usc/mobilib2008072446200807242008-08-20usc/mobilibVPN session, DHCP log, and trap log data from wireless network at USC.This dataset includes VPN session, DHCP log, and tcap log data, for 79 access points and several thousand users at USC.the initial version2008-07-242003-12-232006-04-28hsu-impactData Set WebsiteREADME (trace format)README (trace processing)README (trace format for the Spring 06 trace)README (trace processing for the Spring 06 trace)179180Please note below the terms for using USC traces. You must agree to these terms.
1. While we have made effort to avoid errors in the process of collecting and
manipulating the traces, we cannot guarantee complete correctness in both
the traces and codes used. The users of the traces and codes should be aware
that we do not guarantee the traces or codes are free of bugs, and assume
some risks if you use them 'as is'.
2. Any futher work derived from the trace data should not make the MAC addresses
visible in plain text. Instead, please use self-defined IDs to identify individual
nodes if you have to do so in the work.http://nile.cise.ufl.edu/MobiLib/USC_trace/http://www.crawdad.org/wiki/pmwiki.php?n=Main.Dataset.usc-mobilib802.11authentication logsyslogUser Mobility CharacterizationUsage CharacterizationHuman Behavior Modeling802.11 infrastructureThis data set was collected during 2003-2005 at the USC campus,
where the number of WLAN users was over 4500.At the time of collection, the USC wireless LAN had 79 APs.The authors collected the traces from three sources: VPN session log,
DHCP logs, and trap logs.On Aug. 9, 2005 we stopped collecting the trace due to changes in our campus network, and we will resume trace collection after these changes.70200701082008-07-23the initial version.usc/mobilib/sessionVPN session logs from USC wirelss network.This traceset contains logs for timestamps of (start|stop) of VPN sessions.2007-01-082005-04-192005-08-08User Mobility CharacterizationUsage CharacterizationHuman Behavior ModelingThese traces are logs for timestamps of (start|stop) of VPN sessions.
At USC, wireless users must establish connections to a VPN server before
they can use the network. Hence the session log contains periods of
users potentailly using the network, with its private (dynamic) IP addresses.usc/mobilib219200701082008-07-23the initial versionusc/mobilib/session/vpnVPN session logs from USC wireless network.VPN session logs from USC wireless network.false2007-01-082005-04-192005-08-08These logs of sessions are collected at the VPN server for wireless users at USC.
Before using the network, users must establish a VPN session to the server. The
"Start" and "Stop" timestamps in the trace represents the beginning and the end of
these VPN sessions.The fields in each line of the trace are:
1. Day of the week: Sun, Mon, Tue, Wed, Thu, Fri, Sat
2. Month
3. Day
4. Time: HH:MM:SS
5. Action: "Start" or "Stop" of a session.
6. Private IP in USC network.
7. Public IP given to the host./download/usc/mobilib/USC_sessions.tgzusc/mobilib/session71200701082008-07-23the initial version.usc/mobilib/dhcpDHCP logs from USC wirelss network.This traceset contains The DHCP log of the private IP assignments to MAC addresses.2007-01-082005-04-192005-08-08User Mobility CharacterizationUsage CharacterizationHuman Behavior ModelingThe DHCP log contains the private IP assignments to MAC addresses.usc/mobilib220200701082008-07-23the initial versionusc/mobilib/dhcp/dhcp_logTrace of DHCP logs from USC wirelss network.Trace of DHCP logs from USC wirelss network.false2007-01-082005-04-192005-08-08This log contains the private IP assignments to MAC addresses. The listed
private IP is given to the MAC address at the indicated time.The fields are:
1. Month
2. Day
3. Time: HH:MM:SS
4. Private IP in USC network
5. MAC address/download/usc/mobilib/USC_dhcp.tgzusc/mobilib/dhcp72200701082008-07-23the initial version.usc/mobilib/trapTrap logs from USC wirelss network.This traceset contains the trap log of the (switch port, MAC address) association
when the user is online.2007-01-082005-04-192005-08-08User Mobility CharacterizationUsage CharacterizationHuman Behavior ModelingThe trap log contains the (switch port, MAC address) association
when the user is online. However, if a MAC re-appears at the same switch port
when it was last online, the trap log may NOT record this information.
