A Reliable and Efficient Task Offloading Strategy Based on Multifeedback Trust Mechanism for IoT Edge Computing

Kong, Wenping; Li, Xiaoyong; Hou, Liyang; Yuan, Jie; Gao, Yali; Yu, Shui

doi:10.1109/jiot.2022.3143572

Cited by 40 publications

(9 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the direct evaluation process, a rating score between [0, 1] is awarded to T rustN ode while for the indirect evaluation process, the historical honest behavior of T rustN ode is used for assessing its credibility. Although, direct evaluation is prone to feedback sparseness and misjudgment [29]. However, in this study, time relevance is incorporated in the evaluation process to prevent misjudgment.…”

Section: Consortium Blockchain Systemmentioning

confidence: 99%

An Anonymous IoT-Based E-Health Monitoring System Using Blockchain Technology

Samuel

Omojo²,

Mohsin

et al. 2023

IEEE Systems Journal

View full text Add to dashboard Cite

The Internet of things (IoT) has made it possible for health institutions to have remote diagnosis, reliable, preventive and real-time decision making. However, the anonymity and privacy of patients are not considered in IoT. Therefore, this paper proposes a blockchain-based anonymous system, known as GarliMediChain, for providing anonymity and privacy during COVID-19 information sharing. In GarliMediChain, garlic routing and blockchain are integrated to provide low-latency communication, privacy, anonymity, trust and security. Also, COVID-19 information is encrypted multiple times before transmitting to a series of nodes in the network. To ensure that COVID-19 information is successfully shared, a blockchainbased coalition system is proposed. The coalition system enables health institutions to share information while maximizing their payoffs. In addition, each institution uses the proposed fictitious play to study the strategies of others in order to update its belief by selecting the best responses from them. Furthermore, simulation results show that the proposed system is resistant to security-related attacks and is robust, efficient, and adaptive. From the results, the proposed proof-of-epidemiology-of-interest (PoEoI) consensus protocol has 15.93% less computational cost than 26.30% of proof-of-work (PoW) and 57.77% proof-ofauthority (PoA) consensus protocol, respectively. Nonetheless, the proposed GarliMediChain system promotes global collaborations by combining existing anonymity and trust solutions with the support of blockchain technology.

show abstract

Section: Consortium Blockchain Systemmentioning

confidence: 99%

An Anonymous IoT-Based E-Health Monitoring System Using Blockchain Technology

Samuel

Omojo²,

Mohsin

et al. 2023

IEEE Systems Journal

View full text Add to dashboard Cite

show abstract

“…To combat cyber threats in IoT networks [57], cloud computing [58], and communication networks [59], several researchers have employed ML models. To identify DDoS assaults using two characteristics, A self-adaptive model using RF and LSTM was integrated with a learning strategy by Vedula et al [60].…”

Section: Drone Security Using Machine Learningmentioning

confidence: 99%

IoT Empowered Smart Cybersecurity Framework for Intrusion Detection in Internet of Drones

Ashraf

Manickam

Zia

et al. 2023

Preprint

View full text Add to dashboard Cite

The emergence of drone-based innovative cyber security solutions integrated with the Internet of Things (IoT) has revolutionized navigational technologies with robust data communication services across multiple platforms. This advancement leverages machine learning and deep learning methods for future progress. In recent years, there has been a significant increase in the utilization of IoT-enabled drone data management technology. Industries ranging from industrial applications to agricultural advancements, as well as the implementation of smart cities for intelligent and efficient monitoring. However, these latest trends and drone-enabled IoT technology developments have also opened doors to malicious exploitation of existing IoT infrastructures. This raises concerns regarding the vulnerability of drone networks and security risks due to inherent design flaws and the lack of cybersecurity solutions and standards. The main objective of this study is to examine the latest privacy and security challenges impacting the network of drones (NoD). The research underscores the significance of establishing a secure and fortified drone network to mitigate interception and intrusion risks. The proposed system effectively detects cyber-attacks in drone networks by leveraging deep learning and machine learning techniques. Furthermore, the model's performance was evaluated using well-known drones’ UNSW-NB15, CICIDS2017, and KDDCup 99 datasets. We have tested the multiple hyperparameter parameters for optimal performance and classify data instances and maximum efficacy in the NoD framework. The model achieved exceptional efficiency and robustness in NoD. The system attains precision values of 89.10% and 90.16%, accuracy rates of 91.00% and 91.36%, recall values of 81.13% and 90.11%, and F-measure values of 88.11% and 90.19% for the respective evaluation metrics.

show abstract

“…Supervised, unsupervised, and semi-supervised learning are the three primary types of ML approaches. Many researchers have used ML models to cope with cyberthreats in communication networks [ 34 ], IoT networks [ 35 ], and cloud computing [ 36 ]. Vedula et al [ 37 ] used RF and LSTM (autoencoder) to combine a learning methodology with a self-adaptive model to identify DDoS attacks using two features.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective

Aldaej

Ahanger

Atiquzzaman

et al. 2022

Sensors

View full text Add to dashboard Cite

Drone advancements have ushered in new trends and possibilities in a variety of sectors, particularly for small-sized drones. Drones provide navigational interlocation services, which are made possible by the Internet of Things (IoT). Drone networks, on the other hand, are subject to privacy and security risks due to design flaws. To achieve the desired performance, it is necessary to create a protected network. The goal of the current study is to look at recent privacy and security concerns influencing the network of drones (NoD). The current research emphasizes the importance of a security-empowered drone network to prevent interception and intrusion. A hybrid ML technique of logistic regression and random forest is used for the purpose of classification of data instances for maximal efficacy. By incorporating sophisticated artificial-intelligence-inspired techniques into the framework of a NoD, the proposed technique mitigates cybersecurity vulnerabilities while making the NoD protected and secure. For validation purposes, the suggested technique is tested against a challenging dataset, registering enhanced performance results in terms of temporal efficacy (34.56 s), statistical measures (precision (97.68%), accuracy (98.58%), recall (98.59%), F-measure (99.01%), reliability (94.69%), and stability (0.73).

show abstract

A Reliable and Efficient Task Offloading Strategy Based on Multifeedback Trust Mechanism for IoT Edge Computing

Cited by 40 publications

References 31 publications

An Anonymous IoT-Based E-Health Monitoring System Using Blockchain Technology

An Anonymous IoT-Based E-Health Monitoring System Using Blockchain Technology

IoT Empowered Smart Cybersecurity Framework for Intrusion Detection in Internet of Drones

Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective

Contact Info

Product

Resources

About