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

Fouille de séquences temporelles pour la maintenance prédictive : application aux données de véhicules traceurs ferroviaires

In order to meet the mounting social and economic demands, railway operators and manufacturers are striving for a longer availability and a better reliability of railway transportation systems. Commercial trains are being equipped with state-of-the-art onboard intelligent sensors monitoring various subsystems all over the train. These sensors provide real-time flow of data, called floating train data, consisting of georeferenced events, along with their spatial and temporal coordinates. Once ordered with respect to time, these events can be considered as long temporal sequences which can be mined for possible relationships. This has created a neccessity for sequential data mining techniques in order to derive meaningful associations rules or classification models from these data. Once discovered, these rules and models can then be used to perform an on-line analysis of the incoming event stream in order to predict the occurrence of target events, i.e, severe failures that require immediate corrective maintenance actions. The work in this thesis tackles the above mentioned data mining task. We aim to investigate and develop various methodologies to discover association rules and classification models which can help predict rare tilt and traction failures in sequences using past events that are less critical. The investigated techniques constitute two major axes: Association analysis, which is temporal and Classification techniques, which is not temporal. The main challenges confronting the data mining task and increasing its complexity are mainly the rarity of the target events to be predicted in addition to the heavy redundancy of some events and the frequent occurrence of data bursts. The results obtained on real datasets collected from a fleet of trains allows to highlight the effectiveness of the approaches and methodologies usedDe nos jours, afin de répondre aux exigences économiques et sociales, les systèmes de transport ferroviaire ont la nécessité d'être exploités avec un haut niveau de sécurité et de fiabilité. On constate notamment un besoin croissant en termes d'outils de surveillance et d'aide à la maintenance de manière à anticiper les défaillances des composants du matériel roulant ferroviaire. Pour mettre au point de tels outils, les trains commerciaux sont équipés de capteurs intelligents envoyant des informations en temps réel sur l'état de divers sous-systèmes. Ces informations se présentent sous la forme de longues séquences temporelles constituées d'une succession d'événements. Le développement d'outils d'analyse automatique de ces séquences permettra d'identifier des associations significatives entre événements dans un but de prédiction d'événement signant l'apparition de défaillance grave. Cette thèse aborde la problématique de la fouille de séquences temporelles pour la prédiction d'événements rares et s'inscrit dans un contexte global de développement d'outils d'aide à la décision. Nous visons à étudier et développer diverses méthodes pour découvrir les règles d'association entre événements d'une part et à construire des modèles de classification d'autre part. Ces règles et/ou ces classifieurs peuvent ensuite être exploités pour analyser en ligne un flux d'événements entrants dans le but de prédire l'apparition d'événements cibles correspondant à des défaillances. Deux méthodologies sont considérées dans ce travail de thèse: La première est basée sur la recherche des règles d'association, qui est une approche temporelle et une approche à base de reconnaissance de formes. Les principaux défis auxquels est confronté ce travail sont principalement liés à la rareté des événements cibles à prédire, la redondance importante de certains événements et à la présence très fréquente de "bursts". Les résultats obtenus sur des données réelles recueillies par des capteurs embarqués sur une flotte de trains commerciaux permettent de mettre en évidence l'efficacité des approches proposée

Intelligent Data Understanding for Entry, Descent, and Landing, Architecture Analysis

Designing Planetary Entry, Descent, and Landing Systems requires analyzing a wide range of architectures and scenarios with high fidelity Monte Carlo simulations of performance under uncertainty. Given the complexity of these systems, datasets contain tens of thousands of parameters describing the system and the environment. These datasets are generally manually analyzed by subject matter experts, trying to find interesting correlations and couplings between parameters that explain the behaviors observed. Such analysis work is critical, given that it could lead, for example, to the discovery of major flaws in a design. While the subject matter experts can leverage their knowledge and expertise with past systems to identify issues and features of interest in the current dataset, the next generation of EDL systems will make use of new technologies to address the issue of landing larger payloads, and may present unprecedented challenges that may be missed by the human. In this thesis, we present Daphne, a cognitive assistant, into the process of EDL architecture analysis to support EDL experts by identifying key factors that impact EDL system metrics. Specifically, this thesis describes the current capabilities of Daphne as a platform for EDL architecture analysis by means of a case study of a sample EDL architecture for an ongoing NASA mission, Mars 2020. Given that the work presented in this thesis is in its early development, the thesis focuses on the description of the expert knowledge base and historical database developed for the cognitive assistant, as well as on describing how experts can use it to obtain information relevant to their EDL analysis process by means of natural language or web visual interactions, thus reducing the effort of searching for relevant information from multiple sources. A popular approach to automate the extraction of explanation rules of data is association rule mining, in which rules with high statistical strength are mined from a dataset. However, current rule mining algorithms (e.g., apriori, FP-growth) generate too many rules that are redundant or not useful because they are too complex, too obvious, or don’t make sense to the user. In this thesis, we propose a new approach to improve the comprehensibility, insightfulness, and usefulness of the association rules generated during the analysis of an EDL dataset by leveraging a user-provided knowledge graph. The knowledge graph captures the user knowledge about EDL and the specific problem at hand. We then use a statistical relational learning framework based on probabilistic soft logic to assess the degree of consistency of the rule with our knowledge of the system. We hypothesize that rules that are considered more consistent with the knowledge graph will be perceived by the user as being more comprehensible (making more sense) than rules that are less consistent with the knowledge graph. We test this hypothesis – and more generally the relation between our proposed metric and the perceived usefulness and insightfulness of a rule– in a small study with N=6 subject matter experts. Results support our primary hypothesis and also show interesting relationships between comprehensibility, usefulness, and insightfulness of the extracted rules. These findings can enable a more personalized and adaptive approach to intelligent data understanding, a key enabling technology to help aerospace organizations make sense of the large and heterogeneous datasets that are becoming available in many areas of science and engineering