A Multi-Sink Multi-Hop Wireless Sensor Network Over a Square Region: Connectivity and Energy Consumption Issues

In this paper we present a novel mathematical approach to evaluate the degree of connectivity of a multi- sink wireless sensor network, where sink and sensor nodes are uniformly distributed over a given region. We consider both unbounded and bounded domains, specifically squares, and the impact of border effects is also shown. Random fluctuations as well as a distance-dependent deterministic path-loss are accounted for in our radio channel model. In particular, we deal with randomly shaped wireless footprints, rather than with the less realistic (yet widely adopted) disk model, which is a special case of our channel model. Nodes are organized in a tree-based topology with trees rooted at the sinks (multi-hop communications). The approach allows the computation of the probability that a randomly chosen sensor is not isolated, from which the probability that a certain amount of nodes are connected can be easily derived. The mean energy spent by the network is also accounted for. The model provides guidelines to optimally design the tree-based topology, taking into account connectivity and energy consumption issues.

[1]  Alberto Zanella,et al.  A Statistical Model for the Evaluation of the Distribution of the Received Power in Ad Hoc and Wireless Sensor Networks , 2006, 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks.

[2]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[3]  M. Penrose.  On the Spread-Out Limit for Bond and Continuum Percolation , 1993 .

[4]  D. Stoyan,et al.  Stochastic Geometry and Its Applications , 1989 .

[5]  Rolland Vida,et al.  Deploying Multiple Sinks in Multi-hop Wireless Sensor Networks , 2007, IEEE International Conference on Pervasive Services.

[6]  Christian Bettstetter,et al.  How to achieve a connected ad hoc network with homogeneous range assignment: an analytical study with consideration of border effects , 2002, 4th International Workshop on Mobile and Wireless Communications Network.

[7]  C. Petrioli,et al.  Directed diffusion light: low overhead data dissemination in wireless sensor networks , 2005, 2005 IEEE 61st Vehicular Technology Conference.

[8]  Eylem Ekici,et al.  Probability distribution of multi-hop-distance in one-dimensional sensor networks , 2007, Comput. Networks.

[9]  Paolo Santi,et al.  The Critical Transmitting Range for Connectivity in Sparse Wireless Ad Hoc Networks , 2003, IEEE Trans. Mob. Comput..

[10]  R. Meester,et al.  Continuum percolation: References , 1996 .

[11]  Chee-Yee Chong,et al.  Sensor networks: evolution, opportunities, and challenges , 2003, Proc. IEEE.

[12]  John Orriss,et al.  Probability distributions for the number of radio transceivers which can communicate with one another , 2003, IEEE Trans. Commun..

[13]  Flavio Fabbri,et al.  A statistical model for the connectivity of nodes in a multi-sink wireless sensor network over a bounded region , 2008, 2008 14th European Wireless Conference.