ART efficient path repair mechanism reduces sink's communication overhead but may result in suboptimal source to sink path.
This is the approach followed by (Croes et al., 2006; Faust et al., 2010; van Helden et al., 2002) where the authors concentrate on a pair of discriminating compounds and search for subgraphs corresponding to source-to-sink paths between them.
NQAR is unique in that it uses dynamic network quality parameters as well as static delays in order to estimate the expected event-to-sink path delay.
Since such a 'longest' path is composed of the sites situated on the hull of a percolating cluster, statistical parameters of the source-to-sink path can directly be associated with the fractal properties of this cluster hull.
To effectively localise a source node in a dense wireless tiny-sensor network with an arbitrary 2D/3D node distribution, a novel approach suitable to describe the hop progress of source-to-sink path in such a system is proposed.
Then, by relating statistical parameters (such as mean and variance) of the hop count of the source-to-sink path to the fractal parameters of the percolating cluster, the probability P r,t) indicating a successful arrival of the sensed signal to a sink node spaced at distance r from a source within a specified time t has been mathematically expressed.
The source-to-sink path with the maximum number of hops, which can be rigorously defined through the fractal characteristics of the hull of the percolating cluster, is also important from a communication perspective as its modelling helps to avoid inter-message interference in the WTSN (i.e. the source node must send more than one message to complete its report).
In this paper, we propose a novel method to obtain in a simple manner the worst case number of hops of the source-to-sink path in a very large-scale network with an arbitrary network complexity and the unknown locations of the wirelessly connected nodes.
Due to the complexity and the unknown locations of sensor nodes, determination of the hop count in the source-to-sink path of the WTSN is intractable with existing solutions, which are based on statistical laws (e.g. the Poisson distribution).
After some graph cleanup and simplification, all source-to-sink paths are extracted as candidate haplotypes, up to a limit (default 128).
