Utilization of mobile edge computing on the Internet of Medical Things: A survey

Awad, Ahmed; Fouda, Mostafa M.; Khashaba, Marwa M.; Mohamed, Ehab R.; Hosny, Khalid M.

doi:10.1016/j.icte.2022.05.006

Cited by 41 publications

(14 citation statements)

References 88 publications

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“…Let Q represent the attacker's sets of all mixed policies, where eq. (5,6) is the decision space for the inference probability distribution q.…”

Section: Figure-3 Collaborative Architecture With Hybrid Federated Severmentioning

confidence: 99%

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Optimization algorithms in security and privacy-preserving data disturbance for collaborative edge computing social IoT deep learning architectures

Boopathi

Gupta

et al. 2023

Preprint

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Edge computing (EC) has been viewed as a viable option for addressing cloud computing's shortcomings in supporting Internet-of-Things (IoT) applications as an innovative strategy. However, edge-assisted IoT security requires to be better guaranteed due to the network's instability and increase in attack surfaces. Practice of safeguarding a given network from threats that could compromise its availability is referred to as network security. In addition, we can add that network security must address issues such as unauthorized access to network-accessible resources and even their misuse. This study proposes a novel approach to improving edge computing-based data privacy through secure data transmission and deep learning-based optimization with Internet of Things. Edge network privacy preservation is accomplished through collaborative architecture and hybrid federated sever-based stochastic vector networks. The trust based multiple encryption algorithm is used to secure the transmission of the data after it has been optimized using firefly grey optimization. Throughput, network security analysis, PDR, latency, and energy consumption were all examined as part of the experimental analysis for network data privacy and optimization.

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“…Let Q represent the attacker's sets of all mixed policies, where eq. (5,6) is the decision space for the inference probability distribution q.…”

Section: Figure-3 Collaborative Architecture With Hybrid Federated Severmentioning

confidence: 99%

“…DL services in intelligent edge are part of the goal, which is edge intelligence. Consequently, intelligent edge service throughput as well as resource utilization can benefit edge intelligence [6].…”

mentioning

confidence: 99%

Optimization algorithms in security and privacy-preserving data disturbance for collaborative edge computing social IoT deep learning architectures

Boopathi

Gupta

et al. 2023

Preprint

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“…In [16], they developed the new EDWRR equilibrated extension of the Deficit Weighted Round Robin (DWRR) scheduling method as a new equilibrated variation of the DWRR scheduling algorithm. According to [17], the utilization of mobile edge computing in the IoMT has recently progressed. They made an effort to condense the most sophisticated mobile edge computing-based IoT healthcare solutions.…”

Section: Literature Surveymentioning

confidence: 99%

Reliable Fuzzy-Based Multi-Path Routing Protocol Based on Whale Optimization Algorithm to Improve QOS in 5G Networks for IOMT Applications

Al-Mukhtar

Jaaz

Hamad

2023

Int. J. Interact. Mob. Technol.

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The Internet of Medical Things (IoMT) faces stiff competition from the 5th Generation (5G) communication standard, which includes attributes like short and long transmission ranges, Device to Device (D2D) connectivity, low latency, and high node density. To function in the linked ecosystem, IoMT based on 5G is anticipated to have a diversity of energy and mobility. It is currently difficult to create an IoMT routing system based on 5G that maximizes energy efficiency, lowers transmission latency, and increases network lifespan. The "Quality of Services (QoS)" in 5G-based IoMT is improved by the Reliable Fuzzy-based Multi-path routing system shown in this study. The Whale Optimization Algorithm (WOA) enhances the routing protocol performance. The residual energy-based Cluster Head (CH) selection strategy rotates the CH location among nodes with greater energy levels than the others. The method chooses the following set of CHs for the network that is suitable for IoMT applications by considering initial energy, residual energy, and an ideal value of CHs. According to the simulation results, our suggested routing technique enhances QoS in comparison to current approaches.

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“…IoT plays a significant role in the health maintenance department and is used to connect patients with the health department and monitor the patients [ 10 ]. It is of two concepts: the patient's data are secured and transparency.…”

Section: Introductionmentioning

confidence: 99%

An intelligent blockchain strategy for decentralised healthcare framework

Goel

Neduncheliyan

2023

Peer-to-Peer Netw. Appl.

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Nowadays, securely sharing medical data is one of the significant concerns in blockchain technology. The existing blockchain approaches have faced high time consumption, low confidentiality, and high memory usage for transferring the file in a secure way because of attack harmfulness and large unstructured records. It has ended in security threat, so the integrity of the user data has been lost. Hence, a novel hybrid Deep Belief-based Diffie Hellman (DBDH) security framework was presented to protect medical data from malicious events. Incorporating a deep belief neural system continuously monitors the system and identifies the attacks. Initially, the IoMT dataset was collected from the standard site and imported into the system. Moreover, hash 1 was calculated for the original data and stored in the cloud server for verification. Then, the original data was encrypted with a private key for data hiding. The incorporation of homomorphic property helps to calculate hash 2 for encrypted data. Finally, in the verification module, both hash values are verified. In addition, cryptanalysis was performed by launching an attack to validate the performance of the designed model. Moreover, the estimated outcomes of the presented model were compared with existing approaches to determine the improvement score.

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Utilization of mobile edge computing on the Internet of Medical Things: A survey

Cited by 41 publications

References 88 publications

Optimization algorithms in security and privacy-preserving data disturbance for collaborative edge computing social IoT deep learning architectures

Optimization algorithms in security and privacy-preserving data disturbance for collaborative edge computing social IoT deep learning architectures

Reliable Fuzzy-Based Multi-Path Routing Protocol Based on Whale Optimization Algorithm to Improve QOS in 5G Networks for IOMT Applications

An intelligent blockchain strategy for decentralised healthcare framework

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