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Works that use point cloud avoid wasting time and cost of collection, using simulators and datasets available in the literature. In this way, there is access to an unlimited and organized amount of point clouds, an ideal setting for deep learning networks and Vehicular ad hoc networks (VANETs). However, models trained with synthetic data present problems when applied to real-world data.This work proposes the use of deep learning in the recognition of 3D objects captured with a Light Detection and Ranging (LIDAR), including a pre-processing stage. In addition, it is proposed two datasets, a real-world and a syntetic; each dataset includes three classes. A method of pre-processing is proposed to circumvent the distribution discrepancies of the proposed datasets and the existing datasets from literature, such as ModelNet. We use deep learning with the PointNet method, as it supports raw data from point clouds as input to the network. We performed three evaluation approaches: training and testing steps with the proposed datasets using Lidar3DNetV1, which is a proposed network in this paper, PointNet, and (3) classification of ModelNet datasets using Lidar3DNetV1. The proposed network achieved 98.33% of accuracy and a testing time of $88~\mu \text{s}$ in the synthetic dataset, while in the real-world dataset, the network reached 98.48% and $145~\mu \text{s}$ in accuracy and testing time, respectively. [ABSTRACT FROM AUTHOR]

Cooperative Intelligent Transport System (C-ITS) is a promising technology that aims to improve the traditional transport management systems. In C-ITS infrastructure Autonomous Vehicles (AVs) communicate wirelessly with other AVs, Road Side Units (RSUs) and Traffic Command Centres (TCCs) using an open channel Internet. However, the use of the Internet brings inherent vulnerabilities related to privacy (e.g., adversary performing inference and data poisoning attacks), and security (e.g., AVs can be compromised using advanced hacking techniques) issues and prevents the faster realization of C-ITS applications. To address these challenges, this paper presents a privacy-preserving-based secure framework to provide both privacy and security in C-ITS infrastructure. The proposed framework provides two level of security and privacy using blockchain and deep learning modules. First, a blockchain module is designed to securely transmit the C-ITS data between AVs–RSUs-TCCs, and a smart contract-based enhanced Proof of Work (ePoW) technique is designed to verify data integrity and mitigate data poisoning attacks. Second, a deep-learning module is designed that includes Long-Short Term Memory-AutoEncoder (LSTM-AE) technique for encoding C-ITS data into a new format to prevent inference attacks. The encoded data is used by the proposed Attention-based Recurrent Neural Network (A-RNN), for intrusive events recognition in C-ITS infrastructure. The proposed A-RNN is trained using Truncated Backpropagation Through Time (BPTT) algorithm. The framework is further validated and tested using two publicly available ToN-IoT and CICIDS-2017 datasets. The proposed framework is compared with peer privacy-preserving intrusion detection techniques, and the result shows the effectiveness of the proposed framework over several state-of-the-art techniques in both blockchain and non-blockchain systems. [ABSTRACT FROM AUTHOR]

Cloud computing enables clients to acquire cloud resources dynamically and on demand for their cloud applications and services. For cloud providers, especially, Software as a Service (SaaS) providers, the prediction of future cloud resource requirements, such as CPU usage for their cloud applications, to implement client requests is a complex task because it depends on incoming workloads. Due to workload fluctuations, it is difficult for SaaS cloud providers to predict or forecast future demand for resource usage in the next time interval and, accordingly, to allocate the required resources. Furthermore, cloud computing systems consist of many virtual machines (VMs), which increases the complexity of the prediction problem due to the correlations that exist between the large workload data in these VMs. Therefore, accurate resource usage forecasting remains a challenge, and relatively few studies have explored the prediction of CPU usage for VMs in cloud data centers. This paper proposes an autonomic and intelligent workload forecasting method for cloud resource provisioning based on the concept of autonomic computing and a deep learning approach. In particular, to predict future demand for CPU usage and determine how to respond to workload fluctuations in the next interval, we propose an efficient deep learning model based on a diffusion convolutional recurrent neural network (DCRNN). Existing deep learning models that are widely applied cannot handle accurate real-time forecasting due to the presence of inconsistent and nonlinear workloads in cloud computing systems. The goal of the proposed deep learning model is to improve forecasting accuracy and minimize the error between the predicted and the actual workloads. The effectiveness of the proposed DCRNN-based deep learning model was evaluated using experiments on a real-world dataset of PlanetLab's CPU usage traces. The results indicate that the proposed approach outperformed other existing deep learning models, achieving a mean absolute percentage error of 0.18 and root-mean-square error of 2.40. [ABSTRACT FROM AUTHOR]

