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Troubleshooting Mesh Networks. Lili Qiu Joint Work with Victor Bahl, Ananth Rao, Lidong Zhou Microsoft Research. Mesh Networking Summit 2004. Motivation. Why is it so slow? Cordless phone interference? Neighbors drop traffic? MAC misbehavior? Too much user traffic? Routing problems?

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troubleshooting mesh networks

Troubleshooting Mesh Networks

Lili Qiu

Joint Work withVictor Bahl, Ananth Rao, Lidong Zhou

Microsoft Research

Mesh Networking Summit 2004


Why is it so slow?

Cordless phone interference?

Neighbors drop traffic?MAC misbehavior?

Too much user traffic?

Routing problems?

TCP problems? …


research challenges
Research Challenges

Just knowing link statistics is insufficient

Complicated interactions

  • Between different network elements
  • Between different network protocols
  • Between different faults
  • Signature-based schemes may not capture all the interactions

Need to apply to a wide range of networks

Multi-hop wireless networks

  • Unpredictable physical medium and dynamic topology
  • Limited resources
  • Scale to hundreds of nodes
our approach
Our Approach

Framework: online trace-driven simulation

  • Create a real network inside a simulator
  • Identify root cause by searching for the faults that reproduce the same faulty symptom


  • Applicable to a large class of networks
  • Capture complicated interactions
  • Extensible to diagnose new faults
  • Facilitate what-if analysis
troubleshooting framework
Troubleshooting Framework














Link Loads

Root cause analysis module

common concerns and our approaches for simulation based diagnosis
Common Concerns and Our Approaches for Simulation-Based Diagnosis
  • Simulation accuracy

- Trace-driven simulation

- Remove erroneous data from the trace

2. Too expensive to simulate

- Advances in network simulator

- Focus on long-term faults

- Compression, spatial scoping, adaptive monitoring, multicast

3. Too large fault space

- Develop an efficient search heuristic

data gathering
Data Gathering

What data to collect?

  • Network topology
  • Traffic statistics
  • Physical medium
  • Link performance

Data sources: SNMP, WRAPI, Packet sniffers, NativeWiFi

Dealing with Imperfect Data

  • Neighbor monitoring
  • Using history information
  • Find the smallest number of misbehaving nodes to explain inconsistency in traffic reports
fault diagnosis algorithm
Fault Diagnosis Algorithm


  • Large fault space  brute-force search is infeasible

1. Initialization: diagnosed fault set F = { }

2. while (diff(MeasuredPerf, SimulatedPerf(F)) > threshold) {

Foreach f in F

Adjust f’s magnitudes if necessary

Delete f is its magnitude is too small Add a new candidate fault if necessary



3. Report F

performance evaluation
Performance Evaluation

Effectiveness of data cleaning

  • Detect >80% misbehaving nodes with <15% false positive

Effectiveness of fault diagnosis

Accuracy of detecting combinations of packet dropping,

MAC misbehavior, and external noise in 25-node random topology

performance evaluation13
Performance Evaluation


  • Implemented the technique in a small multi-hop IEEE 802.11a mesh testbed
  • Detected network congestion and random packet dropping
conclusion future work
Conclusion & Future Work

Propose online trace-driven simulation

  • Diagnose faults
  • Test alternative network configurations
  • Our evaluation results show it is promising

Future work

  • Validate it in a larger-scale testbed
  • Extend it to handle mobility
  • Apply it to handle other types of faults
related work
Related Work

Protocols for wireless network management

  • Ad Hoc Network Management Protocol (ANMP)
  • Guerrilla Management Architecture
  • Complementary to our work

Fault management for wireless infrastructure networks

  • AirWave, AirDefense, UniCenter, WNMS, IBM WSA, Wibhu SpectraMon …
  • Different from multihop wireless networks

Detect specific faults in multihop wireless networks

  • Routing misbehavior
  • Mac misbehavior, …
trace driven simulation
Trace-driven Simulation


Fault Injection





Traffic Simulation