Tattle - "Here's How I See It" : Crowd-Sourced Monitoring and Estimation of Cellular Performance Through Local Area Measurement Exchange
Creators: name: Huiguang  Liang
Publisher: name: Carnegie Mellon University
Year: 2015
Subjects: subject: Computer Engineering
subject: 90699 Electrical and Electronic Engineering not elsewhere classified
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: The operating environment of cellular networks can be in a constant state of change due to variations and evolutions of technology, subscriber load, and physical infrastructure. One cellular operator, which we interviewed, described two key difficulties. Firstly, they are unable to monitor the performance of their network in a scalable and fine-grained manner. Secondly, they find difficulty in monitoring the service quality experienced by each user equipment (UE). Consequently, they are unable to effectively diagnose performance impairments on a per-UE basis. They currently expend considerable manual efforts to monitor their network through controlled, small-scale drive-testing. If this is not performed satisfactorily, they risk losing subscribers, and also possible penalties from regulators. In this dissertation, we propose Tattle1, a distributed, low-cost participatory sensing framework for the collection and processing of UE measurements. Tattle is designed to solve three problems, namely coverage monitoring (CM), service quality monitoring (QM) and, per-device service quality estimation and classification (QEC). In Tattle, co-located UEs exchange uncertain location information and measurements using local-area broadcasts. This preserves the context of co-location of these measurements. It allows us to develop U-CURE, as well as its delay-adjusted variant, to discard erroneously-localized samples, and reduce localization errors respectively. It allows operators to generate timely, high-resolution and accurate monitoring maps. Operators can then make informed, expedient network management decisions, such as adjusting base-station parameters, to making long-term infrastructure investment. We propose a comprehensive statistical framework that also allows an individual UE to estimate and classify its own network performance. In our approach, each UE monitors its recent measurements, together with those reported by co-located UEs. Then, through our framework, UEs can automatically determine if any observed impairment is endemic amongst other co-located devices. Subscribers that experience isolated impairments can then take limited remedy steps, such as rebooting their devices. We demonstrate Tattle's effectiveness by presenting key results, using up to millions of real-world measurements. These were collected systematically using current generations of commercial-off-the-shelf (COTS) mobile devices. For CM, we show that in urban built-up areas, GPS locations reported by UEs may have significant uncertainties and can sometimes be several kilometers away from their true locations. We describe how U-CURE can take into account reported location uncertainty and the knowledge of measurement co-location to remove erroneously-localized readings. This allows us to retain measurements with very high location accuracy, and in turn derive accurate, fine-grained coverage information. Operators can then react and respond to specific areas with coverage issues in a timely manner. Using our approach, we showcase high-resolution results of actual coverage conditions in selected areas of Singapore. For QM, we show that localization performance in COTS devices may exhibit non-negligible correlation with network round-trip delay. This can result in localization errors of up to 605.32m per 1,000ms of delay. Naïve approaches that blindly accepts measurements with their reported locations will therefore result in grossly mis-localized data points. This affects the fidelity of any geo-spatial monitoring information derived from these data sets. We demonstrate that using the popular localization approach of combining Global-Positioning System together with Network-Assisted Localization, may result in a median root-mean-square (rms) error increase of over 60%. This is in comparison to simply using the Global-Positioning System on its own. We propose a network-delay-adjusted variant of U-CURE, to cooperatively improve the localization performance of COTS devices. We show improvements of up to 70% in terms of median rms location errors, even while subjected to uncertain real-world network delay conditions, with just 3 participating UEs. This allows us to refine the purported locations of delay measurements, and as a result, derive accurate, fine-grained and actionable cellular quality information. Using this approach, we present accurate cellular network delay maps that are of much higher spatial-resolution, as compared to those naively derived using raw data. For QEC, we report on the characteristics of the delay performance of co-located devices subscribed to 2 particular cellular network operators in Singapore. We describe the results of applying our proposed approach to addressing the QEC problem, on real-world measurements of over 443,500 data points. We illustrate examples where “normal” and “abnormal” performances occur in real networks, and report instances where a device can experience complete outage, while none of its neighbors are affected. We give quantitative results on how well our algorithm can detect an “abnormal” time series, with increasing effectiveness as the number of co-located UEs increases. With just 3 UEs, we are able to achieve a median detection accuracy of just under 70%. With 7 UEs, we can achieve a median detection rate of just under 90%. 1 The meaning of Tattle, as a verb, is to gossip idly. By letting devices communicate their observations with one another, we explore the kinds of insights that can elicited based on this peer-to-peer exchange.