Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Machine Learning Techniques for Energy Efficient IoT Networks
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Machine Learning Techniques for Energy Efficient IoT Networks

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

Deadline for manuscript submissions: closed (30 October 2023) | Viewed by 6976

Special Issue Editors

Prof. Dr. Muhammad Mahtab Alam

E-Mail Website
Guest Editor
Thomas Johann Seebeck Department of Electronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Interests: Internet of Things; 5G and beyond networks; body area networks; low-power communication protocols; machine learning; public safety networks
Dr. Ahmed Zoha

E-Mail Website
Guest Editor
School of Engineering, University of Glasgow, Glasgow G12 8QQ, ‎Scotland‎, ‎UK
Interests: AI & machine learning; 5G networks & beyond; health analytics; IoT

Special Issue Information

Dear Colleagues,

Internet of things (IoT) technologies are becoming part of our daily life thanks to the realization of a large set of applications and services. Most IoT devices are constrained by energy efficiency due to limited battery capacity. Energy-efficient solutions for both IoT devices and IoT networks with device-specific corresponding configurations are critical for viable applications and services.

Machine learning (ML) techniques can pave the way towards energy-efficient IoT networks. A greater understanding is needed regarding how different ML algorithms and methods can improve the performance if suitable data are obtained. Equally important are the methods themselves—how energy efficient they are for their implementation and execution. The objective of this Special Issue is to bring together recent progress in scientific and practical experiences, theory, modeling, design, implementation, deployment, and management of the IoT networks.

  • Energy-efficient machine learning methods for Internet of things.
  • Energy-efficient data prediction in IoT networks.
  • Energy-efficient data analytics in IoT networks.
  • Energy-efficient machine learning methods for edge-IoT networks.
  • Machine-learning-enabled system architectures for IoT applications.
  • Lightweight machine-learning-based security design for IoT networks.
  • Machine-learning-enabled secure and privacy-preserving IoT communications.
  • Low-energy energy-harvesting wireless communication in IoT networks.
  • Machine-learning-based energy-efficient resource allocation in IoT networks.
  • New application implementations by machine learning methods.

Prof. Dr. Muhammad Mahtab Alam
Dr. Ahmed Zoha
Guest Editors

If you have any questions or need further information, please free to contact Special Issue Editor Larissa Zhang <larissa.zhang@mdpi.com>.

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 (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 3246 KiB  
Article
FedBranched: Leveraging Federated Learning for Anomaly-Aware Load Forecasting in Energy Networks
by Habib Ullah Manzoor, Ahsan Raza Khan, David Flynn, Muhammad Mahtab Alam, Muhammad Akram, Muhammad Ali Imran and Ahmed Zoha
Sensors 2023, 23(7), 3570; https://doi.org/10.3390/s23073570 - 29 Mar 2023
Cited by 2 | Viewed by 1696
Abstract
Increased demand for fast edge computation and privacy concerns have shifted researchers’ focus towards a type of distributed learning known as federated learning (FL). Recently, much research has been carried out on FL; however, a major challenge is the need to tackle the [...] Read more.
Increased demand for fast edge computation and privacy concerns have shifted researchers’ focus towards a type of distributed learning known as federated learning (FL). Recently, much research has been carried out on FL; however, a major challenge is the need to tackle the high diversity in different clients. Our research shows that using highly diverse data sets in FL can lead to low accuracy of some local models, which can be categorised as anomalous behaviour. In this paper, we present FedBranched, a clustering-based framework that uses probabilistic methods to create branches of clients and assigns their respective global models. Branching is performed using hidden Markov model clustering (HMM), and a round of branching depends on the diversity of the data. Clustering is performed on Euclidean distances of mean absolute percentage errors (MAPE) obtained from each client at the end of pre-defined communication rounds. The proposed framework was implemented on substation-level energy data with nine clients for short-term load forecasting using an artificial neural network (ANN). FedBranched took two clustering rounds and resulted in two different branches having individual global models. The results show a substantial increase in the average MAPE of all clients; the biggest improvement of 11.36% was observed in one client. Full article
(This article belongs to the Special Issue Machine Learning Techniques for Energy Efficient IoT Networks)
►▼ Show Figures

