فهرست مطالب

Journal of Future Generation of Communication and Internet of Things
Volume:2 Issue: 4, Oct 2023

  • تاریخ انتشار: 1402/11/17
  • تعداد عناوین: 7
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  • Ali Shahriari, Mohammad Davarpour, Mohammad Ahmadinia* Pages 1-9

    The Internet of Things (IoT) refers to the connection of various devices to each other via the internet. Conceptually, the IoT can be defined as a dynamic, self-configuring network infrastructure based on standards and participatory communication protocols. The main goal of the IoT is to lead towards a better and safer community. However, one of the fundamental challenges in developing the IoT is the issue of security, and intrusion detection systems are one of the main methods to create security in the IoT. On the other hand, Convolutional Neural Network (CNN), with its specific features, is one of the best methods for analyzing network data. This network is a type of deep neural network composed of multiple layers that can ultimately reduce the dimensions of features. Additionally, the cuckoo algorithm has parameters required for configuration in the initial search, which are very few and can naturally and efficiently cope with multi-state problems. In this paper, a new method for intrusion detection in the IoT using CNN and feature selection by the cuckoo algorithm is presented. Simulation results indicate the satisfactory performance of the proposed method.

    Keywords: Internet Of Things, Intrusion Detection, Convolutional Neural Network, Cuckoo Algorithm, Dimensionality Reduction
  • Ali Nazari Pages 2-7

    Medical technology has made incredible strides in recent years , altering the way we approach patient care. Intelligent prosthetic limbs have emerged as a game-changer in terms of enhancing patients' motor function among these ground-breaking inventions. These innovative prostheses are pushing the limits of what people with limb loss or disability can do by seamlessly interacting with the Internet of Things (IoT). This review explores the crucial role technology plays in improving patients' motor abilities and provides a look into a future in which prosthetic limbs would automatically adapt to their users' demands and enable them to reclaim their independence.

    Keywords: Internet Of Things (Iot), Intelligent Prosthetic Limbs, Patient Movement Rehabilitation, Motor Function Enhancement
  • Mohammadreza Einollahi Asgarabad * Pages 8-14
    In recent decades, the convergence of Internet of Things (IoT) technology and medical imaging has transformed healthcare. This research explores IoT-enabled medical imaging, its potential, applications, and feasibility in enhancing healthcare services. IoT in medical imaging boosts diagnostic precision and treatment capabilities, ensuring accurate disease diagnoses while preserving patient privacy through secure image uploads with encrypted data. Incorporating artificial intelligence (AI) into IoT-based imaging enhances disease detection and treatment. AI algorithms improve image quality and accuracy, raising the standard of care. IoT enables remote medical imaging and precise patient monitoring, emphasizing the importance of data security through encryption. This research highlights IoT's vital role in healthcare, fostering collaboration between devices and data to enhance public health.
    Keywords: Internet of Things, Medical imaging, Artificial Intelligence, Medical data security
  • Mostafa Sarkabiri * Pages 15-20
    Today, most people use the Internet for web searching, accessing multimedia services, and engaging in social networks, providing smart communication between machines and electronic devices. In fact, the goal of the Internet of Things (IoT) is to connect anything, anytime, anywhere, using any path or network and serving any purpose ideally. The application of IoT in the field of medicine reduces waiting times, tracks patients, doctors, equipment, and more. The aim of this paper is to investigate IoT-based disease prediction and diagnosis systems,artificial intelligence, and machine learning methods.Methods and techniques such as Machine Learning (ML) on IoT data, healthcare datasets, model evaluation, and machine learning description are mentioned for disease prediction and diagnosis systems. Real-world machine learning models for healthcare applications are then discussed in this paper. Some successful applications of machine learning in disease diagnosis through IoT data are presented. Finally, future trends in machine learning for disease diagnosis, collaboration between Artificial Intelligence and the Internet of Things in disease diagnosis, were introduced.
    Keywords: Internet of Things, Machine Learning, Artificial Intelligence, ML
  • MohammadMahdi Marzban * Pages 21-27

    This paper presented the hybrid auto-tuning technique for the brushless direct current motor speed control combining fuzzy logic controller and genetic algorithm (GA). Based on the combination of Genetic algorithm (GA) that belongs to the larger class of evolutionary algorithms with Fuzzy logic controller that operates based on the database rules and intelligent decision making, an optimized speed controller is proposed. In this control framework, the GA-Fuzzy controller contains current feedback loop which is to adjust the torque of the motor and the fuzzy logic controller loop whose control rules are optimized off-line and parameters are adjusted based on the genetic algorithm. The simulation results can proved that the proposed technique has better performance than conventional PID controller

    Keywords: Brushless direct current (BLDC) motors, Genetic-Fuzzy Based Controller, Speed Control, torque ripple reduction
  • Abdullah Jafari Chashmi * Pages 28-35
    Epilepsy is a type of brain disease that can be diagnosed by observing EEG signals. The disease often occurs in children. However, some cases are also seen in adults. Diagnosing this disease in the early stages is a challenging task for doctors. In this work, the authors have classified epileptic and normal EEG signal by adopting deep learning approach. To achieve the efficient features, the dual tree complex wavelet (DTCWT) is considered. Then, the decomposed wavelet coefficients are applied to nonlinear feature extraction. These features are used as input to the Radial Hybrid Basis Function (RBF) class. Using the proposed method, about 99% classification accuracy is observed. This requires significant improvement of the proposed algorithm compared to other previously presented algorithms. It is the first time that nonlinear feature extraction on DT-CWT coefficients of an EEG signal is used to diagnose epilepsy.
    Keywords: epilepsy, k-Means Algorithm, Nonlinear features, radial basis function networks, brain EEG classification, Feature reduction
  • Atefeh Moradi, Mohammad Ahmadinia *, Mohammad Davarpour Pages 36-41

    The Internet of Things (IoT) is an emerging field of study and operation. IoT creates a structure for internet-connected devices. IoT-enabled systems are susceptible to various security and privacy attacks due to their inherently open nature. In IoT-enabled systems, multimedia information moves from one end to another, and computational complexity with constrained environments such as ad-hoc networks, mobile networks, etc., is crucial. Wireless Sensor Networks (WSNs) consist of a large number of sensor nodes, each with very limited computational power and storage capacity. Sensor nodes are typically installed for monitoring activities in unsupervised locations, controlled by one or more gateway nodes. Maintaining the confidentiality of information communication is one of the main challenges, and preserving the privacy of multimedia data from unauthorized access by attackers is a major issue for active WSNs with IoT. In this paper, to leverage the benefits of the Kerberos encryption protocol and reduce overhead for IoT-based wireless sensor networks, users are authenticated using the Kerberos protocol, and then sensor data is encrypted using elliptic curve encryption protocol. The proposed method enhances the security of wireless sensor network by combining Kerberos encryption and elliptic curve encryption techniques.

    Keywords: Internet Of Things, Wireless Sensor Network, Security, Kerberos, Elliptic Curve