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Research Abstracts - 2006
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Network of Floating Sensors in Rivers

Ajay Deshpande & Daniela Rus


Naturally mobile sensor networks are the ones where nodes move under the control of environment, e.g. nodes attached to animals or nodes moving with water currents in rivers and ocean. These networks offers some advantages over static and engineered mobile networks in that they provide some flexibility in data collection due to mobility and at the same time, actuation is provided by natural means. However, communication and sensing capabilities of such networks are constrained by underlying mobility models. This gives rise to several challenging problems in design of such networks. In this work, we focus on design of a network of floating sensors in rivers. Our specific goals are to identify relevant mobility model, come up with a general framework to analyze network properties such as connectivity, coverage and communication and verify results using simulations and experiments. Such a network may have applications in water quality assessment, determining concentration of pollutants, etc. in rivers [1].

Mobility Model

Hydraulic and environmental engineers have proposed several physical and numerical models for describing phenomena of transport and mixing of pollutants, water bodies, etc. in rivers [2]. It is very hard to determine mixing in real rivers as it depends on several factors such as exact geometry of flow, wind currents, obstacles, etc. We approximate river flow as a straight open channel flow. Based on the expressions for surface velocity profile and the values of turbulent diffusion coefficients as described in [2] and [3], we describe motion of a floating particle in rivers in terms of a set of stochastic differential equations. This allows us to simulate trajectories of individual particles.


We have developed a simulator in Java that numerically simulates motion of sensor nodes and allows to simulate a network behavior. In our simulations, we choose model-related parameter values from real field experiments [3]. To begin with, we have observed behavior of the network of 100 nodes under broadcast protocol in two scenarios, presence or absence of base stations along the banks of the river. In both the scenarios, we placed events periodically in the river and as soon as a node detects an event, it triggers the broadcast chain in the network. We also observed the connectivity of the individual nodes in the network. We observed that the network with base stations significantly improves the connectivity and performance of flooding in the network.

Future Work

Currently we are pursuing various directions to predict results observed in simulations analytically. In particular, one direction is based on analyzing hitting times and node inter-meeting times for this mobility model and uses results from [4] and [5]. In future, we plan to simulate and observe coverage properties and network behavior for other communication protocols. We also plan to build a test system and use it to collect data in real rivers.


[1] Workshop: Sensors for Environmental Observatories. Final report available at http://wtec.org/seo/final/Sensors_for_Environmental_Observatories.pdf, University of Washington, Seattle, November 30 - December 2, 2004,

[2] H. B. Fischer, E. J. List, R. C. Y. Koh, K. Imberger and N. H. Brooks. Mixing in inland and coastal waters, 1979.

[3] Zhi-Qiang Deng, Vijay P. Singh and Lars Bengtsson. Longitudinal dispersion coefficient in straight rivers. In Journal of Hydraulic Engineering, Vol. 127, No. 11, pp. 919-927, 2001

[4] David Aldous. Probability approximations via the Poisson clumping heuristic, Springer-Verlag, 1988.

[5] David Aldous and Jim Fill. Reversible Markov Chains and Random Walks on Graphs. Draft of book available at http://www.stat.berkeley.edu/~aldous/RWG/book.html, 2001.

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