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    Advancing Medical Recommendations With Federated Learning on Decentralized Data: A Roadmap for Implementation
    (Institute of Electrical and Electronics Engineers Inc., 2023) Kumari, R.; Sah, D.K.; Gupta, S.; Cengiz, K.; Ivkovic, N.
    This proposal presents a road-map for implementing federated learning for personalized medical recommendations on decentralized data. Federated learning is a privacy-preserving technique allowing multiple parties to train machine learning models collaboratively without sharing their data. Our proposed framework incorporates differential privacy techniques to protect patient privacy. We discuss several evaluation metrics, including KL divergence, fairness, confidence intervals, top-N hit rate, sensitivity analysis, and novelty to evaluate the performance of the federated learning system.These metrics collectively serve as a robust toolbox for assessing the performance of the federated learning system. The proposed framework and evaluation metrics can provide valuable insights into the system’s effectiveness and guide the selection of optimal hyperparameters and model architectures. Our framework incorporates differential privacy methods to safeguard patient information effectively. IEEE
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    Blockchain-Powered Smart E-Healthcare System: Benefits, Use Cases, and Future Research Directions
    (Springer International Publishing, 2023) Malik, A.; Bhushan, B.; Parihar, V.; Karim, L.; Cengiz, K.
    Blockchain technologies are deeply distributed and used in several dominions, including for E-healthcare. Internet of Things (IoT) strategies can arrange real-time sensual information from patients for their treatment. Composed information is aimed to combine for computation, dealing, and storing. Such centralism can be challenging, as it can be the only reason for lack of success, uncertainty, document management, interfering, and confidentiality elusion. Blockchain is able to resolve these kinds of consequent complications by giving distributed computation and proper storage for IoT data records. Consequently, the mixture of blockchain technologies in healthcare can convert into a realistic selection for the scheme of distributed Blockchain-powered smart E-healthcare systems. This paper discusses the background of blockchain technology with its features and categories. The paper explores the collaboration of blockchain with IoT for E-healthcare. Further, this paper highlights some popular consensus algorithms used in blockchain in the circumstance of E-health. Finally, this paper examines some use cases of E-healthcare that illustrate how key characteristics of the IoT and blockchain can be leveraged to maintain healthcare facilities and environments. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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    Efficient Resource Management in IoTs Using Evolutionary and Swarm Intelligence Algorithms
    (Institute of Electrical and Electronics Engineers Inc., 2023) Gupta, K.D.; Cengiz, K.; Awasthi, C.; Ramini, S.A.; Chatterjee, S.; Pavithra, M.
    The proliferation of the Internet of Things (IoT) has ushered in a transformative era of connected devices, emphasizing the critical need for effective resource management. This study introduces an innovative approach that harnesses Evolutionary and Swarm Intelligence algorithms for IoT Task Scheduling, addressing resource optimization challenges. The approach offers superior resource utilization through advanced optimization techniques, reducing energy consumption and enhancing efficiency. Furthermore, it significantly reduces task scheduling execution time, enabling prompt decision-making in dynamic IoT environments. This results in improved task allocation quality, meeting application-specific requirements, and prioritizing critical tasks. Notably, the approach excels in minimizing completion time, making it particularly advantageous for real-time IoT applications. This research contributes to the advancement of IoT resource management, offering an adaptive, efficient, and intelligent solution with broad applicability. In the evolving landscape of IoT, the findings presented here lay a solid foundation for future research and practical implementations, ultimately promoting more responsive, sustainable, and intelligent IoT ecosystems. © 2023 IEEE.
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    Fulfilling the Vision of Worldwide Communication in Healthcare: A Study of Implant Wearable Devices for Healthcare
    (CRC Press, 2023) Nayak, D.S.; Cengiz, K.
