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Smart Anything Everywhere: New Frontiers, Solutions, Issues & Challenges

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 8521

Special Issue Editors


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Guest Editor
Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark
Interests: cyber security; machine learning; deep learning; reinforcement learning; artificial intelligence; blockchain and data mining; information systems; large scale distributed systems (i.e., edge, fog, and cloud, SDNs); the IoT; Industry 4.0; the Internet of Everything (IoE)

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Guest Editor
LIUPPA, IUT of Bayonne, University of Pau and Adour Countries, 64000 Anglet, France
Interests: middleware; software architecture; dynamic adaptation; context-aware; autonomic applications and green computing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Computer Science, Indiana Wesleyan University 4201 S. Washington ST Marion, IN 46953-4974, USA
Interests: crowdsourcing systems; mobile and context-aware applications; recommender systems

Special Issue Information

Dear Colleagues,

‘Smart anything everywhere’ is the next wave of products that integrate digital technology. The main challenge is to accelerate the design, development and uptake of advanced digital technologies in products that include innovative electronic components, software and systems, and especially in sectors where digital technologies are underexploited. To disseminate best practice, to coordinate access to technology, resources, demonstrators and open platforms, and to facilitate the cross development of platforms.

‘Smart anything everywhere’ (Healthcare, Infrastructure Inspection and Maintenance, Agri-Food and Agile Production, Emerging ICT solutions, smart cities and buildings, Internet of Everything (IoE), Internet of things (IoT) Agriculture, Next generation networks, Smart computing, Software defined networking, Grid, Embedded systems, Cyber Physical systems, Home, Hospital, largescale distributed systems (i.e., Cloud, Fog, Edge), Robotics, and Future factories and Industry etc.) has revolutionized the world by mainly bringing smartness to the digital world. Machine learning, Artificial Intelligence (AI), potentials of the Internet of Things (IoT) and Internet of Everything (IoE) together can build an intelligent, adaptive, cost effective, sustainable and smart societies, enterprise and varied future factories and business. Largescale distributed systems such as Cloud Computing, and Fog Computing, plays a vital role to satisfy the computational and storage requirements of the exponentially growing IoT devices. Edge computing can crucially offer a highly scalable infrastructure with delay-aware computing at the edge of the network. The IoT data streams forwarded to the cloud result in high bandwidth usage, long-way data traveling, high traffic on the Internet backbone, unnecessary loop delays, limited mobility support, and higher reliability concerns for successful communication. Hence, the edge resources for data storage and computing services potentially resolve these issues via micro datacenters in the geographical proximity of end devices. In this perspective, edge computing can play a key role to smartly address the real-time computational challenges and issues in cloud-IoT/ Cloud-IoE paradigm.

AI and machine learning together enables smartness by developing intelligent behavior through the processes of learning, reasoning, and self-correction. To make this happen, a substantial amount of processing and storage capabilities are required. Although IoT devices are capable to store and compute; their resource-constrained nature is insufficient to completely leverage the benefits of AI-based and learning mechanisms such as machine learning, deep learning, and reinforcement learning. Moreover, most AI-based solutions are computationally intensive in nature. Additionally, deploying an intelligent learning-based solution at the edge of the network is extremely challenging.

The special issue aims to acquire varied comprehensive state-of-the-art unpublished contributions from both industry and academia to meticulously address the new frontiers, novel solutions, issues, challenges surrounded by implementing ‘Smart anything everywhere’. This special issue equally encourages the proposition and demonstration of use cases, frameworks, applications, and testbeds for Smart X. The topics of interest include, but are not limited to:

Intelligent edge-IoT computing for Smart X

  • Energy efficient Smart X
  • Cyber-physical and embedded systems
  • Artificial Intelligence (AI) based solutions
  • Lightweight cryptographic solutions
  • Customized low energy computing powering CPS and the IoT
  • Flexible and Wearable Electronics
  • Widening Digital Innovation Hubs
  • Data privacy and protection, cyber-security issues (including security by design)
  • Heterogeneous smart resource management at the edge networks
  • Security, trust, and privacy solutions for Smart X
  • Deep learning solutions for edge-IoT
  • Smart solutions for big data analytics
  • Edge-IoT content placement and delivery for Smart X
  • AI-based techniques for Smart X
  • SDN-enabled intelligent edge-IoT
  • Blockchain for intelligent edge-IoT
  • Service provisioning for Smart X
  • Resource management for Smart edge-IoT
  • Data Analytics for intelligent edge-IoT
  • Deep and reinforcement learning based smart solutions
  • Machine learning at the edge for smart X
  • Intelligent Network management for edge-IoT
  • Context Aware Security and Privacy of cyber-physical systems
  • Intelligent adaptive object interaction for Industrial Internet of Things
  • Big Data architectures and solutions for smart manufacturing, supply chain management & co-design
  • Knowledge discovery in decentralized and collaborative Industry 4.0 platforms
  • Predictive maintenance techniques for industrial settings using Artificial Intelligence
  • Energy efficient cyber-physical systems and industry 4.0
  • QoS-aware cyber-physical systems for industry 4.0
  • Future Architectures of Secure cyber-physical systems & IIoT
  • Context-Aware Privacy of cyber-physical systems & IIoT ecosystems
  • QoE-aware cyber-physical systems for industry 4.0
  • Secure & dependable cyber-physical systems for industry 4.0
  • Secure and smart software defined architectures for cyber-physical systems & industry 4.0
  • Novel and innovative intelligent applications for Industrial Internet of Things (IIoT)
  • Novel mechanisms to encounter dynamic and zero-day attacks on cyber-physical systems & IIoT ecosystems
  • Secure by design mobile computing applications for cyber-physical systems & IIoT
  • The application of blockchain technologies in securing cyber-physical systems & IIoT
  • Decentralized Trust management in IIoT ecosystems
  • Machine Learning techniques in enabling trust management in cyber-physical systems & IIoT

