FEC) coding framework dubbed adaptive source-FEC coding over TCP (ESCOT). First, we analytically formulate the optimization problem of joint source-FEC coding to minimize the end-to-end distortion of real-time video communication over TCP. Second, we develop a heuristic solution for effective loss rate approximation, source rate control, and FEC coding adaptation. ESCOT is distinct from existing source-FEC coding schemes in proactively analyzing and leveraging the TCP characteristics. The proposed solution is able to effectively mitigate both consecutive and sporadic video frame drops caused by congestion and random packet losses. We conduct the performance evaluation through extensive emulations in the Exata platform using real-time gaming video encoded by the H.264 codec. Experimental results show that the ESCOT advances the state of the art with noticeable improvements in video peak signal-to-noise ratio, end-to-end delay, goodput, and frame success rate.

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.

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.

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 technology is becoming widely implemented in popular multimedia applications (e.g., Youtube and cloud gaming) to provide a smooth viewing experience, while transmission control protocol (TCP) is pervasively adopted as the transport-layer solution for video communications to achieve firewall traversal and network friendliness. However, it is severely challenging to effectively deliver real-time HFR video over TCP: 1) HFR video streaming features high transmission rate, enhanced frame density, and stringent delay constraint and 2) the packet retransmission and congestion control mechanisms in TCP may cause frequent throughput fluctuations and deadline violations. Motivated by addressing these critical issues, this research presents an application-layer forward error correction (FEC) framework dubbed Raptor coded HFR video over TCP (ROCHET). First, we develop a mathematical model to analyze the frame-level distortion of systematic Raptor code-based HFR video communication over TCP in wireless networks. Second, we propose a joint approximate distortion estimation and Raptor coding adaption solution to minimize the sum of total distortion. The proposed ROCHET is able to effectively leverage unequal error protection and TCP state analysis to enhance streaming video quality. We conduct the performance evaluation through extensive emulations in Exata involving real-time HFR video encoded with H.264 codec. Compared with the existing FEC coding schemes, ROCHET achieves appreciable improvements in terms of video peak signal-to-noise ratio, goodput, and frame success rate. Thus, ROCHET is recommended for TCP-based HFR video transmission over wireless networks.

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.

Delivering high-quality mobile video with the limited radio resources is challenging due to the time-varying channel status and stringent Quality of Service (QoS) requirements. Multi-homing support enables mobile terminals to establish multiple simultaneous associations for enhancing transmission performance. In this paper, we study the multi-homed communication of delay-constrained High Definition (HD) video in heterogeneous wireless networks. The low-delay encoded HD video streaming consists exclusively of Intra (I) and Predicted (P) frames. In the capacity-limited wireless networks, it is highly possible the large-size I frames experience deadline violations and induce severe quality degradations. To address the challenging problem, we propose a novel scheduling framework dubbed delAy Stringent COded Transmission (ASCOT) that featured by frame-level data protection and allocation overmultiple wireless access networks. First, we perform online video distortion estimation to simulate multi-homed transmission impairments according to the feedback channel status and input video data. Second, we control the frame protection level by adapting the Forward Error Correction (FEC) coding redundancy and video data allocation to achieve target quality. The performance of the proposed ASCOT is evaluated through semi-physical emulations in Exata using real-time H.264 video streaming. Experimental results show that ASCOT outperforms existing transmission schemes in improving the video PSNR (Peak Signal-to-Noise Ratio), reducing the end-to-end delay, and increasing the goodput. Or conversely, ASCOTachieves the same video quality with approximately 20 percent bandwidth conservation.

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.

Delivering high-definition (HD) wireless video under stringent delay constraint is challenging with regard to the limited network resources and high transmission rate. Concurrent multipath transfer (CMT) using stream control transmission protocol (SCTP) exploits the multihoming feature of mobile devices to establish associations with different access networks. In this paper, we study the multihomed HD video communication with SCTP over heterogeneous wireless networks. The existing CMT schemes mainly treat the traffic data in a content-agnostic fashion, and thus cannot effectively leverage the scarce wireless resources to maximize the perceived video quality. To address this critical issue, we propose a content-aware CMT (CMT-CA) solution that featured by the unequal frame-level scheduling based on estimated video parameters and feedback channel status. First, we develop an analytical framework to model the total distortion of parallel video transmission over multiple wireless access networks. Second, we introduce a joint congestion control and data distribution scheme to minimize the total distortion based on online quality evaluation and Markov decision process (MDP). The performance of CMT-CA is evaluated through extensive semi-physical emulations in Exata involving HD video encoded with H.264 codec. Experimental results show that CMT-CA outperforms the reference schemes in terms of video peak signal-to-noise ratio (PSNR), end-to-end delay, and goodput. Or conversely, CMT-CA achieves the same video quality with 20 percent bandwidth conservation.

