Indiana University-Purdue University Indianapolis (IUPUI) / Benefited from the booming social network, reading posts from other users over the internet is becoming one of commonest ways for people to intake information. One may also have noticed that sometimes we tend to focus on users provide well-founded analysis, rather than those merely who vent their emotions. This thesis aims at finding a simple and efficient way to recognize reliable information sources among countless internet users by examining the sentiments from their past posts. To achieve this goal, the research utilized a dataset of tweets about Apple's stock price retrieved from Twitter. Key features we studied include post-date, user name, number of followers of that user, and the sentiment of that tweet. Prior to making further use of the dataset, tweets from users who do not have sufficient posts are filtered out. To compare user sentiments and the derivative of Apple's stock price, we use Pearson correlation between them to describe how well each user performs. Then we iteratively increase the weight of reliable users and lower the weight of untrustworthy users, the correlation between overall sentiment and the derivative of stock price will finally converge. The final correlations for individual users are their performance scores. Due to the chaos of real-world data, manual segmentation via data visualization is also proposed as a denoise method to improve performance. Besides our method, other metrics can also be considered as user trust index, such as numbers of followers of each user. Experiments are conducted to prove that our method outperforms others. With simple input, this method can be applied to a wide range of topics including election, economy, and the job market.

Multi-agent systems are increasingly popular for modeling distributed environments that are highly complex and dynamic such as e-commerce, smart buildings, and smart grids. Often in open multi-agent systems, agents interact with other agents to meet their own goals. Trust is considered significant in multi-agent systems to make interactions effectively, especially when agents cannot assure that potential partners share the same core beliefs about the system or make accurate statements regarding their competencies and abilities. This work describes a trust model that augments fuzzy logic with Q-learning, and a suspension technique to help trust evaluating agents select beneficial trustees for interaction in uncertain, imprecise, and the dynamic multi-agent systems. Q-Learning is used to evaluate trust on the long term, fuzzy inferences are used to aggregate different trust factors and suspension is used as a short-term response to dynamic changes. The performance of the proposed model is evaluated using simulation. Simulation results indicate that the proposed model can help agents select trustworthy partners to interact with. It has a better performance compared to some of the popular trust models in the presence of misbehaving interaction partners. When interactions are based on trust, trust establishment mechanisms can be used to direct trustees, instead of trustors, to build a higher level of trust and have a greater impact on the results of interactions. This work also describes a trust establishment model for intelligent agents using implicit feedback that goes beyond trust evaluation to outline actions to guide trustees (instead of trustors). The model uses the retention of trustors to model trustors’ behaviours. For situations where tasks are multi-criteria and explicit feedback is available, we present a trust establishment model that uses a multi-criteria approach to help trustees to adjust their behaviours to improve their perceived trust and attract more interactions with trustors. The model calculates the necessary improvement per criterion when only a single aggregated satisfaction value is provided per interaction, where the model attempts to predicted both the appropriate value per criteria and its importance. Then we present a trust establishment model that integrates the two major sources of information to produce a comprehensive assessment of a trustor’s likely needs in multi-agent systems. Specifically, the model attempts to incorporates explicit feedback, and implicit feed-back assuming multi-criteria tasks. The proposed models are evaluated through simulation, we found that trustees can enhance their trustworthiness, at a cost, if they tune their behaviour in response to feedback (explicit or implicit) from trustors. Using explicit feedback with multi-criteria tasks, trustees can emphasize on important criterion to satisfy need of trustors. Trust establishment based on explicit feedback for multi-criteria tasks, can result in a more effective and efficient trust establishment compared to using implicit feedback alone. Integrating both approaches together can achieve a reasonable trust level at a relatively lower cost.

