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With proliferation of smart devices and wireless applications, the recent few years have witnessed data surge. These massive data needs to be stored, transmitted, and processed in time to exploit their value for decision making. Conventional cloud computing requires transmission of massive amount of data in and out of core network, which can lead to longer service latency and potential traffic congestion. As a new platform, mobile edge computing (MEC) moves computation and storage resources to edge network in proximity to the data source. With MEC, data can be processed locally, and thus mitigate issues of latency and congestion. However, it is very challenging to reap the benefits of MEC everywhere due to geographic constraints, expensive deployment cost, and immoveable base stations. Because of easy deployment and high mobility of unmanned aerial vehicles (UAVs), air-ground integrated mobile edge networks (AGMEN) is proposed, where UAVs are employed to assist the MEC network. Such an AGMEN expects to provide MEC services ubiquitously and reliably. In this article, we first introduce the characteristics and components of UAV. Then, we will review the applications, key challenges, and current research technologies of AGMEN, from perspectives of communication, computation, and caching, respectively. Finally, we will discuss some essential research directions for AGMEN.

With the popularization of the cloud, an increasing number of users and developers need to combine multiple different services to satisfy their requirements in the cloud. Consequently, the question of how to combine these services that are published by different service providers in the cloud has become an active focus of research in service-oriented cloud systems. Service composition is the core technology of service-oriented cloud systems.

Satellite network has become very popular in recent years. The great development of satellite network motivates the research on satellite-air-ground integrated network to accommodate a variety of services. The concept of space service computing is predicated on moving some services towards space network to harness service capabilities that are currently untapped in space nodes, such as satellites, orbital stations, aircrafts and so on. In this paper, we attempt to sketch a big picture about space service computing with a hope of arousing interest of the research community in this emerging field. We first present a space service computing framework. Compared with the traditional service computing, there are many new challenges for space service computing due to the unique space and aerial environment. Therefore, we also discuss the challenges and opportunities in space service computing.

A growing number of mobile services demand intensive computation resources and high energy consumption. Traditional cloud-based task offloading incurs excessive transmission delay. With mobile edge computing, we can offload tasks to the nearby edge servers, thus mitigating long data transmission latency. Current task offloading and resource allocation approaches ignore the extra overhead brought by container instance startup. This naturally leads to a long overall latency and high energy consumption. Motivated by the limitations of existing literature, we address task offloading and resource allocation problem considering container instance startup time. We first examine the startup latency of containers started from different types of images, and present its impact on overall performance. Then, we formulate the problem as optimization problem. The problem is hard to address because mutual effects of offloading decision, resource allocation decision and container instance startup contribute to the overall performance. We resort to Gibbs sampling to decouple offloading decision and resource allocation. Finally, taking into account all potential conditions brought up by container instance startup, an optimal computation resource allocation and uplink power assignment scheme are derived through bisection, and Karush-Kuhn-Tucher (KKT) conditions. Comprehensive evaluations are conducted to verify the performance of the proposed approach, and demonstrate its superiority over current notable solutions.

Geosynchronous Earth Orbit (GEO) satellites, which can relay image data for Low Earth Orbit (LEO) satellites, play an important role in remote sensing. With the development of satellite technologies, the significantly improved computation capabilities of GEO satellites have enabled space service computing, through which GEO satellites can provide data processing services before forwarding to reduce the quantity of transmitted data. In the presence of multiple LEO satellites, how to make effective use of limited communication and computation resources in GEO satellites has become crucial. At present, the research on satellite resource management typically focuses on either communication or computation resources. Existing resource management algorithms are usually of slow convergence speed, which limits their applicability in real-time remote sensing scenarios. Therefore, we propose an aggregated resource management method for remote sensing applications. We first propose models for transmission tasks and processing tasks of remote sensing images. Then we formulate the aggregated resource management for satellite edge computing as a hybrid Stackelberg game and simplify the problem to speed up its convergence speed. Then we propose a distributed resource management algorithm to determine the optimal strategies. Simulation results show that the proposed method can quickly obtain the optimal resource allocation strategy and outperforms typical dynamic iterative algorithms in terms of service quantity and throughput.

