BIG DATA IN SMART CITIES: A SYSTEMATIC MAPPING REVIEW

Big data is an emerging area of research and its prospective applications in smart cities are extensively recognized. In this study, we provide a breadth-first review of the domain “Big Data in Smart Cities” by applying the formal research method of systematic mapping. We investigated the primary sources of publication, research growth, maturity level of the research area, prominent research themes, type of analytics applied, and the areas of smart cities where big data research is produced. Consequently, we identified that empirical research in the domain has been progressing since 2013. The IEEE Access journal and IEEE Smart Cities Conference are the leading sources of literature containing 10.34% and 13.88% of the publications, respectively. The current state of the research is semi-matured where research type of 46.15% of the publications is solution and experience, and contribution type of 60% of the publications is architecture, platform, and framework. Prescriptive is least whereas predictive is the most applied type of analytics in smart cities as it has been stated in 43.08% of the publications. Overall, 33.85%, 21.54%, 13.85%, 12.31%, 7.69%, 6.15%, and 4.61% of the research produced in the domain focused on smart transportation, smart environment, smart governance, smart healthcare, smart energy, smart education, and smart safety, respectively. Besides the requirement for producing validation and evaluation research in the areas of smart transportation and smart environment, there is a need for more research efforts in the areas of smart healthcare, smart governance, smart safety, smart education, and smart energy. Furthermore, the potential of prescriptive analytics in smart cities is also an area of research that needs to be explored

Trusted cloud computing framework for healthcare sector

Cloud computing is rapidly evolving due to its efficient characteristics such as cost-effectiveness, availability and elasticity. Healthcare organizations and consumers lose control when they outsource their sensitive data and computing resources to a third party Cloud Service Provider (CSP), which may raise security and privacy concerns related to data loss and misuse appealing threats. Lack of consumers' knowledge about their data storage location may lead to violating rules and regulations of Health Insurance Portability and Accountability Act (HIPAA) that can cost them huge penalty. Fear of data breach by internal or external hackers may decrease consumers' trust in adopting cloud computing and benefiting from its promising features. We designed a HealthcareTrusted Cloud Computing (HTCC) framework that maintains security, privacy and considers HIPAA regulations. HTCC framework deploys Trusted Computing Group (TCG) technologies such as Trusted Platform Module (TPM), Trusted Software Stack (TSS), virtual Trusted Platform Module (vTPM), Trusted Network Connect (TNC) and Self Encrypting Drives (SEDs). We emphasize on using strong multi-factor authentication access control mechanisms and strict security controls, as well as encryption for data at storage, in-transit and while process. We contributed in customizing a cloud Service Level Agreement (SLA) by considering healthcare requirements. HTCC was evaluated by comparing with previous researchers' work and conducting survey from experts. Results were satisfactory and showed acceptance of the framework. We aim that our proposed framework will assist in optimizing trust on cloud computing to be adopted in healthcare sector

Design and implementation of a privacy preserved off-premises cloud storage

Despite several cost-effective and flexible characteristics of cloud computing, some clients are reluctant to adopt this paradigm due to emerging security and privacy concerns. Organization such as Healthcare and Payment Card Industry where confidentiality of information is a vital act, are not assertive to trust the security techniques and privacy policies offered by cloud service providers. Malicious attackers have violated the cloud storages to steal, view, manipulate and tamper client’s data. Attacks on cloud storages are extremely challenging to detect and mitigate. In order to formulate privacy preserved cloud storage, in this research paper, we propose an improved technique that consists of five contributions such as Resilient role-based access control mechanism, Partial homomorphic cryptography, metadata generation and sound steganography, Efficient third-party auditing service, Data backup and recovery process. We implemented these components using Java Enterprise Edition with Glassfish Server. Finally we evaluated our proposed technique by penetration testing and the results showed that client’s data is intact and protected from malicious attackers

