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“Sociological Orbits”Mobility Profiling and Routingfor Mobile Wireless Networks
Hung Q. Ngo
Computer Science & Engineering
State University of New York at Buffalo
Jul 11, 2006
Acknowledgement
On-going project with Prof. Chunming Qiao
Joy Ghosh Duc Ha S. K. Yoon Dr. Sumesh Philip (former student)
Jul 11, 2006
Outline Mobility Impact on Routing Sociological ORBIT Mobility Framework Mobility Profiling Techniques and
Applications A Fundamental Routing Problem on ICMAN Sociological Orbit aware Location
Approximation and Routing (SOLAR) Conclusion
Jul 11, 2006
Mobility Impact on Routing
Node Mobility Dynamic network topology
Proactive protocols (LS, DV) are inefficient Need to exchange control packets too often Leads to congestion
Reactive protocols (DSR, LAR) are better suited, however Locating a node incurs more delay Route maintenance is tricky as nodes move
To strike a balance need mobility modeling
Jul 11, 2006
Mobility Models in the Literature Random Waypoint, Weighted Random Waypoint
simple, but impractical!! Entity based individual node movement
Jardosh et al., MOBICOM’03 Lin et al., INFOCOM’04
Group based collective group movement Hong et al., MSWIM’99, MCM’01
Scenario based geographical constraints Lam et al., IEEE Comm. Mag. 97 Markulidakis et al., IEEE Per. Comm. 97 Liu et al., IEEE JSAC 98
Jul 11, 2006
Advantages of Node Mobility – Individual node’s view of network
Jul 11, 2006
Advantages of Node Mobility – Node’s view of network through “acquaintances”
Jul 11, 2006
Impact of mobility on protocol performance F. Bai, N. Sadagopan,
and A. Helmy, “Important: a framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks”, Proceedings of IEEE INFOCOM '03, vol. 2, pp. 825-835, March 2003.
Jul 11, 2006
Our MotivationsObservations MANET is often comprised of wireless devices carried by
people living within societies Social activities impose constraints on user movements
Steps to take Study the social influence on user mobility (e.g.,
realization of special regions of some social value) Identify a macro level (thus, lightweight) mobility profile
per user Use this profile to aid macro level soft location
management and routing
Jul 11, 2006
Mobile Users
• influenced by social routines
• visit a few “hubs” /
places (outdoor/indoor) regularly
• “orbit” around (fine to coarse grained) hubs at several levels
Sociological Orbit Framework
Jul 11, 2006
Illustration of A Random Orbit Model
(Random Waypoint + Corridor Path)Conference Track 1
Conference Track 3
Cafeteria
Lounge
Conference Track 2
Conference Track 4
PostersRegistration
Exhibits
Jul 11, 2006
Random Orbit Model
See Ghosh et al., Adhoc NetworksJournal, 2005.
Jul 11, 2006
Hub Based Mobility Profiles and Prediction On any given day, a user may regularly visit a small number of “hubs”
(e.g., locations A and B) Each mobility profile is a weighted list of hubs, where weight = hub visit
probability (e.g., 70% A and 50% B) In any given period (e.g., week), a user may follow a few such “mobility
profiles” (e.g., P1 and P2) Each profile is in turn associated with a (daily) probability (e.g., 60% P1
and 40% P2) Example: P1={A=0.7, B=0.5} and P2={B=0.9, C=0.6}
On an ordinary day, a user may go to locations A, B and C with the following probabilities, resp.: 0.42 (=0.6x0.7), 0.66 (= 0.6x0.5 + 0.4+0.9) and 0.24 (=0.4x0.6)
20% more accurate than simple visit-frequency based prediction Knowing exactly which profile a user will follow on a given day can result in
even more accurate prediction
On any given day, a user may regularly visit a small number of “hubs” (e.g., locations A and B)Each mobility profile is a weighted list of hubs,
where weight = hub visit probability (e.g., 70% A and 50% B)
In any given period (e.g., week), a user may follow a few such “mobility profiles”
(e.g., P1 and P2)Each profile is in turn associated with a (daily) probability (e.g., 60% P1 and 40% P2) Example: P1={A=0.7, B=0.5} and P2={B=0.9, C=0.6}On an ordinary day, a user may go to locations A, B & C with the following probabilities: 0.42 (=0.6x0.7), 0.66 (= 0.6x0.5 + 0.4+0.9), 0.24 (=0.4x0.6)• 20% more accurate than simple visit-frequency based prediction• Knowing exactly which profile a user will follow on a given day can result in even more accurate prediction
Jul 11, 2006
Traces Used Profiling techniques applied to ETH Zurich traces
Duration of 1 year from 4/1/04 till 3/31/05 13,620 wireless users, 391 APs, 43 buildings Grouped users into 6 groups based on degree of activity Selected one sample (most active) user from each group