Smart mobile devices and cloud computing are widely used today. While mobile and portable devices have different capabilities, architectures, operating systems, and communication channels than one another, government data are distributed over heterogeneous systems. This paper proposes a 3-tier mediation framework providing single application to manage all governmental services. The framework is based on private cloud computing for adapting the content of Mobile-Government (M-Government) services using Role-Based Access Control (RBAC) and Derive Unique Key Per Transaction (DUKPT). The 3-layers in the framework are: presence, integration, and homogenization. The presence layer is responsible for adapting the content with regard to four contexts: device, personal, location, and connectivity contexts. The integration layer, which is hosted in a private cloud server, is responsible for integrating heterogeneous data sources. The homogenization layer is responsible for converting data into XML format. The flexibility of the mediation and XML provides an adaptive environment to stream data based on the capabilities of the device that sends the query to the system. [ABSTRACT FROM AUTHOR]

In cloud computing, resources could be provisioned in a dynamic way on demand for cloud services. Cloud providers seek to realize effective SLA execution mechanisms for avoiding SLA violations by provisioning the resources or applications and timely interacting to environmental changes and failures. Sufficient resource provisioning to cloud's services relies on the requirements of the workloads to achieve a high performance for quality of service. Therefore, deciding the suitable amount of cloud's resources for these services to achieve is one of the main works in cloud computing. During the runtime of services, the amount of cloud's resources can be specified and provisioned based on the actual workloads changes. Determining the correct amount of cloud's resources needed for running the services on clouds is not easy task, and it depends on the existing workloads of services. Consequently, it is required to predict the future workloads for dynamic provisioning of resources in order to meet the changes in workloads and demands of services in cloud computing environments. In this paper, we study the possibility of using a cognitive/intelligent approach for cloud resource provisioning which is a combination of the autonomic computing concept, deep learning technique and fuzzy logic control. Deep learning technique is a state-of-the-art in the machine learning field. It achieved promising results in many other fields like image classification and speech recognition. For these reasons, deep learning is proposed in this work to tackle the workload prediction in cloud computing. Additionally, we also propose to use a fuzzy logic-based method in order to make a decision in the case of uncertainty of the workload prediction. We study various exiting works on autonomic cloud resource provisioning and show that there is still an opportunity to improve the current methods. We also present the challenges that may exist on this domain. [ABSTRACT FROM AUTHOR]

Since traffic in networks is growing rapidly, it is difficult for the existing network architecture to support the huge traffic requirement. This article proposes a novel intelligent architecture as a promising paradigm for B5G heterogeneous networks to optimize network resource usage and network performance. The main idea is to build a suitable network model through AI, and integrate edge computing and cloud computing to improve computing performance. In addition, this article gives appropriate recommendations of the deep learning method for different network issues. Since the deep learning method requires a large amount of computing resources, the network resource allocation needs to be paid attention to in this architecture. For complex environments of B5G heterogeneous networks, integrated packet forwarding is one potential technology to improve quality of service. Moreover, we discuss the challenges and open issues for B5G. [ABSTRACT FROM AUTHOR]

In recent years, the smart city concept, which involves multiple disciplines, for example, smart healthcare, smart transportation, and smart community, has become popular because of its ability to improve urban citizens' quality of life. However, most services in these areas of smart cities have become data-driven, thus generating big data that require seamless real-time access, sharing, storing, processing, and analysis anywhere at any time for intelligent decision making to improve living standards. In this scenario, MCC can play a vital role by allowing a mobile device to access and offload big-data-related tasks to powerful cloudlet servers attached to many wireless APs, thus ensuring that the QoS demands of end users are met. However, the connectivity of mobile devices with a given AP is not continuous, but rather sporadic with varying signal strengths. Furthermore, the heterogeneity of the cloudlet resources and the big data application requests place additional challenges in making optimal code execution decision. To cope with this problem, this article proposes a mobility- aware optimal resource allocation architecture, namely Mobi-Het, for remote big data task execution in MCC that offers higher efficiency in timeliness and reliability. The system architecture and key components of the proposed resource allocation service are presented and evaluated. The results of experiments and simulations have demonstrated the effectiveness and efficiency of the proposed Mobi-Het architecture for mobile big data applications. [ABSTRACT FROM AUTHOR]