Trusted cloud computing framework in critical industrial application

Cloud computing facilitates instant online unlimited access to data and computing resources, ubiquitously and pervasively through its various service delivery and deployment models. Despite the significant advantages of cloud computing, still there are concerns regarding Security, Privacy and Trust (SPT) that resulted from consumers’ loss of control over their confidential data since they outsource it to cloud with no knowledge of storage location or who is accessing and maintaining it. This raises the risks of insider and outsider threats besides the data breach and misuse. A Trusted Cloud Computing Framework (TCCF) is designed to overcome these SPT concerns. TCCF proposes the use of Trusted Computing Group (TCG) technologies including, Trusted Platform Module (TPM), Virtual Trusted Platform Module (VTPM), Self-Encrypting Drives (SEDs), Trusted Network Connect (TNC) and Trusted Software Stack (TSS) to initiate a trusted cloud computing platform. In addition, a Multi-Factor Authentication Single Sign on Role Base Access Control (MFA-SSO-RBAC) prototype was developed using a strict security controls. Furthermore, an additional context for cloud Service Level Agreement (SLA) was proposed to support the framework and to ensure the trustworthiness of the cloud computing services to be adopted in critical information industries specifically healthcare sector. TCCF was evaluated by developing a prototype, comprehensive comparison with previous work, compliance with standards and a survey from cloud computing, healthcare and IT security experts. Feedbacks of experts were satisfactory and they agreed with 94% on the overall security techniques used to secure the TCCF three layers. The evaluation proves that TCCF assists in optimizing the trust on cloud computing to be adopted in healthcare sector for best practices

Using virtual machine monitors to overcome the challenges of monitoring and managing virtualized cloud infrastructures

Virtualization is one of the hottest research topics nowadays. Several academic researchers and developers from IT industry are designing approaches for solving security and manageability issues of Virtual Machines (VMs) residing on virtualized cloud infrastructures. Moving the application from a physical to a virtual platform increases the efficiency, flexibility and reduces management cost as well as effort. Cloud computing is adopting the paradigm of virtualization, using this technique, memory, CPU and computational power is provided to clients' VMs by utilizing the underlying physical hardware. Beside these advantages there are few challenges faced by adopting virtualization such as management of VMs and network traffic, unexpected additional cost and resource allocation. Virtual Machine Monitor (VMM) or hypervisor is the tool used by cloud providers to manage the VMs on cloud. There are several heterogeneous hypervisors provided by various vendors that include VMware, Hyper-V, Xen and Kernel Virtual Machine (KVM). Considering the challenge of VM management, this paper describes several techniques to monitor and manage virtualized cloud infrastructure

IDENTIFYING AND ANALYZING THE TRANSIENT AND PERMANENT BARRIERS FOR BIG DATA

Auspiciously, big data analytics had made it possible to generate value from immense amounts of raw data. Organizations are able to seek incredible insights which assist them in effective decision making and providing quality of service by establishing innovative strategies to recognize, examine and address the customers’ preferences. However, organizations are reluctant to adopt big data solutions due to several barriers such as data storage and transfer, scalability, data quality, data complexity, timeliness, security, privacy, trust, data ownership, and transparency. Despite the discussion on big data opportunities, in this paper, we present the findings of our in-depth review process that was focused on identifying as well as analyzing the transient and permanent barriers for adopting big data. Although, the transient barriers for big data can be eliminated in the near future with the advent of innovative technical contributions, however, it is challenging to eliminate the permanent barriers enduringly, though their impact could be recurrently reduced with the efficient and effective use of technology, standards, policies, and procedures

A study on significances of adopting cloud computing paradigm in healthcare sector

Healthcare sector is information critical industry that deals with human lives. Transforming from traditional paper-based to Electronic Health Records (EHRs) was not efficient enough since EHRs require resources, integration, maintenance and high cost implementation. Cloud computing paradigm offers flexible, cost effective, collaborative, multi-tenant infrastructure which assists in transforming electronic healthcare to smart healthcare that consists on the use of latest technologies such as smart mobiles, smart cards, robots, sensors and Tele-health systems via internet on pay-per-use basis for best medical practices. Cloud computing reduces the cost of EHRs in terms of ownership and IT maintenance, also it offers sharing, integration and management of EHRs as well as tracking patients and diseases more efficiently and effectively. This review paper represents the significance and opportunities for implementing cloud computing in healthcare sector

A hybrid model for forecasting communicable diseases in Maldives

The Maldives is an island nation and the islands are scattered over 26 atolls. The government of Maldives is trying to improve health services in the country and improve the accessibility of services throughout the country at the peripheral levels. The healthcare industry collects a large amount of healthcare information, which contains several patterns, such as outbreaks of diseases. However, this data frequently goes unexploited. Accurate forecasting using this past data could help healthcare managers in taking appropriate decisions especially in implementing preventing measures. Due to the geographical nature of Maldives, it is difficult to implement preventive measures in case of an outbreak. There is no single approach to be used for health forecasting; thus, various methods have been used to specific health conditions or healthcare resources. Healthcare comprises of both complex linear and nonlinear patterns, which can affect the forecasting accuracy if only linear models or neural networks are used. In this research, a hybrid of the ARIMA model and Neural Network has been proposed to forecast healthcare data. A dataset comprising of 10 diseases including unique cases reported for each disease, between the years 2012 and 2016 have been used in this research. It was found that the proposed model performed well on 7 out of the 10 diseases