Dr. Adnan Akhunzada
Prof. Dr. Philippe Roose
Dr. André Fonteles
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. Energies 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.

Keywords

  • Smart X
  • Intelligent edge-IoT computing for Smart X
  • Energy efficient Smart X
  • SDN-enabled intelligent edge-IoT
  • Blockchain for intelligent edge-IoT
  • Smart X for big data
  • Smart X for mobile computing
  • Smart X for IIoT

Published Papers (2 papers)

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Research

22 pages, 4443 KiB  
Article
Symptom Analysis Using Fuzzy Logic for Detection and Monitoring of COVID-19 Patients
by Tayyaba Ilyas, Danish Mahmood, Ghufran Ahmed and Adnan Akhunzada
Energies 2021, 14(21), 7023; https://doi.org/10.3390/en14217023 - 27 Oct 2021
Cited by 5 | Viewed by 2909
Abstract
Recent developments regarding the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) opened new horizons of healthcare opportunities. Moreover, these technological advancements give strength to face upcoming healthcare challenges. One of such challenges is the advent of COVID-19, which has [...] Read more.
Recent developments regarding the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) opened new horizons of healthcare opportunities. Moreover, these technological advancements give strength to face upcoming healthcare challenges. One of such challenges is the advent of COVID-19, which has adverse effects beyond comprehension. Therefore, utilizing the basic functionalities of IoT, this work presents a real-time rule-based Fuzzy Logic classifier for COVID-19 Detection (FLCD). The proposed model deploys the IoT framework to collect real-time symptoms data from users to detect symptomatic and asymptomatic Covid-19 patients. Moreover, the proposed framework is also capable of monitoring the treatment response of infected people. FLCD constitutes three components: symptom data collection using wearable sensors, data fusion through Rule-Based Fuzzy Logic classifier, and cloud infrastructure to store data with a possible verdict (normal, mild, serious, or critical). After extracting the relevant features, experiments with a synthetic COVID-19 symptom dataset are conducted to ensure effective and accurate detection of COVID-19 cases. As a result, FLCD successfully acquired 95% accuracy, 94.73% precision, 93.35% recall, and showed a minimum error rate of 2.52%. Full article
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15 pages, 3957 KiB  
Article
Smart Agriculture Cloud Using AI Based Techniques
by Muhammad Junaid, Asadullah Shaikh, Mahmood Ul Hassan, Abdullah Alghamdi, Khairan Rajab, Mana Saleh Al Reshan and Monagi Alkinani
Energies 2021, 14(16), 5129; https://doi.org/10.3390/en14165129 - 19 Aug 2021
Cited by 19 | Viewed by 4283
Abstract
This research proposes a generic smart cloud-based system in order to accommodate multiple scenarios where agriculture farms using Internet of Things (IoTs) need to be monitored remotely. The real-time and stored data are analyzed by specialists and farmers. The cloud acts as a [...] Read more.
This research proposes a generic smart cloud-based system in order to accommodate multiple scenarios where agriculture farms using Internet of Things (IoTs) need to be monitored remotely. The real-time and stored data are analyzed by specialists and farmers. The cloud acts as a central digital data store where information is collected from diverse sources in huge volumes and variety, such as audio, video, image, text, and digital maps. Artificial Intelligence (AI) based machine learning models such as Support Vector Machine (SVM), which is one of many classification types, are used to accurately classify the data. The classified data are assigned to the virtual machines where these data are processed and finally available to the end-users via underlying datacenters. This processed form of digital information is then used by the farmers to improve their farming skills and to update them as pre-disaster recovery for smart agri-food. Furthermore, it will provide general and specific information about international markets relating to their crops. This proposed system discovers the feasibility of the developed digital agri-farm using IoT-based cloud and provides solutions to problems. Overall, the approach works well and achieved performance efficiency in terms of execution time by 14%, throughput time by 5%, overhead time by 9%, and energy efficiency by 13.2% in the presence of competing smart farming baselines. Full article
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