23,433 research outputs found
Big Data and Large-scale Data Analytics: Efficiency of Sustainable Scalability and Security of Centralized Clouds and Edge Deployment Architectures
One of the significant shifts of the next-generation computing technologies will certainly be in
the development of Big Data (BD) deployment architectures. Apache Hadoop, the BD
landmark, evolved as a widely deployed BD operating system. Its new features include
federation structure and many associated frameworks, which provide Hadoop 3.x with the
maturity to serve different markets. This dissertation addresses two leading issues involved in
exploiting BD and large-scale data analytics realm using the Hadoop platform. Namely,
(i)Scalability that directly affects the system performance and overall throughput using
portable Docker containers. (ii) Security that spread the adoption of data protection practices
among practitioners using access controls. An Enhanced Mapreduce Environment (EME),
OPportunistic and Elastic Resource Allocation (OPERA) scheduler, BD Federation Access Broker
(BDFAB), and a Secure Intelligent Transportation System (SITS) of multi-tiers architecture for
data streaming to the cloud computing are the main contribution of this thesis study
Navigating the IoT landscape: Unraveling forensics, security issues, applications, research challenges, and future
Given the exponential expansion of the internet, the possibilities of
security attacks and cybercrimes have increased accordingly. However, poorly
implemented security mechanisms in the Internet of Things (IoT) devices make
them susceptible to cyberattacks, which can directly affect users. IoT
forensics is thus needed for investigating and mitigating such attacks. While
many works have examined IoT applications and challenges, only a few have
focused on both the forensic and security issues in IoT. Therefore, this paper
reviews forensic and security issues associated with IoT in different fields.
Future prospects and challenges in IoT research and development are also
highlighted. As demonstrated in the literature, most IoT devices are vulnerable
to attacks due to a lack of standardized security measures. Unauthorized users
could get access, compromise data, and even benefit from control of critical
infrastructure. To fulfil the security-conscious needs of consumers, IoT can be
used to develop a smart home system by designing a FLIP-based system that is
highly scalable and adaptable. Utilizing a blockchain-based authentication
mechanism with a multi-chain structure can provide additional security
protection between different trust domains. Deep learning can be utilized to
develop a network forensics framework with a high-performing system for
detecting and tracking cyberattack incidents. Moreover, researchers should
consider limiting the amount of data created and delivered when using big data
to develop IoT-based smart systems. The findings of this review will stimulate
academics to seek potential solutions for the identified issues, thereby
advancing the IoT field.Comment: 77 pages, 5 figures, 5 table
Ethical and Social Aspects of Self-Driving Cars
As an envisaged future of transportation, self-driving cars are being
discussed from various perspectives, including social, economical, engineering,
computer science, design, and ethics. On the one hand, self-driving cars
present new engineering problems that are being gradually successfully solved.
On the other hand, social and ethical problems are typically being presented in
the form of an idealized unsolvable decision-making problem, the so-called
trolley problem, which is grossly misleading. We argue that an applied
engineering ethical approach for the development of new technology is what is
needed; the approach should be applied, meaning that it should focus on the
analysis of complex real-world engineering problems. Software plays a crucial
role for the control of self-driving cars; therefore, software engineering
solutions should seriously handle ethical and social considerations. In this
paper we take a closer look at the regulative instruments, standards, design,
and implementations of components, systems, and services and we present
practical social and ethical challenges that have to be met, as well as novel
expectations for software engineering.Comment: 11 pages, 3 figures, 2 table
A European research roadmap for optimizing societal impact of big data on environment and energy efficiency
We present a roadmap to guide European research efforts towards a socially
responsible big data economy that maximizes the positive impact of big data in
environment and energy efficiency. The goal of the roadmap is to allow
stakeholders and the big data community to identify and meet big data
challenges, and to proceed with a shared understanding of the societal impact,
positive and negative externalities, and concrete problems worth investigating.
It builds upon a case study focused on the impact of big data practices in the
context of Earth Observation that reveals both positive and negative effects in
the areas of economy, society and ethics, legal frameworks and political
issues. The roadmap identifies European technical and non-technical priorities
in research and innovation to be addressed in the upcoming five years in order
to deliver societal impact, develop skills and contribute to standardization.Comment: 6 pages, 2 figures, 1 tabl
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