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Performance Implication of Environmental Mobility in Wireless Networks

Maneesh Varshney and Rajive Bagrodia

Department of Computer ScienceUniversity of California, Los Angeles

(Proc. of INFOCOM 2007)

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Motivation: Types of Mobility• Nodal Mobility (NM)

• Movement of the transmitting and/or receiving nodes

• Environmental Mobility (EM)• Ambient movement of entities such as people, vehicles etc. in the vicinity of wireless communication• Communication nodes may or may not be static• Examples:

• customers move about in cafes and offices• students enter and leave classrooms or conferences• vehicles moving on a freeway• animals moving in a forest sensor deployment

Nodal mobility has been widely researched

Environmental mobility has been relatively unexplored

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Focus

1) Effect of EM on wireless link behavior and protocol performance

2) How to model EM? Is the model efficient? Is it also simple?

3) Including EM channel model in network simulation

4) Helping network protocols become aware of the underlying channel conditions

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Talk Outline

• Four Parts

1) Measurement Results

2) Environmental Mobility (EM) Channel Model

3) Qualnet Simulations

4) Cross-layer Optimization: Recovery from Earlier Good State (REGS)

• Conclusions

• Future Work

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Measurement Results

• IBM Thinkpad T-42 laptops • Orinoco Proxim WD-8470 b/g gold card operating in 802.11b mode and Linksys DWL-AG660 card operating in 802.11a mode• The transmitter broadcasts traffic at the rate of 2000 packets/sec for 11a and 500 packets/sec for 11b mode• The receiver captures the Received Signal Strength Indicator (RSSI) for each packet using ethereal software

• About 60 hours of channel traces at different locations on a university campus, during different times of the day and spread over multiple months

• Indoor scenario (the bottom figure)• inside building corridor with classrooms on both sides

• Outdoor scenario (the top figure)• an outdoor portion of an on-campus coffee shop

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Measurement Results (cont’d)• a) No mobility: measurements done late at night

• b) Far mobility: no mobility was allowed within about 10 meters of the radios

• c) Unrestricted mobility: pedestrians were free to move without restriction

a) fairly static channel

b) Multi-path fast fading: modeled well with Ricean distribution

c) The EM channel: different from multi-path fading

- EM channel is a superimposition of low frequency people shadowing (hundreds of ms., person(s) crossing a given line) on multipath fast fading (hundreds of μs.)

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Measurement Results (cont’d)Average error burst duration for fast fading and EM channels

• Avg. error burst duration for EM channels is significantly greater than that of fast fading• Longer error bursts higher correlation in bit errors EM channel has higher memory

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Measurement Results (cont’d)

Observation 1:Real life channels with

pedestrian mobility cannot be sufficiently captured by

multipath fast fading theory and models.

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Measurement Results (cont’d)

• Loss is maximum at the ends and is almost a constant value in the middle• Fading value increases in the case of two people• Fading value does not only depend on the number of people

Experiment 1: Shadowing loss due to motion of multiple people

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Measurement Results (cont’d)

Observation 2:Shadowing loss in an EM

channel depends not only on relative location of people with respect to transmitter and receiver but also with

respect to each other.

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Measurement Results (cont’d)Experiment 2: Effect of EM on prob. distribution of signal strength• People moving randomly close to Rx, and their number varies between 1 and 3• Similar trends are observed when number of people is fixed but their speed varies

• “No movement” curve is conventional Ricean fading distribution

• For the other cases, the fading distribution exhibits a secondary peak

• Key observations: • Entire curve can be thought of as a sum of two distributions where mean value of second component is displaced and its relative weight is dependent on number of people

• Basis for the channel model: EM Channel is modeled as alternating between two states corresponding to the two distributions

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Measurement Results (cont’d)

Observation 3:The fading distribution, in

presence of EM, is distorted to exhibit a secondary peak.

The relative magnitude of the second peak depends on the number of people and their

speed.

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EM Channel ModelA two-state continuous-time Markov process

• The transition between the two states is governed by the dynamics of mobility of the people

• Transition rates between good and bad states were studied using Monte-Carlo simulations

(Unobstructed Link)

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EM Channel Model (cont’d)

• A large terrain of size D x D• ρ: density of the people• ρD2 number of people, placed at random initial positions• v: the speed of movement, follows a random way-point model• d: distance between Tx and Rx

• a line segment of length d is placed in the middle of the terrain

• Consider the time instances of events where any person crosses this line segment

• Found that these events follow the Poisson distribution, implying that the durations when the channel in not shadowed is exponentially distributed• Average duration of good states depend on ρ, v, and d

Average duration of good states

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EM Channel Model (cont’d)Average duration of good states (cont’d)

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EM Channel Model (cont’d)

• Duration of shadowed channel state is also exponential• Mean value determined experimentally to be 300 ms.

• Prob. of more than one person crossing the link simultaneously

• Define Δi to be the difference in time instances between the ith and the (i+1)th crossings• If Δi <= (0.8 * 2W)/v (W is the width of the human body), assume that two people crossed the link simultaneously• Procedure is recursively applied to derive the data for simultaneous crossings of more than two people

Average duration of bad states

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EM Channel Model (cont’d)

• Determined according to the Three Knife-edge diffraction model• M. Varshney, Z. Ji, M. Takai and R. Bagrodia, “Modeling Environmental Mobility and its Effect on Network Protocol Stack”, Proc. of WCNC 2006.