High frame rate (HFR) video is emerging as a new paradigm in popular multimedia applications (e.g., cloud gaming) to achieve smooth viewing experience perceived by end-users. In the context of HFR streaming video, end-to-end distortion and sending frame rate are equally important to the perceptual quality. This study presents a modeling-based approach to optimizing the HFR video transmission over wireless networks. First, we develop an analytical model dubbed FRIED (Frame Rate versus vIdEo Distortion) to characterize the tradeoff between sending frame rate and end-to-end video distortion. Second, we propose a Joint frAme Selection and FEC (Forward Error Correction) cOding (JASCO) approach based on the FRIED model to optimize the transmission performance. The efficacy of the proposed JASCO is evaluated through extensive semi-physical emulations in Exata involving H.264 video streaming. Experimental results show that JASCO outperforms the reference approaches in improving video peak signal-to-noise ratio (PSNR) at the same frame rate. Or conversely, JASCO is able to achieve higher received frame rate while guaranteeing the same video PSNR.

Traditional Complex Event systems (CEP) did not consider the computation requirements of continuous dynamic behavior such as differential equations. In addition, the event composition rules were predefined before the CEP engine began working. The rule defining task is error-prone and cumbersome. In this paper, therefore, a hybrid complex event service is proposed, which deals with not only discrete events but also continuous behavior computation based on IoT (Internet of Things) resource models. In order to satisfy the real-time constraints of processing IoT events, a divide-and-conquer principle is adopted, where we give a combination theorem such that different events can be processed on different IoT resources and then these processed results can be combined to derive complex events. Based on the formal IoT resources and event knowledge, we define interest goals to direct event composition without enumerating event relation to define event composition rules. We finally present event composition algorithms and evaluation to show our idea.

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.

Content-Centric Networking (CCN) has recently emerged as a clean-slate Future Internet architecture which has a completely different communication pattern compared with exiting IP network. Since the World Wide Web has become one of the most popular and important applications on the Internet, how to effectively support the dominant browser and server based web applications is a key to the success of CCN. However, the existing web browsers and servers are mainly designed for the HTTP protocol over TCP/IP networks and cannot directly support CCN-based web applications. Existing research mainly focuses on plug-in or proxy/gateway approaches at client and server sides, and these schemes seriously impact the service performance due to multiple protocol conversions. To address above problems, we designed and implemented a CCN web browser and a CCN web server to natively support CCN protocol. To facilitate the smooth evolution from IP networks to CCN, CCNBrowser and CCNxTomcat also support the HTTP protocol besides the CCN. Experimental results show that CCNBrowser and CCNxTomcat outperform existing implementations. Finally, a real CCN-based web application is deployed on a CCN experimental testbed, which validates the applicability of CCNBrowser and CCNxTomcat.

Summary Even though many IoT (Internet of Things) applications have been implemented based on distributed events, how to construct scalable IoT services is still unclear. In this paper, we first discuss representing physical entities as IoT resources in the cyber world and integrate them into IoT services and then use an event session mechanism to express the coordination logic in an IoT service system. Given their event‐driven models, the scalable IoT services are constructed through decoupling one service at behavior from others, where running atomic service instances with high concurrency is our first focus, and distributed execution of an IoT business process is our second focus. Our last focus is to make each distributed business process execution unit hold its properties in the whole process, which requires modeling the environment as a prerequisite to compute its properties, and some assume‐guarantee rules for composing the services from the environment's perspective. We then propose a platform to support the construction, where according to the behavior decoupling features, one IoT business process is decomposed into pieces of event composition logic and business computation logic, together with separating the data dependency of computation functions in each process fragment, such that it can be concurrently executed and distributed. A practical application is implemented to concept‐prove our work. Copyright © 2015 John Wiley & Sons, Ltd.

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.