Dynamic adaptive video streaming over HTTP (DASH) is widely studied and has been adopted in modern video players to ensure user quality of experience (QoE). In DASH, adaptive bitrate control is a key part whose ultimate goal is to maximize video bitrate while minimizing rebuffering. Throughput prediction plays an important role in helping select the proper video bitrate dynamically. In this paper, we studied the influence of throughput prediction on adaptive video streaming. Because the real-world network is dynamic, different methods need to be tested with large-scale deployments and analyzed statistically. However, this is difficult in academic research. Therefore, we established a reproducible trace-based emulation environment, which enables us to compare different methods quantitatively under the artificially same condition, with limited experiments. The throughput prediction methods are implemented into DASH to evaluate the effect on QoE for video streaming. The results indicate that the prediction method using long short-term memory (LSTM) performs better than the other methods. However, throughput prediction alone is not enough to ensure high QoE. To further improve the QoE, we proposed the decision map method (DMM), where the buffer occupancy is also incorporated to make a selection. By using this decision map, the choice of bitrate can be smarter than that when only prediction information is used. The total QoE is further improved by 32.1% in the ferry trace, which shows the effectiveness of DMM in further improving the performance of throughput prediction in adaptive bitrate control.

With the increasing video demand in daily network traffic, it is an urgent task to develop effective algorithms to facilitate high-quality content delivery service. Recently, numerous adaptive streaming algorithms have been proposed to improve the user perceived experience. However, these algorithms were mainly developed from the perspective of single user. There is not yet systematical evaluation and comparison of the bitrate adaptation methods for multi-user video streaming. Besides, the Quality of Experience (QoE) metrics were not unified.In this work, we propose a new mininet-based testbed framework which is able to conduct real-time video streaming emulation in various multi-user scenarios. Seven state-of-the-art adaptation methods are incorporated into the testbed. Meanwhile, ITU-T P.1203 model, the world’s first standard for measuring QoE of HTTP adaptive streaming, is implemented to calculate the mean opinion scores of different methods. Using the developed testbed, the performance of current adaptation methods in multi-user network is analyzed and compared. A variety of experiments are carried out by changing the user number and network conditions, in which the QoE of different users are investigated. It is found that current algorithms perform inconsistently in various network scenarios. In the excessive user and limited bandwidth cases, machine learning and scheduling techniques show superiority in providing high and equal QoE for all users. While in the high-delay case, the buffer-based approaches show robust performance. Overall, the findings of this work give an insight for designing and choosing adaptive streaming strategies in different multi-user network conditions.

High-speed and low-latency Ethernet interface has always been a key technology for data center. In recent years, the rise of artificial intelligence applications and cloud platforms has put forward higher requirements for data throughput and transmission delay. In this paper, we propose a design of ultra low-latency MAC/PCS IP for high-speed Ethernet based on the existing architecture of MAC and PHY. We first analyze the low-latency technologies used in our IP, then design a simulation experiment and measure the latency based on different platforms. The results show that our MAC/PCS IP has a quite obvious improvement compared with existing solutions.

Cognitive services have revolutionized the way we live, work and interact with the world. In recent years, deep neural networks have become the mainstream approach in cognitive service, and mobile edge computing facilitates a variety of cognitive services for users by offloading computation tasks from resource-limited mobile devices to relatively wealthy edge servers. Combining the two to provide users with a higher quality of cognitive service is an issue worth researching. However, many related studies are not easy to provide fast responses because in these systems, edge servers are only used to pre-process data, and the cloud server is used to perform tasks. In this paper, we aim to study deploying deep neural network models on edge servers to provide fast services. However, a single edge server collects only a small amount of data, which results in low inference accuracy. To address this problem, we propose a cloud and edge collaboration framework. The key idea of the proposed framework is to use a cloud model to assist in training an edge model to improve the latter's inference accuracy and enable the latter to provide fast response and high-performance cognitive service. Experimental results demonstrate the effectiveness of our proposed framework.

Edge computing as a highly distributed multi-tenant computing system relies on its effective interactions with the underlying 5G network to achieve its advantages in service provisioning. On the other hand, edge computing is also transforming 5G network via deploying more computing capabilities at network edge. In this paper, we advocate a holistic vision of service orchestration to facilitate integration of edge computing and 5G network. We attempt to sketch a big picture of the state of the art research enabling the integrated network-edge service architecture. We also discuss technical challenges and identify opportunities for future research in this area.