With the dramatic growth of vehicles around the world, Vehicular Ad-hoc Networks (VANETs) have been proposed as a solution to advance road safety, improve transportation efficiency, and satisfy road users. In the VANET environment, vehicles communicate with each other and with road infrastructure in an ad-hoc manner. This communication may be safety-related or non-safety-related and may often include vehicle information (e.g., location, direction, speed, and control), road conditions, and events. A key component in assessing the veracity of the information is the trustworthiness of the information source. Thus, trust evaluation is one of the main requirements of VANET design. In this work, we investigate performance improvements in the trust evaluation framework of VANETs. First, we propose a risk-based trust evaluation model (RTEAM) to estimate the risk of taking action or refraining from action regarding a reported event (in case of receiving conflicting messages about the event's existence). Some trust metrics such as direct trust, hop-based trust values, proximity to the event, and consequences of acting on a wrong decision are used to estimate the risk of the vehicle’s actions. Vehicles make individual decisions by seeking the action with the lowest risk. Second, we propose a fog-based reputation evaluation model (FREM) to support trust management framework. We promote fog computing as a new paradigm since it can provide several services to users in the edge layer. In our work, Fog supports the decision-making process in the reputation evaluation framework. Fog nodes play a key role in collecting vehicles' reputation records and cooperating with the roadside units (RSUs) to update these records. We propose the use of Digital Trustworthiness Cards (DTC), where the latest reputation evaluation of a vehicle automatically appears on its card. The benefits of the DTC are twofold: 1) the communication load on vehicles is reduced, and 2) historical trust records are established for each vehicle. We also take advantage of fog’s familiarity and greater knowledge of the vehicles that frequently visit its zones; with more intimate knowledge, fog can smartly employ vehicles to perform specific tasks based on their experiences. Further, we implement a strategy for establishing trust based on specific task categories. This permits a nuanced evaluation of the vehicle best suited for the task at hand and has the further benefit of preventing malicious vehicles from being naively trusted based on successful completion of unimportant or non-safety-related tasks. Finally, we expand the role of the fog in the decision-making process when vehicles need to ensure the existence of serious events. We propose a fog-based event validation model (FEVM) to validate the event’s existence through cooperation between vehicles and fog nodes. The vehicles are used as mobile fog nodes, which compute their confidence in events based on the available information. Fog nodes then validate the event after combining vehicles’ confidence values by applying the Extended Dempster-Shafer (EDS) theory of evidence. To test our proposed models, we conduct many experiments to investigate their performance and compare them with other existing models.

The rapid growth of the Internet has resulted in the desperate need for alternative ways to keep electronic transactions secure while at the same time allowing entities that do not know each other to interact. This has, in turn, led to a wide area of interest in the issues of trust and trust modeling to be used by machines. A large amount of work has already been undertaken in this area in an attempt to transfer the trust and interaction decision making processes onto the machine. However this work has taken a number of different approaches with little to no correlation between various models and no standard set of criteria was even proposed that can be used to evaluate the value of such models. The proposed research chooses to use a detailed literature survey to investigate the current models in existence. This investigation focuses on identifying criteria that are required by trust models. These criteria are grouped into four categories that represent four important concepts to be implemented in some manner by trust models: trust representation, initial trust, trust update and trust evaluation. The process of identifying these criteria has led to a second problem. The trust evaluation process is a detailed undertaking requiring a high processing overhead. This process can either result in a value that allows an agent to trust another to a certain extent or in a distrust value that results in termination of the interaction. The evaluation process required to obtain the distrust value is just as process intensive as the one resulting in determining a level of trust and the constraints that will be placed on an interaction. This raises the question: How do we simplify the trust evaluation process for agents that have a high probability of resulting in a distrust value? This research solves this problem by adding a fifth category to the criteria already identified; namely: prejudice filters. These filters have been identified by the literature study and are tested by means of a prototype implementation that uses a specific scenario in order to test two simulation case studies. / Dissertation (MSc)--University of Pretoria, 2008. / Computer Science / unrestricted