Reduction of energy expenditure is becoming an important issue for a cloud provider (CP) when providing cloud services over the Internet. Federation among CPs, whereby a set of CPs cooperating together to provide virtual machine (VM) instances requested by users, can be an effective solution to address this issue. In this paper, we present an effective capacity-sharing mechanism in a federated cloud environment that can lead to a global energy sustainability policy for the federation and encourages them to cooperate. A coalition game theory was utilized to model various interactions among providers. However, unlike the existing approaches, the proposed game model looks for a set of low-energy-cost CPs in a federation such that the social welfare is maximized and provides a fare and suitable revenue for them. In addition, we consider the demand variations of internal users of a CP when sharing VM resources. Moreover, a detailed analysis of various costs and revenue aspects is presented. Experiment results demonstrated that the proposed game model is able to maximize the social welfare of the CPs while satisfying the fairness and stability properties of the federation. [ABSTRACT FROM PUBLISHER]

Nowadays, big media healthcare data processing in cloud has become an effective solution for satisfying QoS demands of medical users. It can support various healthcare services such as pre-processing, storing, sharing, and analysis of monitored data as well as acquiring context-awareness. However, to support energy and cost savings, the union of cloud data centers termed as cloud confederation can be an promising approach, which helps a cloud provider to overcome the limitation of physical resources. However, the key challenge in it is to achieve multiple contradictory objectives, e.g., meeting the required level of services defined in service level agreement, maintaining medial users'application QoS, etc. while maximizing profit of a cloud provider. In this paper, for executing heterogeneous big healthcare data processing requests from users, we develop a local and global cloud confederation model, namely FnF, that makes an optimal selection decision for target cloud data center(s) by exploiting Fuzzy logic. The FnF trades off in between profit of cloud provider and user application QoS in selecting federated data center(s). In addition, FnF enhances its decision accuracy by precisely estimating the resource requirements for the big data processing tasks using multiple linear regression. The proposed FnF model is validated through numerical as well as experimental evaluations. Simulation results depict the effectiveness and efficiency of the FnF model compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The advances in information technology have witnessed great progress on healthcare technologies in various domains nowadays. However, these new technologies have also made healthcare data not only much bigger but also much more difficult to handle and process. Moreover, because the data are created from a variety of devices within a short time span, the characteristics of these data are that they are stored in different formats and created quickly, which can, to a large extent, be regarded as a big data problem. To provide a more convenient service and environment of healthcare, this paper proposes a cyber-physical system for patient-centric healthcare applications and services, called Health-CPS, built on cloud and big data analytics technologies. This system consists of a data collection layer with a unified standard, a data management layer for distributed storage and parallel computing, and a data-oriented service layer. The results of this study show that the technologies of cloud and big data can be used to enhance the performance of the healthcare system so that humans can then enjoy various smart healthcare applications and services. [ABSTRACT FROM PUBLISHER]

The exponentially growing healthcare costs coupled with the increasing interest of patients in receiving care in the comfort of their own homes have prompted a serious need to revolutionize healthcare systems. This has prompted active research in the development of solutions that enable healthcare providers to remotely monitor and evaluate the health of patients in the comfort of their residences. However, existing works lack flexibility, scalability, and energy efficiency. This article presents a pervasive patient health monitoring (PPHM) system infrastructure. PPHM is based on integrated cloud computing and Internet of Things technologies. In order to demonstrate the suitability of the proposed PPHM infrastructure, a case study for real-time monitoring of a patient suffering from congestive heart failure using ECG is presented. Experimental evaluation of the proposed PPHM infrastructure shows that PPHM is a flexible, scalable, and energy-efficient remote patient health monitoring system. [ABSTRACT FROM PUBLISHER]

Cloud computing offers sufficient computing and storage resources that can be used to provide multimedia services. Migrating the existing multimedia service to cloud brings a new challenging issue, i.e., remote display of video contents. To reduce the bandwidth consumption especially for mobile users, it is desired to encode video before sending to client. Existing encoding methods have unique advantages and disadvantages, differing their performance under varying situations. Thus, we propose to use multi-encoder method to solve the real-time remote display problem for remote multimedia cloud. To select the most appropriate encoder, factors including cost, application requirement, network, client device and codec implementation are considered. In this paper, we form a non-linear programming model, and provide an example to illustrate how to apply the proposed model for getting desired optimization. [ABSTRACT FROM AUTHOR]

