Decentralized Movement Analysis - Computational Movement Analysis - page 74

a functional nature. For instance, in Both et al. ( P19 . 2013 ) the generation of infor-
mation is separated from the querying of that information. Quite often information
is also separated from the rather limited and constrained nodes. In both the fish in
Both et al. ( P19 . 2013 ) and the flocking agents in Laube et al. ( P6 . 2008 ) information
tokens are separated from their carriers such that information can move beyond the
constraints of the individual nodes. Hence, separation clearly is a precondition for
mobility compensation and mobility diffusion .
4.3.3.5 Heuristics
The fundamental question as to whether decentralized spatial computing in general
can perform as good as conventional spatial computing, with respect to efficiency,
effectiveness, accuracy, or scalability, is still subject of ongoing research ( P7 . Laube
et al. 2009 ). Irrespective of this question, approximation approaches or heuristics
can be an appropriate way to address tricky tasks in arguably constrained decentral-
ized environments. For instance, Laube et al. ( P6 . 2008 ) argue that heuristics are
a suitable way of compensating for limited perception of individual sensor nodes.
The heuristic extrapolates the presence of a flock, reaching beyond any single node's
limited perception range. Duckham ( 2012 ) follows up on this initial approach and
argues that the apparently simple nkr -flock is computationally intractable such that
even centralized spatial information systems require heuristics in order to compute
such patterns (Duckham 2012 ).
4.4 Related Work
This section summarizes further related work relevant to the topics covered in this
chapter. The chapter then concludes with insights and lessons learned from both the
research covered in this chapter and in related work.
Wireless sensor networks and geosensor networks . The textbook “Wireless Sen-
sor Networks—An Information Processing Approach” by Zhao and Guibas ( 2004 )
offers an excellent entry point into the wider research area. Most research in wire-
less sensor networks is concerned with lower-level engineering tasks establishing
the system infrastructure and maintaining communication (physical, data link, net-
work, transport layers, Zhao and Guibas 2004 ). The top-most application level, the
focus of this topic, still receives less attention. More recently Nittel ( 2009 ) has sum-
marized the field's progress in monitoring geographic phenomena and advances
in dynamic environmental monitoring. She makes very clear that the key task of
geosensor networks is to sense, monitor and track dynamic phenomena in real-time
in the environment. Clearly, this not only refers to change, but also mobility.