A trust assurance technique for Internet of things based on human behavior compliance

Abstract

The advent of the Internet of Things (IoT) has radically changed the way in which computations and communications are carried out. People are just becoming another component of IoT environments and, in turn, IoT environments are becoming a mixture of platforms, software, services, things, and people. The price we pay for such dynamic and powerful environment is an intrinsic uncertainty and low trustworthiness due to its opaque perimeter, the multitude of different data sources with unknown providers, and uncertain responsibilities. Trustworthiness of observables collected by smart devices (from minuscle sensors to bigger machines) is fundamental to build a chain of trust on a decision process taken according to these observables. Some assurance solutions evaluate the quality of collected data, though they are difficult to apply in IoT environments for performance and cost reasons. In this paper, we take a different approach and put forward the idea that, in many cases, the behavior of people owning smart devices can contribute to the evaluation of the trustworthiness of collected data and, in turn, of the whole decision process. We therefore define an assurance methodology based on data analytics evaluating the compliance of people to behavioral policies. The more people behavior is compliant, the higher the trustworthiness of data collected through their smart devices.

Special issue on real‐time behavioral monitoring in IoT applications using big data analytics

“…The price one pays for such dynamic and powerful environment is an intrinsic uncertainty and low trustworthiness due to its opaque perimeter, the multitude of different data sources with unknown providers, and uncertain responsibilities. In the contribution by Anisetti et al, “A Trust Assurance Technique for IoT based on Human Behavior Compliance,” the authors took a different approach and put forward the idea that, in many cases, the behavior of people owning smart devices can contribute to the evaluation of the trustworthiness of collected data and, in turn, of the whole decision process 16 . The authors therefore defined an assurance methodology based on data analytics evaluating the compliance of people to behavioral policies.…”

Dynamic Bargain Game Theory in the Internet of Things for Data Trustworthiness

“…In this context, node trustworthiness is fundamental to the development of IoT networks for decision processes based on observation. Although some security methods provide high data trustworthiness, they are difficult to apply in an IoT environment due to cost and performance reasons [7].…”

“…Direct trust and indirect trust were measured from the network, and the best node was found for transmission. An assurance policy template [7] was applied for the trust measure based on the data collection process and human behavior. The assurance policy instance is applied in the assurance policy template for the selection of node.…”

“…The developed method had lower performance in terms of the efficiency of the network. The trust-based methods [6,7] showed a strong performance in node selection and a lower performance in malicious node detection. The routing protocol [8] had higher performance in detecting malicious node and lower efficiency in the dynamic network.…”

“…Node Payoff: The acceleration and deceleration of nodes affect the efficiency and safety of the nodes. The acceleration and deceleration change is denoted in Equation (7).…”