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Energy-Efficient IoT (Internet of Things) and Big Data Challenges for...
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Energy-Efficient IoT (Internet of Things) and Big Data Challenges for Connected Intelligence

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 11384

Special Issue Editors

Prof. Dr. Anupam Kumar Bairagi

E-Mail Website
Guest Editor
1. Department of Computer Science and Engineering, Kyung Hee University, 1732 Deokyoungdaero, Giheung-gu, Yongin-si 17014, Gyeonggi-do, Republic of Korea
2. Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
Interests: artificial intelligence; machine learning; wireless resource management in 5G; cooperative communication; game theory; health informatics; IIoT; UAV communication
Special Issues, Collections and Topics in MDPI journals
Dr. Md. Golam Rabiul Alam

E-Mail Website
Guest Editor
1. Department of Computer Science and Engineering, Kyung Hee University, 1732 Deokyoungdaero, Giheung-gu, Yongin-si 17014, Gyeonggi-do, Republic of Korea
2. Department of Computer Science and Engineering, School of Data and Sciences, BRAC University, Dhaka 1212, Bangladesh
Interests: healthcare-IoT networks; mental-health informatics; ambient intelligence systems; mobile-cloud computing; edge computing; affective computing; UAV image processing
Dr. Anselme Ndikumana

E-Mail Website
Guest Editor
Département génie des systèmes, École de Technologie Supérieure, Montréal, QC H3C 1K3, Canada
Interests: deep learning; multiaccess edge computing; information-centric networking; in-network caching
Dr. Md. Shirajum Munir

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Kyung Hee University, 1732 Deokyoungdaero, Giheung-gu, Yongin-si 17014, Gyeonggi-do, Republic of Korea
Interests: artificial intelligence and stochastic models; Internet of Things; wireless network resource management; sustainable edge computing; intelligent healthcare system; industrial-IoT; resilient smart grid

Special Issue Information

Dear Colleagues,

With the ongoing development of the Internet of Things (IoT), artificial intelligence (AI), and AI as a service, we are rapidly moving towards connected intelligence, where the roles of Big Data become crucial, since individuals and critical cyber-physical systems completely rely on behavior and intuition of data. In future IoT and communication systems, energy-efficient, rational, trustworthy, and data-informed AI models become the key enablers to automatic IoT network and service management. Therefore, to support vertical IoT applications for the modern citizen, AI-supported methods, architectures, and system models are needed, where the system must meet a set of KPIs such as self-sustainable, self-powered, and self-organized qualities. In particular, the mechanisms for analyzing Big Data for critical cyber-physical systems such as Industrial IoT, smart homes, smart grid, intelligent healthcare, connected and autonomous vehicle systems, and smart factories are essential so that system can infer knowledge and be capable to execute in real-time. Further, the modern IoT system must be scalable for adopting 5G and beyond communication systems such as THz sensing and communication, integration of non-terrestrial communication, effective edge, and fog computing. The envision of this special issue is to investigate energy-efficient IoT systems, AI models for analysis and evaluation, use-cases and case studies, as well as the comprehensive review on the roles of Big Data for leaping forward to connected intelligence. We encourage the research community to submit original research articles and comprehensive review articles for receiving high-quality feedback (peer review) from editorial board members. We believe that, together, we can contribute high-quality research to the community by publishing with the journal Big Data and Cognitive Computing (ISSN 2504-2289). Note that papers will be published on an ongoing basis.

The topic of “Energy-Efficient IoT (Internet of Things) and Big Data Challenges for Connected Intelligence” includes, but is not limited to, the following topics:

