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Adversarial Sensor Network Simulation

Adversarial Sensor Network Simulation. CS252 Project Pitch Zach Anderson Henry Lin. The Problem. Current sensor network algorithm robustness testing: Realistic bit error models Varying sensor density, network diameter Other things can go wrong or vary: Packet duplication

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Adversarial Sensor Network Simulation

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  1. Adversarial Sensor Network Simulation CS252 Project Pitch Zach Anderson Henry Lin

  2. The Problem • Current sensor network algorithm robustness testing: • Realistic bit error models • Varying sensor density, network diameter • Other things can go wrong or vary: • Packet duplication • Packet reordering • Movement of sensors • Placement of the root mote • Correlated/non-correlated mote failure • Cross traffic • Large variations in transmission delay • Noise in data used by algorithms • Times • Positions

  3. The Solution • How robust are the algorithms we use? • Use TOSSIM to simulate adversarial behaviors. • Compare the behavior of algorithms under different strengths of adversaries. • Allow the finding and elimination of weaknesses.

  4. Evaluation • Examine algorithms for possible weaknesses • Run several algorithms under adversarial conditions • FTSP, TPSN – Time Synchronization • MintRoute, AODV – Routing • TAG, Synopsis Diffusion – Aggregation

  5. Expected Results • We expect to find: • For each algorithm • For selected adveraries • How the strength of the adversary affects the results of the algorithm. • Possible ideas for improvement.

  6. Related Work • TAG was tested with random placement, and a realistic loss model. • AODV was tested with a random waypoint movement model. • TPSN tested against varying network diameters • FTSP was tested against varying network diameter, root failure, and non-correlated failure. • Trickle was tested against asymmetric loss models and varying sensor density • TinyBench proposes a standard benchmark suite for sensor networks

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