With the increasing number of in-vehicle sensors and the adoption of various communication interfaces, vehicles have become an ambient sensing platform for many applications such as traffic and weather monitoring, urban surveillance, road safety, and driver behavior analysis. Furthermore, a large number of vehicles can be coordinated to sense large-scale information precisely and efficiently. Such coordination requires enormous communication, computing, and power resources. In this paper, we propose a virtual vehicle (VV) coordination approach based on the theory of group consensus to sense environment information efficiently. Specifically, we first propose a discovery algorithm to find the optimal VV groups. Then, based on multi-group consensus theory, we devise a coordination algorithm to control VV groups to achieve multi-group coordination by adjusting the communication relationships among VVs. Finally, the precise environment information is acquired through the coordination of multiple VV groups. Extensive simulations are provided to demonstrate the effectiveness of the proposed schemes.

The recent developments in networking research leverage the principles of virtualization and service-orientation to enable fundamental changes in network architecture, which forms a trend of network cloudification that enables network systems to be realized using cloud technologies and network services to be provisioned following the cloud service model. On the other hand, the latest progress in cloud and fog computing has made networking an indispensable ingredient for cloud/fog service delivery. Convergence of networking and cloud/fog computing technologies enables unification of network and cloud/fog service provisioning, which has become an active research area that attracts interest from both academia and industry. In this paper, we introduce the notion of network-cloud/fog service unification and propose an architectural framework for unified network-cloud/fog service provisioning. Then we present a survey that reflects the state of the art of research on enabling network-cloud/fog service unification. We also discuss challenges to realizing such unification and identify some opportunities for future research, with a hope to arouse the research community's interest in this exciting interdisciplinary field.

Throughput prediction is essential for ensuring high quality of service for video streaming transmissions. However, current methods are incapable of accurately predicting throughput in mobile networks, especially for moving user scenarios. Therefore, we propose a TCP throughput prediction method TRUST using machine learning for mobile networks. TRUST has two stages: user movement pattern identification and throughput prediction. In the prediction stage, the long short-term memory (LSTM) model is employed for TCP throughput prediction. TRUST takes all the communication quality factors, sensor data and scenario information into consideration. Field experiments are conducted to evaluate TRUST in various scenarios. The results indicate that TRUST can predict future throughput with higher accuracy than the conventional methods, which decreases the throughput prediction error by maximum 44% under the moving bus scenario.

Hierarchical Task Network (HTN) planning is an AI planning technique, which can be employed to implement service composition. Current HTN-based service composition systems fail to solve the problem comprehensively for only considering functional properties and ignoring the constraints of QoS. In this paper, we address this issue and solve the problem by exploiting the HTN planner JSHOP2. We implement an automatic service composition system by extending JSHOP2 with the consideration of both functional and non-functional properties. Furthermore, we conduct experiments on the system. Experiments show the effectiveness of our system.

Mobile Edge Computing (MEC) is designed to extend the edge of the cloud network to decrease latency and network congestion, which would significantly improve the quality of experience (QoE) of adaptive streaming service for mobile users. This paper proposes QCSS, a QoE-aware control plane for adaptive streaming service over MEC infrastructures. QCSS aims to assure high QoE delivery of online streaming service to mobile users. The design of QCSS features: 1) a timeslot system with a look-ahead window for calculating cost of edge node switch and video quality adaption (to balance network load and reduce latency); 2) conducting service adaption via a set of cooperative action components running on client devices, edge nodes, and center nodes (to ensure a smooth viewing experience); 3) constructing a flexible QoE model and extending the scope and meaning of user-perceived experience. The effectiveness of QCSS has been validated via three real datasets. The validation results show that the proposed QCSS can improve QoE performance and network load performance for adaptive streaming service over MEC Infrastructures.

Edge server placement problem is a hot topic in mobile edge computing. In this paper, we study the problem of energy-aware edge server placement and try to find a more effective placement scheme with low energy consumption. Then, we formulate the problem as a multi-objective optimization problem and devise a particle swarm optimization based energy-aware edge server placement algorithm to find the optimal solution. We evaluate the algorithm based on the real dataset from Shanghai Telecom and the results show our algorithm can reduce more than 10% energy consumption with over 15% improvement in computing resource utilization, compared to other algorithms.