Additional loss due to blocking (Ldiffraction) in the bad state

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EM Channel Model (cont’d)

• Radius of curvature at edges of human body are negligible when compared with communication range knife-edge

• Fading due to one person• a three knife-edge model: head, left arm and right arm of a person

• Fading due to multiple people• Use the Epstein-Peterson model [Parsons’01] of multiple knife-edge diffraction• E.g., for the case of two obstacles, the two losses are added and an extra correction term is added to the sum

• Model extension to account for various combinations of paths that the waves may take to reach the receiver

• E.g., in Figure (b), there are 5 paths from Tx to Rx, each encountering 2 diffraction edges. Components are added to produce the total loss

Brief overview of the diffraction model [Varshney et al. 2006]

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EM Channel Model: ValidationExperiment 1 (Revisited)

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EM Channel Model: Validation (cont’d)Experiment 2 (Revisited)

Note: Fraction of time that channel was in good state was taken as 0.75 and 0.65 respectively

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Qualnet Simulation

Objectives:

- Effect of EM channel behavior on classes of protocols that maintain state via feedback from the channel

- Is it possible that the link can switch to a good state while the protocol is still operating under the conditions of the bad state recorded in the recent past?

- If yes, then protocols are NOT able to adequately utilize the good state performance degradation

- How to remedy that? How effective is the solution?

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Qualnet Simulation (cont’d)

• Interaction of SampleRate [Bicket’05] with the EM 2-state Markov channel model• One transmitter sending a backlogged UDP traffic over 802.11a link to a single receiver• Tx-Rx separation varies: 50-300m. Pathloss: Two Ray

• Expected performance (throughput)

• f: fraction of time that the channel remains in the good state• Good, Bad and EM respectively refer to exclusive good state, exclusive bad state and EM channel (alternation of previous two)• Rationale: Any deviation from this expected value implies that the protocol maintains some “memory” of past channel conditions which prevents it from utilizing current conditions efficiently

MAC layer data rate adaptation

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Qualnet Simulation (cont’d)MAC layer data rate adaptation (cont’d)

as high as 50% performancedegradation due to the “memory” effect

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Qualnet Simulation (cont’d)Brief overview of SampleRate [Bicket 2005]

• State maintained for each data rate• whether four consecutive losses have occurred• average packet transmission time (using the packet length, bit-rate, and the number of retries (including 802.11 back-off)).

• An averaging window (10 sec.) is used to avoid stale info.

• After a packet has been transmitted:• based on information from the device driver, update the average transmission time of the current data rate and record if 4 consecutive losses have occurred

• After every 10th packet:• switch randomly to one of the remaining data rates that have lower lossless transmission time than the average transmission time of the current data rate

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Qualnet SimulationMAC layer data rate adaptation (cont’d)

• After 4 consecutive losses, the 36Mbps will not be used for the next 10 seconds!

• Avg. Tx time for the 24Mbps increases (due to large number of retries), but it is not able to reset fast enough

• Protocol ends up using the lower 18Mbps rate

bad states

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Qualnet Simulation (cont’d)

Observation 4:Protocols that maintain state can

acquire negative information in the bad state which is discarded only very slowly or not at all even when the

channel conditions improve. The effect of this phenomenon is that the protocols are not able to utilize

adequately the durations when the channel conditions are good.

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Cross Layer Optimization

• Basic Idea:• “Forgetting the bad memories” of the bad state• Operation in the good state is not influenced by decisions made or state variables changes in the bad state performance is not degraded in the good state

• Cross-layer Operation:• LL monitors incoming packets and infers current channel state from RSS

• maintains smoothed trace of signal strength• utilizes Hidden Markov Model training and is able to predict state transitions without false positives or negatives and with a latency of less than 5 ms.

• LL announces this information. Higher layers subscribe to these announcements• Switch from a good state to a bad state:

• A REGS-enabled protocol checkpoints the current protocol state• Switch from a bad state to a good state:

• A REGS-enabled protocol discards all the protocol memory acquired since the previous announcement and starts to operate from the earlier checkpointed state

Recovery from Earlier Good State (REGS)

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Cross Layer Optimization (cont’d)

• Switch from a good state to a bad state:• REGS-enabled SampleRate marks a checkpoint at the current index in the history

• Switch from a bad state to a good state:• REGS-enabled SampleRate goes back in the history and erases everything until it encounters the marked checkpoint• Statistics such as average transmission are then recalculated

Application of REGS to SampleRate

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Cross Layer Optimization (cont’d)Qualnet Simulation of SampleRate (Revisited)

Performance of REGS-enabled SampleRate is close to expected performance

Memory acquired during the bad state was the cause of performance degradation.

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Conclusions

• A systematic study to analyze the effect of EM on wireless link behavior and protocol performance using:

• measurements: channel traces• analysis model: two-state Markov Process, simple but efficient model• simulations: thanks to its simplicity and efficiency, the model was

incorporated into the Qualnet network simulator

• Several case studies at different layers of the protocol stack• MAC layer data rate adaptation in a wireless network• Routing (AODV and DSR) in ad-hoc networks• TCP flow in wired-in-wireless path of a mixed network• See http://pcl.cs.ucla.edu/papers/ for more details

• A cross-layer optimization scheme, called Recovery from Earlier Good State (REGS)

• REGS allows protocols to become aware of the underlying channel conditions

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Future Work

• Role of nodal mobility in relation with environmental mobility:

• nodes themselves are moving and can shadow other communications • resulting impact on protocols at the different layers of the protocol stack

• Impact of shadowing by objects with dimensions larger than humans

• vehicles in Vehicular Ad Hoc Networks (VANETs)