Advancements in medical science and technology, medicine and public health coupled with increased consciousness about nutrition and environmental and personal hygiene have paved the way for the dramatic increase in life expectancy globally in the past several decades. However, increased life expectancy has given rise to an increasing aging population, thus jeopardizing the socio-economic structure of many countries in terms of costs associated with elderly healthcare and wellbeing. In order to cope with the growing need for elderly healthcare services, it is essential to develop affordable, unobtrusive and easy-to-use healthcare solutions. Smart homes, which incorporate environmental and wearable medical sensors, actuators, and modern communication and information technologies, can enable continuous and remote monitoring of elderly health and wellbeing at a low cost. Smart homes may allow the elderly to stay in their comfortable home environments instead of expensive and limited healthcare facilities. Healthcare personnel can also keep track of the overall health condition of the elderly in real-time and provide feedback and support from distant facilities. In this paper, we have presented a comprehensive review on the state-of-the-art research and development in smart home based remote healthcare technologies.

Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges

Cyber-physical-social systems (CPSSs) represent an emerging paradigm encompassing the cyber world, physical world and social world. One of the main purposes of CPSSs is to provide high-quality, proactive, and personalized services for humans. For CPSSs to realize this purpose, a novel services framework is needed. In this article, we present a tensor-based cloud-edge computing framework that mainly includes the cloud and edge planes. The cloud plane is used to process large-scale, long-term, global data, which can be used to obtain decision making information such as the feature, law, or rule sets. The edge plane is used to process small-scale, short-term, local data, which is used to present the real-time situation. Also, personalized services will be directly provided for humans by the edge plane according to the obtained feature, law, or rule sets and the local high-quality data obtained in the edge plane. Then a tensor-based services model is proposed to match the requirement of users in the local CPSS. Finally, a case study about CPSS services is proposed to demonstrate the application features of the proposed framework.

A Cloud-Edge Computing Framework for Cyber-Physical-Social Services

An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles

Deep learning, as the most important architecture of current computational intelligence, achieves super performance to predict the cloud workload for industry informatics. However, it is a nontrivial task to train a deep learning model efficiently since the deep learning model often includes a great number of parameters. In this paper, an efficient deep learning model based on the canonical polyadic decomposition is proposed to predict the cloud workload for industry informatics. In the proposed model, the parameters are compressed significantly by converting the weight matrices to the canonical polyadic format. Furthermore, an efficient learning algorithm is designed to train the parameters. Finally, the proposed efficient deep learning model is applied to the workload prediction of virtual machines on cloud. Experiments are conducted on the datasets collected from PlanetLab to validate the performance of the proposed model by comparing with other machine-learning-based approaches for workload prediction of virtual machines. Results indicate that the proposed model achieves a higher training efficiency and workload prediction accuracy than state-of-the-art machine-learning-based approaches, proving the potential of the proposed model to provide predictive services for industry informatics.

An Efficient Deep Learning Model to Predict Cloud Workload for Industry Informatics

Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images

With an objective to provide the proactive and personalized services for human beings, Cyber-Physical-Social Systems (CPSS), which combine the cyber space, physical space, and social space together, need to process the large scale heterogenous data first. Tensor, as an appropriate data representation tool, has been widely used for representation of heterogeneous Cyber-Physical-Social big data. When computationally processing such tensor, many necessary constraints have to be taken into account, e.g., the execution time, energy consumption, economic cost, security as well as reliability. However, the systematic integration of these constraints and then the modelling of general optimization for tensor processing become more challenging. In this paper, with such constraints being considered together, a general model for tensor computation that optimizes the execution time, energy consumption, and economic cost with acceptable security and reliability is proposed. From diverse perspectives of user requirements, a case study for the tree-based distributed High-Order Singular Value Decomposition (HOSVD) is measured. With the focus on multi-objective combination, the experimental results validate the applicability and generality of the proposed model.