Effective virtual machine placement algorithms can improve network resource utilization in cloud data centers. In order to design a more effective VM placement algorithm, we usually rely on a large-scale experimental platform to evaluate and verify its performance. However, large-scale experiment may have a huge impact on data center network, which makes it very unlikely to happen. Therefore, researchers need an experiment platform that can support large-scale environment to meet the above requirements. In order to evaluate the performance of virtual machine deployment algorithm, this paper first designs and implements a cloud data center network experiment system, WebCloudSim, which can support the joint experimental verification of real environment and simulation environment. Then, based on WebCloudSim, the paper implements three classic algorithms for virtual machine deployment, and verifies and analyzes the results. The experimental results show that WebCloudSim can effectively support the performance evaluation of virtual machine placement algorithm.

With the development of mobile Internet, mobile service is emerging one after another, and the problem of information overload is becoming ever more serious. As an important tool to alleviate information overload, mobile service recommendation has attracted more and more attention. However, traditional recommendation algorithms always recommend popular services to users, which result into a rich-get-richer problem and become a barrier for the unpopular services to startup and growth. In order to promote the healthy development of the service ecosystem, it is necessary to guarantee the fairness of unpopular services. To address this problem, this paper proposes a fairness-aware mobile service recommendation method (FMSR), which gives a relatively fair recommendation opportunity for unpopular services. FMSR makes a tradeoff between recommendation accuracy and fairness, and can recommend popular services and unpopular services respectively. For unpopular services, we design a fair efficiency function and use combinatorial optimization techniques to achieve recommendations. For popular services, bias matrix factorization is utilized to implement recommendations. Experimental results based on real-world demonstrate that FMSR significantly improve the fairness of mobile service recommendation in the evolving mobile service ecosystem.

Mobile Edge Computing (MEC) offers a new paradigm to improve vehicular services and augment the capabilities of vehicles. In this paper, to reduce the latency of the computation offloading of vehicles, we study multiple vehicles computation offloading problem in vehicular edge networks. We formulate the problem as a multi-user computation offloading game problem, prove the existence of Nash equilibrium (NE) of the game and propose a distributed computation offloading algorithm to compute the equilibrium. We analyze the price of anarchy of the game algorithm and evaluate the performance of the game algorithm using extensive simulations. Numerical results show that the proposed algorithm can greatly reduce the computation overhead of vehicles.

The proliferation of real-time applications, such as online gaming, video chatting, brings unprecedented pressure for multi-cloud application providers to secure the good quality of experience. These applications are designed for cooperative group users/members/clients and are expected to have low data transmission latency due to frequent interactions. We use total data transmission latency (TDTL) to represent the group latency in this paper. However, existing approaches perform poorly since they often ignore the feature of the cooperative group scenario and cannot provide a flexible rental cost model for application providers. Minimizing TDTL is challenging due to the difficulties in 1) Finding an effective method to transmit data through multi-cloud datacenters, 2) Taking the cooperative group scenario into consideration, 3) Providing a flexible transmission rental cost model. To this end, we propose CMIP, an innovative approach that aims to minimize the TDTL by renting virtual machines of well-connected datacenters as the proxy for the cooperative group scenario in multi-cloud providers. First, a weighted graph is constructed to illustrate the multi-cloud network. Second, we define a path latency model and a rental cost model for the cooperative group. After that, Yen’s algorithm and mixed integer programming are adopted to optimize the TDTL with rental cost constraint. To study the performance of CMIP, we conduct extensive evaluations using multiple real data traces and compare it with related approaches. The comprehensive evaluation analysis shows that the CMIP achieves much lower total latency and is more flexible than other approaches.

As communication and sensing capabilities of mobile devices increase, mobile applications are becoming increasingly complex. The ability of computation offloading, which is one of the main features of mobile edge computing gains relevance as a technique to improve the battery lifetime of mobile devices and increase the performance of applications. In this paper, we describe the offloading system model and present an innovative architecture, called "MVR", contributing to computation offloading in mobile edge computing.

