Group detection in mobility traces

Chen, Yung-Chih; Rosensweig, Elisha J.; Kurose, Jim; Towsley, D.

doi:10.1145/1815396.1815597

Cited by 15 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1. The group mobility model has been verified as a realistic mobility model [7] and applied to many practical scenarios, such as campus networks [8] and ad-hoc networks [9] [10]. Our goal is to dynamically allocate a finite number of mesh nodes to cover as many mobile clients as possible, while maintaining the connectivity between the groups of clients.…”

Section: Ammnet Overviewmentioning

confidence: 99%

Autonomous Mobile Mesh Networks

Shen¹,

Chen²,

Lin³

et al. 2014

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

Abstract-Mobile ad-hoc networks (MANETs) are ideal for situations where a fixed infrastructure is unavailable or infeasible. Today's MANETs, however, may suffer from network partitioning. This limitation makes MANETs unsuitable for applications such as crisis management and battlefield communications, in which team members might need to work in groups scattered in the application terrain. In such applications, inter-group communication is crucial to the team collaboration. To address this weakness, we introduce in this paper a new class of ad-hoc network called Autonomous Mobile Mesh Network (AMMNET). Unlike conventional mesh networks, the mobile mesh nodes of an AMMNET are capable of following the mesh clients in the application terrain, and organizing themselves into a suitable network topology to ensure good connectivity for both intra-and inter-group communications. We propose a distributed client tracking solution to deal with the dynamic nature of client mobility, and present techniques for dynamic topology adaptation in accordance with the mobility pattern of the clients. Our simulation results indicate that AMMNET is robust against network partitioning and capable of providing high relay throughput for the mobile clients.

show abstract

Section: Ammnet Overviewmentioning

confidence: 99%

Autonomous Mobile Mesh Networks

Shen¹,

Chen²,

Lin³

et al. 2014

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

show abstract

“…Group membership detection in combination with mobility over time can help track group motion loyalty, as briefly described in [28]. Knowing something about an individual's existing groups or preferences allows recommending group membership, as described in [29], where known preferences are leveraged in constructing a target number of output groups.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Bluetooth co-location traces in group discovery algorithms

Boston¹,

Mardenfeld²,

Pan³

et al. 2014

Pervasive and Mobile Computing

View full text Add to dashboard Cite

Smart phones can collect and share Bluetooth co-location traces to identify ad hoc or semi-permanent social groups, which can enhance recommender systems or allow detection of epidemic events. Group discovery using Bluetooth co-location is practical due to low power consumption, short range, and applicability to decentralization. This paper presents the Group Discovery using Co-location traces (GDC) and Decentralized GDC (DGDC) algorithms, which leverage user meeting frequency and duration to accurately detect groups. GDC and DGDC are validated on one month of data collected from 141 smart phones carried by students on our campus, and by comparison against ground-truth groups.

show abstract

“…While the most recent work on modeling 802.11 wireless networks focuses on characterizing the network usage and user behavioral pattern [11], [12], [13], [5], this paper propose a peculiar performance estimation metric and provide some learning techniques not only to predict the target performance variable, but also to discern the discriminative structure of the data in various parts of the network.…”

Section: Related Workmentioning

confidence: 99%

Predicting short 802.11 sessions from RADIUS usage data

Allahdadi

Morla

Aguiar

et al. 2013

38th Annual IEEE Conference on Local Computer Networks - Workshops

View full text Add to dashboard Cite

The duration of 802.11 user sessions has been widely studied in the context of analyzing user behavior and mobility. Short (smaller-than-5-minutes) sessions are never used or characterized in these analyses as they are unrelated to user behavior and considered as artifacts introduced by the wireless network. In this paper we characterize short 802.11 sessions as recorded through RADIUS authentication. We show that 50% of access points have 70% of smaller than 5 minutes sessions in a 5 months trace from the Eduroam academic wireless network in the University of Porto. Exactly because they are artifacts introduced by the network, short sessions are an important indicator for network management and the quality of the wireless access. Network managers typically do not collect and process session information but rely on SNMP to provide summaries of 802.11 usage data. We develop a modeling framework to provide predictions for the number of short sessions from SNMP data. We model the data stream of each access point using two methods of regression and one classification technique. We evaluate these models based on short session prediction accuracy. The models are trained on the 5 months data and the best results show prediction accuracy of 95.27% in polynomial regression at degree of 3.

show abstract

Group detection in mobility traces

Cited by 15 publications

References 8 publications

Autonomous Mobile Mesh Networks

Autonomous Mobile Mesh Networks

Leveraging Bluetooth co-location traces in group discovery algorithms

Predicting short 802.11 sessions from RADIUS usage data

Contact Info

Product

Resources

About