Showing papers in "Future Generation Computer Systems in 2018"

TL;DR: This paper focuses on the relationship between blockchain and IoT, investigates challenges in blockchain IoT applications, and surveys the most relevant work in order to analyze how blockchain could potentially improve the IoT.

TL;DR: The main goal of this study is to holistically analyze the security threats, challenges, and mechanisms inherent in all edge paradigms, while highlighting potential synergies and venues of collaboration.

TL;DR: A survey of IoT and Cloud Computing with a focus on the security issues of both technologies is presented, and it shows how the Cloud Computing technology improves the function of the IoT.

TL;DR: This paper proposes to exploit the concept of Fog Computing in Healthcare IoT systems by forming a Geo-distributed intermediary layer of intelligence between sensor nodes and Cloud and presents a prototype of a Smart e-Health Gateway called UT-GATE.

TL;DR: It is proposed that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other, and needs a multi-layer architecture.

TL;DR: The changing cloud infrastructure is discussed and the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers is considered, leading to a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.

TL;DR: This paper presents a smartphone inertial sensors-based approach for human activity recognition that was compared with traditional expression recognition approaches such as typical multiclass Support Vector Machine (SVM) and Artificial Neural Network (ANN) where it outperformed them.

TL;DR: This paper first introduces existing major security and forensics challenges within IoT domain and then briefly discusses about papers published in this special issue targeting identified challenges.

TL;DR: The potential of using Recurrent Neural Network (RNN) deep learning in detecting IoT malware by using RNN to analyze ARM-based IoT applications’ execution operation codes (OpCodes) is explored.

TL;DR: A novel deep learning approach for ECG beat classification is proposed that is not only more efficient than the state of the art in terms of accuracy, but also competitive in Terms of sensitivity and specificity.

TL;DR: The proposed system of supply chain management will be able to overcome all challenges of traditional SCM and provide secure environment of SCM processes and a framework which integrates DEMATEL and AHP in neutrosophic environment to deal effectively with vague, uncertain and incomplete information is presented.

TL;DR: The results show that the chain of agricultural supply chain based on double chain structure can take into account the openness and security of transaction information and the privacy of enterprise information, and greatly enhance the credibility of the public service platform and the overall efficiency of the system.

TL;DR: The architectures of Fog computing are discussed and analyzes, and the related potential security and trust issues are indicated.

TL;DR: This paper proposes a novel hybrid network architecture for the smart city by leveraging the strength of emerging Software Defined Networking and blockchain technologies and proposes a Proof-of-Work scheme in the model to ensure security and privacy.

TL;DR: A taxonomy of recent offloading schemes that have been proposed for domains such as fog, cloud computing, and IoT is presented and the middleware technologies that enable offloading in a cloud-IoT cases and the factors that are important for offload in a particular scenario are discussed.

TL;DR: A new systematic approach is used for the diabetes diseases and the related medical data is generated by using the UCI Repository dataset and the medical sensors for predicting the people who has affected with diabetes severely and a new classification algorithm called Fuzzy Rule based Neural Classifier is proposed for diagnosing the disease and the severity.

TL;DR: An architecture for patient monitoring health-care system in WMSN is proposed and an anonymity-preserving mutual authentication protocol for mobile users is designed and it is demonstrated that the proposed protocol is efficient and robust.

TL;DR: A new model to optimize virtual machines selection in cloud-IoT health services applications to efficiently manage a big amount of data in integrated industry 4.0 applications is proposed and outperforms on the state-of-the-art models in total execution time and the system efficiency.

TL;DR: This paper proposes a light field imaging approach for solving underwater imaging problems in a low-intensity light environment by using deep convolutional neural networks with depth estimation and a spectral characteristic-based color correction method for recovering the color reduction.

TL;DR: Security vulnerabilities of the multi-server cloud environment of the protocols proposed by Xue et al. and Chuang et al are shown and an informal cryptanalysis confirms that the protocol is protected against all possible security threats.

TL;DR: The proposed hybrid whale algorithm (HWA) is incorporated with Nawaz–Enscore–Ham (NEH) to improve the performance of the algorithm and it is observed that HWA gives competitive results compared to the existing algorithms.

TL;DR: A one-to-many group authentication protocol and a group key establishment algorithm between personal digital assistance (PDA) and each of sensor nodes with energy efficiency and low computational cost and the validation of the proposed protocol can be proved.

TL;DR: The experiments show that the ECC-based healthcare system provides a better user experience and optimizes the computing resources reasonably, as well as significantly improving in the survival rates of patients in a sudden emergency.

TL;DR: A methodology is proposed to segment and classify the brain tumor using magnetic resonance images (MRI) using deep Neural Networks (DNN) based architecture for tumor segmentation.

TL;DR: This paper discusses the similarities and differences among Big Data technologies used in different IoT domains, suggests how certain Big Data technology used in one IoT domain can be re-used in another IoT domain, and develops a conceptual framework to outline the critical Big data technologies across all the reviewed IoT domains.

TL;DR: A lightweight ECC based authentication scheme for smart grid communication that not only provides mutual authentication with low computation and communication cost but also withstand against all known security attacks.

TL;DR: A novel tool for an automated differentiation of shockable and non-shockable ventricular arrhythmias from 2 s electrocardiogram (ECG) segments is proposed and indicates that shockable life-threatening arrhythmia can be immediately detected and thus increase the chance of survival while CPR or AED-based support is performed.

TL;DR: The purpose of this special issue is to analyze the top concerns in IoT technologies that pertain to smart sensors for health care applications; particularly applications targeted at individualized tele-health interventions with the goal of enabling healthier ways of life.

TL;DR: The aim is to solve the performance estimation problem and improve the quality of services by creating a strong competition between cloud providers by providing a neutrosophic multi-criteria decision analysis (NMCDA) approach for estimating thequality of cloud services.

TL;DR: A workload prediction model using neural network and self adaptive differential evolution algorithm that outperforms well known back propagation network approach in accuracy and is able to achieve significant reduction in the prediction error.