10/11/10

Challenge the future Delft University of Technology

Predictive QoS Routing to Mobile Sinks in Wireless Sensor Networks

Gargi Prasad

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Problem Statement

Routing in networks with significant mobility: •  High link Volatility •  Scarcity of resources •  Energy constraints

Existing approaches do not suffice for drastic connectivity in such networks.

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Predictive QoS Routing

•  Algorithm for data delivery to mobile nodes. •  Mobile node act only as data sinks

•  Do not forward information •  • Semi-static nodes

•  Individual links have high volatility •  Gradual long term connectivity change

•  Use of a relay node within radio distance of the mobile node •  Finding the best relay node to send packets.

• Based on a routing tree, defined as a gradient of information potentials.

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Information Potential

•  A Real function on the nodes; defined on the relay node •  Value 1 at sink, 0 at other nodes or the avg. of neighbors values

•  Guass Siedel Iterative method is used

• Each node communicates with its 1-hop neighbors •  Stores its own potential

•  avg. value of its neighbors.

•  Periodically updates its potential

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•  Length of the shortest path and Robustness are accounted for

•  Stable to small changes in connectivity

•  Forwarding a packet to the neighbor with highest potential value guarantees delivery.

•  Initial values arbitrary •  For dynamic updates

• Can be Asynchronous Updates at regular intervals

Computing Information Potentials

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Background/Assumptions

•  Static Sensor nodes; but subject to typical WSN constraints

• Relay node acts as a proxy to a mobile node

• When relay node changes, information potentials are adapted

• Users carry a mobile computing device to communicate with surrounding sensor nodes. •  Allows observing trajectories of users using RSSI.

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Mobility Patterns

• Cooperating nodes measure Signal strength •  For each packet •  From each user •  At each time interval

• Best connected node highest signal strength

• Trajectory of a user -> observational sequence

•  Sequence of best connected nodes can be derived

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Mobility Graph

•  Encodes mobility patterns of mobile nodes •  Using observational sequences

• Determines relay node transitions

•  Predict future relay nodes

•  Improves reliability

•  Ensures seamless transition to new relay node

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Mobility Graph

• Adds an edge between vertices m and n iff: for all; i : {B(ti) = m} ∧ {B(ti+1) = n}

•  Edge divides observational sequences to segments showing transition

•  Same vertex set as network connectivity graph

• Mobility Vs Connectivity •  Extra edges when user moves through areas with no network •  Missing edges in case of areas with obstacles/walls.

•  Edges only in the mobility graph- used for predictive routing algorithm

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Mobility

Vs

Connectivity

Graph

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Mobility Graph Extraction

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•  Pattern matching used for finding current position in graph.

•  For each edge e a set of observational sequences Se is determined during training phase/mobility extraction

•  For prediction of next relay node: •  current RSSI trace is measured with the stored RSSI segments of all

relevant edges •  Only, set of out going vertices is considered [E→(vt)]

•  t: time •  vt : current relay node •  R(t0:t) : RSSI measurement since last change •  emin : edge with the best matching segment.

•  End vertex of emin is the next predicted relay node

Mobility Prediction

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• Determines best matching outgoing edge •  hence, predicts next relay node

• Used in cases of considerable speed differences

Dynamic Time Warping

Expected time to transition: current RSSI trace= R(t1 : tk) & the best match RG(t1 : tl).

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• New sink is 1-hop neighbor to the old sink •  Routing tree changes by a few edges

• New node far away •  Some performance penalty but data delivery happens

• With Mobility prediction potentials can be computed in advance

•  Each node stores 2 potentials •  Current potential used for routing •  Predicted potential

•  Precomputing gives a good start value •  Precomputed, for each node if it doesn’t already exist in its k-hop

neighborhood.

Updates and Precomputation

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•  2 Potentials/ node updating at frequency of 4 iterations / sec.

•  State changes in the network •  New prediction •  Relay node changes

• Messages propagated through network-wide flood

•  Piggybacked with information potential updates.

•  Every node has a timer •  estimating time to transition •  achieving synchronous transition

Implementation

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•  10 MicaZ motes in an office space • Area of 30m X 25 m • Tiny OS 2.1 • Messages broadcasted at 0.6 sec period • Constant transmission power • Regular working hours

Accuracy of Prediction – Low initially With increase in no. of data points improves by approx. 90%

Change in walking speed by 30%- prediction suffers No change in routing performance

Test Bed

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Evaluation Impact of size and variability of training data.

Impact of size and variability of training data. Prediction accuracy depending on the variability of the training set.

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• Achieve gains in routing performance •  Novel Algorithm :Mobility Prediction •  A variant of Gradient based routing

• Mobility Graph is a valuable tool •  Understanding and optimizing wireless networks

• Reliability in data delivery improves.

Conclusion

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Thank You