Analyzing Different Mobile Applications in Time and Space: a City-Wide Scenario

Okic, Armin; Redondi, Alessandro E. C.; Galimberti, Iacopo; Foglia, Francesco; Venturini, Luisa

doi:10.1109/wcnc.2019.8885902

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…As a result, they created MobiPupose, a system that could track app network requests and classify data collection purposes based on app traffic patterns. Walelgne et al [205] and Okic et al [206] showed that different app categories have different traffic patterns. In terms of traffic volume, entertainment and social media apps are the most traffic-intensive, while education and weather apps are the least traffic-intensive [205].…”

Section: B App Traffic Patternsmentioning

confidence: 99%

Smartphone App Usage Analysis: Datasets, Methods, and Applications

Tong

Wang

et al. 2022

IEEE Commun. Surv. Tutorials

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As smartphones have become indispensable personal devices, the number of smartphone users has increased dramatically over the last decade. These personal devices, which are supported by a variety of smartphone apps, allow people to access Internet services in a convenient and ubiquitous manner. App developers and service providers can collect fine-grained app usage traces, revealing connections between users, apps, and smartphones. We present a comprehensive review of the most recent research on smartphone app usage analysis in this survey. Our survey summarizes advanced technologies and key patterns in smartphone app usage behaviors, all of which have significant implications for all relevant stakeholders, including academia and industry. We begin by describing four data collection methods: surveys, monitoring apps, network operators, and app stores, as well as nine publicly available app usage datasets. We then systematically summarize the related studies of app usage analysis in three domains: app domain, user domain, and smartphone domain. We make a detailed taxonomy of the problem studied, the datasets used, the methods used, and the significant results obtained in each domain. Finally, we discuss future directions in this exciting field by highlighting research challenges.

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Section: B App Traffic Patternsmentioning

confidence: 99%

Smartphone App Usage Analysis: Datasets, Methods, and Applications

Tong

Wang

et al. 2022

IEEE Commun. Surv. Tutorials

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“…Several works in the literature suggest a strong relationship that correlates end-users mobility patterns with cellular network statistics [6]- [8] in urban environments. In a similar way, the mobility patterns identified on highways present repetitive trends following regular working routines.…”

Section: B Temporal Patterns Considerationmentioning

confidence: 99%

$π$-ROAD: a Learn-as-You-Go Framework for On-Demand Emergency Slices in V2X Scenarios

Okic,

Zanzi,

Sciancalepore

et al. 2020

Preprint

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Vehicle-to-everything (V2X) is expected to become one of the main drivers of 5G business in the near future. Dedicated network slices are envisioned to satisfy the stringent requirements of advanced V2X services, such as autonomous driving, aimed at drastically reducing road casualties. However, as V2X services become more mission-critical, new solutions need to be devised to guarantee their successful service delivery even in exceptional situations, e.g. road accidents, congestion, etc. In this context, we propose π-ROAD, a deep learning framework to automatically learn regular mobile traffic patterns along roads, detect non-recurring events and classify them by severity level. π-ROAD enables operators to proactively instantiate dedicated Emergency Network Slices (ENS) as needed while re-dimensioning the existing slices according to their service criticality level. Our framework is validated by means of real mobile network traces collected within 400 km of a highway in Europe and augmented with publicly available information on related road events. Our results show that π-ROAD successfully detects and classifies nonrecurring road events and reduces up to 30% the impact of ENS on already running services.

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“…Understanding the system efficiency in the wild has only been possible in reduced scenarios involving very few devices [11], or by making assumption on the real patterns, modelling user movements and service requests with random processes [37]. The only works that employ a data source comparable to ours are the one in [38] and our seminal work in [39].…”

Section: Related Workmentioning

confidence: 99%

Resource Sharing Efficiency in Network Slicing

Márquez

Gramaglia

Fiore³

et al. 2019

IEEE Trans. Netw. Serv. Manage.

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The economic sustainability of future mobile networks will largely depend on the strong specialization of its offered services. Network operators will need to provide added value to their tenants, by moving from the traditional one-sizefits-all strategy to a set of virtual end-to-end instances of a common physical infrastructure, named network slices, which are especially tailored to the requirements of each application. Implementing network slicing has significant consequences in terms of resource management: service customization entails assigning to each slice fully dedicated resources, which may also be dynamically reassigned and overbooked in order to increase the cost-efficiency of the system. In this paper, we adopt a data-driven approach to quantify the efficiency of resource sharing in future sliced networks. Building on metropolitanscale real-world traffic measurements, we carry out an extensive parametric analysis that highlights how diverse performance guarantees, technological settings, and slice configurations impact the resource utilization at different levels of the infrastructure in presence of network slicing. Our results provide insights on the achievable efficiency of network slicing architectures, their dimensioning, and their interplay with resource management algorithms at different locations and reconfiguration timescales.

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Analyzing Different Mobile Applications in Time and Space: a City-Wide Scenario

Cited by 8 publications

References 11 publications

Smartphone App Usage Analysis: Datasets, Methods, and Applications

Smartphone App Usage Analysis: Datasets, Methods, and Applications

$π$-ROAD: a Learn-as-You-Go Framework for On-Demand Emergency Slices in V2X Scenarios

Resource Sharing Efficiency in Network Slicing

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