Latency and connectivity analysis tools for wireless mesh networks

There has been a recent rise in interest in building networked control systems over a wireless network, whether they be for robot navigation, multi-robot systems, or traditional industrial automation. The wireless networks in these systems must deliver packets between the controller and the actuators/sensors reliably and with low latency. Furthermore, they should be amenable to modeling and characterization so they can be designed as part of a complete control system. Mesh networks are particularly suited for control applications because they provide greater reliability through path diversity. This paper introduces tools for characterizing the end-to-end connectivity of two points in a wireless mesh network as a function of latency. In particular, we use tools derived from Markov chain models to compare end-to-end connectivity in two routing protocols running on the Data Link/MAC layer provided by Dust Network's Time Synchronized Mesh Protocol (TSMP): Directed Staged Flooding (DSF) and Dust Network's Unicast Path Diversity (UPD). These models also allow us to calculate the traffic load, the sensitivity of end-to-end connectivity to link estimation error, and the robustness of the network to node failure. The paper gives an example of how these tools can be used to evaluate the feasibility of running control applications over sensor networks.

[1]  L. Schenato.  Optimal estimation in networked control systems subject to random delay and packet loss , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[2]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[3]  Kristofer S. J. Pister,et al.  TSMP: TIME SYNCHRONIZED MESH PROTOCOL , 2008 .

[4]  Robert Szewczyk,et al.  System architecture directions for networked sensors , 2000, ASPLOS IX.

[5]  João Pedro Hespanha,et al.  A Survey of Recent Results in Networked Control Systems , 2007, Proceedings of the IEEE.

[6]  Deborah Estrin,et al.  Highly-resilient, energy-efficient multipath routing in wireless sensor networks , 2001, MOCO.

[7]  Abtin Keshavarzian,et al.  Load balancing in ad hoc networks: single-path routing vs. multi-path routing , 2004, IEEE INFOCOM 2004.

[8]  J. Werb,et al.  Radio Channel Quality in Industrial Wireless Sensor Networks , 2005, 2005 Sensors for Industry Conference.

[9]  Yaping Li,et al.  ARRIVE: Algorithm for Robust Routing in Volatile Environments , 2003 .

[10]  John N. Tsitsiklis,et al.  Introduction to Probability , 2002 .

[11]  David E. Culler,et al.  Taming the underlying challenges of reliable multihop routing in sensor networks , 2003, SenSys '03.

[12]  Mani Srivastava,et al.  Overview of sensor networks , 2004 .

[13]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[14]  Deborah Estrin,et al.  Guest Editors' Introduction: Overview of Sensor Networks , 2004, Computer.

[15]  Paul J. M. Havinga,et al.  Trade-off between traffic overhead and reliability in multipath routing for wireless sensor networks , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[16]  Deborah Estrin,et al.  Directed diffusion: a scalable and robust communication paradigm for sensor networks , 2000, MobiCom '00.

[17]  Bruno Sinopoli,et al.  Kalman filtering with intermittent observations , 2004, IEEE Transactions on Automatic Control.

[18]  William J. Stewart,et al.  Introduction to the numerical solution of Markov Chains , 1994 .

[19]  H. M. Taylor,et al.  An introduction to stochastic modeling , 1985 .

[20]  Samir Ranjan Das,et al.  Performance of Multipath Routing for On-Demand Protocols in Mobile Ad Hoc Networks , 2001, Mob. Networks Appl..