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Big Data Analysis for Green Environment Using Internet of Things

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (21 April 2023) | Viewed by 3057

Special Issue Editors

Department of Computer Science, Air University, Islamabad 44000, Pakistan
Interests: machine learning; Artificial Intelligence and Deep Learning; big data analytics; demosaicking and denoising; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Department of Embedded Systems Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea
Interests: remote sensing; deep learning; artificial intelligence; image processing; signal processing
Special Issues, Collections and Topics in MDPI journals
Department of Information Security National University of Science and Technology, Islamabad 44000, Pakistan
Interests: information security; IoT security; cryptography; SDN security; machine learning

Special Issue Information

Dear Colleagues,

Internet advancements allow the coordination and sharing of a common communication medium and a complete framework comprising different kinds of sensor arrangements, including smart home sensors, vehicular networks, climate and water sensors, smart parking sensors, and surveillance objects. Subsequently, expansions in the worldwide population have led to expanded interest for items and administration, which calls for more compelling methods of utilizing existing normal assets and materials. The new advancement of data and correspondence innovations, which enormously affect numerous regions, additionally affecting the climate and human wellbeing. Subsequently, societies are advancing toward a greener future by diminishing the utilization of nonrenewable materials, unrefined components, and assets while simultaneously diminishing energy contamination and utilization. Since data innovation is viewed as a way to address natural challenges, the green Internet of things is assuming an indispensable role in making a sustainable home. A data analysis investigation is needed to acquire an important outline of the enormous and different information created by the green IoT. The assembled data will be derived from dynamic and different exercises identified with keen metropolitan administrations and afterward added to the relentless improvement of green IoT innovation. Along these lines, regardless of whether economical and smart urban areas become a reality, the green IoT approach and the information acquired through huge information examination will make urban communities more maintainable, more secure, and more astute.

Therefore, to achieve a green environment, the volume of information made day by day using IoT takes us toward the speed of information creation, and the expanding interest for information stockpiling and handling have raised critical difficulties for analysts around the world. Applied and experimental examinations into the investigation of enormous information and prescient are, as yet, lacking. Contrasting and gathering the results of these examinations to arrive at quick discoveries is testing. Supportability examinations and arranging present a small bunch of replies to resolve arising issues by permitting the utilization of displaying, arranging, recreation, controlling, and advancement to plan more practical things and techniques. In the period of digitalization, information examination and related strategies are propelling the wide space of the cultural, monetary, and natural parts of people's lives.
Therefore, this Special Issue presents a platform for researchers and practitioners to explore the big data investigation and prescient logical strategies identified with the multidimensional parts of manageability. Moreover, it examines valuable strategies for big data analysis, dynamic, and exploration headings towards resolving the current issues identified with manageability, for instance, environmental change, green innovation, smart urban areas, and ecological issues by zeroing in on the promising techniques for information examination and prescient investigation. In particular, this Special Issue aims to expect the most original achievements and headways in big data revelation and examination with regard to supportability. Generally, this Special Issue will cover but is not limited to the following themes:

  • IoT-based environmental big data processing and analysis
  • IoT-based green city big data governance and management
  • Environment-aware application, analytics, and visualization
  • Knowledge-based systems for green IoT
  • Big data analysis of energy distributions in smart cities
  • Predictive analytics in green information systems
  • Knowledge-based systems, computing, and visualization for green IoT
  • Internet of things (IoT) technologies for sustainability
  • Internet of vehicles for green environment
  • Passenger sensing, control, and management
  • Big data modeling, storage, management, and analytics
  • Geography big data mining and exploration
  • Large-scale sustainable infrastructure and smart buildings

Dr. Awais Ahmad
Dr. Gwanggil Jeon
Dr. Waseem Iqbal
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

30 pages, 875 KiB  
Article
Delay Optimal Schemes for Internet of Things Applications in Heterogeneous Edge Cloud Computing Networks
Sensors 2022, 22(16), 5937; https://doi.org/10.3390/s22165937 - 09 Aug 2022
Cited by 12 | Viewed by 2055
Abstract
Over the last decade, the usage of Internet of Things (IoT) enabled applications, such as healthcare, intelligent vehicles, and smart homes, has increased progressively. These IoT applications generate delayed- sensitive data and requires quick resources for execution. Recently, software-defined networks (SDN) offer an [...] Read more.
Over the last decade, the usage of Internet of Things (IoT) enabled applications, such as healthcare, intelligent vehicles, and smart homes, has increased progressively. These IoT applications generate delayed- sensitive data and requires quick resources for execution. Recently, software-defined networks (SDN) offer an edge computing paradigm (e.g., fog computing) to run these applications with minimum end-to-end delays. Offloading and scheduling are promising schemes of edge computing to run delay-sensitive IoT applications while satisfying their requirements. However, in the dynamic environment, existing offloading and scheduling techniques are not ideal and decrease the performance of such applications. This article formulates joint and scheduling problems into combinatorial integer linear programming (CILP). We propose a joint task offloading and scheduling (JTOS) framework based on the problem. JTOS consists of task offloading, sequencing, scheduling, searching, and failure components. The study’s goal is to minimize the hybrid delay of all applications. The performance evaluation shows that JTOS outperforms all existing baseline methods in hybrid delay for all applications in the dynamic environment. The performance evaluation shows that JTOS reduces the processing delay by 39% and the communication delay by 35% for IoT applications compared to existing schemes. Full article
(This article belongs to the Special Issue Big Data Analysis for Green Environment Using Internet of Things)
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