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Electronics
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Empowering Sensor Applications with AI and Big Data Analytics
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Empowering Sensor Applications with AI and Big Data Analytics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 2067

Special Issue Editors

Prof. Dr. Mo Saraee

E-Mail Website
Guest Editor
School of Science, Engineering and Environment, University of Salford-Manchester, Salford M5 4NT, UK
Interests: data science; data mining; medical data mining; big data analytics; advanced databases
Dr. Surbhi Bhatia Khan

E-Mail Website
Guest Editor
School of Science, Engineering and Environment, University of Salford-Manchester, Salford M5 4NT, UK
Interests: knowledge management; information systems; machine learning; data science

Special Issue Information

Dear Colleagues,

The convergence of artificial intelligence (AI), machine learning, and big data technology has garnered significant attention in the realm of Internet of Things (IoT) sensor applications. Data scientists and engineers alike have recognized the immense potential of leveraging these technologies to harness the vast amount of data collected through sensors. With the emergence of next-generation IoT operations, such as smart facility management, IoT predictive maintenance, smart cities, cyber security, intelligence computation for security measures, and blockchain, the process of data collection through sensors and subsequent data processing using AI is commonly referred to as big data analysis in the context of sensors and applications.

This Special Issue aims to provide a prominent international platform for researchers to showcase cutting-edge advancements and results pertaining to AI, machine learning, big data analytics, and cyber security technologies in the field of sensors. The focus will be on exploring recent progress, current trends, and future directions of AI applications in sensors and associated applications. Moreover, this Special Issue seeks to foster collaboration and the exchange of ideas among researchers from computer science and various engineering disciplines, facilitating the discovery of common research topics and fostering cross-disciplinary exploration.

This Special Issue welcomes submissions covering a wide range of topics related to AI, machine learning, big data analytics, and cyber security in sensor applications. Potential areas of interest include, but are not limited to:

  1. Advanced AI algorithms and models for sensor data analysis.
  2. Machine learning techniques for sensor data processing and interpretation.
  3. Big data analytics methodologies for IoT sensor networks.
  4. Cyber security measures for safeguarding sensor data transmission and storage.
  5. Intelligent computation for enhancing security measures in sensor applications.
  6. Blockchain-based solutions for secure and transparent sensor data management.
  7. IoT predictive maintenance using AI and big data analytics.
  8. AI-driven smart facility management and optimization.
  9. Smart city applications leveraging AI, machine learning, and big data analytics.
  10. Cross-disciplinary research topics and collaborative efforts in sensor applications.

We look forward to receiving your contributions. 

Prof. Dr. Mo Saraee
Dr. Surbhi Bhatia Khan
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. Electronics 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 2400 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.

Keywords

  • artificial intelligence
  • cyber security
  • data analytics
  • big data
  • IoT

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Published Papers (1 paper)

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Research

31 pages, 7030 KiB  
Article
OptiDJS+: A Next-Generation Enhanced Dynamic Johnson Sequencing Algorithm for Efficient Resource Scheduling in Distributed Overloading within Cloud Computing Environment
by Pallab Banerjee, Sharmistha Roy, Umar Muhammad Modibbo, Saroj Kumar Pandey, Parul Chaudhary, Anurag Sinha and Narendra Kumar Singh
Electronics 2023, 12(19), 4123; https://doi.org/10.3390/electronics12194123 - 2 Oct 2023
Cited by 3 | Viewed by 1260
Abstract
The continuously evolving world of cloud computing presents new challenges in resource allocation as dispersed systems struggle with overloaded conditions. In this regard, we introduce OptiDJS+, a cutting-edge enhanced dynamic Johnson sequencing algorithm made to successfully handle resource scheduling challenges in cloud computing [...] Read more.
The continuously evolving world of cloud computing presents new challenges in resource allocation as dispersed systems struggle with overloaded conditions. In this regard, we introduce OptiDJS+, a cutting-edge enhanced dynamic Johnson sequencing algorithm made to successfully handle resource scheduling challenges in cloud computing settings. With a solid foundation in the dynamic Johnson sequencing algorithm, OptiDJS+ builds upon it to suit the demands of modern cloud infrastructures. OptiDJS+ makes use of sophisticated optimization algorithms, heuristic approaches, and adaptive mechanisms to improve resource allocation, workload distribution, and task scheduling. To obtain the best performance, this strategy uses historical data, dynamic resource reconfiguration, and adaptation to changing workloads. It accomplishes this by utilizing real-time monitoring and machine learning. It takes factors like load balance and make-up into account. We outline the design philosophies, implementation specifics, and empirical assessments of OptiDJS+ in this work. Through rigorous testing and benchmarking against cutting-edge scheduling algorithms, we show the better performance and resilience of OptiDJS+ in terms of reaction times, resource utilization, and scalability. The outcomes underline its success in reducing resource contention and raising service quality generally in cloud computing environments. In contexts where there is distributed overloading, OptiDJS+ offers a significant advancement in the search for effective resource scheduling solutions. Its versatility, optimization skills, and improved decision-making procedures make it a viable tool for tackling the resource allocation issues that cloud service providers and consumers encounter daily. We think that OptiDJS+ opens the way for more dependable and effective cloud computing ecosystems, assisting in the full realization of cloud technologies’ promises across a range of application areas. In order to use the OptiDJS+ Johnson sequencing algorithm for cloud computing task scheduling, we provide a two-step procedure. After examining the links between the jobs, we generate a Gantt chart. The Gantt chart graph is then changed into a two-machine OptiDJS+ Johnson sequencing problem by assigning tasks to servers. The OptiDJS+ dynamic Johnson sequencing approach is then used to minimize the time span and find the best sequence of operations on each server. Through extensive simulations and testing, we evaluate the performance of our proposed OptiDJS+ dynamic Johnson sequencing approach with two servers to that of current scheduling techniques. The results demonstrate that our technique greatly improves performance in terms of makespan reduction and resource utilization. The recommended approach also demonstrates its ability to scale and is effective at resolving challenging work scheduling problems in cloud computing environments. Full article
(This article belongs to the Special Issue Empowering Sensor Applications with AI and Big Data Analytics)
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