Chatting with nearby interested strangers instantly in location-based mobile social network (LMSN) has become increasingly popular. Currently, friend recommendation relies only on the simple and limited user profiles, and is agnostic to users' offline behaviors in the real world. For the first time, we focus on utilizing the user's check-in behaviors in the real world, instead of the general acquaintance-based social circles, to instantly recommend nearby strangers to make friends. However, bridging nearby strangers with similar check-in behaviors instantly has some new characteristics, such as lack of common friends and interaction histories, temporal, spatial and user three-dimensional correlation, and sparseness of check-ins. Most existing work about friend recommendations mainly focuses on making friends within the acquaintance-based social circles, and has not fully considered these new characteristics mentioned above. Therefore, how to catch the ephemeral opportunity to recommend nearby interested strangers instantly remains a challenge. In this paper, we present to use “Encounter” probability to measure the behavior similarity of two strangers in the real world based on their check-in histories. To address the sparseness challenge of check-in data, a Kernel Density Estimation (KDE)-based user check-in probability estimation method considering the spatiotemporal dimensions is proposed to estimate each user's check-in probability distribution with time at each spot. Finally, we use a large-scale user check-in dataset of Gowalla to validate the effectiveness of this approach. The experimental results show that our approach outperforms other commonly used similarity computation methods.

Transmission control protocol (TCP) is pervasively employed as the transport-layer solution in popular video applications (e.g., Skype, Google+, HTTP-based adaptive video streaming, etc.) for firewall traversal and network-friendliness. To remedy the shortcomings of the data retransmission mechanism in TCP, forward error correction (FEC) coding is commonly used as the application-layer error-resilient scheme in live media streaming systems. An important measurement study reveals that TCP exhibits delay-friendliness (i.e., delay-performance bias) towards traffic flows composed of small-size packets. Motivated by leveraging the delay-friendliness of TCP to optimize the real-time streaming video quality, we propose a novel FEC coding scheme dubbed Coded Live vide OStreaming ov Er TCP (CLOSET). To achieve the optimal video quality over the lossy communication networks, we analytically formulate the constrained optimization problem of joint FEC coding and packet interleaving to minimize the effective packet loss rate. Then, we provide an approximate analysis to derive the solution for online adaption of FEC redundancy, packet size, and interleaving level. The performance of CLOSET is evaluated through extensive semi-physical emulations in Exata involving real-time encoded H.264 video streaming. Experimental results show that CLOSET outperforms the reference FEC coding schemes in terms of video peak signal-to-noise ratio (PSNR), end-to-end delay, ratio of overdue video frames, and goodput. Therefore, we recommend CLOSET for TCP-based real-time video communication systems.

High-frame-rate (HFR) video is emerging in popular gaming applications to enhance the smooth experience perceived by end users. However, it is challenging to guarantee the delivery quality of HFR video in mobile cloud gaming scenarios because of the high transmission rate and limited wireless resources. To address this critical problem, we develop a novel transmission scheduling framework dubbed AdaPtive HFR vIdeo Streaming (APHIS). The term adaptive indicates this scheme's capability in dynamically adjusting the video traffic load and forward error correction (FEC) coding. First, we propose an online video frame selection algorithm to minimize the total distortion based on the network status, input video data, and delay constraint. Second, we introduce an unequal FEC coding scheme to provide differentiated protection for Intra (I) and Predicted (P) frames with low-latency cost. The proposed APHIS framework is able to appropriately filter video frames and adjust data protection levels to optimize the quality of HFR video streaming. We conduct extensive emulations in Exata involving HFR video encoded with H.264 codec. Experimental results show that APHIS outperforms the reference transmission schemes in terms of video peak signal-to-noise ratio, end-to-end delay, and goodput. Therefore, we recommend APHIS for delivering HFR video streaming in mobile cloud gaming systems.

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.

With the increasing popularity of web services on the Internet, besides functionalities, Quality of Service (QoS) is becoming an important concern for describing characteristics of web services. QoS rankings provide valuable information for making optimal service selection and recommendation from a set of functionally similar or equivalent service candidates. However, in order to obtain such rankings, a huge number of invocations on the services are usually required, which is extremely expensive and even impractical in reality. To tackle this challenge, this paper proposes a scheme to derive the global ranking from observations of QoS rankings on subsets of the services, while the observations may also be contaminated by noise and errors. We introduce a pairwise comparison model to describe the relationships between services, and thus the ranking can be formulated as random walks over the services. A Markov chain based approach is proposed, and algorithms for deriving global rankings are designed. The efficacy of our approach is validated by both mathematical analysis and simulation experiments.

