Reducing the complexity of NASA's space communications infrastructure

This report describes the range of activities performed during the annual reporting period in support of the NASA Code O Success Team - Lifecycle Effectiveness for Strategic Success (COST LESS) team. The overall goal of the COST LESS team is to redefine success in a constrained fiscal environment and reduce the cost of success for end-to-end mission operations. This goal is more encompassing than the original proposal made to NASA for reducing complexity of NASA's Space Communications Infrastructure. The COST LESS team approach for reengineering the space operations infrastructure has a focus on reversing the trend of engineering special solutions to similar problems

CoMon: Cooperative Ambience Monitoring Platform with Continuity and Benefit Awareness

Mobile applications that sense continuously, such as location monitoring, are emerging. Despite their usefulness, their adoption in real-world deployment situations has been extremely slow. Many smartphone users are turned away by the drastic battery drain caused by continuous sensing and processing. Also, the extractable contexts from the phone are quite limited due to its position and sensing modalities. In this paper, we propose CoMon, a novel cooperative ambience monitoring platform, which newly addresses the energy problem through opportunistic cooperation among nearby mobile users. To maximize the benefit of cooperation, we develop two key techniques, (1) continuity-aware cooperator detection and (2) benefit-aware negotiation. The former employs heuristics to detect cooperators who will remain in the vicinity for a long period of time, while the latter automatically devises a cooperation plan that provides mutual benefit to cooperators, while considering running applications, available devices, and user policies. Through continuity- and benefit-aware operation, CoMon enables applications to monitor the environment at much lower energy consumption. We implement and deploy a CoMon prototype and show that it provides significant benefit for mobile sensing applications

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

Toward a Mobile Platform for Pervasive Games

Emerging pervasive games will be immersed into real-life situations and leverage new types of contextual interactions therein. For instance, a player's punching gesture, running activity, and fast heart rate conditions can be used as the game inputs. Although the contextual interaction is the core building blocks of pervasive games, individual game developers hardly utilize a rich set of interactions within a game play. Most challenging, it is significantly difficult for developers to expect dynamic availability of input devices in real life, and adapt to the situation without system-level support. Also, it is challenging to coordinate its resource use with other gaming logics or applications. To address such challenges, we propose Player Space Director (PSD), a novel mobile platform for pervasive games. PSD facilitates the game developers to incorporate diverse contextual interactions in their game without considering complications in player's real-life situations, e.g., heterogeneity, dynamics or resource scarcity of input devices. We implemented the PSD prototype on mobile devices, diverse set of sensors, and actuators. On top of PSD, we developed three exploratory applications, ULifeAvatar, Swan Boat, U-Theater, and showed the effectiveness of PSD through extensive deployment of those games.

Understanding customer malling behavior in an urban shopping mall using smartphones

Abstract This paper presents a novel customer malling behavior modeling framework for an urban shopping mall. As an automated computing framework using smartphones, it is designed to provide comprehensive understanding of customer behavior. We prototype the framework in a real-world urban shopping mall. Development consists of three steps; customer data collection, customer trace extraction, and behavior model analysis. We extract customer traces from a collection of 701-hour sensor data from 195 in-situ customers who installed our logging application at Android Market. The practical behavior model is created from the real traces. It has a multi-level structure to provide the holistic understanding of customer behavior from physical movement to service semantics. As far as we know, it is the first work to understand complex customer malling behavior in offline shopping malls

Data Driven Analysis of Lithium-ion Battery Internal Resistance Towards Reliable State of Health Prediction

Accurately predicting the lifetime of lithium-ion batteries in the early stage is critical for faster battery production, tuning the production line, and predictive maintenance of energy storage systems and battery-powered devices. Diverse usage patterns, variability in the devices housing the batteries, and diversity in their operating conditions pose significant challenges for this task. The contributions of this paper are three-fold. First, a public dataset is used to characterize the behavior of battery internal resistance. Internal resistance has non-linear dynamics as the battery ages, making it an excellent candidate for reliable battery health prediction during early cycles. Second, using these findings, battery health prediction models for different operating conditions are developed. The best models are more than 95\% accurate in predicting battery health using the internal resistance dynamics of 100 cycles at room temperature. Thirdly, instantaneous voltage drops due to multiple pulse discharge loads are shown to be capable of characterizing battery heterogeneity in as few as five cycles. The results pave the way toward improved battery models and better efficiency within the production and use of lithium-ion batteries.Peer reviewe

PADA: Power-aware development assistant for mobile sensing applications

� 2016 ACM. We propose PADA, a new power evaluation tool to measure and optimize power use of mobile sensing applications. Our motivational study with 53 professional developers shows they face huge challenges in meeting power requirements. The key challenges are from the significant time and effort for repetitive power measurements since the power use of sensing applications needs to be evaluated under various real-world usage scenarios and sensing parameters. PADA enables developers to obtain enriched power information under diverse usage scenarios in development environments without deploying and testing applications on real phones in real-life situations. We conducted two user studies with 19 developers to evaluate the usability of PADA. We show that developers benefit from using PADA in the implementation and power tuning of mobile sensing applications.N