Cellular Data Usage Research Articles

Group anomaly detection in mobile app usages: A spatiotemporal convex hull methodology

Analysing mobile apps communications can unleash significant information on both the communication infrastructure state and the operations of mobile computing services. A wide variety of events can engender unusual mobile communication patterns possibly interesting for pervasive computing applications, e.g., in smart cities. For instance, local events, national events, and network outages can produce spatiotemporal load anomalies that could be taken into consideration by both mobile applications and infrastructure providers, especially with the emergence of edge computing frameworks where the two environments merge. Being able to detect and timely treat these anomalies is therefore a desirable feature for next-generation cellular and edge computing networks, with regards to security, network and application performance, and public safety. We focus on the detection of mobile access spatiotemporal anomalies by decomposing, aggregating and grouping cellular data usage features time series. We propose a methodology to detect first raw anomalies, and group them in a spatiotemporal convex hull , further refining the anomaly detection logic, with a novel algorithmic framework. We show how with the proposed framework we can unveil details about broad-category mobile events timeline, their spatiotemporal spreading, and their impacted apps. We apply our technique to extensive real-world data and open source our code. By linkage with ground-truth special events that happened in the observed period, we show how our methodology is able to detect them. We also evidence the existence of five main categories of anomalies, finely characterising them. Finally, we identify global patterns in the anomalies and assess their level of unpredictability, based on the nature of the impacted mobile applications.

Does the improvement of public Wi-Fi technology undermine mobile network operators’ profits? Evidence from consumer preferences

Cellular data consumption by users is the main revenue source for mobile network operators (MNOs). On the one hand, technological development and expansion in Wi-Fi coverage can reduce cellular data usage. On the other hand, Wi-Fi offloading, which entails redirecting cellular traffic to a Wi-Fi network, can prevent network quality degradation as a result of explosive cellular traffic. Thus, the advancement and expansion of public Wi-Fi are both beneficial and detrimental for MNOs. Therefore, in this study, we analyze consumer preferences based on changes in the properties of Wi-Fi and cellular networks by employing a mixed logit model with stated preference data. The results indicate high consumer preference when the cellular and Wi-Fi networks are fast, Wi-Fi usability range is wide, and price is low. To analyze the impact of improving public Wi-Fi technology on MNOs, we conduct a scenario analysis. When the Wi-Fi service speed increases to 100 Mbps or more and its coverage expands to span an entire public transportation area, consumers reduce the amount of their monthly cell phone plan by 1347 Korean won (KRW) (1.15 USD) and 5141 KRW (4.40 USD), respectively. The results of this study can facilitate the determination of appropriate public Wi-Fi speeds, establish the quality of cellular networks by considering the effect on MNOs, and improve social welfare by expanding public Wi-Fi networks.

CellPred

Cellular data usage consumption prediction is an important topic in cellular networks related researches. Accurately predicting future data usage can benefit both the cellular operators and the users, which can further enable a wide range of applications. Different from previous work focusing on statistical approaches, in this paper, we propose a scheme called CellPred to predict cellular data usage from an individual user perspective considering user behavior patterns. Specifically, we utilize explicit user behavioral tags collected from subscription data to function as an external aid to enhance the user's mobility and usage prediction. Then we aggregate individual user data usage to cell tower level to obtain the final prediction results. To our knowledge, this is the first work studying cellular data usage prediction from an individual user behavior-aware perspective based on large-scale cellular signaling and behavior tags from the subscription data. The results show that our method improves the data usage prediction accuracy compared to the state-of-the-art methods; we also comprehensively demonstrate the impacts of contextual factors on CellPred performance. Our work can shed light on broad cellular networks researches related to human mobility and data usage. Finally, we discuss issues such as limitations, applications of our approach, and insights from our work.

Crowd-Cache: Leveraging on spatio-temporal correlation in content popularity for mobile networking in proximity

Mobile capped plans are being increasingly adopted by mobile operators due to an exponential data traffic growth. Users then often suffer high data consumption costs as well as poor quality of experience. In this paper, we introduce a novel content access scheme, Crowd-Cache, which enables mobile networking in proximity by exploiting the transient co-location of devices, the epidemic nature of content popularity, and the capabilities of smart mobile devices. Crowd-Cache provides mobile users access to popular content cheaply with low latency while improving the overall quality of experience. We model the Crowd-Cache system in a probabilistic framework using a real-life dataset of video content access. The simulation results show that, in a public transportation scenario, more than 80% of the passengers can save at least 40% on their cellular data usage during a typical average city bus commute of 10 minutes. Finally, we show the practical viability of the system by implementing and evaluating the system on Android devices.

