The Effects of Motion on Applications in Mobile Ad-Hoc Sensor Networks

A set of mobile wireless sensors observe their environment as they move about. We consider the subset of these sensors that each made observations when they were all at approximately the same time/location. As they continue to move, one of them processes its observations and decides that an event that must be reported has taken place. To reduce the probability of a false alarm, this sensor assumes the role of a Cluster-Head (CH) and requests that all other sensors that collected observations at that time/location send it their decisions. The motion of each sensor determines how many hops its decision data must make to reach the CH. We analyze this effect of motion in the 1D case by modeling each sensor's motion as a Correlated Random Walk (CRW), which can account for transient behavior, geographical restrictions, and nonzero drift. Quantities, such as the energy required to collect the decision from all relevant sensors, can then be determined as a function of time.

[1]  Walter Böhm.  The correlated random walk with boundaries: A combinatorial solution , 2000 .

[2]  S. Goldstein.  ON DIFFUSION BY DISCONTINUOUS MOVEMENTS, AND ON THE TELEGRAPH EQUATION , 1951 .

[3]  Tracy Camp,et al.  A survey of mobility models for ad hoc network research , 2002, Wirel. Commun. Mob. Comput..

[4]  Do Young Eun,et al.  Toward stochastic anatomy of inter-meeting time distribution under general mobility models , 2008, MobiHoc '08.

[5]  Edward J. Coyle,et al.  Transient analysis of quasi-birth-death processes , 1989 .

[6]  Edward J. Coyle,et al.  Minimizing communication costs in hierarchically clustered networks of wireless sensors , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[7]  Eric Renshaw,et al.  THE CORRELATED RANDOM WALK , 1981 .

[8]  Ahmed Helmy,et al.  A survey of mobility modeling and analysis in wireless adhoc networks , 2004 .

[9]  E. Coyle,et al.  Stochastic Properties of Mobility Models in Mobile Ad Hoc Networks , 2006 .

[10]  Edward J. Coyle,et al.  Spatio-temporal sampling rates and energy efficiency in wireless sensor networks , 2005, IEEE/ACM Transactions on Networking.

[11]  D. G. Lampard,et al.  A random walk problem with correlation , 1972 .

[12]  Yuan Lin Zhang,et al.  Some problems on a one-dimensional correlated random walk with various types of barrier , 1992, Journal of Applied Probability.

[13]  J. Gillis,et al.  Correlated random walk , 1955, Mathematical Proceedings of the Cambridge Philosophical Society.

[14]  U. Narayan Bhat,et al.  Some explicit results for correlated random walks , 1989, Journal of Applied Probability.