Service platforms have disadvantages such as they have long construction periods, low resource utilizations and isolated constructions. Migrating service platforms into clouds can solve these problems. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The automatic scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user SLA while keeping scaling costs low.

We consider the problem of high definition video transmission in a heterogeneous wireless network from a video server to a multihomed client. On one hand, a single wireless network is limited in transmission performance (e.g., available bandwidth and link delay); On the other hand, the high definition video streaming is characterized by high transmission rate and large-size video frames. Thus, the end-to-end video frame delay becomes a severely challenging problem which is critical for real-time video applications. In this paper, we propose a novel scheduling approach named SFL, which deliberately splits large-size video frames into sub-frames and dispatches each of them onto a different wireless network to the multihomed client. This approach is able to improve the frame-level delay performance for enhancing video quality. We formulate a optimization problem of video streaming allocation for minimizing the end-to-end delay based on the network calculus and derive its solution with the water filling algorithm. We evaluate the performance of the proposed SFL through Exata emulations using real-time H.264 video streaming. Emulation results show that SFL outperforms existing frame level scheduling approaches in improving frame-level delay as well as in enhancing video quality in terms of PSNR (Peak Signal-to-Noise Ratio).

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.

As cloud-based services become more numerous and dynamic, service deployment becomes more and more challenging. For the reason of economic, the service deployment strategy is crucial for maximize the system utility and profit gained. In this paper, considered both the cost and quality, the service deployment in cloud is modeled as a congestion game, where every service has several components is considered as a player and his strategy is to select the subset of resources to deploy his components. Based on the congestion game, a game theoretic method is proposed to optimize both the overall cost and quality. To solve the congestion game, efficient algorithms are proposed to achieve the equilibrium in polynomial time.

Load distribution is vital to the performance of multipath transport. The task becomes more challenging in real-time multimedia applications (RTMA), which impose stringent delay requirements. Two key issues to be addressed are: 1) how to minimize end-to-end delay, and 2) how to alleviate packet reordering that incurs additional recovery time at the receiver. In this paper, we propose SPMLD, a new model that splits traffic at the granularity of sub-packet. The Sub-Packet based Multipath Load Distribution (SPMLD) model aims to minimize total packet delay by effectively aggregating multiple parallel paths as a single virtual path. First, we formulate the packet splitting over multiple paths as a constrained optimization problem and derive its solution based on progressive approximation method. Second, in the solution, we analyze queuing delay by introducing D/M/1 model and obtain the expression of dynamic packet splitting ratio for each path. The performances of SPMLD are evaluated through extensive simulations in QualNet using real-time H.264 video streaming. Experimental results demonstrate that: SPMLD outperforms previous flow and packet based load distribution models in total packet delay, end-to-end delay and seldom induces packet reordering. Besides, SPMLD's extra overhead is tiny compared to the input video streaming.

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.

With the increasing amount of web services on the Internet, personalized web service selection and recommendation are becoming more and more important. In this paper, we present a new similarity measure for web service similarity computation and propose a novel collaborative filtering approach, called normal recovery collaborative filtering, for personalized web service recommendation. To evaluate the web service recommendation performance of our approach, we conduct large-scale real-world experiments, involving 5,825 real-world web services in 73 countries and 339 service users in 30 countries. To the best of our knowledge, our experiment is the largest scale experiment in the field of service computing, improving over the previous record by a factor of 100. The experimental results show that our approach achieves better accuracy than other competing approaches.

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.

With the rapid application of service-oriented technologies, service and data outsourcing has become a practical and useful computing paradigm. Combined use of access control and cryptography was proposed by many researchers to protect sensitive information in this outsourcing scenario. However, the rigid combination in existing approaches has difficulty in satisfying the flexibility requirement of access control for diverse applications. In this paper, we propose a flexible authorization by generating public re-decryption trapdoor. The features of our solution are as follows: simple key management without the need of key derivation for users to decrypt cipher texts, grouping users during authorization to reduce management workload, composing conditions for accessing new resources without generating new keys if the re-encryption keys for atomic conditions have been produced.

