Dynamic Credit Investment in Partially Observed Markets

We consider the problem of maximizing expected utility for a power investor who can allocate his wealth in a stock, a defaultable security, and a money market account. The dynamics of these security prices are governed by geometric Brownian motions modulated by a hidden continuous time finite state Markov chain. We reduce the partially observed stochastic control problem to a complete observation risk sensitive control problem via the filtered regime switching probabilities. We separate the latter into pre-default and post-default dynamic optimization subproblems, and obtain two coupled Hamilton-Jacobi-Bellman (HJB) partial differential equations. We prove existence and uniqueness of a globally bounded classical solution to each HJB equation, and give the corresponding verification theorem. We provide a numerical analysis showing that the investor increases his holdings in stock as the filter probability of being in high growth regimes increases, and decreases his credit risk exposure when the filter probability of being in high default risk regimes gets larger

Energy-Efficient Data Acquisition in Mobile Crowdsensing Systems

Mobile Crowdsensing (MCS) is one of the most promising paradigms for monitoring phenomena in urban environments. The success of a MCS campaign relies on large participation of citizens, who may be reluctant in joining a campaign due to sensing and reporting costs they sustain. Hence, it is fundamental to propose efficient data collection frameworks (DCFs). In the first stages of our work, we proposed an energyefficient DCF that aims to minimize energy consumption while maximizing the utility of contributed data. Then, we developed an Android application and proposed a methodology to compare several DCFs, performing energy- and network-related measures with Power Monitor and Wireshark. Currently, we are investigating collaborative data delivery as a more efficient solution than the individual one. The key idea is to form groups of users and elect a responsible for aggregated data delivery. To this end, it is crucial to analyze device to device (D2D) communications and propose efficient policies for group formation and owner election. To evaluate the performance in realistic urban environments we exploit CrowdSenSim, which runs large-scale simulations in citywide scenarios

User Rewarding and Distributed Payment Platforms for Mobile Crowdsensing Systems

Mobile Crowdsensing (MCS) has become in the last years one of the most prominent paradigms for urban sensing. In MCS systems, citizens actively participate in the sensing process by contributing data from their mobile devices. To make e ective a MCS campaign, large participation is fundamental. Users sustain costs to contribute data and they may be reluctant in joining the sensing process. Hence, it is essential to incentivize participants. Several incentive mechanisms have been investigated, such as monetary rewarding. In this context, distributed payment platforms based on custom built blockchains assume a fundamental role. We aim to develop a platform to distribute micro-payments following rewarding schemes. The key idea is to di erentiate between users through several parameters, such as the amount of acquired data and the Quality of Information (QoI), according to the particular campaign and the need of the organizers

Energy Efficient Data Collection in Opportunistic Mobile Crowdsensing Architectures for Smart Cities

Smart cities employ latest information and communication technologies to enhance services for citizens. Sensing is essential to monitor current status of infrastructures and the environment. In Mobile Crowdsensing (MCS), citizens participate in the sensing process contributing data with their mobile devices such as smartphones, tablets and wearables. To be effective, MCS systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and energy-efficient framework for data collection in opportunistic MCS architectures. Opportunistic sensing systems require minimal intervention from the user side as sensing decisions are application- or device-driven. The proposed framework minimizes the cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. We evaluate performance of the framework with simulations, performed in a real urban environment and with a large number of participants. The simulation results verify cost-effectiveness of the framework and assess efficiency of the data generation process

Acute decompensated heart failure in the emergency department: Identification of early predictors of outcome

Identification of clinical factors that can predict mortality and hospital early readmission in acute decompensated heart failure (ADHF) patients can help emergency department (ED) physician optimize the care-path and resource utilization.We conducted a retrospective observational study of 530 ADHF patients evaluated in the ED of an Italian academic hospital in 2013.Median age was 82 years, females were 55%; 31.1% of patients were discharged directly from the ED (12.5% after short staying in the observation unit), while 68.9% were admitted to a hospital ward (58.3% directly from the ED and 10.6% after a short observation). At 30 days, readmission rate was 17.7% while crude mortality rate was 9.4%; this latter was higher in patients admitted to a hospital ward in comparison to those who were discharged directly from the ED (12.6% vs. 2.4%, P\u200a 104\u200amm Hg, POS\u200a>\u200a94%, may guide the ED physician to identify low-risk patients who can be safely discharged directly from the emergency room or after observation unit stay

A Cost-Effective Distributed Framework for Data Collection in Cloud-based Mobile Crowd Sensing Architectures

Mobile crowd sensing received significant attention in the recent years and has become a popular paradigm for sensing. It operates relying on the rich set of built-in sensors equipped in mobile devices, such as smartphones, tablets and wearable devices. To be effective, mobile crowd sensing systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and sustainable framework for gathering information in cloud-based mobile crowd sensing systems with opportunistic reporting. The proposed framework minimizes cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. Analytical and simulation results provide performance evaluation for the proposed framework by providing a fine-grained analysis of the energy consumed. The simulations, performed in a real urban environment and with a large number of participants, aim at verifying the performance and scalability of the proposed approach on a large scale under different user arrival patterns

Why Energy Matters? Profiling Energy Consumption of Mobile Crowdsensing Data Collection Frameworks

Mobile Crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. The citizens actively participate in the sensing process by contributing data with their mobile devices. To produce data, citizens sustain costs, i.e., the energy consumed for sensing and reporting operations. Hence, devising energy efficient data collection frameworks (DCF) is essential to foster participation. In this work, we investigate from an energy-perspective the performance of different DCFs. Our methodology is as follows: (i) we developed an Android application that implements the DCFs, (ii) we profiled the energy and network performance with a power monitor and Wireshark, (iii) we included the obtained traces into CrowdSenSim simulator for large-scale evaluations in city-wide scenarios such as Luxembourg, Turin and Washington DC. The amount of collected data, energy consumption and fairness are the performance indexes evaluated. The results unveil that DCFs with continuous data reporting are more energy-efficient and fair than DCFs with probabilistic reporting. The latter exhibit high variability of energy consumption, i.e., to produce the same amount of data, the associated energy cost of different users can vary significantly

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas