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Adversarial Sensor Network Simulation Study for Algorithm Robustness Testing

This project aims to evaluate the robustness of sensor network algorithms by simulating adversarial behaviors. Testing various factors such as bit errors, sensor density, and packet handling will help identify weaknesses. By comparing algorithm behavior under different adversary strengths, weaknesses can be uncovered and improved upon. Evaluation includes running multiple algorithms under adversarial conditions for time synchronization, routing, and data aggregation. Expected results include insights on adversary impact and ideas for enhancement.

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Adversarial Sensor Network Simulation Study for Algorithm Robustness Testing

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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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