Mapped APs into buildings based on AP’s coordinates, and each building becomes a “hub” Converted AP-based traces into hub-based traces
Other traces Expect similar results from Dartmouth’s traces No sufficient AP location info from other traces UMass’s traces are for buses, more predictable than users Need to obtain actual users’ traces with GPS
Jul 11, 2006
Orbital Mobility Profiling Obtain each user’s daily hub lists as binary vectors Represent each hub list (binary vector) as a point in
a n-dimensional space (n = total number of hubs) Cluster these points into multiple clusters, each with
a mean Using the Expectation-Maximization (EM) algorithm to train
the model based on a Mixture of Bernoulli’s distribution Probe other classification methods: Bayesian-Bernoulli’s
Each cluster mean represents a mobility profile, described as a probabilistic hub visitation list
User’s mobility is aptly modeled using a mixture of mobility profiles with certain “mixing proportions”
Jul 11, 2006
Profiling illustration
Obtain daily hub stay durations
Translate to binary hub visitation vectors
Apply clustering algorithm to find mixture of profiles
Jul 11, 2006
Profile parameters for all sample users
Jul 11, 2006
Hub-based Location Predictions - I Unconditional Hub-visit Prediction
Prediction Error = Incorrect hubs predicted over Total hubs SPE – Statistical based Prediction Error
SPE-ALL: (n+1)th day prediction based on hub-visit frequency from day 1 through day n
SPE-W7 : (n+1)th day prediction based on hub-visit frequency within last week, i.e., day (n-7) through day n
PPE – Profile based Prediction Error PPE-W7 : (n+1)th day prediction based on profiles of the last
week, i.e., day (n-7) through day n Prediction Improvement Ratio (PIR)
PIR-ALL = (SPE-ALL – PPE-ALL) / SPE-ALL PIR-W7 = (SPE-W7 – PPE-W7) / SPE-W7
Jul 11, 2006
Unconditional Prediction Results
The profile mixing proportions vary with every window of n days
Jul 11, 2006
Hub-based Location Predictions - II Conditional Hub-visit Prediction
Improvement given current profile is known/identifiable It is possible sometimes to infer profile from current hub
information alone Our method effectively leverages information when available
Sample user categoriesTarget Hub ID: will the user visit this hub?The current day in questionPredicted probability using visit frequency Indicator (Current) HubCurrent ProfilePredicted probability based on profileActually visited Ht on day D or not
Jul 11, 2006
Hub-based Location Predictions - III Hub sequence prediction based on hub transitional probability
Prediction Accuracy = 1 – (incorrect predictions / total predictions) Scenario 1: only starting hub is known for sequence prediction Scenario 2: hub prediction is corrected at every hub in sequence Better performance with increasing knowledge – intuitive
Statistical based Prediction Accuracy (SPA) – no profile informationProfile based Prediction Accuracy (PPA) – no time informationTime based Prediction Accuracy (TPA) – temporal profiles
Jul 11, 2006
Applications of Orbital Mobility Profiles Location Predictions and Routing within MANET and ICMAN
We will discuss an example of routing on ICMAN We have several other papers in this area (see website at the end)
Anomaly based intrusion detection unexpected movement (in time or space) sets off an alarm
Customizable traffic alerts alert only the individuals who might be affected by a specific traffic condition
Targeted inspection examine only the persons who have routinely visited specific regions
Environmental/health monitoring identify travelers who can relay data sensed at remote locations with no APs
Others
Jul 11, 2006
Routing challenges in ICMAN ICMAN (Intermittently Connected MANET)
Features of DTN/ICN + MANET Lack of infrastructure and any central control May not have an end-to-end path from source to
destination at any given point in time Conventional MANET routing strategies fail User mobility may not be deterministic or
controllable Devices are constrained by power, memory, etc. Applications need to be delay/disruption tolerant
Jul 11, 2006
On Problem’s Complexity Basic model:
G = (V,E) be a directed graph V = ICMAN users;
E = probabilistic contact between users Let A be a routing algorithm and G(A) be the delivery
sub-graph induced by A subject to some constraint Basic tradeoff: overhead vs delivery probability Possible constraints to limit overhead
Constraint 1: each intermediate node forwards packets to at most k downstream neighbors
Constraint 2: G(A) has at most k edges
Jul 11, 2006
On Problem’s Complexity (cont’) The centralized version: given G and k, find a delivery subgraph H where Conn2(H) is maximized, subject to |E(H)| ≤ k