Hence trap logmust be used in conjunction with session log to discover
all association sessions.
The file [Mapping] is the mapping between switch (IP, port) and the building code of USC campus.
USC campus map is available through university website.WARNING: The trap log alone does NOT contain all user online events!
If a user comes online at the same switch port repeatedly, it does NOT
create separate trap log for each new online event. Also, the trap log
only records the online epoch, but not online duration information of any kind.There is a hole in this data from Sep. 28, 2004 to Oct. 18, 2004./download/usc/mobilib/Mapping.in.txtusc/mobilib221200701082008-07-23the initial versionusc/mobilib/trap/trap_logTrace of trap logs collected from USC wirelss network during 2005.Trace of trap logs collected from USC wirelss network during 2005.false2007-01-082005-04-192005-08-08The trap log contains the (switch port, MAC address) association when the user
is online. This log records the approximate location of nodes, since the switch ports
correspond to buildings in USC network. However, if a node reappears repeatedly at
the same switch port, a new trap entry may not be generated. Hence the trap log is
mainly used as an indication of the "last seen" location of the node, and we assume it
does not move unless indicated otherwise by a new trap entry.The fields are:
1. Month
2. Day
3. Time: HH:MM:SS
4. Switch IP
5. Switch port (switch IP + switch port is used to locate the node on USC
campus map, the Mapping file is also available online)
6. MAC address/download/usc/mobilib/USC_traps.tgzusc/mobilib/trap222200701082008-07-23the initial versionusc/mobilib/trap/old_trap_logTrace of trap logs collected from USC wirelss network during 2003-2005.Trace of trap logs collected from USC wirelss network during 2003-2005.false2007-01-082003-12-232005-04-17The trap log contains the (switch port, MAC address) association when the user
is online. This log records the approximate location of nodes, since the switch ports
correspond to buildings in USC network. However, if a node reappears repeatedly at
the same switch port, a new trap entry may not be generated. Hence the trap log is
mainly used as an indication of the "last seen" location of the node, and we assume it
does not move unless indicated otherwise by a new trap entry.The fields are:
1. Month
2. Day
3. Time: HH:MM:SS
4. Switch IP
5. Switch port (switch IP + switch port is used to locate the node on USC
campus map, the Mapping file is also available online)
6. MAC address/download/usc/mobilib/USC_old_trap.tgzusc/mobilib/trap73200807242008-08-20the initial version.usc/mobilib/associationAssociation history from USC wirelss network.This traceset contains "association history" traces for individual MAC addresses,
which consist of start times and end times of a MAC associated with various locations.2008-07-242005-04-192006-04-28User Mobility CharacterizationUsage CharacterizationHuman Behavior ModelingFrom the raw traces (session, dhcp, and trap) it is possible to find out
user locations (at per switch port granularity, which roughly corresponds
to buildings on campus) when they are online.
This "association history" trace for individual MAC addresses
consists of start times and end times of a MAC associated with various locations.
The location granularity is per switch port, roughly corresponding to buildings on campus.
There are three files related with generation of association history traces.
(1) session file: Records of start/stop of a association session, with the corresponding
private IP address.
(2) dhcp file: Records of private IPs to MAC address binding.
(3) trap file: Records of MAC address showing up at switch ports.
The conversion involves getting session durations from (1), then converting the IP
address in (1) to MAC address using (2), finally finding the locations of these MAC
addresses using (3).
The file [Processing code] is the program code we used for trace processing.
For more detail about the trace processing, please see [Memo of USC trace processing]./download/usc/mobilib/trace_processing_code.tgz/download/usc/mobilib/Memo_USC_trace_processing.pdfusc/mobilib223200701082008-07-23the initial versionusc/mobilib/association/duration_logTrace of association history from USC wirelss network for one month.Trace of association history from USC wirelss network for one month.true2007-01-082005-04-202005-05-19For the processed trace, we have the association history for each MAC address in
a separate file.The fields in these files are:
1. Start timestamp: The starting time of an association record. The timestamp is
defined as the elapsed time since Apr. 1, 2005 in unit of seconds.
