A secure energy-efficient routing protocol for disease data transmission using IoMT

Singh, Samayveer; Nandan, Aridaman Singh; Sikka, Geeta; Malik, Arun; Tiwari, Prayag

doi:10.1016/j.compeleceng.2022.108113

Cited by 19 publications

(8 citation statements)

References 22 publications

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“…Their energy-efficient fuzzy approach was able to achieve up to 77% and 60% reductions in energy consumption and delay, respectively. Also, the authors in [21] presented a genetic algorithm to provide a secure and energy-saving method to detect communicable infectious diseases using a wireless sensor network (WSN). In [22], the authors developed an optimization model (mixed-integer non-linear programming (MINLP) model) and applied an enhanced deep reinforcement learning approach to find an optimal strategy for resource allocation, computation offloading, and minimizing energy consumption.…”

Section: 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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Section: 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 another study [7], three supervised ML algorithms are trained and evaluated for detecting rank and Blackhole attacks in IoT networks. At the same time, there are also studies to improve network security by strengthening routing strategies [19][20][21]. Considering the periodic topology changes of satellite networks, Pan et al [19] propose OPSPF, which makes use of the regularity of constellation and performs periodic routing calculations for the generation of instantaneous routing tables.…”

Section: Related Workmentioning

confidence: 99%

A Digital-Twin-Based Detection and Protection Framework for SDC-Induced Sinkhole and Grayhole Nodes in Satellite Networks

Qiao,

Zhuang,

et al. 2023

Aerospace

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In the space environment, cosmic rays and high-energy particles may cause a single-event upset (SEU) during program execution, and further cause silent data corruption (SDC) errors in program outputs. After extensive research on SEU and SDC errors, it has been found that SDC errors in the routing program in satellite networks may lead to the emergence of Sinkhole (SH) and Grayhole (GH) nodes in the network, which may cause damage to satellite networks. To find and solve the problems in time, a digital-twin-based detection and protection framework for SDC-induced SH and GH nodes in satellite networks is proposed. First, the satellite network fault model under SEU and the generation mechanism of SH and GH nodes induced by SDC errors are described. Then, the data structure based on digital twins required by the proposed detection and protection framework is designed, and the detection methods of SH and GH nodes induced by SDC errors are proposed. SKT and LLFI simulation tools are used to build a simulated Iridium satellite network and carry out fault injection experiments. Experiment results show that the accuracy of the proposed detection method is 98–100%, and the additional time cost of routing convergence caused by the proposed framework is 3.1–28.2%. Compared with existing SH and GH detection methods, the proposed methods can timely and accurately detect faults during the routing update stage.

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“…The drawbacks are a lack of retransmission and data recovery. IOMT, RAB-CRP, and cluster-Based routing protocols (19)(20)(21) are deployed in large-scale networks where nature-inspired concepts are used to minimize delay and maximize the life of nodes. In the IoT environment, the nodes are deployed at a minimum distance for effective communication and successful packet delivery from source to destination.…”

Section: Introductionmentioning

confidence: 99%

Energy Aware Adaptive Sleep Scheduling and Secured Data Transmission Protocol to enhance QoS in IoT Networks using Improvised Firefly Bio-Inspired Algorithm (EAP-IFBA)

Nithyanandh,

Omprakash,

Megala

et al. 2023

IJST

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Objectives: To propose a suitable bio-inspired algorithm for energy-aware adaptive sleep scheduling and secured data transmission in IoT networks. Machine learning with bio-inspired technique is employed to schedule sleep periods for sensor nodes to maximize the lifetime of the IoT network, minimize energy consumption, and ensure robust data security during attacks. Methods: Improvised Firefly Bio-Inspired Algorithm (IFBA) is employed for adaptive sleep scheduling, and Dynamic Key Distribution Management (DKDM) with the elliptic curve method is used for secured and reliable data transmission between sensor nodes. Enhanced Recurrent Neural Networks (ERNN) with the N-Key method is deployed to identify the abnormal patterns associated with attacks and topology changes. Mean Square Error Data Recovery (MSEDR) is utilized to evaluate the error in data recovery, and Q-Learning Technique (QLT) with action sets is used to identify the finest path to ensure fast transmission of data. OMNETC++ simulator software is used to evaluate the performance of the proposed EAP-IFBA IoT network protocol with baseline protocols such as IWD-ARP, ECC-ILEACH, and RLSSA-CDGP. Findings: The proposed EAP-IBFA sleep scheduling and secured data transmission algorithm outperforms the prevailing methods IWD-ARP, ECC-ILEACH, and RLSSA-CDGP with an energy depletion rate of 8%, 97.5% alive nodes, 98% network life span in an IoT environment, 97.6% data transmission speed, 98% quick sleep scheduling, and 96.5% robustness to attacks. Novelty: The comprehensive solution of EAP-IFBA enhances QoS in IoT sensor networks. The proven results show that the proposed novel sleep scheduling and secured https://www.indjst.org/ 2753

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A secure energy-efficient routing protocol for disease data transmission using IoMT

Cited by 19 publications

References 22 publications

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

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

A Digital-Twin-Based Detection and Protection Framework for SDC-Induced Sinkhole and Grayhole Nodes in Satellite Networks

Energy Aware Adaptive Sleep Scheduling and Secured Data Transmission Protocol to enhance QoS in IoT Networks using Improvised Firefly Bio-Inspired Algorithm (EAP-IFBA)

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