Two Negative Theorems1. Computing Conn2(H) is #P-complete
2. Finding H maximizing Conn2(H) is #P-hard What do we do now?
Approximate Conn2(H) by another poly-time computable function f, then find H that maximizes f(H)
Develop heuristics routing algorithms
Jul 11, 2006
Our Problem’s Setting
Slightly different from the basic problem just discussed Mobility profiles give contact probabilities But, contact probabilities do not give mobility
profiles We make use of the mobility profiles in our
routing heuristics!
Jul 11, 2006
User level routing strategies Deliver packets to the destination itself Intermediate users store-carry-forward the packets Mobility profiles used to compute pair wise user contact probability P(u,v) via
Markov Process Form weighted graph G with edge weights w(u,v) = log (1/P(u,v)) Apply modified Dijkstra’s on G to obtain k-shortest paths (KSP) with
corresponding Delivery probability under following constraints Paths are chosen in increasing order of total weights (i.e., minimum first) Each path must have different next hop from source
S-SOLAR-KSP (static) protocol Source only stores set of unique next-hops on its KSP Forwards only to max k users of the chosen set that come within radio range within
time T D-SOLAR-KSP (dynamic) protocol
Source always considers the current set of neighbors Forwards to max k users with higher delivery probability to destination
Jul 11, 2006
Hub level routing strategy Deliver packets to the hubs visited by destination Intermediate users store-carry-forward the packets Packet stored in a hub by other users staying in
that hub (or using a fixed hub storage device if any) Mobility profiles used to obtain delivery probabilities
(DP), not the visit probability, of a user to a given hub i.e. user may either directly deliver to hub by traversing to
the hub, or may pass onto other users who can deliver to the hub
Fractional data delivered to each hub proportional to the probability of finding the destination in it
Routing Strategy SOLAR-HUB protocol
Jul 11, 2006
SOLAR-HUB Protocol Pd
nihj: delivery probability (DP) of user ni to hub hj
Ptnihj: probability of user ni to travel to hub hj
h(ni): hub that user ni is going to visit next Pc
nink(hj): probability of contact between users ni & nj in hub hj
N(ni): neighbors of user ni
Pdnihj = max(Pt
nihj, maxk(Pcnink(h(ni))*Pt
nkhj)) Source ns will pick ni as next hop to hub hj as:
{ni | max(Pdnihj), ni Є N(ns)} iff P
dnihj > Pd
nshj
Packet Delivery Scheme Source transmits up to k copies of message
k/2 to neighbors with higher DP to “most visited” hub k/2 to neighbors with higher DP to “2nd most visited” hub
Downstream users forward up to k users with higher DP to the hub chosen by upstream node
Jul 11, 2006
Simulation Parameters for GloMoSim
Jul 11, 2006
Performance – Number of Hubs
• Overhead of EPIDEMIC is much more than others and had to be omitted from plot
• Overall D-SOLAR-KSP performs best
Jul 11, 2006
Performance – Number of Users
• Overhead of EPIDEMIC is much more than others and had to be omitted from plot
• Overall D-SOLAR-KSP performs best like before because it is the most opportunistic in forwarding to any of its current neighbors
Jul 11, 2006
Performance – Cache Size (Only SOLAR)
• All versions fair better with more cache
• Overall D-SOLAR-KSP performs best
Jul 11, 2006
Performance – Cache Timeout (Only SOLAR)
• All versions fair better with larger timeout
• Overall D-SOLAR-KSP performs best
Jul 11, 2006
Conclusion Mobility has severe impact on routing performance A practical mobility framework should the sociological
influence on user movement into account Wireless users can be profiled based on their social
activities Mobility profiles are useful not only for routing (e.g.,
SOLAR protocols) but also other applications such as location prediction, resource allocation, etc.
SOLAR Project: (for more information) http://www.cse.buffalo.edu/~joyghosh/solar.html
Thank You!
Questions?