Analysis of Human Mobility Based on Cellular Data

Nowadays not only adult but even teenager and children have then own mobile phones. This phenomena indicates that the mobile phone becomes an important part of everyday’s life. Based on these indication, the amount of cellular data also increased rapidly. Cellular data defined as the data that records communication among mobile phone users. Cellular data is easy to obtain because the telecommunications company had made a record of the data for the billing system of the company. Billing data keeps a log of the users cellular data usage each time. We can obtained information from the data about communication between users. Through data visualization process, an interesting pattern can be seen in the raw cellular data, so that users can obtain prior knowledge to perform data analysis. Cellular data processing can be done using data mining to find out human mobility patterns and on the existing data. In this paper, we use frequent pattern mining and finding association rules to observe the relation between attributes in cellular data and then visualize them. We used weka tools for finding the rules in stage of data mining. Generally, the utilization of cellular data can provide supporting information for the decision making process and become a data support to provide solutions and information needed by the decision makers.

Utilizing Geospatial Information in Cellular Data Usage for Key Location Prediction

Previous research on the identification of key locations (e.g., home and workplace) for a user largely relies on call detail records (CDRs). Recently, cellular data usage (i.e., mobile internet) is growing rapidly and offers fine-grained insights into various human behavior patterns. In this study, we introduce a novel dataset containing both voice and mobile data usage records of mobile users. We then construct a new feature based on the geospatial distribution of cell towers connected by mobile users and employ bivariate kernel density estimation to help predict users’ key locations. The evaluation results suggest that augmented features based on both voice and mobile data usage improve the prediction precision and recall.

DAWN: Delay-Aware Wi-Fi Offloading and Network Selection

To accommodate the explosive growth in mobile data traffic, both mobile cellular operators and mobile users are increasingly interested in offloading the traffic from cellular networks to Wi-Fi networks. However, previously proposed offloading schemes mainly focus on reducing the cellular data usage, without paying too much attention on the quality of service (QoS) requirements of the applications. In this paper, we study the Wi-Fi offloading problem with delay-tolerant applications under usage-based pricing. We aim to achieve a good tradeoff between the user's payment and its QoS characterized by the file transfer deadline. We first propose a general Delay-Aware Wi-Fi Offloading and Network Selection (DAWN) algorithm for a general single-user decision scenario. We then analytically establish the sufficient conditions, under which the optimal policy exhibits a threshold structure in terms of both the time and file size. As a result, we propose a monotone DAWN algorithm that approximately solves the general offloading problem, and has a much lower computational complexity comparing to the optimal algorithm. Simulation results show that both the general and monotone DAWN schemes achieve a high probability of completing file transfer under a stringent deadline, and require the lowest payment under a non-stringent deadline as compared with three heuristic schemes.

Does Bigger Screen Lead to More Cellular Data Usage?

This study utilizes a unique dataset from a telecommunications company to examine the relationship between screen size and cellular data consumption for smartphone users. We first find an overall positive correlation based on population-level cross-sectional regressions, but after adopting a quasi-experimental design, there is no significant relationship between screen size and cellular data consumption measured by the amount of data transmitted. Motivated by the theory of task-technology fit, our additional analyses of the QQ browser mobile app suggest that screen size has a positive effect on mobile web browsing. Our study provides important implications for mobile network operators in understanding consumer behavior and promoting mobile data services to users with different devices.

Managing cellular congestion using incentives

Mobile data traffic is expected to grow exponentially in the next few years due to the explosive growth of mobile web and video traffic on smartphones. Wireless operators have invested heavily to make infrastructural improvements by installing new cell towers and offloading cellular data traffic to Wi-Fi to resolve congestion. They are also exploring the use of behavioral and economic interventions to manage congestion. To understand the role of interventions, we distributed smartphones to students at Purdue University, loaded with applications to perform monitoring and location tracking with user consent. We conducted two experiments: first with 14 phones of one type, then with 30 phones of two types. Wi-Fi traffic and cellular network data usage were collected and analyzed to characterize and quantify the changes in usage behaviors; the second experiment also captured location data during compliance/non-compliance to incentive messages. The trial seeks not only to experiment with incentives and disincentives to observe their effectiveness, but also to understand current mobile broadband and Wi-Fi usage behaviors in a campus environment. Our results indicate a high level of compliance with economic incentives and disincentives. Detailed analysis further showed correlation with two psychological measures of each user (agreeableness and neuroticism). In addition, we found schemes with probabilistic payments of higher incentive amounts getting more positive results compared to schemes with definite payments with lower incentive amounts, despite similar total payout.