A Tensor Computation and Optimization Model for Cyber-Physical-Social Big Data

Big service is an extremely important application of service computing to provide predictive and needed services to humans. To operationalize big services, the heterogeneous data collected from Cyber-Physical-Social Systems (CPSS) must be processed efficiently. However, because of the rapid rise in the volume of data, faster and more efficient computational techniques are required. Therefore, in this paper, we propose a multi-order distributed high-order singular value decomposition method (MDHOSVD) with its incremental computational algorithm. To realize the MDHOSVD, a tensor blocks unfolding integration regulation is proposed. This method allows for the efficient analysis of large-scale heterogeneous data in blocks in an incremental fashion. Using simulation and experimental results from real-life, the high-efficiency of the proposed data processing and computational method, is demonstrated. Further, a case study about cyber-physical-social system data processing is illustrated. The proposed MDHOSVD method speeds up data processing, scales with data volume, improves the adaptability and extensibility over data diversity and converts low-level data into actionable knowledge.

A Multi-Order Distributed HOSVD with Its Incremental Computing for Big Services in Cyber-Physical-Social Systems

The rapid advances of information, computing, and communication technologies have led to significant enhancements to human living environments. Enhanced living environments (ELEs) encompass the coupling of information technologies (cyber), intelligent devices (physical), and human society (social) for enhanced quality of life. Together, these spaces are referred to as cyber-physical-social systems (CPSSs). For CPSSs to provide high-quality services, improved service frameworks are needed. The framework presented in this article includes a sensing plane, cloud plane, and application plane. In the sensing plane, the relationship of objects in every local CPSS is represented by a local tensor, which is cleaned and uploaded to the cloud plane. In the cloud plane, a global tensor is constructed by integrating all local tensors together. Next, the application plane provides the corresponding high-quality services. A case study using a typical CPSS smart home illustrates a simple application of the proposed service framework.

A Tensor-Based Big Service Framework for Enhanced Living Environments

Smart city, a focus of many researchers from academia and industry, is a successful example of Cyber-Physical-Social Systems (CPSS). Based on the rapid and efficient processing of large-scale data, Smart city, an example of CPSS, has revolutionized the service provision model by providing proactive services for humans. However, to operationalize the services provided in smart cities, a comprehensive analysis of heterogeneous and large-scale big data is required. Further, to speed up data processing and improve the adaptability and extensibility of big data, CPSS big data processing should be realized in the form of blocks and avoid redundant computing on historical data. In this paper, as an extension of multi-order distributed and incremental High-Order Singular Value Decomposition (HOSVD) computing, Ring-based Tree algorithm and Tree-based Tree algorithm are proposed for the problems of increasing scale of processable data and computational efficiency. The experimental and simulation results demonstrate that the proposed algorithms have high performance in terms of error, improvement factor, and improvement factor ratio. At last, to demonstrate the performance of our improved algorithms, a case study about CPSS big data processing is provided.

Improved Multi-Order Distributed HOSVD with Its Incremental Computing for Smart City Services

Mobile cloud computing (MCC) is extensively ubiquitous in the mobile Internet era and embraces complex environments because of the heterogeneity of devices and complexity of communications. Balancing the costs of different influencing objectives (e.g., energy consumption, system reliability, and quality of experience (QoE)) in MCC faces great challenges. This paper focuses on reasonably allocating computational tasks to suitable cores of mobile devices or cloud in MCC to minimize the total energy consumption, and maximize the system reliability and QoE. Concretely, this paper 1 proposes a holistic mobile cloud optimization model including energy consumption, system reliability, and QoE; 2 presents a DVFS-enabled and thermal-aware global energy consumption model which simultaneously considers the synergy of multiple factors concerning mobile devices, cloud, and networks; 3 constructs a tensor-based representation model to comprehensively reflect the complex relationship of multiple influencing factors and cope with their heterogeneity; and 4 proposes a customized optimization framework and two heuristic single-objective optimization (SOO) and triple-objective optimization (TOO) algorithms based on simulated annealing. Experimental results demonstrate that the proposed scheme outperforms the state-of-the-art scheduling schemes in SOO and the Pareto front in TOO can provide appropriate solutions to satisfy different application requirements.

Xingyu Chen

Papers

A Tensor Computation and Optimization Model for Cyber-Physical-Social Big Data

A Multi-Order Distributed HOSVD with Its Incremental Computing for Big Services in Cyber-Physical-Social Systems

A Tensor-Based Big Service Framework for Enhanced Living Environments

Improved Multi-Order Distributed HOSVD with Its Incremental Computing for Smart City Services

A Holistic Optimization Framework for Mobile Cloud Task Scheduling