    Wearable sensor technology today has a variety of applications, due to its steadily growing applicability. Since sensors can often analyse and enumerate the wearer’s functioning, they are frequently engaged in human movement finding and quantified self-assessment. More and more, wearable sensors are being developed to observe the patient’s health, accelerate disease analysis, and observe the regular improvement in the patient outcomes. Clinicians ask patients to self-report their symptoms and perform standardised tests to measure their functional abilities. These tests depend on the patient’s memory and take a lot of time and money. Additionally, measurements might not be an accurate representation of the patient’s level of functionality at the household. Wearable sensors can be used to recognise and measure precise arrangements in a range of applications. When tracking ambulatory movement over time, wearable sensors can gather enormous amounts of data in the form of tuples. Various human body movements can be tracked and recorded using the methods discussed in this chapter. It also discusses techniques for determining movement and sleep levels from long-term data composed by wearable tech gadgets. © 2024 selection and editorial matter, Arun Kumar, Sumit Chakravarty, Aravinda K., and Mohit Kumar Sharma; individual chapters, the contributors.
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    The industrial internet of things: Advanced analytic framework
    (IGI Global, 2023) Bhushan, S.; Shanker, S.; Cengiz, K.
    Volumes of data produced by the dumb machines in industrial environment are used by smart machine for real time analysis. The ultimate result is far better than human handling and analysis of data, and hence enhancement and accuracy in business decisions. IIoT (industrial internet of things) also referred to as industry 4.0 is developed to enhance the accuracy and performance of industrial processes by using smart sensors and actuators. This chapter shed light on key technologies of IoT ecosystem and also discusses the architecture and development details of an IIoT solution its components, applications, and case studies. © 2023, IGI Global.
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    Optimizing Resource Utilization Using Vector Databases in Green Internet of Things
    (Institute of Electrical and Electronics Engineers Inc., 2023) Kumari, R.; Sah, D.K.; Cengiz, K.; Nauman, A.; Ivkovi?, N.; Mihaljevi?, I.
    With the rapid proliferation of Internet of Things (IoT) devices and the ever-increasing volume of sensor data, optimizing resource utilization has become crucial for building sustainable and efficient IoT systems. In this study, we propose a novel approach for optimizing resource utilization in Green IoT through efficient storage and retrieval in vector databases. Our approach leverages specialized data structures, including k-d trees and ball trees, to achieve improved storage efficiency and accelerated retrieval performance for high-dimensional sensor data. We conducted extensive experiments to evaluate the effectiveness of our proposal, comparing it with traditional database approaches. The results demonstrate significant improvements in storage efficiency, with vector databases requiring considerably less storage space compared to traditional databases. Moreover, our approach enables fast and accurate retrieval of high-dimensional sensor data, reducing query times and enhancing real-time data analysis and decision-making capabilities. The technical achievements of our proposal offer promising prospects for the development of sustainable and efficient IoT systems in various domains, such as environmental monitoring, healthcare, and smart cities. Our work contributes to advancing the field of Green IoT by addressing the challenges of resource utilization and query performance through efficient storage and retrieval in vector databases. © 2023 IEEE.
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    A Symmetric TDMA Mechanism to Optimize the Performance of the Body Sensor Network for Sports Application
    (Institute of Electrical and Electronics Engineers Inc., 2023) Mustafa, M.M.; Khalifa, A.A.; Cengiz, K.
    The body sensor network plays a vital role in the analysis of the gait analysis of human, sensing the various events happening in the human body. The communication that takes place between the sensed device and the processing system is very important in which the nodes worn on the human body communicate with the sink node placed in the center of the human body. A wireless communication mechanism TDMA was used and the results gave around 60 percent reliability among the nodes. The dynamic TDMA gave a reliability of 90 percent and retransmission mechanism of 95 percent. Our research work focused on to improve the reliability between the nodes and to develop an application for the users to retrieve the data from the sink node. A Symmetric TDMA algorithm was used in which the reliability was increased up to 2 percent resulted in 97 percent. Also, a Bayesian model was developed to identify the probability of nodes initiating to transmit at the same time. The model proved that the chances of nodes transmitting at the same time are 8 percent when comparing to all other techniques. The reliability of the network was also increased. Further, the work will be developed to bring the security concepts into the transmission of data packets, so that the loss can be minimized. © 2023 IEEE.