It is challenging to conduct quality of experience (QoE) evaluations of web-based streaming video services effectively and efficiently. Aiming to overcome this challenge, we have created QoECenter, a web-based visual platform that innovatively facilitates comprehensive QoE evaluations of the streaming video services. QoECenter offers a holistic approach to conducting the QoE evaluations via an integrated set of technologies for source video classification, QoS realization of video encoding and network transmission, and context-aware user experience data gathering and analysis. From a QoECenter consumer's viewpoint, three kinds of data are required for an end-to-end streaming video QoE evaluation: video source level data, system process level data, and end user level data. QoECenter provides visual interfaces for parameter setting and data acquisition for each data level, and supports both objective and subjective datadriven QoE analyses. A QoECenter consumer can easily conduct comparative QoE evaluations like running easy-to-use visual applications. The effectiveness and efficiency design objectives of QoECenter have been validated by various real experiments.

One App running on mobile devices often needs to invoke several frequently-used Application Programming Interfaces (APIs) to perform service provision. However, when these mobile devices roam around another city, these APIs' QoS performance changes or degradation often make App failure. Hence, accurate QoS prediction before these APIs are invoked becomes an important issue for App developers. In this paper, we propose an accurate API QoS prediction approach by using user similarity computation and base-station similarity computing. The experimental results demonstrate the efficiency and effectiveness of our approach.

Mobile Cloud Computing (MCC) offers a new paradigm to relieve the pressure of soaring data demands and augment the capabilities of resource-poor mobile devices. In this paper, we study the multi-user computation offloading problem in MCC to reduce the total cost including energy consumption, time consumption and monetary cost on mobile devices. We formulate the computation offloading decision making problem among multiple mobile device users as a multi-user computation offloading game problem. We present a distributed computation offloading algorithm and show the existence of Nash equilibrium of the game. We show that the proposed algorithm can achieve efficient computation offloading performance.

This poster addresses the problem of access point ranking for cloudlet placement in edge computing environment. Access point ranking is an important step for cloudlets placement. Currently, there is no method that can rank the access point effectively for ignoring the connection features of the access points. To address the problem, we propose an adaptive and comprehensive access point ranking method by investigating the connection features of the access points. We introduce the system model and the technical detail of the proposed access point ranking method in this paper. The experimental results validate the effectiveness of our proposed access point ranking method.

The widespread utilization of cloud computing services has brought in the emergence of cloud service reliability as an important issue for both cloud providers and users. To enhance cloud service reliability and reduce the subsequent losses, the future status of virtual machines should be monitored in real time and predicted before they crash. However, most existing methods ignore the following two characteristics of actual cloud environment, and will result in bad performance of status prediction: 1. cloud environment is dynamically changing, 2. cloud environment consists of many heterogeneous physical and virtual machines. In this paper, we investigate the predictive power of collected data from cloud environment, and propose a simple yet general machine learning model StaP to predict multiple machine status. We introduce the motivation, the model development and optimization of the proposed StaP. The experimental results validated the effectiveness of the proposed StaP.

In this paper, we propose a Skyline service selection approach based on QoS prediction. We first consider the QoS history records as time series and predict the QoS values by using Autoregressive Integrated Moving Average (ARIMA) model to provide more accurate QoS attributes values. And then we calculate the uncertainty of the prediction result by adopting an improved Coefficient of Variation. In order to downsize the search space, we employ Skyline computing to prune redundant services and then perform Skyline service selection by using Mixed Integer Programming. Extensive experimental results show that our approach has a better performance than other approaches.

Given a service composition, it is challenging but important to have a runtime adaptation, due to the complicated execution environment and evolving feature of Web service. In this paper, we present a runtime adaptive service composition approach, taking execution time minimization and revenue maximization into consideration. Based on dynamic programming, we deduce the optimal policy. Through this policy, orchestrator selects one concrete service for per task on runtime. The experimental results show that the proposed approach outperforms previous approach.

Personalized service recommendation becomes increasingly essential because of the growing number of services. To enhance the performance of personalized service recommendation in collaborative tagging systems, not only tag information but also time and social relations information should be considered. In this paper, we propose a hybrid method aiming at taking advantage of tag, time and users' social relations information for a preferable service recommendation. We first improve a simple tag-based recommendation method by a time-decay function. Then we develop a temporal social-based recommendation method which analyzes user familiarity and user preference similarity between friends. Based on these two steps we integrate them as a temporal tag-and social-based (TTS) recommendation algorithm. Experiment results indicate that our method outperforms general tag-based and social-based recommendation methods.