Due to the significant advancement of wireless and mobile technology, the applications targeted for these are getting more and more complex and demanding of high power and resources. The cloud-assisted software-defined wireless sensor networking (CSDWSN) paradigm has emerged as a promising solution to perform these highly demanding tasks with the help of powerful cloud servers. However, due to the dynamic variation in sensor users demands, it is becoming challenging for a single CSDWSN provider to always provide a sufficient amount of computing resources to fulfill users needs. This paper exhibits an approach of resource and revenue sharing with coalition development among CSDWSN providers. The proposed method models the collaborations among the CSDWSN providers as a coalition game. As opposed to existing methodologies, the game model goes for amplifying the individual revenue of CSDWSN providers with minimum energy consumption by analyzing their internal users demand and dynamic pricing strategy. In addition, the proposed game enables a socially optimal and stable coalition structure, which leads to both increased total profit of a federation and improved equality among participating CSDWSN providers. Various simulation experiments were completed to demonstrate the viability of the proposed cooperative game model. [ABSTRACT FROM PUBLISHER]

Due to the significant advancement of Smartphone technology, the applications targeted for these devices are getting more and more complex and demanding of high power and resources. Mobile cloud computing (MCC) allows the Smart phones to perform these highly demanding tasks with the help of powerful cloud servers. However, to decide whether a given part of an application is cost-effective to execute in local mobile device or in the cloud server is a difficult problem in MCC. It is due to the trade-off between saving energy consumption while maintaining the strict latency requirements of applications. Currently, 5th generation mobile network (5G) is getting much attention, which can support increased network capacity, high data rate and low latency and can pave the way for solving the computation offloading problem in MCC. In this paper, we design an intelligent computation offloading system that takes tradeoff decisions for code offloading from a mobile device to cloud server over the 5G network. We develop a metric for tradeoff decision making that can maximize energy saving while maintain strict latency requirements of user applications in the 5G system. We evaluate the performances of the proposed system in a test-bed implementation, and the results show that it outperforms the state-of-the-art methods in terms of accuracy, computation and energy saving. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of efficient federation formation by the cloud providers (CPs) with an aim to fulfill the dynamic resource demands of users for supporting data-intensive workloads. Existing works only focus on forming federations based on the highest profit gained by each of the CPs in a federation. Therefore, these approaches often suffer from the risk of selecting unreliable CPs in the federation resulting in additional penalty cost and loss of CPs's reputation due to service level agreement violation between the users and the federation. In contrast, we argue that a trust model is necessary to find the most promising cloud collaborators. Accordingly, we propose a novel cloud federation formation mechanism by utilizing a trust-based cooperative game theory, which enables the CPs to dynamically form a federation based on profit maximization and penalty cost minimization as a result of selecting the trustworthy CPs. Simulation results show that the cloud federation formed by the proposed mechanism is stable, satisfies the fairness property, and yields higher profit for the participating CPs in the long run without incurring penalty cost as compared with the state-of-the-art approaches. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Vehicular Cloud Computing (VCC) facilitates real-time execution of many emerging user and intelligent transportation system (ITS) applications by exploiting under-utilized on-board computing resources available in nearby vehicles. These applications have heterogeneous time criticality, i.e., they demand different Quality-of-Service levels. In addition to that, mobility of the vehicles makes the problem of scheduling different application tasks on the vehicular computing resources a challenging one. In this article, we have formulated the task scheduling problem as a mixed integer linear program (MILP) optimization that increases the computation reliability even as reducing the job execution delay. Vehicular on-board units (OBUs), manufactured by different vendors, have different architecture and computing capabilities. We have exploited MapReduce computation model to address the problem of resource heterogeneity and to support computation parallelization. Performance of the proposed solution is evaluated in network simulator version 3 (ns-3) by running MapReduce applications in urban road environment and the results are compared with the state-of-the-art works. The results show that significant performance improvements in terms of reliability and job execution time can be achieved by the proposed task scheduling model. [ABSTRACT FROM AUTHOR]

Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. However, there is only one copy for each file stored in cloud even if such a file is owned by a huge number of users. As a result, deduplication system improves storage utilization while reducing reliability. Furthermore, the challenge of privacy for sensitive data also arises when they are outsourced by users to cloud. Aiming to address the above security challenges, this paper makes the first attempt to formalize the notion of distributed reliable deduplication system. We propose new distributed deduplication systems with higher reliability in which the data chunks are distributed across multiple cloud servers. The security requirements of data confidentiality and tag consistency are also achieved by introducing a deterministic secret sharing scheme in distributed storage systems, instead of using convergent encryption as in previous deduplication systems. Security analysis demonstrates that our deduplication systems are secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement the proposed systems and demonstrate that the incurred overhead is very limited in realistic environments. [ABSTRACT FROM PUBLISHER]

The promising potential and emerging applications of cloud gaming have drawn increasing interest from academia, industry, and the general public. However, providing a high-quality gaming experience in the cloud gaming framework is a challenging task because of the tradeoff between resource consumption and player emotion, which is affected by the game screen. We tackle this problem by leveraging emotion-aware screen effects in the cloud gaming framework and combining them with remote display technology. The first stage in the framework is the learning or training stage, which establishes a relationship between screen features and emotions using Gaussian mixture model-based classifiers. In the operating stage, a linear programming model provides appropriate screen changes based on the real-time user emotion obtained in the first stage. Our experiments demonstrate the effectiveness of the proposed framework. The results show that our proposed framework can provide a high quality gaming experience while generating an acceptable amount of workload for the cloud server in terms of resource consumption. [ABSTRACT FROM AUTHOR]

Recently, multimedia cloud is being considered as a new effective serving mode in e-Health area that meets the requirement of scalable and economic multimedia service for e-health. It can provide a flexible stack of powerful Virtual Machine (VM) resources of cloud like CPU, memory, storage, network bandwidth etc. on demand to manage e-health media services and applications (e.g. medical image/video retrieval, health video transcoding, streaming, video rendering, sharing and delivery) at lower cost. However, one major issue here is how to efficiently allocate VM resources dynamically based on e-health applications' QoS demands and support energy and cost savings by optimizing the number of servers in use. In order to solve this problem, we propose a cost effective and dynamic VM allocation model based on Nash bargaining solution. With extensive simulations it is shown that the proposed mechanism can reduce the overall cost of running servers while at the same time guarantee QoS demand and maximize resource utilization in various dimensions of server resources. [ABSTRACT FROM AUTHOR]

Recommender systems assist the e-commerce providers for services computing in aggregating user profiles and making suggestions tailored to user interests from large-scale data. This is mainly achieved by two primary schemes, i.e., memory-based collaborative filtering and model-based collaborative filtering. The former scheme predicts user interests over the entire large-scale data records and thus are less scalable. The latter scheme is often unsatisfactory in recommendation accuracy. In this paper, we propose Large-scale E-commerce Recommendation Using Smoothing and Fusion (CFSF) for e-commerce providers. CFSF is divided into an offline phase and an online phase. During the offline phase, CFSF creates a global item similarity matrix (GIS) and user clusters, where user ratings within each cluster is smoothed. In the online phase, when a recommendation needs to be made, CFSF dynamically constructs a locally-reduced item-user matrix for the active user item by selecting the top M similar items from GIS and top the K like-minded users from user clusters. Our empirical study shows that CFSF outperforms existing CF approaches in terms of recommendation accuracy and scalability. [ABSTRACT FROM AUTHOR]

With the development of e-commerce, a growing number of people prefer to purchase medicine online for the sake of convenience. However, it is a serious issue to purchase medicine blindly without necessary medication guidance. In this paper, we propose a novel cloud-assisted drug recommendation (CADRE), which can recommend users with top-N related medicines according to symptoms. In CADRE, we first cluster the drugs into several groups according to the functional description information, and design a basic personalized drug recommendation based on user collaborative filtering. Then, considering the shortcomings of collaborative filtering algorithm, such as computing expensive, cold start, and data sparsity, we propose a cloud-assisted approach for enriching end-user Quality of Experience (QoE) of drug recommendation, by modeling and representing the relationship of the user, symptom and medicine via tensor decomposition. Finally, the proposed approach is evaluated with experimental study based on a real dataset crawled from Internet. [ABSTRACT FROM AUTHOR]

Monitoring patients who have noncommunicable diseases is a big challenge. These illnesses require a continuous monitoring that leads to high cost for patients’ healthcare. Several solutions proposed reducing the impact of these diseases in terms of economic with respect to quality of services. One of the best solutions is mobile healthcare, where patients do not need to be hospitalized under supervision of caregivers. This paper presents a new hybrid framework based on mobile multimedia cloud that is scalable and efficient and provides cost-effective monitoring solution for noncommunicable disease patient. In order to validate the effectiveness of the framework, we also propose a novel evaluation model based on Analytical Hierarchy Process (AHP), which incorporates some criteria from multiple decision makers in the context of healthcare monitoring applications. Using the proposed evaluation model, we analyzed three possible frameworks (proposed hybrid framework, mobile, and multimedia frameworks) in terms of their applicability in the real healthcare environment. [ABSTRACT FROM AUTHOR]

Distributed resource allocation is a very important and complex problem in emerging horizontal dynamic cloud federation (HDCF) platforms, where different cloud providers (CPs) collaborate dynamically to gain economies of scale and enlargements of their virtual machine (VM) infrastructure capabilities in order to meet consumer requirements. HDCF platforms differ from the existing vertical supply chain federation (VSCF) models in terms of establishing federation and dynamic pricing. There is a need to develop algorithms that can capture this complexity and easily solve distributed VM resource allocation problem in a HDCF platform. In this paper, we propose a cooperative game-theoretic solution that is mutually beneficial to the CPs. It is shown that in non-cooperative environment, the optimal aggregated benefit received by the CPs is not guaranteed. We study two utility maximizing cooperative resource allocation games in a HDCF environment. We use price-based resource allocation strategy and present both centralized and distributed algorithms to find optimal solutions to these games. Various simulations were carried out to verify the proposed algorithms. The simulation results demonstrate that the algorithms are effective, showing robust performance for resource allocation and requiring minimal computation time. [ABSTRACT FROM AUTHOR]

Recently, multimedia cloud is being considered as a new effective serving mode in multimedia domain. It can provide a flexible stack of powerful Virtual Machine (VM) resources of cloud like CPU, memory, storage, network bandwidth etc. on demand to manage media services and applications (e.g. image/video retrieval, video transcoding, streaming, video rendering, sharing and delivery) at lower cost. However, one major issue here is how to efficiently allocate VM resources dynamically based on applications' QoS demands and support energy and cost savings by optimizing the number of servers in use. In order to solve this problem, we propose a cost effective and dynamic VM allocation model based on Nash bargaining solution. With various simulations it is shown that the proposed mechanism can reduce the overall cost of running servers while at the same time guarantee QoS demand and maximize resource utilization in various dimensions of server resources. [ABSTRACT FROM AUTHOR]

Virtual Machine (VM) resource management is crucial to satisfy the Quality of Service (QoS) demands of various multimedia services in a media cloud platform. To this end, this paper presents a VM resource allocation model that dynamically and optimally utilizes VM resources to satisfy QoS requirements of media-rich cloud services or applications. It additionally maintains high system utilization by avoiding the over-provisioning of VM resources to services or applications. The objective is to 1) minimize the number of physical machines for cost reduction and energy saving; 2) control the processing delay of media services to improve response time; and 3) achieve load balancing or overall utilization of physical resources. The proposed VM allocation is mapped into the multidimensional bin-packing problem, which is NP-complete. To solve this problem, we have designed a Mixed Integer Linear Programming (MILP) model, as well as heuristics for quantitatively optimizing the VM allocation. The simulation results show that our scheme outperforms the existing VM allocation schemes in a media cloud environment, in terms of cost reduction, response time reduction and QoS guarantee. [ABSTRACT FROM AUTHOR]

Highlights: [•] A cloud-based gaming is proposed to monitor health condition of obese people. [•] The game enables ubiquitous and real-time access of health data by the therapists. [•] Therapist-mediated dynamic change of game level and recommendation is supported. [•] The gaming impact on cognitive behavior of obese people is measured. [•] The results show the obese become self-aware and motivated for weight loss. [Copyright &y& Elsevier]

Obesity has become an outstanding public health issue in most countries around the world. Many attempts have been made to address this issue that ranges from taking medication to doing exercise to following a diet plan to playing games. Few approaches combine exercise and game to engage the obese people in playing fun-based games or purposeful games, also known as serious games, while monitoring their biosignals. However, existing work hardly provides a configurable, scalable and context-aware serious game framework that can be used as a support for obesity treatment. In this paper, we take an attempt to propose such a framework. The proposed framework facilitates bio-signal monitoring based on body sensor network, context-awareness based on pervasive sensors, and on-the-spot activity recommendation based on current game-playing context. It uses the cloud computing platform as infrastructural support that ensures the scalability of the framework. In order to demonstrate the suitability of the proposed framework, we developed a sample serious game; deploy it over a cloud platform; and experiment with it by capturing some psycho-physical data while the obese are engaged in game-play. We observed that the obese people were very much engaged in game-play and they had positive experience using the system. [ABSTRACT FROM AUTHOR]

