On Nodal Encounter Patterns in Wireless LAN Traces

Hsu, Wei-jen

doi:10.1109/tmc.2010.123

Cited by 75 publications

(51 citation statements)

References 24 publications

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“…We also would like to construct a systematic way to automatically generate the configuration files, such that the communities and time periods of nodes are set to capture the inter-node encounter properties we observe in various traces (for example, the Small World encounter patterns observed in WLAN traces [13]). …”

Section: Discussionmentioning

confidence: 99%

“…Along this line, researchers have exploited existing wireless network infrastructure, such as wireless LANs (e.g., [2], [25], [10]) or cellular phone networks (e.g., [7]), to track user mobility by monitoring their locations. Such traces can be replayed as input mobility patterns for simulations of network protocols [13]. More recently, DTNspecific testbeds [6], [4], [19] aim at collecting encounter events between mobile nodes instead of the mobility patterns.…”

Section: Related Workmentioning

confidence: 99%

“…If we assume perfect overlap and a single community from both nodes, we arrive at (14). If we assume no overlap, we result in (13). Also note in the general expressions presented in this paper, the whole simulation area is also considered as a community.…”

Section: Derivation Of the Meeting Timementioning

confidence: 99%

See 2 more Smart Citations

Modeling Spatial and Temporal Dependencies of User Mobility in Wireless Mobile Networks

Hsu

Spyropoulos

Psounis

et al. 2009

IEEE/ACM Trans. Networking

142

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Abstract-Realistic mobility models are fundamental to evaluate the performance of protocols in mobile ad hoc networks. Unfortunately, there are no mobility models that capture the nonhomogeneous behaviors in both space and time commonly found in reality, while at the same time being easy to use and analyze. Motivated by this, we propose a time-variant community mobility model, referred to as the TVC model, which realistically captures spatial and temporal correlations. We devise the communities that lead to skewed location visiting preferences, and time periods that allow us to model time dependent behaviors and periodic re-appearances of nodes at specific locations.To demonstrate the power and flexibility of the TVC model, we use it to generate synthetic traces that match the characteristics of a number of qualitatively different mobility traces, including wireless LAN traces, vehicular mobility traces, and human encounter traces. More importantly, we show that, despite the high level of realism achieved, our TVC model is still theoretically tractable. To establish this, we derive a number of important quantities related to protocol performance, such as the average node degree, the hitting time, and the meeting time, and provide examples of how to utilize this theory to guide design decisions in routing protocols.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Spatial and Temporal Dependencies of User Mobility in Wireless Mobile Networks

Hsu

Spyropoulos

Psounis

et al. 2009

IEEE/ACM Trans. Networking

142

View full text Add to dashboard Cite

show abstract

“…Goodness of fit tests indicate that both models provide good fit. In addition, we find hyperexponential models provide better match to the empirical data than BiPareto models [21], [28], [27] that have been proposed for NHDs.…”

Section: Introductionmentioning

confidence: 86%

Session lengths and IP address usage of smartphones in a university campus WiFi network: Characterization and analytical models

Chen¹,

Lipsky²,

Suh

et al. 2013

2013 IEEE 32nd International Performance Computing and Communications Conference (IPCCC)

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Abstract-Smart mobile handheld devices (MHDs) are being adopted at a fast speed. Compared to wireless non-handheld devices (NHDs), MHDs tend to be more mobile and can be used more opportunistically. In this paper, we study two important network usage characteristics of MHDs, namely session lengths and IP address usage, in a university campus WiFi network. Specifically, we analyze two five-week long DHCP traces collected from the network, characterize session lengths of MHDs, and develop two hyper-exponential models to capture the distribution of session lengths. We further characterize the IP address usage of MHDs, and develop two analytical models to predict the number of concurrent IP addresses that are being used by MHDs at one point of time. Goodness of fit tests indicate that our analytical models of session lengths provide good fit, and evaluation results demonstrate that the predictions from our models for IP address usage are accurate. Our results provide important insights on managing MHDs as they are being adopted rapidly in WiFi networks.

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“…Since the node mobility patterns are highly volatile and difficult to characterize or predict, node contact process [19] is also exploited, as abstraction of node mobility, to calculate relay selection metrics. More specifically, the nodes' capability of contacting others in the future is predicted based on their cumulative contact records from the past.…”

Section: Related Workmentioning

confidence: 99%

Social-Aware Multicast in Disruption-Tolerant Networks

Gao

Zhao

et al. 2012

IEEE/ACM Trans. Networking

110

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Abstract-Node mobility and end-to-end disconnections in disruption-tolerant networks (DTNs) greatly impair the effectiveness of data forwarding. Although social-based approaches can address the problem, most existing solutions only focus on forwarding data to a single destination. In this paper, we study multicast with single and multiple data items in DTNs from a social network perspective, develop analytical models for multicast relay selection, and furthermore investigate the essential difference between multicast and unicast in DTNs. The proposed approach selects relays according to their capabilities, measured by social-based metrics, for forwarding data to the destinations. The design of social-based metrics exploits social network concepts such as node centrality and social community, and the selected relays ensure achieving the required data delivery ratio within the given time constraint. Extensive trace-driven simulations show that the proposed approach has similar data delivery ratio and delay to that of Epidemic routing, but significantly reduces data forwarding cost, measured by the number of relays used.

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On Nodal Encounter Patterns in Wireless LAN Traces

Cited by 75 publications

References 24 publications

Modeling Spatial and Temporal Dependencies of User Mobility in Wireless Mobile Networks

Modeling Spatial and Temporal Dependencies of User Mobility in Wireless Mobile Networks

Session lengths and IP address usage of smartphones in a university campus WiFi network: Characterization and analytical models

Social-Aware Multicast in Disruption-Tolerant Networks

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