Novel model for boosting security strength and energy efficiency in internet-of-things using multi-staged game

Security as well as energy efficiency is one of the most inevitable and challenging problems when it comes it large scale network deployment like INternet-of-Things (IoT). After reviewing existing research work on IoT, it was found that there are discrete set of solution for security as well as for energy. However, there is little research work that has jointly investigated both the problems with respect to IoT. Apart from this, there are also various form of attacks that cost energy of sensors that constitutes core physical devices in IoT. Therefore, these manuscripts present a novel idea for identifying and resisting the security breach within an IoT system ensuring energy efficiency too. Harnessing the modelling capability of game-theory, the proposed system offers a joint solution towards these problems. The simulated outcome of the study is found to offer balance performance for better energy efficiency and robust threat mitigation capability when compared with existing approaches

SCTSC: A Semicentralized Traffic Signal Control Mode With Attribute-Based Blockchain in IoVs

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordAssisting traffic control is one of the most important applications on the Internet of Vehicles (IoVs). Traffic information provided by vehicles is desired since drivers or vehicle sensors are sensitive in perceiving or detecting nuances on roads. However, the availability and privacy preservation of this information are critical while conflicted with each other in the vehicular communication. In this paper, we propose a semicentralized mode with attribute-based blockchain in IoVs to balance the tradeoff between the availability and the privacy preservation. In this mode, a method of control-by-vehicles is used to control signals of traffic lights to increase traffic efficiency. Users are grouped their attributes such as locations and directions before starting the communication. The users reach an agreement on determining a temporary signal timing by interacting with each other without leaking privacy. Final decisions are verifiable to all users, even if they have no a priori agreement and processes of consensus. The mode not only achieves the aim of privacy preservation but also supports responsibility investigation for historical agreements via ciphertext-policy attribute-based encryption (CP-ABE) and blockchain technology. Extensive experimental results demonstrated that our mode is efficient and practical.National Key R&D Program of ChinaNatural Science Foundation of ChinaFundamental Research Funds for the Central Universities of Chin

Software agents & human behavior

People make important decisions in emergencies. Often these decisions involve high stakes in terms of lives and property. Bhopal disaster (1984), Piper Alpha disaster (1988), Montara blowout (2009), and explosion on Deepwater Horizon (2010) are a few examples among many industrial incidents. In these incidents, those who were in-charge took critical decisions under various ental stressors such as time, fatigue, and panic. This thesis presents an application of naturalistic decision-making (NDM), which is a recent decision-making theory inspired by experts making decisions in real emergencies. This study develops an intelligent agent model that can be programed to make human-like decisions in emergencies. The agent model has three major components: (1) A spatial learning module, which the agent uses to learn escape routes that are designated routes in a facility for emergency evacuation, (2) a situation recognition module, which is used to recognize or distinguish among evolving emergency situations, and (3) a decision-support module, which exploits modules in (1) and (2), and implements an NDM based decision-logic for producing human-like decisions in emergencies. The spatial learning module comprises a generalized stochastic Petri net-based model of spatial learning. The model classifies routes into five classes based on landmarks, which are objects with salient spatial features. These classes deal with the question of how difficult a landmark turns out to be when an agent observes it the first time during a route traversal. An extension to the spatial learning model is also proposed where the question of how successive route traversals may impact retention of a route in the agent’s memory is investigated. The situation awareness module uses Markov logic network (MLN) to define different offshore emergency situations using First-order Logic (FOL) rules. The purpose of this module is to give the agent the necessary experience of dealing with emergencies. The potential of this module lies in the fact that different training samples can be used to produce agents having different experience or capability to deal with an emergency situation. To demonstrate this fact, two agents were developed and trained using two different sets of empirical observations. The two are found to be different in recognizing the prepare-to-abandon-platform alarm (PAPA ), and similar to each other in recognition of an emergency using other cues. Finally, the decision-support module is proposed as a union of spatial-learning module, situation awareness module, and NDM based decision-logic. The NDM-based decision-logic is inspired by Klein’s (1998) recognition primed decision-making (RPDM) model. The agent’s attitudes related to decision-making as per the RPDM are represented in the form of belief, desire, and intention (BDI). The decision-logic involves recognition of situations based on experience (as proposed in situation-recognition module), and recognition of situations based on classification, where ontological classification is used to guide the agent in cases where the agent’s experience about confronting a situation is inadequate. At the planning stage, the decision-logic exploits the agent’s spatial knowledge (as proposed in spatial-learning module) about the layout of the environment to make adjustments in the course of actions relevant to a decision that has already been made as a by-product of situation recognition. The proposed agent model has potential to be used to improve virtual training environment’s fidelity by adding agents that exhibit human-like intelligence in performing tasks related to emergency evacuation. Notwithstanding, the potential to exploit the basis provided here, in the form of an agent representing human fallibility, should not be ignored for fields like human reliability analysis

Desarrollo y Evaluación de un Sistema de Recomendación Basado en una Aproximación Push.

El exceso de información disponible en la actualidad puede desbordar a los usuarios cuando tienen que tomar una decisión y escoger entre diversas opciones. Los sistemas de recomendación, (RS, Recommender Systems) tienen como objetivo sugerir ítems a los usuarios según sus preferencias y circunstancias, y resultan de gran interés en el campo de la investigación y en el mercado. Estos abordan una perspectiva bidimensional basada en Usuario-Ítem. Existen también los sistemas de recomendación dependientes del contexto (CARS, Context-Aware Recommender Systems), cuya perspectiva es tridimensional Usuario-Ítem-Contexto, en la que incorporan el contexto del usuario en el proceso de recomendación para adaptar mejor las recomendaciones proporcionadas. El sistema desarrollado consiste en un sistema de recomendación dependiente del contexto y proactivo (que recomienda ítems sin que el usuario intervenga) para usuarios móviles. Este proyecto se ha realizado sobre el prototipo desarrollado en el Trabajo de Fin de Grado Desarrollo de un Prototipo de Aplicación Móvilpara Sistemas de Recomendación Proactivos, que trabaja sobre un modelo de arquitectura de un sistema de recomendación proactivo y que incluye un prototipo móvil capaz de recibir recomendaciones, gestionar las actividades recomendadas de diversas categorías y comunicarse con el encargado de realizar recomendaciones a los usuarios (el gestor de entorno). El foco de este proyecto previo no era la parte de recomendación.Este proyecto amplía el prototipo móvil anterior haciéndolo capaz de detectar qué tipos de recomendación deben activarse sin la intervención del usuario. Esto permite tomar este tipo de decisiones en el propio dispositivo y beneficiar la privacidad del usuario, ya que, si este lo desea, puede decidir no compartir información del contexto con el recomendador externo. Además, ofrece la posibilidad de definir reglas personalizadas para decidir qué tipos de recomendación deben activarse según su contexto. Para ello, se ha realizado una búsqueda de tecnologías que permitan la definición de estas reglas y puedan funcionar en un dispositivo Android. Esto, junto con la posibilidad de establecer prioridades entre los distintos tipos de recomendación, hace que el sistema sea capaz de ofrecer una experiencia más personalizada al usuario. Para evaluar el prototipo se ha implementado un gestor de entorno de prueba, se ha definido un escenario real y se han comprobado sus resultados. Además, se completaron pruebas de rendimiento de la tecnología de reglas en un dispositivo móvil.<br /