2. Location: the building code of the association record.
3. Duration: duration of the association record, in unit of seconds./download/usc/mobilib/USC_duration_trace.tgzusc/mobilib/association224200701082008-07-23the initial versionusc/mobilib/association/summer_duration_logTrace of association history from USC wirelss network during 2005 summer.Trace of association history from USC wirelss network during 2005 summer.true2007-01-082005-04-192005-08-08For the processed trace, we have the association history for each MAC address in
a separate file. This trace is a longer processed trace for the whole summer.
Please note that the summer vacation is from mid-May to mid-Aug for USC, and
the WLAN activity significantly reduced during the summer vacation.The fields in these files are:
1. Start timestamp: The starting time of an association record. The timestamp is
defined as the elapsed time since Apr. 1, 2005 in unit of seconds.
2. Location: the building code of the association record.
3. Duration: duration of the association record, in unit of seconds./download/usc/mobilib/USC_2005_summer.tgzusc/mobilib/association225200807242008-08-20the initial versionusc/mobilib/association/spring_2006_duration_logTrace of association history from USC wirelss network during Spring 2006.Trace of association history from USC wirelss network during Spring 2006.true2008-07-242006-01-252006-04-28This data set contains 25,481 users that appeared during Jan. 25, 2006 to Apr. 28, 2006.
During this time frame, there were 137 unique locations in the trace.
Each location roughly corresponds to a building on campus, and it is encoded
in the format of IP_port (the actual switch port that controls traffic to/from this location).The fields in these files are:
1. Start timestamp: The starting time of an association record. The timestamp is
defined as the elapsed time since Jan. 1, 2006 in unit of seconds.
2. Location: the format of IP_port (the actual switch port that controls
traffic to/from this location).
3. Duration: duration of the association record, in unit of seconds.
For more information on the trace format and the processing procedure,
please refer to the documents [Memo Format USC06] and [Memo processing USC06]./download/usc/mobilib/Memo_format_USC06.pdf/download/usc/mobilib/Memo_processing_USC06.pdf/download/usc/mobilib/USC_06spring_trace.tar.gzusc/mobilib/association179usc/mobilibWei-jen Hsuwjhsu@ufl.eduUniversity of FloridaComputer and Information Science and Engineering (CISE) DepartmentPh.D student352-392-2744http://nile.cise.ufl.edu/~weijenhs/180usc/mobilibAhmed Helmyhelmy@ufl.eduUniversity of FloridaComputer and Information Science and Engineering (CISE) DepartmentAssociate Professor352-392-6860http://www.cise.ufl.edu/~helmy/hsu-associationsWei-Jen HsuAhmed HelmyOn Modeling User Associations in Wireless LAN Traces on University CampusesProceedings of the Second Workshop on Wireless Network Measurements (WiNMee 2006)Boston, MA, USA--04--2006http://www.winmee.org/papers/01-05.pdfmeasurementwirelessdartmouth_campusibm_watsoncrawdadusc/mobilib20060401hsu-behavioral-groupsWei-jen HsuDebojyoti DuttaAhmed HelmyMining behavioral groups in large wireless LANsMobiCom '07: Proceedings of the 13th annual ACM international conference on Mobile computing and networking2007338-341Montreal, Quebec, Canadameasurementwirelessdartmouth_campuscrawdadhttp://doi.acm.org/10.1145/1287853.1287899http://doi.acm.org/10.1145/1287853.1287899ACM PressRecent years have witnessed significant growth in the adoption of portable
wireless communication and computing devices (e.g., laptops, PDAs, smart
phones) and large-scale deployment of wireless networks (e.g., cellular,
WLANs). We envision that future usage of mobile devices and services will be
highly personalized. Users will incorporate these new technologies into their
daily lives, and the way they use new devices and services will reflect their
personality and lifestyle. Therefore it is imperative to study and characterize
