Trustworthy Edge Storage Orchestration in Intelligent Transportation Systems Using Reinforcement Learning

A large scale fast-growing data generated in intelligent transportation systems (ITS) has become a ponderous burden on the coordination of heterogeneous transportation networks, which makes the traditional cloud-centric storage architecture no longer satisfy new data analytics requirements. Meanwhile, the lack of storage trust between ITS devices and edge servers could lead to security risks in the data storage process. However, a unified data distributed storage architecture for ITS with intelligent management and trustworthiness is absent in the previous works. To address these challenges, this paper proposes a distributed trustworthy storage architecture with reinforcement learning in ITS, which also promotes edge services. We adopt an intelligent storage scheme to store data dynamically with reinforcement learning based on trustworthiness and popularity, which improves resource scheduling and storage space allocation. Besides, trapdoor hashing based identity authentication protocol is proposed to secure transportation network access. Due to the interaction between cooperative devices, our proposed trust evaluation mechanism is provided with extensibility in the various ITS. Simulation results demonstrate that our proposed distributed trustworthy storage architecture outperforms the compared ones in terms of trustworthiness and efficiency

IEEE Access Special section Editorial: Mobile edge computing and mobile cloud computing: Addressing heterogeneity and energy issues of compute and network resources

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Trust Management for Internet of Things: A Systematic Literature Review

Internet of Things (IoT) is a network of devices that communicate with each other through the internet and provides intelligence to industry and people. These devices are running in potentially hostile environments, so the need for security is critical. Trust Management aims to ensure the reliability of the network by assigning a trust value in every node indicating its trust level. This paper presents an exhaustive survey of the current Trust Management techniques for IoT, a classification based on the methods used in every work and a discussion of the open challenges and future research directions.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

StabTrust-A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks

Vehicular Ad-hoc Network (VANET) is a modern era of dynamic information distribution among societies. VANET provides an extensive diversity of applications in various domains, such as Intelligent Transport System (ITS) and other road safety applications. VANET supports direct communications between vehicles and infrastructure. These direct communications cause bandwidth problems, high power consumption, and other similar issues. To overcome these challenges, clustering methods have been proposed to limit the communication of vehicles with the infrastructure. In clustering, vehicles are grouped together to formulate a cluster based on certain rules. Every cluster consists of a limited number of vehicles/nodes and a cluster head (CH). However, the significant challenge for clustering is to preserve the stability of clusters. Furthermore, a secure mechanism is required to recognize malicious and compromised nodes to overcome the risk of invalid information sharing. In the proposed approach, we address these challenges using components of trust. A trust-based clustering mechanism allows clusters to determine a trustworthy CH. The novel features incorporated in the proposed algorithm includes trust-based CH selection that comprises of knowledge, reputation, and experience of a node. Also, a backup head is determined by analyzing the trust of every node in a cluster. The major significance of using trust in clustering is the identification of malicious and compromised nodes. The recognition of these nodes helps to eliminate the risk of invalid information. We have also evaluated the proposed mechanism with the existing approaches and the results illustrate that the mechanism is able to provide security and improve the stability by increasing the lifetime of CHs and by decreasing the computation overhead of the CH re-selection. The StabTrust also successfully identifies malicious and compromised vehicles and provides robust security against several potential attacks.This work was supported by the Deanship of Scientific Research, King Saud University through the Vice Deanship of Scientific Research Chairs. The authors are grateful to the Deanship of Scientific Research, King Saud University for funding through Vice Deanship of Scientific Research Chairs

Indeterminacy-aware prediction model for authentication in IoT.

The Internet of Things (IoT) has opened a new chapter in data access. It has brought obvious opportunities as well as major security and privacy challenges. Access control is one of the challenges in IoT. This holds true as the existing, conventional access control paradigms do not fit into IoT, thus access control requires more investigation and remains an open issue. IoT has a number of inherent characteristics, including scalability, heterogeneity and dynamism, which hinder access control. While most of the impact of these characteristics have been well studied in the literature, we highlighted “indeterminacy” in authentication as a neglected research issue. This work stresses that an indeterminacy-resilient model for IoT authentication is missing from the literature. According to our findings, indeterminacy consists of at least two facets: “uncertainty” and “ambiguity”. As a result, various relevant theories were studied in this work. Our proposed framework is based on well-known machine learning models and Attribute-Based Access Control (ABAC). To implement and evaluate our framework, we first generate datasets, in which the location of the users is a main dataset attribute, with the aim to analyse the role of user mobility in the performance of the prediction models. Next, multiple classification algorithms were used with our datasets in order to build our best-fit prediction models. Our results suggest that our prediction models are able to determine the class of the authentication requests while considering both the uncertainty and ambiguity in the IoT system

HoliTrust-A holistic cross-domain trust management mechanism for service-centric internet of things

Internet of Things (IoT) is proposed and used in diverse application domains. In IoT, nodes commonly have a low capacity to maintain security on their own expenses, which increases the vulnerability for several attacks. Many approaches have been proposed that are based on privacy and trust management to reduce these vulnerabilities. Existing approaches neglect the aspects of cross-domain node communications and the significance of cross-domain trust management. In this paper, we propose a Holistic Cross-domain trust management model (HoliTrust) that is based on multilevel central authorities. To provide multilevel security, the HoliTrust divides domains into communities on the basis of similarities and interests. Every community has its dedicated server to calculate and manage the degree of trust. In addition, these domains also have their dedicated servers to manage their specific domains, to communicate with the trust server, and to sustain trust among other domain servers. The trust sever is introduced in the HoliTrust that controls the domains, calculates the domain trust, manages the trust values, and distributes standard trust certificates to domains based on a degree of trust. Trust computation is performed on the basis of direct and indirect trust parameters. Furthermore, if a trustor communicates through the community, then the community server includes community trust of the trustee during the trust evaluation. If the communication of the trustor is across the domain, then the community server includes the domain trust along with the community trust of the trustee comprising direct and indirect observations. The overall trust evaluation of communities and domains is time-driven and the responsible authority computes trust after a specific interval of time. We have also compared the HoliTrust with the existing trust mechanisms by focusing on several holistic trust objectives, such as trust relation and decision, data perception trust, and privacy preservation. 2019 IEEE.This work was supported in part by the SEP-CONACyT Research Project under Grant 255387, in part by the School of Engineering and Sciences and the Telecommunications Research Group, Tecnologico de Monterrey, and in part by the Deanship of Scientific Research, King Saud University, Riyadh, Saudi Arabia.Scopus2-s2.0-8506610459