CMPE 257 Spring 2003 1

CMPE 257: Wireless and Mobile Networking

Spring 2003Lecture 17

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Announcements Project status update 2. Graded exams. Hw 4 (?) Project report.

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Today Bluetooth. Location Management.

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Location Management

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Why is location management needed? In wired networks, hosts don’t move.

Constant association between host (id, address) and its location.

In mobile wireless networks, hosts can move. Host id/address no longer provides

location information. Need location tracking mechanism to

deliver information destined to host.

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Location databases Home Location Register (HLR). Visitor Location Register (VLR).

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Location management tasks Location registration. Call delivery.

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Home location register (HLR)

One HLR for each network. User permanently associated to a

HLR. HLR keeps user profiles for each user.

Location information. Services subscribed. Billing information.

User profiles can be centralized in HLRs.

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Visitor location register (VLR) VLR stores information (obtained

from HLR) about MHs visiting the area.

Number and placement of VLRs vary. One per network. Tradeoffs?

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Location registration Update location databases (HLR

and VLRs). MH authentication when location

info available.

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More on location registration… MH performs location update.

Coverage area split into registration (or location) areas (RAs or LAs).

RA consists of several cells (several BSs) under same Mobile Switching Center (MSC).

VLR covers a number of RAs.

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Location registration procedure MH moves to new cell and sends location

update to new BS. BS informs MSC which contacts its VLR. VLR updates user profile for MH. If new RA belongs to same VLR, update profile

with new RA info. Else, VLR contacts MH’s HLR and updates MH’s

HLR’s location information. HLR authenticates MH and sends ACK to new

VLR; HLR also de-registers MH with old VLR Old VLR sends an ACK.

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Call delivery 2 steps:

Finding current VLR. Locating the MH current cell.

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Call delivery procedure Caller contacts callee’s MSC through

nearby BS. MSC finds address of callee’s HLR and

sends location request to HLR. HLR finds callee’s current VLR and MSC. Connection is set up between caller and

callee’s current MSC. Polling to find where callee is within RA

(paging). Callee responds.

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Issues [Akyildiz96] Location registration and call

delivery are expensive. Signaling traffic.

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Solutions Still keeping the 2-level database model.

Caching MH’s VLR at MSC level (caller’s MSC). MH’s profile replication. Pointer forwarding: setting pointers from

previous VLR to new VLR. Avoids updating the HLR every time MH moves. Maximum forwarding chain length.

Local anchor: use “nearby” VLR to receive location updates from MH.

HLR keeps pointer to local anchor. “Localizes” signaling traffic.

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Solutions (Cont’d) Deeper hierarchies.

Tree of location databases. Leaf databases contain information on local MHs. Higher level databases contain pointers (MH

id+database id) to next lower level database storing profile or pointer to lower level.

In the worse case, query travels all the way to root, down the appropriate subtree.

Partitioned databases. Groups of location databases. No location update if MH moves within same

partition.

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HiPER [Jannink97] Life-long numbering.

Same number irrespective of provider/location.

Hierarchical location database. No concept of a “home site” (HRL/VRL). Leaf databases store user profiles in a zone. Higher-level databases store pointers to

lower-level. Root stores pointer to every user. Scalability? Partitioning.

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Locating a user… When A calls B, query for B’s location

propagates up the hierarchy from A’s zone to first database containing pointer to B; then, down that subtree.

Drawbacks? When a user moves, its record is sent to the

appropriate leaf database; databases along the way to the least common ancestor for old and new zones are updated. Locality?

How expensive is this?

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Replication Replicate user profile at other

databases in the hierarchy. Tradeoff: lookup latency versus

update and storage cost. “Lazy” consistency. Where to replicate?

Locality of calls and mobility. Also use intermediate nodes in the

tree.

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Replication parameters Number of replicas.

Bound on storage requirements and/or number of updates.

Replication cost-benefit. Local call-to-mobility ratio (LCMR).