the fundamental structure of wireless user behavior in order to model, manage,
leverage and design efficient mobile networks and services. In this study,
using our systematic TRACE approach, we analyze wireless users' behavioral
patterns by extensively mining wireless network logs from two major university
campuses. We represent the data using location-preference vectors, and utilize
unsupervised learning (clustering) to classify trends in user behavior using
novel similarity metrics. Matrix decomposition techniques are used to identify
(and differentiate between) major patterns. We discover multi-modal user
behavior and hundreds of distinct groups with unique behavioral patterns in
both campuses, and their sizes follow a power-law distribution. Our methods and
findings might provide new directions in network management and behavior-aware
network protocols and applications, to name a few.usc/mobilib20070001hsu-impactWei-jen HsuAhmed HelmyIMPACT: Investigation of Mobile-user Patterns Across University Campuses using WLAN Trace Analysis--07--2005Electrical Engineering Department, University of Southern Californiahttp://nile.usc.edu/MobiLib/Trace_analysis_TR.pdfWe conduct the most comprehensive study of WLAN traces to date. Measurements
collected from four major university campuses are analyzed with the aim of
developing fundamental understanding of realistic user behavior in wireless
networks. Both individual user and inter-node (group) behaviors are
investigated and two classes of metrics are devised to capture the underlying
structure of such behaviors. For individual user behavior we observe distinct
patterns in which most users are 'on' for a small fraction of the time, the
number of access points visited is very small and the overall online user
mobility is quite low. We clearly identify categories of heavy and light users.
In general, users exhibit high degree of similarity over days and weeks. For
group behavior, we define metrics for encounter patterns and friendship.
Surprisingly, we find that a user, on average, encounters less than 6\% of the
network user population within a month, and that encounter and friendship
relations are highly asymmetric. We establish that number of encounters follows
a biPareto distribution, while friendship indexes follow an exponential
distribution. We capture the encounter graph using a small world model, the
characteristics of which reach steady state after only one day. We hope for our
study to have a great impact on realistic modeling of network usage and
mobility patterns in wireless networks.crawdadmeasurementwirelessdartmouth_campusibm_watsoncrawdadusc/mobilib20050701hsu-mobilityWei-jen HsuThrasyvoulos SpyropoulosKonstantinos PsounisAhmed HelmyModeling Time-variant User Mobility in Wireless Mobile NetworksProceedings of the 26th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM)--03--2007Anchorage, AlaskaIEEEhttp://nile.cise.ufl.edu/~weijenhs/publication/INFOCOM_camera_final.pdfwireless-meas,crawdadmeasurementwirelessdartmouth_campusibm_watsoncrawdadusc/mobilib20070301hsu-nodalWei-Jen HsuAhmed HelmyOn Nodal Encounter Patterns in Wireless LAN TracesProceedings of the Second Workshop on Wireless Network Measurements (WiNMee 2006)Boston, MA, USA--04--2006http://www.winmee.org/papers/02-03.pdfmeasurementwirelessdartmouth_campusibm_watsoncambridge_hagglecrawdadusc/mobilib20060401hsu-profile-castWei-jen HsuDebojyoti DuttaAhmed HelmyProfile-cast: behavior-aware mobile networkingSIGMOBILE Mob. Comput. Commun. Rev.80211crawdadusc_mobilibmeasurementmobilitynetwork-measurementwirelesswireless-lan12120081559-166252-54http://doi.acm.org/10.1145/1374512.1374529http://doi.acm.org/10.1145/1374512.1374529ACMNew York, NY, USAusc/mobilib20080001hsu-structure-posterWeijen HsuDebojyoti DuttaAhmed HelmyMobiCom Poster: On the Structure of User Association Patterns in Wireless LANs2006--09--crawdadPoster presentation at MobiCom 2006http://portal.acm.org/citation.cfm?id=1282239measurementwirelessdartmouth_campuscrawdadusc/mobilib20060901