Special Issue "Advancements in Lightweight AI for Constrained Internet of Things Devices for Smart Cities"

Submission Deadline: 30 September 2021 (closed)
Guest Editors
Dr. Shabir Ahmad, Gachon University, South Korea.
Dr. Muhammad Fayaz, University of Central Asia, Russia.
Dr. Faheem Khan, University of Laki Marwat, Pakistan.


The Internet of Things (IoT) has been playing a vital role in adding value to human lives. In recent years, IoT applications have been coupled with Machine Learning techniques to form Intelligent IoT applications. However, for intelligent IoT nodes, the machine learning technologies should be lightweight to meet the constrained capabilities of the embedded hardware. This Special Issue aims to highlight advances in the open research topics in this field, which include, but are not limited to, the following:

1. Optimize Existing Machine Learning architecture for embedded IoT devices;

2. Lightweight Machine Learning architecture and frameworks;

3. Distributed Predictive Optimization;

4. Communication network design and optimization;

5. Energy saving and energy harvesting methods and techniques;

6. Blockchain for security and privacy;

7. Data collection and management methods (big data and data retrieval);

8. Data replication and distribution management on IoT edge nodes;

9. Lightweight Intelligent IoT service orchestration;

10. Intelligent IoT for lightweight driver assistance systems in Electric Vehicles.

Model Optimization
Smart Cities
Internet of Things
Service Orchestration
Computer Vision
Medical Image Processing
Digital Twin

Published Papers
  • Edge Metric Dimension of Honeycomb and Hexagonal Networks for IoT
  • Abstract Wireless Sensor Network (WSN) is considered to be one of the fundamental technologies employed in the Internet of things (IoT); hence, enabling diverse applications for carrying out real-time observations. Robot navigation in such networks was the main motivation for the introduction of the concept of landmarks. A robot can identify its own location by sending signals to obtain the distances between itself and the landmarks. Considering networks to be a type of graph, this concept was redefined as metric dimension of a graph which is the minimum number of nodes needed to identify all the nodes of the graph. This… More
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  • Hybrid Renewable Energy Resources Management for Optimal Energy Operation in Nano-Grid
  • Abstract Renewable energy resources are deemed a potential energy production source due to their cost efficiency and harmless reaction to the environment, unlike non-renewable energy resources. However, they often fail to meet energy requirements in unfavorable weather conditions. The concept of Hybrid renewable energy resources addresses this issue by integrating both renewable and non-renewable energy resources to meet the required energy load. In this paper, an intelligent cost optimization algorithm is proposed to maximize the use of renewable energy resources and minimum utilization of non-renewable energy resources to meet the energy requirement for a nanogrid infrastructure. An actual data set comprising… More
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  • Blockchain Based Secured Load Balanced Task Scheduling Approach for Fitness Service
  • Abstract In recent times, the evolution of blockchain technology has got huge attention from the research community due to its versatile applications and unique security features. The IoT has shown wide adoption in various applications including smart cities, healthcare, trade, business, etc. Among these applications, fitness applications have been widely considered for smart fitness systems. The users of the fitness system are increasing at a high rate thus the gym providers are constantly extending the fitness facilities. Thus, scheduling such a huge number of requests for fitness exercise is a big challenge. Secondly, the user fitness data is critical thus securing… More
  •   Views:36       Downloads:25        Download PDF

  • An Energy-Efficient Mobile Agent-Based Data Aggregation Scheme for Wireless Body Area Networks
  • Abstract Due to the advancement in wireless technology and miniaturization, Wireless Body Area Networks (WBANs) have gained enormous popularity, having various applications, especially in the healthcare sector. WBANs are intrinsically resource-constrained; therefore, they have specific design and development requirements. One such highly desirable requirement is an energy-efficient and reliable Data Aggregation (DA) mechanism for WBANs. The efficient and reliable DA may ultimately push the network to operate without much human intervention and further extend the network lifetime. The conventional client-server DA paradigm becomes unsuitable and inefficient for WBANs when a large amount of data is generated in the network. Similarly, in… More
  •   Views:432       Downloads:222        Download PDF

  • EEG-Based Neonatal Sleep Stage Classification Using Ensemble Learning
  • Abstract Sleep stage classification can provide important information regarding neonatal brain development and maturation. Visual annotation, using polysomnography (PSG), is considered as a gold standard for neonatal sleep stage classification. However, visual annotation is time consuming and needs professional neurologists. For this reason, an internet of things and ensemble-based automatic sleep stage classification has been proposed in this study. 12 EEG features, from 9 bipolar channels, were used to train and test the base classifiers including convolutional neural network, support vector machine, and multilayer perceptron. Bagging and stacking ensembles are then used to combine the outputs for final classification. The proposed… More
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  • Fuzzy-Based Automatic Epileptic Seizure Detection Framework
  • Abstract Detection of epileptic seizures on the basis of Electroencephalogram (EEG) recordings is a challenging task due to the complex, non-stationary and non-linear nature of these biomedical signals. In the existing literature, a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning algorithms. Some characterizing features of epileptic and non-epileptic EEG signals overlap; therefore, it requires that analysis of signals must be performed from diverse perspectives. Few studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG signals. To pose the challenge mentioned… More
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  • Improved Sequencing Heuristic DSDV Protocol Using Nomadic Mobility Model for FANETS
  • Abstract Most interesting area is the growing demand of flying-IoT mergers with smart cities. However, aerial vehicles, especially unmanned aerial vehicles (UAVs), have limited capabilities for maintaining node energy efficiency. In order to communicate effectively, IoT is a key element for smart cities. While improving network performance, routing protocols can be deployed in flying-IoT to improve latency, packet drop rate, packet delivery, power utilization, and average-end-to-end delay. Furthermore, in literature, proposed techniques are very much complex which cannot be easily implemented in real-world applications. This issue leads to the development of lightweight energy-efficient routing in flying-IoT networks. This paper addresses the… More
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  • IoT Devices Authentication Using Artificial Neural Network
  • Abstract User authentication is one of the critical concerns of information security. Users tend to use strong textual passwords, but remembering complex passwords is hard as they often write it on a piece of paper or save it in their mobile phones. Textual passwords are slightly unprotected and are easily attackable. The attacks include dictionary, shoulder surfing, and brute force. Graphical passwords overcome the shortcomings of textual passwords and are designed to aid memorability and ease of use. This paper proposes a Process-based Pattern Authentication (PPA) system for Internet of Things (IoT) devices that does not require a server to maintain… More
  •   Views:346       Downloads:226        Download PDF

  • Intelligent Microservice Based on Blockchain for Healthcare Applications
  • Abstract Nowadays, the blockchain, Internet of Things, and artificial intelligence technology revolutionize the traditional way of data mining with the enhanced data preprocessing, and analytics approaches, including improved service platforms. Nevertheless, one of the main challenges is designing a combined approach that provides the analytics functionality for diverse data and sustains IoT applications with robust and modular blockchain-enabled services in a diverse environment. Improved data analytics model not only provides support insights in IoT data but also fosters process productivity. Designing a robust IoT-based secure analytic model is challenging for several purposes, such as data from diverse sources, increasing data size,… More
  •   Views:748       Downloads:599        Download PDF