2,009 research outputs found
IoT trust and reputation: a survey and taxonomy
IoT is one of the fastest-growing technologies and it is estimated that more
than a billion devices would be utilized across the globe by the end of 2030.
To maximize the capability of these connected entities, trust and reputation
among IoT entities is essential. Several trust management models have been
proposed in the IoT environment; however, these schemes have not fully
addressed the IoT devices features, such as devices role, device type and its
dynamic behavior in a smart environment. As a result, traditional trust and
reputation models are insufficient to tackle these characteristics and
uncertainty risks while connecting nodes to the network. Whilst continuous
study has been carried out and various articles suggest promising solutions in
constrained environments, research on trust and reputation is still at its
infancy. In this paper, we carry out a comprehensive literature review on
state-of-the-art research on the trust and reputation of IoT devices and
systems. Specifically, we first propose a new structure, namely a new taxonomy,
to organize the trust and reputation models based on the ways trust is managed.
The proposed taxonomy comprises of traditional trust management-based systems
and artificial intelligence-based systems, and combine both the classes which
encourage the existing schemes to adapt these emerging concepts. This
collaboration between the conventional mathematical and the advanced ML models
result in design schemes that are more robust and efficient. Then we drill down
to compare and analyse the methods and applications of these systems based on
community-accepted performance metrics, e.g. scalability, delay,
cooperativeness and efficiency. Finally, built upon the findings of the
analysis, we identify and discuss open research issues and challenges, and
further speculate and point out future research directions.Comment: 20 pages, 5 Figures, 3 tables, Journal of cloud computin
A comparative analysis of scalable and context-aware trust management approaches for internet of things
© Springer International Publishing Switzerland 2015. The Internet of Things - IoT - is a new paradigm in technology that allows most physical ‘things’ to contact each other. Trust between IoT devices is a critical factor. Trust in the IoT environment can be modeled using various approaches, such as confidence level and reputation parameters. Furthermore, trust is an important element in engineering reliable and scalable networks. In this paper, we survey scalable and context-aware trust management for IoT from three perspectives. First, we present an overview of the IoT and the importance of trust in relation to it, and then we provide an in-depth trust/reliable management protocol for the IoT and evaluate comparable trust management protocols. We also investigate a scalable solution for trust management in the IoT and provide a comparative evaluation of existing trust solutions. We then pre-sent a context-aware assessment for the IoT and compare the different trust solutions. Lastly, we give a full comparative analysis of trust/reliability management in the IoT. Our results are drawn from this comparative analysis, and directions for future research are outlined
A Brain-Inspired Trust Management Model to Assure Security in a Cloud based IoT Framework for Neuroscience Applications
Rapid popularity of Internet of Things (IoT) and cloud computing permits
neuroscientists to collect multilevel and multichannel brain data to better
understand brain functions, diagnose diseases, and devise treatments. To ensure
secure and reliable data communication between end-to-end (E2E) devices
supported by current IoT and cloud infrastructure, trust management is needed
at the IoT and user ends. This paper introduces a Neuro-Fuzzy based
Brain-inspired trust management model (TMM) to secure IoT devices and relay
nodes, and to ensure data reliability. The proposed TMM utilizes node
behavioral trust and data trust estimated using Adaptive Neuro-Fuzzy Inference
System and weighted-additive methods respectively to assess the nodes
trustworthiness. In contrast to the existing fuzzy based TMMs, the NS2
simulation results confirm the robustness and accuracy of the proposed TMM in
identifying malicious nodes in the communication network. With the growing
usage of cloud based IoT frameworks in Neuroscience research, integrating the
proposed TMM into the existing infrastructure will assure secure and reliable
data communication among the E2E devices.Comment: 17 pages, 10 figures, 2 table
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