A Probabilistic Routing Protocol for Mobile Ad Hoc Networks

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Presentation transcript:

A Probabilistic Routing Protocol for Mobile Ad Hoc Networks Abdallah Jabbour • James Psota • Alexey Radul {ajabbour, psota, axch}@mit.edu Jabbour, Psota, Radul 6.829 Final Project

presentation notes (delete this slide) slides are currently not formatted; formatting will occur after content is more finalized; please don’t worry about that for now total presentation time: ~15 minutes (5 min questions)  10-15 slides total should give protocol a name! need to add figures, but will talk to teammates first may need to consolidate/merge slides… I’d like to add an animation/diagram of the protocol in action Jabbour, Psota, Radul 6.829 Final Project

Routing in Ad Hoc Networks Most routing protocols… Use fixed route to send all packets from a given source to a given destination Send along path with minimum hop count Use two main types of packets Data packets Control (routing) packets <one reason why these are bad> We think we can do better! Jabbour, Psota, Radul 6.829 Final Project

Outline Related Routing Protocols Shortcomings of Related Protocols DSDV, DSR, AODV Probabilistic Routing Protocols Shortcomings of Related Protocols Protocol Description Simulation Overview and Results Conclusion Jabbour, Psota, Radul 6.829 Final Project

Related Routing Protocols Destination-Sequenced Distance Vector Loop-free, hop-by-hop distance vector Routes prioritized by sequence numbers Dynamic Source Routing Employs source routing Floods route requests Maintains routes by link breakage notification Ad Hoc On-Demand Distance Vector Combines sequence numbers from DSDV and Route Discovery from DSR Jabbour, Psota, Radul 6.829 Final Project

Probabilistic Routing Protocols Control and routing information (“ants”) sent randomly Data forwarded deterministically to path with best metric Examples Ant-Based Control (ABC) AntNet Ant-Colony-Based Routing Algorithm (ARA) Jabbour, Psota, Radul 6.829 Final Project

Drawbacks and Limitations of Above Protocols Routing packets hinder performance Decreases available bandwidth Increases transmission latency High recovery latency due to static routes DSDV, DSR, AODV Probabilistic protocols incorrectly assume symmetric traffic Above protocols use shortest hop routes Tends to choose routes with less capacity than optimal ones Tends to use marginal links Jabbour, Psota, Radul 6.829 Final Project

Our Protocol’s Design Goals Minimize routing packets Especially those interfering with traffic Use a better measure of network state than minimum hop count to make forwarding decisions Better cope with link outages our goals are to increase performance by… Jabbour, Psota, Radul 6.829 Final Project

Protocol Overview Minimize control packets by prepending protocol-level headers onto all data packets Both when originating and forwarding a packet Nodes promiscuously listen to all traffic, using protocol headers to update state Base routing decisions on link loss ratios Modular in both choice of metric and metric to probability mapping Use ETX instead of minimum hop count Jabbour, Psota, Radul 6.829 Final Project

Random Routing Nodes forward probabilistically to neighbor ni with probability pi n1 routing table p1 = 0.1 dest p1 p2 p3 d 0.1 0.4 0.5 dest p1 p2 p3 d 0.1 0.4 0.5 dest p1 p2 p3 d 0.1 0.4 0.5 s n2 d p1 = 0.4 p3 = 0.5 n3 Route is not fixed, so packets can still reach destination immediately upon link breakage Jabbour, Psota, Radul 6.829 Final Project

Random Routing Nodes forward probabilistically to neighbor ni with probability pi n1 routing table p1 = 0.3 dest p1 p2 p3 d 0.1 0.3 0.4 0.0 0.5 0.7 x s n2 d p1 = 0.4 x x x link breaks! p3 = 0.7 n3 Update forwarding probability upon link breakage (nodes see infinite loss ratio on link) Jabbour, Psota, Radul 6.829 Final Project

Deterministic Routing Forward ALL packets along path with best metric Our flexible infrastructure allowed simulation of both First to compare Random to Deterministic Routing Jabbour, Psota, Radul 6.829 Final Project

Node State Nodes maintain the following state Dynamically-updated set of neighbors Loss ratios to and from each neighbor Routing state Metric values for each destination and each neighbor-destination pair Probability of forwarding to a certain neighbor in order to reach a desired destination Requests and fulfillments thereof for information about destinations Jabbour, Psota, Radul 6.829 Final Project

Protocol Header Contents Each outgoing packet contains a protocol-level header Jabbour, Psota, Radul 6.829 Final Project

State Update Nodes update state Probability Distribution Updates Upon sending Upon receiving Periodically Refresh stale state and alert neighbors that you’re still alive Probability Distribution Updates Probability distribution and metric values updated along with other node state Values evolve in response to nodes entering and leaving the system and changes in link quality Jabbour, Psota, Radul 6.829 Final Project

Simulation Environment 50 mobile nodes in 1500m x 300m area Random waypoint movement model 900s simulation time We investigated… Pause times Node speeds Connection patterns Packet sizes Used 30 CBR UDP sources Avoid TCP because YYY Jabbour, Psota, Radul 6.829 Final Project

Evaluation Measures Packet delivery ratio Packet delivery latency Goodput Number of bytes of overhead Path length optimality Transmission overhead Route acquisition latency Jabbour, Psota, Radul 6.829 Final Project

Simulation Results Jabbour, Psota, Radul 6.829 Final Project

Conclusions Jabbour, Psota, Radul 6.829 Final Project

insert title slide Jabbour, Psota, Radul 6.829 Final Project

Backup Slides Jabbour, Psota, Radul 6.829 Final Project

Implementation Overview ns-2 with Monarch mobility extensions used to model mobile ad hoc network Implemented protocol in C++ Data generated by 30 CBR UDP sources Metrics Jabbour, Psota, Radul 6.829 Final Project

Routing and Distribution Updates Random Routing Each node sends probabilistically to neighbor i with probability pi for destination d Deterministic Routing Each node sends exclusively to neighbor i when probability pi for destination d is highest Our flexible infrastructure allowed simulation of both Jabbour, Psota, Radul 6.829 Final Project