  • Trustworthy artificial intelligence model for energy-efficient IoT network management using Big Data;
  • Neurosymbolic artificial intelligence for complex cyber-physical system;
  • Role of Big Data for IoT network traffic characterization, measurement, and monitoring;
  • Light-weight knowledge graph modeling for IoT services using Big Data;
  • Energy-harvesting model for self-powered IoT;
  • Role of satellite communication for IoT;
  • 6G approaches to serve massive IoT;
  • IoT applications and services to convergence with ground, space, and maritime networks;
  • Connected intelligence framework designing for smart factory;
  • Data-informed intelligent system model for sustainable smart city;
  • Cellular IoT with sustainable energy;
  • AI methods for quality of experience (QoE) and quality of service (QoS) IoT service delivery;
  • Experimental analysis on IoT-based smart energy management;
  • Multi-agent intelligent system design for mitigating the tradeoffs between exploration and exploitation;
  • AI model performance evaluation of smart grid communications and demand response;
  • Tradeoff analysis for solving big data problem with small amount of data for the resilient healthcare system;
  • Information-centric networking for connected and autonomous vehicle (CAV) system;
  • Testbed and prototype design for target-oriented smart services such as intelligent caching, smart energy, and emergent services for smart citizens;
  • Energy-efficient and data-centric AR/VR/MR control design;
  • Game theory-based AI model for semantic IoT communication;
  • UAV-aided IoT system for energy saving;
  • Statistical machine learning algorithm design for performance analysis of IoT network;
  • Personalized behavior analysis for infotainment contents delivery to CAV systems;
  • Blockchain for target-oriented security, reliability, privacy, and trust enhancement for connected intelligent;
  • Public dataset generation for IoT network evaluation;
  • Trend analysis for beyond 5G networks of energy-efficient IoT network;
  • Performance and energy consumption tradeoffs analysis model using Big Data;
  • AI model for priority-based IoT service provisioning;
  • Role of Big Data for cellular IoT and Edge computing;
  • IoT Use-case analysis for evolvable AI model for multi-access edge computing (MEC) and fog computing infrastructure;
  • Trend and performance analysis for sensing and wireless resource management in THz;
  • Multi-model AI system design for complex cyber-physical systems;
  • AI model for real-time IoT services.

Prof. Dr. Anupam Kumar Bairagi
Dr. Md. Golam Rabiul Alam
Dr. Anselme Ndikumana
Dr. Md. Shirajum Munir
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. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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 1800 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

  • Internet of Things (IoT)
  • connected intelligence
  • energy-efficient IoT
  • energy harvesting
  • self-powered IoT
  • artificial intelligence (AI)
  • trustworthy AI
  • neurosymbolic artificial intelligence
  • semantic-based AI
  • knowledge Graph
  • evolvable AI
  • multi-model AI system
  • explainable AI model
  • machine intelligent
  • security, reliability, privacy, and trust for IoT
  • connected and autonomous vehicle
  • personalized behavior analysis
  • cellular IoT
  • smart healthcare
  • smart city
  • complex cyber-physical system
  • Big Data fusion
  • quality of experience (QoE) and quality of service (QoS) IoT service delivery
  • game theory for Big Data
  • smart sensing
  • THz for IoT
  • satellite communication for IoT
  • convergence with the ground, space, and maritime networks
  • sustainable IoT network
  • infotainment for CAV
  • self-powered IoT
  • real-time IoT services
  • dataset of IoT network

Published Papers (3 papers)