Web Service is one of implementation modes of distributed systems and is becoming the next generation web-based application. As the amount of Web services is increasing, service experience problem is emerging. After Web services are implemented and published, service providers do not know their running effect and user satisfactory degree. So in order to provide better services and improve user experience quality, this paper proposes a three-step service experience approach with feedback to service accessing, in which service experience coefficient is used to measure the running effect of service and user satisfaction degree. At the same time, the test data of the feedback are sent to service providers to create new services or improve services in order to provide better services. With the data of the web site WS-DREAM, the service experience approach to service accessing is designed and implemented. The result indicates that the approach is useful to improve better services.

MapReduce is an important programming model for large-scale data-intensive applications such as web indexing, scientific simulation, and data mining. Hadoop is an open-source implementation of MapReduce enjoying wide adoption. Partition function is an important component of Hadoop which split outputs of maps into bulks that place the input data of reduces. Based on the assumptions that cluster nodes are homogeneous and perform work at roughly the same rate, its default partition function splits intermediate keys into reduces. However, in practice the homogeneity assumptions seldom hold and cluster nodes usually perform work at different rate. In this paper, we design a heterogeneity-load-aware partition function named proportional partition function (PPF). Besides the dynamic loading of cluster nodes, PPF considers the capacity diversity of cluster nodes such as CPU processing speed and disk writing speed.

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.

During the design procedure of BPEL (Business Process Execution Language) processes, there may be control dependency deadlocks among the activities in BPEL. We design a novel Agree/Refuse Matrix (ARM) to real-timely detect control dependency deadlocks. Online detection approach can be integrated into Active BPEL Designer to extend their functions and improve the accuracy of BPEL processes. Most importantly, this method's real-time property can help avoiding detecting the whole process from scratch caused by process's partial modifications.

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.

Since there are many Web services on the Internet, personalized Web service selection and recommendation is very important. In this paper, we present a new similarity measure for Web service similarity computation and propose a normal recovery collaborative filtering (NRCF) method for personalized Web service recommendation.

Traditional graph configuration software builds monitor screen by integrating user interface components in a block-building way. It is domain-specific, such as a graph configuration system for coal mine. And usually the control logic (also called business) of a graph configuration system for coal mine is not suitable for electricity, yet its user interface and control logic (also called business) are coupled together. In order to solve these problems this paper proposes a UI service composition method based on SCA (Service Component Architecture) and DTS (Domain, Task and Show) models. SCA is going to decouple business from user interface, while DTS is going to model user interface thinking about its reuse. Through experiments, we can see that this system not only can significantly reduce the cost of UI development by DTS, but also provide efficient support for users to participate in business processes.

To solve the problems of traditional reporting system, such as difficult manipulation, high levels of data coupling, this paper presents an ontology-based semantic reporting system. It builds a semantic layer between report designer and data source, where enterprise professional information, relevant properties and metadata about data source are described in ontology language. On the report designer interface, semantic information that saved in ontology library is shown and can be dragged to design the report. Ontology-based semantic layer greatly improves design efficiency and makes manipulation easy, because it seals the technical details of data source.

With the rapid development of Service-Oriented Architecture (SOA), more and more enterprise applications are built on the Enterprise Services Bus (ESB). However, ESB would not be able to deal with the exchange of data among multiple ESBs. In this paper, we propose an Enterprise Cloud Bus (ECB) framework that combines the WS-Notification with cloud computing. On the basis of WS-Notification specification and cloud queue model, we design and implement the Subscribe Request Queue cloud service model that supports real-time communication among distributed ESB, and supports running non-Service components or composition services deployed on distributed ESB or in different business process. On the basis of cloud platform, our framework shows better scalability and reliability.

Web Service is becoming the next generation of web-based application. With enhancement of quality of services and increasing quantity of services, how to recommend the suitable services according to personalized requirement becomes an urgent question. In the existing approaches of service recommendation, the result of service recommendation is the service list in which there is no evaluation standard that we can use to distinguish services with high relevancy and low relevancy. So in real-world, the user may obtain low relative services. To address the problem, in this paper, membership function is analyzed and recommendation measure standard is proposed. With dynamic programming theory, an ontology based approach of service recommendation is provided. In the result of service recommendation, membership as measure index is used to divide high relative services, medium relative services and low relative services. High relative services are recommended to the user. So the recommended services are accurate and available.