Minimize communication cost. Benefits: number of local calls to user. Cost: number of moves during given time period. LCMRi,j = Ci,j/Mi. Min and max threshold.

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More parameters… Highest hierarchical level user

profile may be replicated. If site j replicates user i’s profile, all

ancestors of j will also be replicas. L sets upper bound on replication

level.

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Off-line replica allocation Inputs: database topology and

estimated LCMR. Output: replication plan which is sent to

the databases. 1st. phase: bottom-up traversal.

For each user i, assign it to replica j if LCMRij >= Rmax.

If n<N, additional replicas below L with largest LCMRij-Rmin assigned to user i.

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Evaluation Implemented their own event-driven

simulator (Pleiades). User movement models. User calling models.

Simulations. Model of the Bay Area geography and

demographics. Compared several location management

techniques: HLR/VLR, centralized, caching, full replication, simple hierarchy (no replication), hiper.

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Location Management by [Prakash96] System model: cellular architecture.

Cells, base stations, MHs, location servers.

Node wishing to communicate with MH needs to find MH’s location (cell).

Once location is determined, info sent to BS (over wired network), who relays to MH.

BS co-located with location servers.

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Motivation Distribute location management

load evenly across location servers. Even though location information

distributed, location tracking responsibility may not be spread equally.

MH density may be uneven. Some MHs maybe called more often than

others. Avoid “hot spots”.

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Problem statement“Which location servers will store an MH’s

location?” Single location server? Multiple statically assigned location servers? Multiple location servers based on MH’s

location? Multiple location servers based on location

and identity. Location servers change as MH moves. MHs in same cell will map to different sets of

servers.

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Problem statement (cont’d)“Given an MH and its current location (given

by the current BS), determine set of location servers given the MH’s id and its location.”, or

h: BSxMH -> SBS. Function h determines the read set (replicas

to query when trying to locate MH) and write set (replicas to update when MH moves) for MH.

Multiple id’s assigned to popular MHs. Why?

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Assigning location servers Mapping MH id to virtual id.

“Cold” MHs have single id. “Hot” MHs have multiple (two) ids.

Use hash function to map MH’s virtual id and its BS id to set of BSs.

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“Active” location management Previously described systems rely

on MHs to update their location. Some more recent systems take a

proactive approach: detect user and figure out location (positioning).

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Location for the Active Office [Ward97] Indoor sensor system that tracks

location of: people (active badge), equipment (equipment tags), etc.

Requirements: accurate (within 15cm), 3 dimensions, scalable (number of objects locatable, area covered), cost.

RF communication.

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System components Transmitters attached to every

locatable object. Matrix of receiver elements in all

rooms where objects are to be tracked.

Controller which polls one mobile object at a time.

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Operation Periodically, mobile node is polled. Polled mobile broadcasts signal. Controller synchronizes receivers,

who listen for some time to detect the peak of mobile’s transmission.

Controller polls receivers for the measured time interval between the sync signal and the signal peak (if any).

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Distance computation Time measured by receiver

composed of: time to transmit the polling signal (from controller to mobile)+time to transmit pulse (function of distance being calculated)+processing time.

Distance between mobile and receiver calculated. Empirically computed speed of sound in

the room and service times.

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Position calculation Triangulation using 4 receivers to

determine a point in 3 dimensional space as estimate of position.

In this particular set up, since all receivers are in the ceiling, only 3 distances required.

Extra reported distances can be used for higher accuracy.

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Evaluation Experiments with prototype show 95% of

readings within 14cm accuracy. Even better accuracy for averaged readings.

Addresses limit number of trackable objects. Large number of receivers and ultrasound

nature of transmission from mobile proved to pay off regarding accuracy.

Power savings mode minimizes maintenance. Low interference levels from office

equipment.

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RADAR [Bahl et al.] Similar to the [Ward97] paper. Provide indoor location service.

RF. Use received signal strength &

triangulation. Low cost. Off-the-shelf hardware.

