ContextLabeler Dataset: physical and virtual sensors data collected from smartphone usage in-the-wild

This paper describes a data collection campaign and the resulting dataset derived from smartphone sensors characterizing the daily life activities of 3 volunteers in a period of two weeks. The dataset is released as a collection of CSV files containing more than 45K data samples, where each sample is composed by 1332 features related to a heterogeneous set of physical and virtual sensors, including motion sensors, running applications, devices in proximity, and weather conditions. Moreover, each data sample is associated with a ground truth label that describes the user activity and the situation in which she was involved during the sensing experiment (e.g., working, at restaurant, and doing sport activity). To avoid introducing any bias during the data collection, we performed the sensing experiment in-the-wild, that is, by using the volunteers' devices, and without defining any constraint related to the user's behavior. For this reason, the collected dataset represents a useful source of real data to both define and evaluate a broad set of novel context-aware solutions (both algorithms and protocols) that aim to adapt their behavior according to the changes in the user's situation in a mobile environment

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Conservation of Mobile Energy and Optimum Usage of Radio Resources in Cellular Networks Idle Leverage

While assessing the nature of cellular networks, it is noticed that inactivity timers are employed to control the release of radio assets. It is commonly noticed that there is disruption during the period of inactivity timers. This disruption is called Tail time. This results in the wastage of a large proportion of energy in client gadgets and a lot of radio assets. This research study proposes that this problem called Tail time can be overcome by employing a technique known as Idle Leverage. This Idle Leverage is for batching and prefetching which in turn would reduce energy consumption. Idle Leverage provides a customized application programming interface to distinguish requests and then schedules delay tolerant and prefetchable requests in the Tail time to save energy. Idle Leverage utilizes a virtual Tail time mechanism to identify the amount of Tail time that can be used and a dual queue scheduling algorithm to schedule transmissions. This research study implements Idle Leverage in the Network Simulator with a model for estimating energy consumption that is based on parameters measured from handsets. As a result of this experiment, it has been proved that Idle Leverage can achieve significant savings on battery energy and avoid wastage in the case of radio assets. As per assessment 70% of savings has been achieved in mobile battery energy, and it dedicates radio assets up to 60%, compared to the existing system

The Role of Patents in Information and Communication Technologies (ICTs). A survey of the Literature.

During the last decades, the number of ICT related patents has increased considerably. In association with a great fragmentation in IP rights, the increasing number of patents has generated a series of potentially problematic consequences. Patent thickets, royalty stacking, the emergence of patent assertion entities, increased patent litigation \u2013 in particular around standard essential patents \u2013 and the difficulties in the definition of fair, reasonable and non-discriminatory (FRAND) licensing terms are among the most debated issues in the literature that we review in this paper. We devote a specific section of our survey to patents involving software products, where the above problems are amplified by the high level of abstraction of computer algorithms. In our analysis we mix theoretical and empirical arguments with a more policy-oriented reasoning. This allows us to better position the different issues in the relevant political and economic context

Understanding, Discovering, and Mitigating Habitual Smartphone Use in Young Adults

People, especially young adults, often use their smartphones out of habit: They compulsively browse social networks, check emails, and play video-games with little or no awareness at all. While previous studies analyzed this phenomena qualitatively, e.g., by showing that users perceive it as meaningless and addictive, yet our understanding of how to discover smartphone habits and mitigate their disruptive effects is limited. Being able to automatically assess habitual smartphone use, in particular, might have different applications, e.g., to design better “digital wellbeing” solutions for mitigating meaningless habitual use. To close this gap, we first define a data analytic methodology based on clustering and association rules mining to automatically discover complex smartphone habits from mobile usage data. We assess the methodology over more than 130,000 phone usage sessions collected from users aged between 16 and 33, and we show evidence that smartphone habits of young adults can be characterized by various types of links between contextual situations and usage sessions, which are highly diversified and differently perceived across users. We then apply the proposed methodology in Socialize, a digital wellbeing app that (i) monitors habitual smartphone behaviors in real time and (ii) uses proactive notifications and just-in-time reminders to encourage users to avoid any identified smartphone habits they consider as meaningless. An in-the-wild study with 20 users (ages 19–31) demonstrates that Socialize can assist young adults in better controlling their smartphone usage with a significant reduction of their unwanted smartphone habits

Battery Health Estimation for IoT Devices using V-Edge Dynamics

Deployments of battery-powered IoT devices have become ubiquitous, monitoring everything from environmental conditions in smart cities to wildlife movements in remote areas. How to manage the life-cycle of sensors in such large-scale deployments is currently an open issue. Indeed, most deployments let sensors operate until they fail and fix or replace the sensors post-hoc. In this paper, we contribute by developing a new approach for facilitating the life-cycle management of large-scale sensor deployments through online estimation of battery health. Our approach relies on so-called V-edge dynamics which capture and characterize instantaneous voltage drops. Experiments carried out on a dataset of battery discharge measurements demonstrate that our approach is capable of estimating battery health with up to 80% accuracy, depending on the characteristics of the devices and the processing load they undergo. Our method is particularly well-suited for the sensor devices, operating dedicated tasks, that have constant discharge during their operation.Peer reviewe