Socio-Technological factors affecting user’s adoption of eHealth functionalities: A case study of China and Ukraine eHealth systems

Recent studies have shown the rapid adoption of digital health software applications worldwide. However, researchers are yet to fully understand users’ rationale of eHealth systems. Therefore, the objective of this study is to analyze user attitudes to eHealth applications in China and eHealth system in Ukraine, and then provide insights and suggestions to the development of an eHealth application (eZdorovya) for health information services in general. The study includes a survey conducted by Chinese and Ukrainian users, after which thorough data analyses were conducted. Based on the Technology Acceptance Model (TAM), this research framework explores the influence of socio-technical factors affecting user’s adoption of eHealth functionalities. Serial Multiple Mediator Model 6 (SMMM6) and a deep neural network-based approach were used to analyze the eHealth software users’ rationale with the sample size of survey 236 end-users from China and 124 end-users from Ukraine. The key findings from the data analysis are: (1) if the software application is covering an important service function and is interesting to use, Chinese users will continue using it, (2) given an eHealth software with important or interesting function, it is inconclusive whether Ukrainian users will switch to use the application. (3) Deep neural network shows highly accurate prediction results and was given applied suggestions for Chinese and Ukrainian providers in the case of improving eHealth systems based on a raw prediction

Smartwatch-Based Legitimate User Identification for Cloud-Based Secure Services

Smartphones are ubiquitously integrated into our home and work environment and users frequently use them as the portal to cloud-based secure services. Since smartphones can easily be stolen or coopted, the advent of smartwatches provides an intriguing platform legitimate user identification for applications like online banking and many other cloud-based services. However, to access security-critical online services, it is highly desirable to accurately identifying the legitimate user accessing such services and data whether coming from the cloud or any other source. Such identification must be done in an automatic and non-bypassable way. For such applications, this work proposes a two-fold feasibility study; (1) activity recognition and (2) gait-based legitimate user identification based on individual activity. To achieve the above-said goals, the first aim of this work was to propose a semicontrolled environment system which overcomes the limitations of users' age, gender, and smartwatch wearing style. The second aim of this work was to investigate the ambulatory activity performed by any user. Thus, this paper proposes a novel system for implicit and continuous legitimate user identification based on their behavioral characteristics by leveraging the sensors already ubiquitously built into smartwatches. The design system gives legitimate user identification using machine learning techniques and multiple sensory data with 98.68% accuracy

Differentially Private High-Dimensional Data Publication in Internet of Things

Internet of Things and the related computing paradigms, such as cloud computing and fog computing, provide solutions for various applications and services with massive and high-dimensional data, while produces threatens on the personal privacy. Differential privacy is a promising privacy-preserving definition for various applications and is enforced by injecting random noise into each query result such that the adversary with arbitrary background knowledge cannot infer sensitive input from the noisy results. Nevertheless, existing differentially private mechanisms have poor utility and high computation complexity on high-dimensional data because the necessary noise in queries is proportional to the size of the data domain, which is exponential to the dimensionality. To address these issues, we develop a compressed sensing mechanism (CSM) that enforces differential privacy on the basis of the compressed sensing framework while providing accurate results to linear queries. We derive the utility guarantee of CSM theoretically. An extensive experimental evaluation on real-world datasets over multiple fields demonstrates that our proposed mechanism consistently outperforms several state-of-the-art mechanisms under differential privacy

Energy Efficient In-network RFID Data Filtering Scheme in Wireless Sensor Networks

RFID (Radio frequency identification) and wireless sensor networks are backbone technologies for pervasive environments. In integration of RFID and WSN, RFID data uses WSN protocols for multi-hop communications. Energy is a critical issue in WSNs; however, RFID data contains a lot of duplication. These duplications can be eliminated at the base station, but unnecessary transmissions of duplicate data within the network still occurs, which consumes nodes’ energy and affects network lifetime. In this paper, we propose an in-network RFID data filtering scheme that efficiently eliminates the duplicate data. For this we use a clustering mechanism where cluster heads eliminate duplicate data and forward filtered data towards the base station. Simulation results prove that our approach saves considerable amounts of energy in terms of communication and computational cost, compared to existing filtering schemes

A Quantum Safe Key Hierarchy and Dynamic Security Association for LTE/SAE in 5G Scenario

Millions of devices are going to participate in 5G producing a huge space for security threats. The 5G specification goals require rigid and robust security protocol against such threats. Quantum cryptography is a recently emerged term in which we test the robustness of security protocols against Quantum computers. Therefore, in this paper, we propose a security protocol called Quantum Key GRID for Authentication and Key Agreement (QKG-AKA) scheme for the dynamic security association. This scheme is efficiently deployed in Long Term Evolution (LTE) architecture without any significant modifications in the underlying base system. The proposed QKGAKA mechanism is analyzed for robustness and proven safe against quantum computers. The simulation results and performance analysis show drastic improvement regarding security and key management over existing schemes

Detail-preserving switching algorithm for the removal of random-valued impulse noise

© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. This paper presents a new algorithm for the denoising of images corrupted with random-valued impulse noise (RVIN). It employs a switching approach that identifies the noisy pixels in the first stage and then estimates their intensity values to restore them. Local statistics of the textons in distinct orientations of the sliding window are exploited to identify the corrupted pixels in an iterative manner; using an adaptive threshold range. Textons are formed by using an isometric grid of minimum local distance that preserves the texture and edge pixels of an image, effectively. At the noise filtering stage, fuzzy rules are used to obtain the noise-free pixels from the proposed tri-directional pixels to estimate the intensity values of identified corrupted pixels. The performance of the proposed denoising algorithm is evaluated on a variety of standard gray-scale images under various intensities of RVIN by comparing it with state-of-the-art denoising methods. The proposed denoising algorithm also has robust denoising and restoration power on biomedical images such as, MRI, X-Ray and CT-Scan. The extensive simulation results based on both quantitative measures and visual representations depict the superior performance of the proposed denoising algorithm for various noise intensities

A Novel Framework and Enhanced QoS Big Data Protocol for Smart City Applications

Various heterogeneous devices or objects will be integrated for transparent and seamless communication under the umbrella of Internet of things (IoT). This would facilitate the open access of data for the growth of various digital services. Building a general framework of IoT is a complex task because of the heterogeneity in devices, technologies, platforms and services operating in the same system. In this paper, we mainly focus on the framework for Big Data analytics in Smart City applications, which being a broad category specifies the different domains for each application. IoT is intended to support the vision of Smart City, where advance technologies will be used for communication to improve the quality of life of citizens. A novel approach is proposed in this paper to enhance energy conservation and reduce the delay in Big Data gathering at tiny sensor nodes used in IoT framework. To implement the Smart City scenario in terms of Big Data in IoT, an efficient (optimized in quality of service) wireless sensor network (WSN) is required where communication of nodes is energy efficient. Thus, a new protocol, QoS-IoT(quality of service enabled IoT), is proposed on the top layer of the proposed architecture (the five-layer architecture consists of technology, data source, data management, application and utility programs) which is validated over the traditional protocols

Internet of Things (IoT)-Based Wastewater Management in Smart Cities

Wastewater management is a mechanism that is used to extract and refine pollutants from wastewater or drainage that can be recycled to the water supply with minimal environmental effects. New methods and techniques are required to ensure safe and smart wastewater management systems in smart cities because of the present deteriorating environmental state. Wireless sensor networks and the Internet of Things (IoT) represent promising wastewater treatment technologies. The elaborated literature survey formulates a conceptual framework with an Internet of Things (IoT)-based wastewater management system in smart cities (IoT-WMS) using blockchain technology. Blockchain technology is now being used to store information to develop an incentive model for encouraging the reuse of wastewater. Concerning the quality and quantity of recycled wastewater, tokens are issued to households/industries in smart cities. Nevertheless, this often encourages tampering with the information from which these tokens are awarded to include certain rewards. Anomaly detector algorithms are used to identify the possible IoT sensor data which has been tampered with by intruders. The model employs IoT sensors together with quality metrics to measure the amount of wastewater produced and reused. The simulation analysis shows that the proposed method achieves a high wastewater recycling rate of 96.3%, an efficiency ratio of 88.7%, a low moisture content ratio of 32.4%, an increased wastewater reuse of 90.8%, and a prediction ratio of 92.5%

Network aware and power-based resource allocation in mobile Ad Hoc computational grid

Three resource allocation schemes are described for allocation of data-intensive dependent tasks on mobile ad hoc computational Grid. The first resource scheme exploits transmission power control mechanism in order to improve energy efficiency and network capacity while the second resource allocation scheme takes into account the connection quality and network traffic in order to reduce data transfer time. To balance the trade-off between first two schemes a network-aware and power-based resource allocation scheme is proposed which takes into account transmission power and network characteristics in order to make allocation decisions. The relationship between data transfer time and transmission energy consumption has also been investigated in detail. The schemes are validated in a simulated environment using various workloads and parameters