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Testbed Single floor (10500 sq. ft.) with 50+

rooms. 3 base stations covering entire

floor. Lucent WaveLAN RF technology.

2 Mbps. 1-2 ms one-way delay. 200m and 25m range (open/close

environments).

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Functionality Off-line and real-time functions.

Off-line: derive and validate accurate signal propagation models.

Real-time: user location.

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What is being collected? Signal strength (in dBm).

s (Watts) = 10*log10(s/.001) (dBm)

Signal-to-noise ratio (SNR) (in dB). SNR (dB) = 10*log 10 (s/n) (dB).

For each received packet, SS recorded.

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Data collection process Mobile broadcasts beacons

periodically. Base stations record SS and SNR.

Different than the ORL system. Scalability? Path asymmetry.

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More on data collection All clocks synchronized. Mobile broadcasts packets (4 pkt/sec). BS records (t, bs, ss).

Off-line: mobile also provides its location by using a floor map.

Orientation is important (LoS, obstruction, etc.).

In off-line phase, collected SS in all 4 directions at 70 different floor locations. For each (x, y, d), 20 ss samples.

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Processing data Off-line data used to build signal

propagation model. Validation of assumption that from signal

strength location can be inferred. How is location determined?

Signal strengths from 3 BSs. Compare to floor layout/energy map. Pick location that minimizes (Euclidian)

distance between measured and recorded set of ss’s.

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Results “Empirical” method performs better

than random and strongest BS. Error approx. size of a room…

Taking “k” nearest neighbors shows some improvement.

Analysis of impact orientation, number of data points, and number of samples.

User tracking.

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Radio propagation model Model of indoor signal propagation.

No need for empirical data. Indoor propagation:

Reflection, diffraction, scattering. Multipath effect.

Receiver gets signal from multiple paths. Distorted signal.

Challenges: dependency on layout, material, obstacles (number and type), etc.

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Radio propagation model (cont’d)

Adaptation of existing model to single floor. Consider effects of walls.

Signal strength varies with distance AND number (and type) of obstacles.

Empirical characterization of wall attenuation. Use (corrected) empirical data and linear

regression to determine other parameters. Similar values for different BSs (location,

surroundings, etc.) Less accurate results than empirical model, but

more practical.

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Localization in Sensor Networks [Bulusu01]

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What are sensor networks? Large number of small, low-power

devices (wirelessly) connected. Applications:

Monitoring, surveillance, tracking, etc. Typically ad-hoc deployable,

unattended operation. Data-centric (instead of node-

centric).

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Localization Estimation of physical position

(coordinates). Why is this important?

Data usually identified by location (temperature of a given area, target tracking, signal processing applications).

No a priori knowledge of location. GPS?

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Approaches Multilateration: nodes measure enough

pair-wise distance estimates. Combination of radio and acoustic signals.

Proximity-based: “beacon” nodes periodically broadcast position; nearby nodes then estimate their position.

Iterative multilateration: beacon information propagated multi-hop. Beacon density sparse in some areas.

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Self-configuring localized algorithms Adjust to current conditions (load,

environment, etc). Localized algorithms: distributed

computation where communication is restricted to given neighborhood. Node density. Multiple modalities. Environmental adaptation.

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Density Trade-off: sparse vs. dense networks. Controlling density: transmit power.

Higher power makes networks more dense. Multiple power levels for tiered structure.

Problem: right balance between number of beacons (for coverage) and good localization. Power conservation. Interference.

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Sensor modalities Use different modalities (acoustic

sensors, cameras, etc) to overcome environmental unpredictability.

Example: acoustic sensors and acoustic/visual sensors. Acoustic sensing prefers LoS. Cameras can help by determining LoS

sensors.

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Adapting to the environment Not only to dynamics but also to

fixed characteristics (e.g., obstructions, terrain, etc.). Example: boundary beacon can extend

its lifetime by cutting down its duty cycle.

Example: adapting to the dynamics of wireless channel using learning algorithms.