A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

Lakhan, Abdullah; Sodhro, Ali Hassan; Majumdar, Arnab; Khuwuthyakorn, Pattaraporn; Thinnukool, Orawit

doi:10.3390/s22062379

Cited by 19 publications

(3 citation statements)

References 35 publications

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“…Lakhan et al [15] developed an LSEOS method called lightweight secure efficient offloading scheduling. LSEOS has lightweight and secured offloading and scheduling methodologies whose offloading delay will be lesser compared to prevailing techniques.…”

Section: Related Workmentioning

confidence: 99%

Internet of Medical Things with a Blockchain-Assisted Smart Healthcare System Using Metaheuristics with a Deep Learning Model

Albakri

Alqahtani

2023

Applied Sciences

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The Internet of Medical Things (IoMT) is a network of healthcare devices such as wearables, diagnostic equipment, and implantable devices, which are linked to the internet and can communicate with one another. Blockchain (BC) technology can design a secure, decentralized system to store and share medical data in an IoMT-based intelligent healthcare system. Patient records were stored in a tamper-proof and decentralized way using BC, which provides high privacy and security for the patients. Furthermore, BC enables efficient and secure sharing of healthcare data between patients and health professionals, enhancing healthcare quality. Therefore, in this paper, we develop an IoMT with a blockchain-based smart healthcare system using encryption with an optimal deep learning (BSHS-EODL) model. The presented BSHS-EODL method allows BC-assisted secured image transmission and diagnoses models for the IoMT environment. The proposed method includes data classification, data collection, and image encryption. Initially, the IoMT devices enable data collection processes, and the gathered images are stored in BC for security. Then, image encryption is applied for data encryption, and its key generation method can be performed via the dingo optimization algorithm (DOA). Finally, the BSHS-EODL technique performs disease diagnosis comprising SqueezeNet, Bayesian optimization (BO) based parameter tuning, and voting extreme learning machine (VELM). A comprehensive set of simulation analyses on medical datasets highlights the betterment of the BSHS-EODL method over existing techniques with a maximum accuracy of 98.51%, whereas the existing methods such as DBN, YOLO-GC, ResNet, VGG-19, and CDNN models have lower accuracies of 94.15%, 94.24%, 96.19%, 91.19%, and 95.29% respectively.

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Section: Related Workmentioning

confidence: 99%

Internet of Medical Things with a Blockchain-Assisted Smart Healthcare System Using Metaheuristics with a Deep Learning Model

Albakri

Alqahtani

2023

Applied Sciences

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“…since the edge cloud collaboration scenario is developing so quickly, its data sharing technology has been applied to the industrial Internet of things [1], intelligent transportation systems [2], and medical Internet of things [3]. Additionally, resourceconstrained intelligent terminals in the edge cloud collaboration scenario can realize improved computing power elasticity, and the core data sharing technology satisfies the entity users in the scenario to carry out high-precision, fast-response, low-latency transmission and safe information exchange.…”

Section: Introductionmentioning

confidence: 99%

A secure data sharing model against keyword guessing attacks in edge-cloud collaboration scenarios

Zhang,

Xiong,

et al. 2024

Preprint

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In the context of edge cloud collaboration scenarios, data sharing is an essential technological tool, and smart devices face significant challenges when it comes to data sharing security. In scenarios involving edge clouds, attribute-based keyword search (ABKS) is utilized to enable fine-grained access control over shared data and keyword retrieval by users who meet the required privileges. The implementation of secure data sharing is threatened since most of the current ABKS protocols are unable to fend against Keyword Guessing Attacks (KGA), which can be launched by an untrusted cloud server and result in the exposure of sensitive personal information. Using attribute-based encryption (ABE) as the foundation, we build a secure data exchange paradigm in this work that withstands the keyword guessing attack.In our paper, we provide a secure data sharing framework that resists keyword guessing attacks and uses attribute-based encryption (ABE) as the foundation to achieve fine-grained access control to resources in the ciphertext. To avoid malicious guessing of keywords by cloud servers, two encryption keys computed by edge layer key negotiation mechanism re-encrypt the user's private key and keyword trapdoor, respectively. The model is implemented using the JPBC library. According to the security analysis, the model is able to resist Keyword Guessing Attacks (KGA) in the random oracle model. The model's performance examination demonstrates its feasibility and lightweight nature, as well as its large computing advantages and lower storage consumption.

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“…A lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model is developed in [22]. It consists of a set of lightweight, secure schedule techniques that have less response time than previous techniques and aims to implement application workflows on the contract in order to reduce system latency and business risks.…”

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confidence: 99%

A hybrid lightweight security approach in internet of things for healthcare application

2022

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The internet of things (IoT) is a rapidly developing area that consists of a globally linked network architecture based on the Internet. The internet of healthcare things (IoHT) is a subset of IoT that comprises of smart healthcare devices that are critical in monitoring, processing, storing, and transferring sensitive data. It is confronted with new issues in terms of data privacy protection. To safeguard healthcare information, this work proposes hybrid lightweight ciphers (PRESENT and TEA) that leverage elliptic curve cryptography (ECC) in the key generation phase. The proposed system evaluated using the main network evaluation parameters as throughput in Kbps, delay in ms, packet loss rate (%). The proposed approach provides secure data transmission of IoT devices based on the used lightweight security algorithms, in addition it provides conserving network performance, improving channel resource usage, network latency is increased due to the security level added by PRESENT and TEA with ECC, and decrease number of loss packets compared without security case study.

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A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

Cited by 19 publications

References 35 publications

Internet of Medical Things with a Blockchain-Assisted Smart Healthcare System Using Metaheuristics with a Deep Learning Model

Internet of Medical Things with a Blockchain-Assisted Smart Healthcare System Using Metaheuristics with a Deep Learning Model

A secure data sharing model against keyword guessing attacks in edge-cloud collaboration scenarios

A hybrid lightweight security approach in internet of things for healthcare application

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