Dans le cadre des systèmes de transport intelligents (STI), les véhicules peuvent avoir beaucoup de capteurs (caméras, lidars, radars, etc.) et d’applications (évitement des collisions, surveillance du trafic, etc.) générant des données. Ils représentent alors une source d’information importante. Les applications locales peuvent augmenter considérablement leur efficacité en partageant une telle information au sein du réseau. La précision des données, la confiance et la pertinence peuvent être vérifiées lors de la réception de données provenant d’autres nœuds. Par conséquent, nous croyons qu’une question importante à répondre dans ce contexte est: “Comment partager efficacement les données dans un tel environnement?” Le partage de données est une tâche complexe dans les réseaux dynamiques. De nombreuses problèmes telles que les connexions intermittentes, la variation de la densité du réseau et la congestion du médium de communication se posent. Une approche habituelle pour gérer ces problèmes est basée sur des processus périodiques. En effet, un message envoyé plusieurs fois peut atteindre sa destination même avec des connexions intermittentes et des réseaux à faible densité. Néanmoins, dans les réseaux à haute densité, ils peuvent entraîner une congestion du médium de communication. Dans cette thèse, nous abordons le problème du partage de données dans des réseaux dynamiques en nous appuyant sur des horizons de pertinence. Un horizon est défini comme une zone dans laquelle une information devrait être reçue. Nous commençons par nous concentrer sur le partage de données au sein des voisins directs (à 1 saut de distance). Ensuite, nous proposons une solution pour construire une carte des voisins, centrée sur le nœud ego, dans un horizon à n sauts. Enfin, nous relâchons la définition de l’horizon pour la définir de façon dynamique, où différents éléments de données peuvent atteindre des distances différentes (sauts). En ce qui concerne la solution pour les horizons à 1 saut, notre technique adaptative prend en compte la dynamique des nœuds et la charge du réseau. Afin d’assurer une diffusion efficace des données dans différents scénarios, la fréquence d’envoi des messages est définie en fonction des mouvements des véhicules et d’une estimation du taux de perte du réseau. Après, nous nous concentrons sur la carte des voisins jusqu’à n sauts de distance. Comme la communication avec des nœuds éloignés apporte des problèmes supplémentaires (actions de transfert, retards plus importants, informations périmées), une évaluation de confiance des nœuds identifiés et une estimation de fiabilité du chemin vers chaque voisin sont ajoutées à la carte. Au lieu d’exécuter des processus de diffusion séparés, notre troisième contribution porte sur une stratégie de coopération dont l’objectif principal est de diffuser des données tout en satisfaisant la plupart des nœuds. À cette fin, une trame unique est transmise de nœud en nœud. Sa charge utile est mise à jour localement afin qu’elle contienne les éléments de données les plus pertinents en fonction de certains critères (par exemple, urgence, pertinence). Une telle stratégie définit ainsi un horizon centré sur les données. Nous validons nos propositions au moyen d’émulations de réseaux réalistes. De toutes nos études et des résultats obtenus, nous pouvons affirmer que notre approche apporte des perspectives intéressantes pour le partage de données dans des réseaux dynamiques comme les VANET. / In the context of Intelligent Transportation Systems - ITS, vehicles may have a lot of sensors (e.g. cameras, lidars, radars) and applications (collision avoidance, traffic monitoring, etc.) generating data. They represent then an important source of information. Local applications can significantly increase their effectiveness by sharing such an information within the network. Data accuracy, confidence and pertinence can be verified when receiving data from other nodes. Therefore, we believe that an important question to answer in this context is: “How to efficiently share data within such an environment?” Data sharing is a complex task in dynamic networks. Many concerns like intermittent connections, network density variation and communication spectrum congestion arise. A usual approach to handle these problems is based on periodic processes. Indeed, a message sent many times can reach its destination even with intermittent connections and low density networks. Nevertheless, within high density networks, they may lead to communication spectrum scarcity. In this thesis we address the problem of data sharing in dynamic networks by relying in so-called horizons of pertinence. A horizon is defined as an area within which an information is expected to be received. We start focusing on data sharing within direct neighbors (at 1-hop of distance). Then we propose a solution to construct a map of neighbors, centered in the ego-node, within a horizon of n-hops. Finally, we relax the horizon definition to a dynamic defined one where different data items may reach different distances (hops). Regarding the solution for 1-hop horizons, our adaptive technique takes into account nodes’ dynamics and network load. In order to ensure an effective data dissemination in different scenarios, the sending messages frequency is defined according to vehicles movements and an estimation of the network loss rate. Following, we focus on the map of neighbors up to n-hops of distance. As communicationwith distant nodes brings additional concerns (forwarding actions, larger delays, out-of-date information), a trust evaluation of identified nodes and a reliability estimation of the multi-hop path to each neighbor is added to the map. Instead of running separated disseminating processes, our third contribution deals with a cooperative strategy with the main goal of disseminating data while satisfying most of the nodes. For this purpose a unique frame is forwarded from node to node. Its payload is locally updated so that it contains the most relevant data items according to some criteria (e.g. urgency, relevance). Such a strategy defines thus a data-centered horizon. We validate our proposals by means of realistic network emulations. From all our studies and achieved results we can state that our approach brings interesting insights for data sharing in dynamic networks like VANETs.