Silvia Giordano

Bio: Silvia Giordano is an academic researcher from SUPSI. The author has contributed to research in topics: Social influence & Application layer. The author has an hindex of 12, co-authored 44 publications receiving 569 citations. Previous affiliations of Silvia Giordano include Lahti University of Applied Sciences.

Probabilistic Routing Protocol for Intermittently Connected Mobile Ad hoc Network (PROPICMAN)

Abstract: Routing in intermittently connected mobile ad hoc networks (ICMAN) is a very challenging problem because disconnections are prevalent and the lack of knowledge about network dynamics hinders good decision making. In ICMAN one of the most important decisions is how to choose the most suitable intermediate(s) to forward the message to the destination. We propose in this document a new algorithm (PROPICMAN) that, based on context information, allows the sender to select the neighbor(s) such that the message has the highest probability to reach the destination.

Smartphone-centred wearable sensors network for monitoring patients with bipolar disorder

Abstract: Bipolar Disorder is a severe form of mental illness. It is characterized by alternated episodes of mania and depression, and it is treated typically with a combination of pharmacotherapy and psychotherapy. Recognizing early warning signs of upcoming phases of mania or depression would be of great help for a personalized medical treatment. Unfortunately, this is a difficult task to be performed for both patient and doctors. In this paper we present the MONARCA wearable system, which is meant for recognizing early warning signs and predict maniac or depressive episodes. The system is a smartphone-centred and minimally invasive wearable sensors network that is being developing in the framework of the MONARCA European project.

Tapping into the Vibe of the city using VibN, a continuous sensing application for smartphones

Abstract: We present VibN, a mobile sensing application deployed at large scale through the Apple App Store and the Android Market. VibN has been built to determine "what's going on" around the user in real-time by exploiting multiple sensor feeds. The application allows its users to explore live points of interest of the city by presenting real-time hotspots from sensor data. Each hotspot is characterized by a demographics breakdown of inhabitants and a list of short audio clips. The audio clips augment traditional microblogging methods by allowing users to automatically and manually provide rich audio data about their locations. VibN also allows one to browse historical points of interest and view how locations in a city evolve over time. Additionally, VibN automatically determines a user's personal points of interest, which are a means for building a user's breadcrumb diary of locations where they have spent significant amount of time. In this paper, we present the design, evaluation, and results from the large scale deployment of VibN through the popular Apple App Store and Android Market.

SCAMPI: service platform for social aware mobile and pervasive computing

Abstract: Allowing mobile users to find and access resources available in the surrounding environment opportunistically via their smart devices could enable them to create and use a rich set of services. Such services can go well beyond what is possible for a mobile phone acting alone. In essense, access to diverse resources such as raw computational power, social networking relationships, or sensor readings across a set of different devices calls for distributed task execution. In this paper, we discuss the SCAMPI architecture designed to support distributed task execution in opportunistic pervasive networks. The key elements of the architecture include leveraging human social behavior for efficient opportunistic interaction between a variety of sensors, personal communication devices and resources embedded in the local environment. The SCAMPI architecture abstracts resources as service components following a service-oriented model. This enables composing rich applications that utilize a collection of service components available in the environment.

StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones

Abstract: Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day-to-day and week-by-week impact of workload on stress, sleep, activity, mood, sociability, mental well-being and academic performance of a single class of 48 students across a 10 week term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant correlations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while positive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web.

The Smartphone in Medicine: A Review of Current and Potential Use Among Physicians and Students

Abstract: Background: Advancements in technology have always had major impacts in medicine. The smartphone is one of the most ubiquitous and dynamic trends in communication, in which one’s mobile phone can also be used for communicating via email, performing Internet searches, and using specific applications. The smartphone is one of the fastest growing sectors in the technology industry, and its impact in medicine has already been significant. Objective: To provide a comprehensive and up-to-date summary of the role of the smartphone in medicine by highlighting the ways in which it can enhance continuing medical education, patient care, and communication. We also examine the evidence base for this technology. Methods: We conducted a review of all published uses of the smartphone that could be applicable to the field of medicine and medical education with the exclusion of only surgical-related uses. Results: In the 60 studies that were identified, we found many uses for the smartphone in medicine; however, we also found that very few high-quality studies exist to help us understand how best to use this technology. Conclusions: While the smartphone’s role in medicine and education appears promising and exciting, more high-quality studies are needed to better understand the role it will have in this field. We recommend popular smartphone applications for physicians that are lacking in evidence and discuss future studies to support their use. [J Med Internet Res 2012;14(5):e128]

Cabernet: vehicular content delivery using WiFi

Abstract: Cabernet is a system for delivering data to and from moving vehicles using open 802.11 (WiFi) access points encountered opportunistically during travel. Using open WiFi access from the road can be challenging. Network connectivity in Cabernet is both fleeting (access points are typically within range for a few seconds) and intermittent (because the access points do not provide continuous coverage), and suffers from high packet loss rates over the wireless channel. On the positive side, WiFi data transfers, when available, can occur at broadband speeds.In this paper, we introduce two new components for improving openWiFi data delivery to moving vehicles: The first, QuickWiFi, is a streamlined client-side process to establish end-to-end connectivity, reducing mean connection time to less than 400 ms, from over 10 seconds when using standard wireless networking software. The second part, CTP, is a transport protocol that distinguishes congestion on the wired portion of the path from losses over the wireless link, resulting in a 2x throughput improvement over TCP. To characterize the amount of open WiFi capacity available to vehicular users, we deployed Cabernet on a fleet of 10 taxis in the Boston area. The long-term average transfer rate achieved was approximately 38 Mbytes/hour per car (86 kbit/s), making Cabernet a viable system for a number of non-interactive applications.

Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.

Abstract: Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions.