Power vs. Spectrum 2-D Sensing in Energy Harvesting Cognitive Radio Networks

Energy harvester based cognitive radio is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption patterns are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In this paper, the Markovian behavior of the primary users is considered, based on which we adopt a hidden input Markov model to specify the primary vs. secondary dynamics in the system. Accordingly, we propose a 2-D spectrum and power (harvested) sensing scheme to improve the primary user detection performance, which is also capable of estimating the primary transmit power level. Theoretical and simulated results demonstrate the effectiveness of the proposed scheme, in term of the performance gain achieved by considering the new power dimension. To the best of our knowledge, this is the first work to jointly consider the spectrum and power dimensions for the cognitive primary user detection problem

Online vs. In-Store Shopping: How Problem Solving Strategies of Decision Support Systems influence Confidence in Purchase Decisions

Several studies have investigated the relevance of Decision Support Systems (DSS) on purchasebehaviour. Even though these studies show several aspects of the utility of DSS, they are limited toonline purchase situations, the use of one decision making strategy and one DSS technology. In thispaper, we therefore develop a theoretical model that measures the impact of DSS strategies relative toa given purchase problem and an adequate use of DSS technology on consumers’ perceivedconfidence in purchase decisions for both online and in-store purchase situations. Further, threemediating decision process variables are considered: perceived personalization of a DSS, perceivedrelevance of recommendations, and cognitive trust in DSS competence. As this paper represents awork in progress, the theoretical model has still to be tested empirically with regard to the proposedmethod. But as a result, we expect that the model not only allows evaluating different kinds ofpurchase-directed DSS but let researchers also draw conclusions on the appropriate use of technologyand decision strategies of one individual DSS

A personalized system for scalable distribution of multimedia content in multicast wireless networks

The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-014-2139-3This paper presents a novel architecture for scalable multimedia content delivery over wireless networks. The architecture takes into account both the user preferences and context in order to provide personalized contents to each user. In this way, third-party applications filter the most appropriate contents for each client in each situation. One of the key characteristics of the proposal is the scalability, which is provided, apart from the use of filtering techniques, through the transmission in multicast networks. In this sense, content delivery is carried out by means of the FLUTE (File Delivery over Unidirectional Transport) protocol, which provides reliability in unidirectional environments through different mechanisms such as AL-FEC (Application Layer Forward Error Correction) codes, used in this paper. Another key characteristic is the context-awareness and personalization of content delivery, which is provided by means of context information, user profiles, and adaptation. The system proposed is validated through several empirical studies. Specifically, the paper presents evaluations of two types that collect objective and subjective measures. The first evaluate the efficiency of the transmission protocol, analyzing how the use of appropriate transmission parameters reduces the download time (and thus increasing the Quality of Experience), which can be minimized by using caching techniques. On the other hand, the subjective measures present a study about the user experience after testing the application and analyze the accuracy of the filtering process/strategy. Results show that using AL-FEC mechanisms produces download times until four times lower than when no protection is used. Also, results prove that there is a code rate that minimizes the download time depending on the losses and that, in general, code rates 0.7 and 0.9 provide good download times for a wide range of losses. On the other hand, subjective measures indicate a high user satisfaction (more than 80 %) and a relevant degree of accuracy of the content adaption.This work is supported in part by the Ministerio de Economia y Competitividad of the Government of Spain under project COMINN (IPT-2012-0883-430000) and by the project PAID/2012/313 from the PAID-05-12 program of the Vicerrectorado de Investigacion of the Universitat Politecnica de Valencia.De Fez Lava, I.; Gil Pascual, M.; Fons Cors, JJ.; Guerri Cebollada, JC.; Pelechano Ferragud, V. (2014). A personalized system for scalable distribution of multimedia content in multicast wireless networks. 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An Aspect-Oriented Approach to Securing Distributed Systems

International audienceThe increasing size and complexity of distributed systems create a need to raise the level of abstraction for their development. This need becomes critical for pervasive computing where non-functional properties, such as security, must be guaranteed. Architecture description languages (ADLs) propose a promising approach to coping with the size and complexity of pervasive computing systems. A system is defined by a high-level description that may be used to produce a programming framework. However, non-functional properties are not specifically addressed by existing ADL works. To address this issue aspect-oriented programming is a well-proven technique to properly modularize non-functional concerns that can be dealt with by weaving dedicated code into a program. In this paper, we present DiaAspect, an aspect-oriented language for our ADL, DiaSpec. Like most ADLs, DiaSpec exposes features enabling an accurate coordination of aspects. We demonstrate the expressiveness of DiaAspect with two examples of security policies in pervasive computing. We also show how, combining the knowledge of the architecture description with aspect code, improves aspect weaving in the implementation code

Activity recognition from smartphone sensing data

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Intelligent Design in Wireless System

We are living in an era full of data services, and the advancement in statistical learning encourages the development of intelligent system design algorithms based on practical data. In our work, we plan to study two potential applications with intelligent design in wireless systems based on statistical and machine learning techniques. The first application we study is the spectrum sensing problem in energy harvesting based cognitive radio networks, which is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption pattern are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In our work, the Markovian behavior of the primary users is considered, based on which we adopt a hidden input Markov model to specify the primary vs. secondary dynamics in the system. Accordingly, we propose a 2-D spectrum vs. power (harvested) sensing scheme to improve the primary user detection performance, which is also capable of estimating the primary transmit power level. Theoretical and simulated results demonstrate the effectiveness of the proposed scheme, in terms of the performance gain achieved by considering the new power dimension. To the best of our knowledge, this is the first work to jointly consider the spectrum and power dimensions for the cognitive primary user detection problem. The second work is about spatio-temporal base station traffic prediction with machine learning. Accurate prediction of user traffic in cellular networks is crucial to improve the system performance in terms of energy efficiency and resource utilization. However, existing work mainly considers the temporal traffic correlations within each cell while neglecting the spatial correlation across neighboring cells. In this work, machine learning models that jointly explore the spatio-temporal correlations are proposed, where a multitask learning approach is adopted to explore the commonalities and differences across cells in improving the prediction performance. Base on real data, we demonstrate the benefits of joint learning over spatial and temporal dimensions

Intelligent Design in Wireless System

We are living in an era full of data services, and the advancement in statistical learning encourages the development of intelligent system design algorithms based on practical data. In our work, we plan to study two potential applications with intelligent design in wireless systems based on statistical and machine learning techniques. The first application we study is the spectrum sensing problem in energy harvesting based cognitive radio networks, which is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption pattern are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In our work, the Markovian behavior of the primary users is considered, based on which we adopt a hidden input Markov model to specify the primary vs. secondary dynamics in the system. Accordingly, we propose a 2-D spectrum vs. power (harvested) sensing scheme to improve the primary user detection performance, which is also capable of estimating the primary transmit power level. Theoretical and simulated results demonstrate the effectiveness of the proposed scheme, in terms of the performance gain achieved by considering the new power dimension. To the best of our knowledge, this is the first work to jointly consider the spectrum and power dimensions for the cognitive primary user detection problem. The second work is about spatio-temporal base station traffic prediction with machine learning. Accurate prediction of user traffic in cellular networks is crucial to improve the system performance in terms of energy efficiency and resource utilization. However, existing work mainly considers the temporal traffic correlations within each cell while neglecting the spatial correlation across neighboring cells. In this work, machine learning models that jointly explore the spatio-temporal correlations are proposed, where a multitask learning approach is adopted to explore the commonalities and differences across cells in improving the prediction performance. Base on real data, we demonstrate the benefits of joint learning over spatial and temporal dimensions

ICE-MILK: Intelligent Crowd Engineering using Machine-based Internet of Things Learning and Knowledge Building

Title from PDF of title page viewed June 1, 2022Dissertation advisor: Sejun SongVitaIncludes bibliographical references (pages 136-159)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2022The lack of proper crowd safety control and management often leads to spreading human casualties and infectious diseases (e.g., COVID-19). Many Machine Learning (ML) technologies inspired by computer vision and video surveillance systems have been developed for crowd counting and density estimation to prevent potential personal injuries and deaths at densely crowded political, entertaining, and religious events. However, existing crowd safety management systems have significant challenges and limitations on their accuracy, scalability, and capacity to identify crowd characterization among people in crowds in real-time, such as a group characterization, impact of occlusions, mobility and contact tracing, and distancing. In this dissertation, we propose an Intelligent Crowd Engineering platform using Machine-based Internet of Things Learning, and Knowledge Building approaches (ICE-MILK) to enhance the accuracy, scalability, and crowd safety management capacity in real-time. Specifically, we design an ICE-MILK structure with three critical layers: IoT-based mobility characterization, ML-based video surveillance, and semantic information-based application layers. We built an IoT-based mobility characterization system by predicting and preventing potential disasters through real-time Radio Frequency (RF) data characterization and analytics. We tackle object group identification, speed, direction detection, and density for the mobile group among the many crowd mobility characteristics. Also, we tackled an ML-based video surveillance approach for effective dense crowd counting by characterizing scattered occlusions, named CSONet. CSONet recognizes the implications of event-induced, scene-embedded, and multitudinous obstacles such as umbrellas and picket signs to achieve an accurate crowd analysis result. Finally, we developed a couple of group semantics to track and prevent crowd-caused infectious diseases. We introduce a novel COVID-19 tracing application named Crowd-based Alert and Tracing Services (CATS) and a novel face masking and social distancing monitoring system for Modeling Safety Index in Crowd (MOSAIC). CATS and MOSAIC apply privacy-aware contact tracing, social distancing, and calculate spatiotemporal Safety Index (SI) values for the individual community to provide higher privacy protection, efficient penetration of technology, greater accuracy, and effective practical policy assistance.Introduction -- Literature review -- IoT-based mobility characterization -- ML-based video/image surveillance -- Semantic knowledge information-based tracing application -- Conclusions and future directions -- Appendi