Nowadays, wireless sensor network (WSN) applications have been used in several important areas, such as healthcare, military, critical infrastructure monitoring, environment monitoring, and manufacturing. However, due to the limitations of WSNs in terms of memory, energy, computation, communication, and scalability, efficient management of the large number of WSNs data in these areas is an important issue to deal with. There is a need for a powerful and scalable high-performance computing and massive storage infrastructure for real-time processing and storing of the WSN data as well as analysis (online and offline) of the processed information under context using inherently complex models to extract events of interest. In this scenario, cloud computing is becoming a promising technology to provide a flexible stack of massive computing, storage, and software services in a scalable and virtualized manner at low cost. Therefore, in recent years, Sensor-Cloud infrastructure is becoming popular that can provide an open, flexible, and reconfigurable platform for several monitoring and controlling applications. In this paper, we present a comprehensive study of representative works on Sensor-Cloud infrastructure, which will provide general readers an overview of the Sensor-Cloud platform including its definition, architecture, and applications. The research challenges, existing solutions, and approaches as well as future research directions are also discussed in this paper. [ABSTRACT FROM AUTHOR]

Currently, the number of traffic accidents has been increased globally. One of the main reasons for this increase is the distraction of the driver on the road. Distracted driving can cause collisions and cause injury, death, or property damage. New techniques can help to mitigate this problem, and one of the recent approaches is to employ body wearable sensors or camera sensors in the vehicle for real-time monitoring and detection of drivers’ distraction and behaviors, such as cell phone use, talking, eating, drinking, radio tuning, navigation interaction, or even combing hair while driving. However, this type of approach requires not only a powerful training module but also a lightweight module for real-time detection and analyzing the captured data. Data need to be collected from specific wearable or camera sensors in order to detect drivers’ distraction and ensure immediate feedback by the administrator for safe driving. Therefore, in this paper, we propose an effective camera-based framework for real-time identification of drivers’ distraction by using a deep learning approach with edge and cloud computing technologies. More specifically, the framework consists of three modules, including the distraction detection module deployed on edge devices in the vehicle environment, the training module deployed in the cloud environment, and finally the analyzing module implemented in the monitoring environment (administrator side) connected with a telecommunication network. The proposed framework is developed using two deep learning models. The first is a custom deep convolutional neural network (CDCNN) model, and the second one is a visual geometry group-16 (VGG16)-based fine-tuned model. Several experiments are conducted on a public large-scale driver distraction dataset to evaluate the two models. The experimental results show that the accuracy rates were 99.64% for the first model and 99.73% for the second model using a holdout test set of 10%. In addition, the first and second models have achieved accuracy rates of 99.36% and 99.57% using a holdout test set of 30%. The results confirmed the applicability and appropriateness of the adopted deep learning models for designing the proposed driver distraction detection framework. [ABSTRACT FROM AUTHOR]

Computer-Supported Collaborative Learning (CSCL) is concerned with how Information and Communication Technology (ICT) might facilitate learning in groups which can be co-located or distributed over a network of computers such as Internet. CSCL supports effective learning by means of communication of ideas and information among learners, collaborative access of essential documents, and feedback from instructors and peers on learning activities. As the cloud technologies are increasingly becoming popular and collaborative learning is evolving, new directions for development of collaborative learning tools deployed on cloud are proposed. Development of such learning tools requires access to substantial data stored in the cloud. Ensuring efficient access to such data is hindered by the high latencies of wide-area networks underlying the cloud infrastructures. To improve learners’ experience by accelerating data access, important files can be replicated so a group of learners can access data from nearby locations. Since a cloud environment is highly dynamic, resource availability, network latency, and learner requests may change. In this paper, we present the advantages of collaborative learning and focus on the importance of data replication in the design of such a dynamic cloud-based system that a collaborative learning portal uses. To this end, we introduce a highly distributed replication technique that determines optimal data locations to improve access performance by minimizing replication overhead (access and update). The problem is formulated using dynamic programming. Experimental results demonstrate the usefulness of the proposed collaborative learning system used by institutions in geographically distributed locations. [ABSTRACT FROM AUTHOR]