Personal Mobility Profiles of cellphone users play a crucial role in a wide range of context-aware applications. Transportation mode, a representative feature of users' mobility, can provide much richer context information to the pervasive computing applications. This paper presents a data driven classification model to detect transportation mode from sparse GPS trajectory data. The contributions of this paper lie in the following two aspects. On one hand, we propose a transportation mode classification model based on a deep architecture formed by an SAE model to further improve the inference performance. On the other hand, the relevant features related to the underlying transportation network information are redefined and considered to improve the final classification efficiency and effectiveness. Five representative travel modes-walking, cycling, taking a bus, taking a subway, and driving-are distinguished. Four different models including Bayesian Net, Decision Tree, Random Forest, and Support Vector Machine are compared with our model and tested with the GPS trajectory data in Beijing. As a result, we achieved the average precision and recall accuracy being greater than other models, which are 93.58% and 93.32%, respectively.

Latent Factor Model (LFM) is extensively used in dealing with user-item bipartite networks in service recommendation systems. To alleviate the limitations of LFM, this papers presents a novel unsupervised learning model, Latent Interest and Topic Mining model (LITM), to automatically mine the latent user interests and item topics from user-item bipartite networks. In particular, we introduce the motivation and objectives of this bipartite network based approach, and detail the model development and optimization process of the proposed LITM. This work not only provides an efficient method for latent user interest and item topic mining, but also highlights a new way to improve the accuracy of service recommendation. Experimental studies are performed and the results validate the LITM's efficiency in model training, and its ability to provide better service recommendation performance based on user-item bipartite networks are demonstrated.

Selective attention is the idea that visual attention is not granted equally to all regions of the visual field, but rather focuses on high-attention regions. In this paper, we propose that selectively improving the performance of video in regions that are given high attention by users can significantly improve the quality of experience (QoE) of video services. We investigate users' attention regions, and choose the subtitle region as a representative high-attention region to present a subtitle-based measurement study. This study concentrates on how and how much the subtitle region affects the QoE of video. First of all, we verify that changes in performance of the subtitle region can cause an illusion about users' judgment on video bitrate. Secondly, we present a logarithmic relationship between video bitrate and the QoE of video. Thirdly, we measure the improved QoE by improving the performance of the subtitle region. Finally, we demonstrate that selectively improving the performance of the subtitle region can significantly improve the QoE of video at little cost.

Many factors affect the time cost of Cloud computing tasks. One of the most serious factors is data transmission latency, which reduces the efficiency of Cloud computing. Existing notable schemes ignore the communication cost among virtual machines (VMs) in the MapReduce environment. In this paper, we propose a VM placement approach to reduce data transmission latency with the communication cost among VMs. We first construct bipartite graph and classify VMs as two groups according to their transmission latency with data nodes. Then we propose two VM placement optimization algorithms to minimize the total data transmission latency (TDTL) and the maximum data transmission latency (MDTL) in the MapReduce environment. Finally, we place VMs for Reduce phase. The evaluation results show that our approach reduces the average data transmission latency by 26.3% compared with other approaches.

Fast deployment of mobile Internet makes Web services often consumed under a multi-dimensional spatiotemporal model, wherein a specific service client could keep active while its location is changing. Recommending Web services for such clients must be able to predict unknown QoS values with the target client's service requesting time and location taken into account, e.g., Performing the prediction via a set of measured multi-dimensional QoS data. Most QoS prediction methods focus on the QoS characteristics for one specific dimension, e.g., Time or location, and do not exploit the structural relationships among the multi-dimensional QoS data. This paper proposes an integrated QoS prediction approach which unifies the modeling of multi-dimensional QoS data via multi-linear-algebra based concepts of tensor and enables efficient service recommendation for Web service based mobile clients via tensor decomposition and reconstruction optimization algorithms. Comparative experimental evaluation results show that the proposed QoS prediction approach could result in much better accuracy in recommending Web services than several other representative ones.

Exiting schemes rarely take into account coordinated problem among multiple virtual machines (VMs) which collectively complete a task, since if these VMs are uncoordinated, the execution results of a task are not correct. To solve the problem, we first exploit a proactive prediction scheme to predict the VM's status. Then, if VM's status is deteriorating, coordinated checkpoint is adopted to suspend the current task to search an optimal target host. Finally, we introduce an efficient heuristic algorithm to solve the optimal target host selection problem. The experimental results demonstrate the efficiency and effectiveness of our proposed approach.