An energy-efficient and secure framework for IoMT: An application of smart cities

Mangla, Cherry; Rani, Shalli; Herencsár, Norbert

doi:10.1016/j.seta.2022.102335

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…On the other hand, drones can fly autonomously or be remotely controlled. Integrating IRS and drones presents a promising solution for various communication and networking applications, such as enhancing network coverage [66], increasing network capacity [66], and reducing energy consumption [67], [68]. In particular, the deployment of IRS in drones can allow on-demand radio environment control and dynamic adjustments to communication links, leading to improved network performance.…”

Section: Irs-dronesmentioning

confidence: 99%

Federated Learning Meets Intelligence Reflection Surface in Drones for Enabling 6G Networks: Challenges and Opportunities

Shvetsov,

Alsamhi,

Hawbani

et al. 2023

IEEE Access

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The combination of drones and Intelligent Reflecting Surfaces (IRS) have emerged as potential technologies for improving the performance of six Generation (6G) communication networks by proactively modifying wireless communication through smart signal reflection and manoeuvre control. By deploying the IRS on drones, it becomes possible to improve the coverage and reliability of the communication network while reducing energy consumption and costs. Furthermore, integrating IRS with Federated Learning (FL) can further boost the performance of the drone network by enabling collaborative learning among multiple drones, leading to better and more efficient decision-making and holding great promise for enabling 6G communication networks. Therefore, we present a novel framework for FL meets IRS in drones for enabling 6G. In this framework, multiple IRS-equipped drone swarm are deployed to form a distributed wireless network, where FL techniques are used to collaborate with the learning process and optimize the reflection coefficients of each drone-IRS. This allows drone swarm to adapt to changing communication environments and improve the coverage and quality of wireless communication services. Integrating FL and IRS into drones offers several advantages over traditional wireless communication networks, including rapid deployment in emergencies or disasters, improved coverage and quality of communication services, and increased accessibility to remote areas. Finally, we highlight the challenges and opportunities of integrating FL and IRS into drones for researchers interested in drone networks. We also help drive innovation in developing 6G communication networks.

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Section: Irs-dronesmentioning

confidence: 99%

Federated Learning Meets Intelligence Reflection Surface in Drones for Enabling 6G Networks: Challenges and Opportunities

Shvetsov,

Alsamhi,

Hawbani

et al. 2023

IEEE Access

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“…Furthermore, an integrated edge-fog-cloud healthcare framework was developed in [10] to minimize the service latency and power consumption of using IoMT applications by approximately 28% and 27%, respectively. In [20], an effective framework based on the Remora Optimization algorithm was developed to jointly minimize latency and power consumption for IoMT applications. Also, a novel message exchange procedure with load balancing was proposed in [4] to offload IoMT applications via a cloud-fog architecture with the aim of reducing energy consumption and delay.…”

Section: Related Workmentioning

confidence: 99%

“…• Security [20] Proposed an energy-efficient and secure strategy for IoMT. • Meta-heuristic approach (e.g., Remora Optimization Algorithm (ESRO))…”

Section: Table 1: Related Workmentioning

confidence: 99%

Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

Alharbi¹,

Yosuf²,

Aldossary³

et al. 2023

Computer Systems Science and Engineering

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In this paper, the Internet of Medical Things (IoMT) is identified as a promising solution, which integrates with the cloud computing environment to provide remote health monitoring solutions and improve the quality of service (QoS) in the healthcare sector. However, problems with the present architectural models such as those related to energy consumption, service latency, execution cost, and resource usage, remain a major concern for adopting IoMT applications. To address these problems, this work presents a four-tier IoMT-edge-fog-cloud architecture along with an optimization model formulated using Mixed Integer Linear Programming (MILP), with the objective of efficiently processing and placing IoMT applications in the edge-fog-cloud computing environment, while maintaining certain quality standards (e.g., energy consumption, service latency, network utilization). A modeling environment is used to assess and validate the proposed model by considering different traffic loads and processing requirements. In comparison to the other existing models, the performance analysis of the proposed approach shows a maximum saving of 38% in energy consumption and a 73% reduction in service latency. The results also highlight that offloading the IoMT application to the edge and fog nodes compared to the cloud is highly dependent on the tradeoff between the network journey time saved vs. the extra power consumed by edge or fog resources.

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“…The IoMT is a subarea developed and applied within the IoT to focus on different current medical applications. This process uses the acquisition, processing, transmission, and storage of medical information through specific devices and the security of patient data [11][12][13][14][15]. The IoMT processes and contains patients' confidential data.…”

Section: Introductionmentioning

confidence: 99%

Triboelectric and Piezoelectric Nanogenerators for Self-Powered Healthcare Monitoring Devices: Operating Principles, Challenges, and Perspectives

et al. 2022

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The internet of medical things (IoMT) is used for the acquisition, processing, transmission, and storage of medical data of patients. The medical information of each patient can be monitored by hospitals, family members, or medical centers, providing real-time data on the health condition of patients. However, the IoMT requires monitoring healthcare devices with features such as being lightweight, having a long lifetime, wearability, flexibility, safe behavior, and a stable electrical performance. For the continuous monitoring of the medical signals of patients, these devices need energy sources with a long lifetime and stable response. For this challenge, conventional batteries have disadvantages due to their limited-service time, considerable weight, and toxic materials. A replacement alternative to conventional batteries can be achieved for piezoelectric and triboelectric nanogenerators. These nanogenerators can convert green energy from various environmental sources (e.g., biomechanical energy, wind, and mechanical vibrations) into electrical energy. Generally, these nanogenerators have simple transduction mechanisms, uncomplicated manufacturing processes, are lightweight, have a long lifetime, and provide high output electrical performance. Thus, the piezoelectric and triboelectric nanogenerators could power future medical devices that monitor and process vital signs of patients. Herein, we review the working principle, materials, fabrication processes, and signal processing components of piezoelectric and triboelectric nanogenerators with potential medical applications. In addition, we discuss the main components and output electrical performance of various nanogenerators applied to the medical sector. Finally, the challenges and perspectives of the design, materials and fabrication process, signal processing, and reliability of nanogenerators are included.

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An energy-efficient and secure framework for IoMT: An application of smart cities

Cited by 7 publications

References 25 publications

Federated Learning Meets Intelligence Reflection Surface in Drones for Enabling 6G Networks: Challenges and Opportunities

Federated Learning Meets Intelligence Reflection Surface in Drones for Enabling 6G Networks: Challenges and Opportunities

Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

Triboelectric and Piezoelectric Nanogenerators for Self-Powered Healthcare Monitoring Devices: Operating Principles, Challenges, and Perspectives

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