The relationship-focused and credential-focused identity managements are both user-centric notions in Service-oriented architecture (SOA). For composite services, pure user-centric identity management is inefficient because each subservice may authenticate and authorize users and users need participate in every identity provisioning transaction. If the above two paradigms are unified into the universal identity management where identity information and privileges are delegatable, user centricity will be more feasible in SOA. The credential-focused system is a good starting point for constructing a universal identity management system. However, how to implement a practical delegation scheme is still a challenge although there are some delegatable anonymous credential schemes that were theoretically constructed. This paper aims to propose a practical delegation solution for universal identity management. For this, a pseudonym-based signature scheme is first designed where pseudonyms are self-generated and unlinkable for realizing user's privacy. Next, a proxy signature is presented with the pseudonyms as public keys where delegation can be achieved through certificate chains. Finally, the security of our scheme is analyzed and proved in the random oracle model.

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.

Service discovery is the process of finding suitable services and selecting the best alternative for a given task, and service matching algorithm is the kernel technology of the step. Nowadays there are no quantity index of service Query Complete Rate (QCR) and Query Accuracy Rate (QAR), and the efficiency of service matching is low. This paper defines QCR and QAR, and provides a new ontology-based service matching algorithm that uses the theory of query rewriting from key-words to ontology to select suitable services.

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.

Web services have led a revolution of Internet technology architecture by their platform-independence, language- independence and other characters. But traditional Web service architecture is based on "Client/Server" model, where server is always providing service reactively. Software agents are now increasingly used in commercial applications to solve complex engineering problems, for their autonomous, proactive and social capabilities. And these applications often make use of Web services. As such, this paper presents a Web service invocation infrastructure based on software agents. The infrastructure is a hybrid peer-to-peer model, using agents to describe service providers and service customers. This invocation model is more flexible than traditional Web service model, for (l)agents can invoke services in a proactive manner no matter whether they act like service customers or providers, and(2)agents can also act as multi-role actors in service domain. And with inspiration from Aspect-Oriented programming, web services are mapped as aspects, while agents are mapped as node. In this way, Web service policies in an agent can be considered to be form an filter chain, either incoming filter chain or outgoing filter chain, which is used to describe agent's request or response filter policies. When service contractor satisfies both the incoming filter chain and the outgoing filter chain at the same time, the corresponding service can be invoked dynamically. And experiments show that dynamic service invocation can be achieved in our infrastructure.

In order to naturally incorporate the information provided by Web services into the data integration systems, we propose a novel model, uniform query, to uniformly describe the data query/view and the semantic of information-providing Web services. With the proper matching algorithm we have implemented a prototype in which Web services act as normal data sources (e.g. RDF datasets), and data queries could be answered through dynamic invocation of matched services.

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.

With the wide application of Web services, its performance has caused the extensive concern and becomes one of the key factors of determining whether it is widely applied further. Based on the idea of differentiating Web services, the paper presents a SLA-based Web services performance guarantee model to improve Web services runtime environment, and expatiates on the key techniques of realizing the model: the request class utility function, the predicting model of response time, the genetic algorithm-based resource allocation and the dynamic weight-based weighted round-robin scheduling algorithm. Experiments prove the model can guarantee the response performance of Web services requests and improve the stability of the system.

Goal Web services description languages and service discovery algorithms are two keys to deal with the service discovery problem. Based on the analysis of the current Web services description languages, the paper designs a goal Web services description ontology which can depict the functional features, the performance features and the semantic features of Web services. On the principle of enhancing efficiency and quality of the service discovery, the paper presents a Web services feature elements-based service discovery model, and implements a prototype system in the tourist domain

With the development of Web service, it is critical to compose existing Web services dynamically based on the need of users. This paper analyzes semantic association between services and proposes a dynamic Web service composition approach based on dynamic semantic association between services (DSAC). DSAC discovers the dynamic semantic of services when they are executing, integrates IO interface-match method and services' semantic relation ontology to get candidate successive services. This semi-automatically method could get an optimized service matchmaking results, ensure efficiency while composing services. The prototype and experiments show that DSAC can help user select proper services simply and is quite feasible for solving problems with real world sizes

Next generation wireless network would be a service driven network. As SoftSwitch system could play a very important roll in wireless network, SoftSwitch system also possesses great capabilities in providing services. In this paper, based on the analysis of the architecture of SoftSwitch based wireless network, a variety of service solutions are presented. Moreover, a design scheme of SoftSwitch, which could integrate all service solutions stated in this paper, is proposed.