Showing papers by "Eemil Lagerspetz published in 2018"

Evidence-Aware Mobile Computational Offloading

Abstract: Computational offloading can improve user experience of mobile apps through improved responsiveness and reduced energy footprint. A fundamental challenge in offloading is to distinguish situations where offloading is beneficial from those where it is counterproductive. Currently, offloading decisions are predominantly based on profiling performed on individual devices . While significant gains have been shown in benchmarks, these gains rarely translate to real-world use due to the complexity of contexts and parameters that affect offloading. We contribute by proposing crowdsensed evidence traces as a novel mechanism for improving the performance of offloading systems. Instead of limiting to profiling individual devices, crowdsensing enables characterizing execution contexts across a community of users, providing better generalisation and coverage of contexts. We demonstrate the feasibility of using crowdsensing to characterize offloading contexts through an analysis of two crowdsensing datasets. Motivated by our results, we present the design and development of the EMCO toolkit and platform as a novel solution for computational offloading. Experiments carried out on a testbed deployment in Amazon EC2 Ireland demonstrate that EMCO can consistently accelerate app execution while at the same time reduce energy footprint. We also demonstrate that EMCO provides better scalability than current cloud platforms, being able to serve a larger number of clients without variations in performance. Our framework, use cases, and tools are available as open source from GitHub.

The hidden image of mobile apps: geographic, demographic, and cultural factors in mobile usage

Abstract: While mobile apps have become an integral part of everyday life, little is known about the factors that govern their usage. Particularly the role of geographic and cultural factors has been understudied. This article contributes by carrying out a large-scale analysis of geographic, cultural, and demographic factors in mobile usage. We consider app usage gathered from 25,323 Android users from 44 countries and 54,776 apps in 55 categories, and demographics information collected through a user survey. Our analysis reveals significant differences in app category usage across countries and we show that these differences, to large degree, reflect geographic boundaries. We also demonstrate that country gives more information about application usage than any demographic, but that there also are geographic and socio-economic subgroups in the data. Finally, we demonstrate that app usage correlates with cultural values using the Value Survey Model of Hofstede as a reference of cross-cultural differences.

Pervasive Communities in the Internet of People

Abstract: The Internet has traditionally been a device-oriented architecture where devices with IP addresses are first-class citizens, able to serve and consume content or services, and their owners take part in the interaction only through those devices The Internet of People (IoP) is a recent paradigm where devices become proxies of their users, and can act on their behalf To realize IoP, new policies and rules for how devices can take actions are required The role of context information grows as devices act autonomously based on the environment and existing social relationships between their owners In addition, the social profiles of device owners determine eg how altruistic or resourceconserving they are in collaborative computing scenarios In this paper we focus on community formation in IoP, a prerequisite for enabling collaborative scenarios, and discuss main challenges and propose potential solutions

GeoMatch: Efficient Large-scale Map Matching on Apache Spark

Abstract: We develop GeoMatch as a novel, scalable, and efficient big-data pipeline for large-scale map matching on Apache Spark. GeoMatch improves existing spatial big-data solutions by utilizing a novel spatial partitioning scheme inspired by Hilbert space-filling curves. Thanks to its partitioning scheme, GeoMatch can effectively balance operations across different processing units and achieve significant performance gains. GeoMatch also incorporates a dynamically adjustable error-correction technique that provides robustness against positioning errors. We demonstrate the effectiveness of GeoMatch through rigorous and extensive empirical benchmarks that consider large-scale urban spatial datasets ranging from 166,253 to 3.78B location measurements. We separately assess execution performance and accuracy of map matching and develop a benchmark framework for evaluating large-scale map matching. Results of our evaluation show up to 27.25-fold performance improvements compared to previous works while achieving better processing accuracy than current solutions. We also showcase the practical potential of GeoMatch with two urban management applications. GeoMatch and our benchmark framework are open-source.

MegaSense: Megacity-scale Accurate Air Quality Sensing with the Edge

Abstract: This demo presents MegaSense, an air pollution monitoring system for realizing low-cost, near real-time and high resolution spatio-temporal air pollution maps of urban areas. MegaSense involves a novel hierarchy of multi-vendor distributed air quality sensors, in which accurate sensors calibrate lower cost sensors. Current low-cost air quality sensors suffer from measurement drift and they have low accuracy. We address this significant open problem for dense urban areas by developing a calibration scheme that detects and automatically corrects drift. MegaSense integrates with the 5G cellular network and leverages mobile edge computing for sensor management and distributed pollution map creation. We demonstrate MegaSense with two sensor types, a state of the art air quality monitoring station and a low-cost sensor array, with calibration between the two to improve the accuracy of the low-cost device. Participants can interact with the sensors and see air quality changes in real-time, and observe the mechanism to mitigate sensor drift. Our re-calibration method minimizes the error for NO2 and O3 81% of the time (vs single calibration) and reduces the mean relative error by 25%-45%.

Demo: Managing Sensing Resources at the Edge using Cloud OSes

Abstract: Environmental sensing is an important use case for edge computing and mobile networks. Specifically, edge computing approaches and mobile networks are expected to be used by sensing applications to collect data from fixed environment monitoring stations along with the sensors mounted on mobile sensing platforms. These mobile sensing platforms are in turn expected to leverage micro-servers such as Raspberry Pis for collecting the data, performing some initial computation, and disseminating their results for further processing. In this demonstration we show that OpenStack can be used to manage containers on Rasperry Pis. This setup enables a) micro-servers at the network edge to support multitenancy like their counterparts in data centers, and b) Cloud OSes to manage the resources at the network edge along with the resources in data centers. Specifically, such a hybrid cloud setup can be useful in augmenting the computational resources in private and public clouds with their counterparts in the network edge.