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22 pages, 3764 KiB  
Article
A Guide to Data Collection for Computation and Monitoring of Node Energy Consumption
by Alberto del Rio, Giuseppe Conti, Sandra Castano-Solis, Javier Serrano, David Jimenez and Jesus Fraile-Ardanuy
Big Data Cogn. Comput. 2023, 7(3), 130; https://doi.org/10.3390/bdcc7030130 - 11 Jul 2023
Viewed by 1492
Abstract
The digital transition that drives the new industrial revolution is largely driven by the application of intelligence and data. This boost leads to an increase in energy consumption, much of it associated with computing in data centers. This fact clashes with the growing [...] Read more.
The digital transition that drives the new industrial revolution is largely driven by the application of intelligence and data. This boost leads to an increase in energy consumption, much of it associated with computing in data centers. This fact clashes with the growing need to save and improve energy efficiency and requires a more optimized use of resources. The deployment of new services in edge and cloud computing, virtualization, and software-defined networks requires a better understanding of consumption patterns aimed at more efficient and sustainable models and a reduction in carbon footprints. These patterns are suitable to be exploited by machine, deep, and reinforced learning techniques in pursuit of energy consumption optimization, which can ideally improve the energy efficiency of data centers and big computing servers providing these kinds of services. For the application of these techniques, it is essential to investigate data collection processes to create initial information points. Datasets also need to be created to analyze how to diagnose systems and sort out new ways of optimization. This work describes a data collection methodology used to create datasets that collect consumption data from a real-world work environment dedicated to data centers, server farms, or similar architectures. Specifically, it covers the entire process of energy stimuli generation, data extraction, and data preprocessing. The evaluation and reproduction of this method is offered to the scientific community through an online repository created for this work, which hosts all the code available for its download. Full article
(This article belongs to the Special Issue Energy-Efficient IoT (Internet of Things) and Big Data Challenges for Connected Intelligence)
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10 pages, 1071 KiB  
Article
Adoption Case of IIoT and Machine Learning to Improve Energy Consumption at a Process Manufacturing Firm, under Industry 5.0 Model
by Andrés Redchuk, Federico Walas Mateo, Guadalupe Pascal and Julian Eloy Tornillo
Big Data Cogn. Comput. 2023, 7(1), 42; https://doi.org/10.3390/bdcc7010042 - 24 Feb 2023
Cited by 3 | Viewed by 2389
Abstract
Considering the novel concept of Industry 5.0 model, where sustainability is aimed together with integration in the value chain and centrality of people in the production environment, this article focuses on a case where energy efficiency is achieved. The work presents a food [...] Read more.
Considering the novel concept of Industry 5.0 model, where sustainability is aimed together with integration in the value chain and centrality of people in the production environment, this article focuses on a case where energy efficiency is achieved. The work presents a food industry case where a low-code AI platform was adopted to improve the efficiency and lower environmental footprint impact of its operations. The paper describes the adoption process of the solution integrated with an IIoT architecture that generates data to achieve process optimization. The case shows how a low-code AI platform can ease energy efficiency, considering people in the process, empowering them, and giving a central role in the improvement opportunity. The paper includes a conceptual framework on issues related to Industry 5.0 model, the food industry, IIoT, and machine learning. The adoption case’s relevancy is marked by how the business model looks to democratize artificial intelligence in industrial firms. The proposed model delivers value to ease traditional industries to obtain better operational results and contribute to a better use of resources. Finally, the work intends to go through opportunities that arise around artificial intelligence as a driver for new business and operating models considering the role of people in the process. By empowering industrial engineers with data driven solutions, organizations can ensure that their domain expertise can be applied to data insights to achieve better outcomes. Full article
(This article belongs to the Special Issue Energy-Efficient IoT (Internet of Things) and Big Data Challenges for Connected Intelligence)
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28 pages, 772 KiB  
Article
Incentive Mechanisms for Smart Grid: State of the Art, Challenges, Open Issues, Future Directions
by Sweta Bhattacharya, Rajeswari Chengoden, Gautam Srivastava, Mamoun Alazab, Abdul Rehman Javed, Nancy Victor, Praveen Kumar Reddy Maddikunta and Thippa Reddy Gadekallu
Big Data Cogn. Comput. 2022, 6(2), 47; https://doi.org/10.3390/bdcc6020047 - 27 Apr 2022
Cited by 26 | Viewed by 5793
Abstract
Smart grids (SG) are electricity grids that communicate with each other, provide reliable information, and enable administrators to operate energy supplies across the country, ensuring optimized reliability and efficiency. The smart grid contains sensors that measure and transmit data to adjust the flow [...] Read more.
Smart grids (SG) are electricity grids that communicate with each other, provide reliable information, and enable administrators to operate energy supplies across the country, ensuring optimized reliability and efficiency. The smart grid contains sensors that measure and transmit data to adjust the flow of electricity automatically based on supply/demand, and thus, responding to problems becomes quicker and easier. This also plays a crucial role in controlling carbon emissions, by avoiding energy losses during peak load hours and ensuring optimal energy management. The scope of big data analytics in smart grids is huge, as they collect information from raw data and derive intelligent information from the same. However, these benefits of the smart grid are dependent on the active and voluntary participation of the consumers in real-time. Consumers need to be motivated and conscious to avail themselves of the achievable benefits. Incentivizing the appropriate actor is an absolute necessity to encourage prosumers to generate renewable energy sources (RES) and motivate industries to establish plants that support sustainable and green-energy-based processes or products. The current study emphasizes similar aspects and presents a comprehensive survey of the start-of-the-art contributions pertinent to incentive mechanisms in smart grids, which can be used in smart grids to optimize the power distribution during peak times and also reduce carbon emissions. The various technologies, such as game theory, blockchain, and artificial intelligence, used in implementing incentive mechanisms in smart grids are discussed, followed by different incentive projects being implemented across the globe. The lessons learnt, challenges faced in such implementations, and open issues such as data quality, privacy, security, and pricing related to incentive mechanisms in SG are identified to guide the future scope of research in this sector. Full article
(This article belongs to the Special Issue Energy-Efficient IoT (Internet of Things) and Big Data Challenges for Connected Intelligence)
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