Routing topology inference for wireless sensor networks

Authors:
Yao Liang;Rui Liu
Affiliations:
Indiana University & Purdue University Indianapolis, Indianapolis, Indiana, USA;Indiana University & Purdue University Indianapolis, Indianapolis, Indiana, USA
Venue:
ACM SIGCOMM Computer Communication Review
Year:
2013

Citing 13
Cited 0

Multicast-based inference of network-internal delay distributions

IEEE/ACM Transactions on Networking (TON)
Connecting the Physical World with Pervasive Networks

IEEE Pervasive Computing
Loss inference in wireless sensor networks based on data aggregation

Proceedings of the 3rd international symposium on Information processing in sensor networks
An algebraic approach to practical and scalable overlay network monitoring

Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
MPIDA: A Sensor Network Topology Inference Algorithm

CIS '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security
The β-factor: measuring wireless link burstiness

Proceedings of the 6th ACM conference on Embedded network sensor systems
Topology Tomography in Wireless Sensor Networks Based on Data Aggregation

CMC '09 Proceedings of the 2009 WRI International Conference on Communications and Mobile Computing - Volume 02
Compressed Network Monitoring

SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Decoding by linear programming

IEEE Transactions on Information Theory
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

IEEE Transactions on Information Theory
Compressed sensing

IEEE Transactions on Information Theory
A survey on sensor networks

IEEE Communications Magazine
A factor graph approach to link loss monitoring in wireless sensor networks

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider an important problem of wireless sensor network (WSN) routing topology inference/tomography from indirect measurements observed at the data sink. Previous studies on WSN topology tomography are restricted to static routing tree estimation, which is unrealistic in real-world WSN time-varying routing due to wireless channel dynamics. We study general WSN routing topology inference where routing structure is dynamic. We formulate the problem as a novel compressed sensing problem. We then devise a suite of decoding algorithms to recover the routing path of each aggregated measurement. Our approach is tested and evaluated though simulations with favorable results. WSN routing topology inference capability is essential for routing improvement, topology control, anomaly detection and load balance to enable effective network management and optimized operations of deployed WSNs.