Figure 1

30 pages, 11450 KiB  
Article
A Two-Stage Multi-Agent EV Charging Coordination Scheme for Maximizing Grid Performance and Customer Satisfaction
by Adil Amin, Anzar Mahmood, Ahsan Raza Khan, Kamran Arshad, Khaled Assaleh and Ahmed Zoha
Sensors 2023, 23(6), 2925; https://doi.org/10.3390/s23062925 - 08 Mar 2023
Cited by 1 | Viewed by 2647
Abstract
Advancements in technology and awareness of energy conservation and environmental protection have increased the adoption rate of electric vehicles (EVs). The rapidly increasing adoption of EVs may affect grid operation adversely. However, the increased integration of EVs, if managed appropriately, can positively impact [...] Read more.
Advancements in technology and awareness of energy conservation and environmental protection have increased the adoption rate of electric vehicles (EVs). The rapidly increasing adoption of EVs may affect grid operation adversely. However, the increased integration of EVs, if managed appropriately, can positively impact the performance of the electrical network in terms of power losses, voltage deviations and transformer overloads. This paper presents a two-stage multi-agent-based scheme for the coordinated charging scheduling of EVs. The first stage uses particle swarm optimization (PSO) at the distribution network operator (DNO) level to determine the optimal power allocation among the participating EV aggregator agents to minimize power losses and voltage deviations, whereas the second stage at the EV aggregator agents level employs a genetic algorithm (GA) to align the charging activities to achieve customers’ charging satisfaction in terms of minimum charging cost and waiting time. The proposed method is implemented on the IEEE-33 bus network connected with low-voltage nodes. The coordinated charging plan is executed with the time of use (ToU) and real-time pricing (RTP) schemes, considering EVs’ random arrival and departure with two penetration levels. The simulations show promising results in terms of network performance and overall customer charging satisfaction. Full article
(This article belongs to the Special Issue Machine Learning Techniques for Energy Efficient IoT Networks)
►▼ Show Figures

Figure 1

14 pages, 8623 KiB  
Article
On Coverage of Critical Nodes in UAV-Assisted Emergency Networks
by Maham Waheed, Rizwan Ahmad, Waqas Ahmed, Muhammad Mahtab Alam and Maurizio Magarini
Sensors 2023, 23(3), 1586; https://doi.org/10.3390/s23031586 - 01 Feb 2023
Cited by 7 | Viewed by 1691
Abstract
Unmanned aerial vehicle (UAV)-assisted networks ensure agile and flexible solutions based on the inherent attributes of mobility and altitude adaptation. These features render them suitable for emergency search and rescue operations. Emergency networks (ENs) differ from conventional networks. They often encounter nodes with [...] Read more.
Unmanned aerial vehicle (UAV)-assisted networks ensure agile and flexible solutions based on the inherent attributes of mobility and altitude adaptation. These features render them suitable for emergency search and rescue operations. Emergency networks (ENs) differ from conventional networks. They often encounter nodes with vital information, i.e., critical nodes (CNs). The efficacy of search and rescue operations highly depends on the eminent coverage of critical nodes to retrieve crucial data. In a UAV-assisted EN, the information delivery from these critical nodes can be ensured through quality-of-service (QoS) guarantees, such as capacity and age of information (AoI). In this work, optimized UAV placement for critical nodes in emergency networks is studied. Two different optimization problems, namely capacity maximization and age of information minimization, are formulated based on the nature of node criticality. Capacity maximization provides general QoS enhancement for critical nodes, whereas AoI is focused on nodes carrying critical information. Simulations carried out in this paper aim to find the optimal placement for each problem based on a two-step approach. At first, the disaster region is partitioned based on CNs’ aggregation. Reinforcement learning (RL) is then applied to observe optimal placement. Finally, network coverage over optimal UAV(s) placement is studied for two scenarios, i.e., network-centric and user-centric. In addition to providing coverage to critical nodes, the proposed scheme also ensures maximum coverage for all on-scene available devices (OSAs). Full article
(This article belongs to the Special Issue Machine Learning Techniques for Energy Efficient IoT Networks)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop