Abstract:
One of the most fundamental aspects of wireless sensor networking based
applications is that they are either designed to monitor physical quantities,
observe various phenomena, disseminate useful information to autonomous
or semi-autonomous agents, or simply gather information in
their surrounding environment. The collected information may be used
by a cyber-physical system or transmitted via a data network to a remote
location for subsequent data processing. In both cases, the information
can become meaningless if the current location of the sending
sensor node is not known or the reported information or observation is
not accurately location stamped. In addition to this, there are certain
tracking applications, monitoring applications, and geographical routing
protocols that put a stringent demand that the location of sensor nodes
should be known a priori. This work proposes a distributed localization
algorithm that describes how a small sub-region in a sensing eld can
construct a spatial map of the locations of all the neighbouring nodes
based on inter-node distances and how each sub-region can then stitch
its own map with those of all other sub-regions in its close proximity with
the outcome that the collection of stitched maps forms a consistent coordinate
system. The proposed localization algorithm employs concepts
of range lookup, multidimensional scaling, and least-squares tting to
compute locations of static sensor nodes. The proposed algorithm can
compute relative coordinates without the use of any anchor nodes and
is also capable of converting the relative coordinates into absolute coordinates
if a certain minimum number of anchor nodes become available
at a later stage. The proposed localization scheme is only one component
of a proposed framework which aims to enhance road tra c safety
by employing static roadside sensors. In addition to the localization service,
three more components have been proposed for the road tra c
safety framework namely a road segment surveillance scheme to detect
vehicles on two-way roads, an adaptive data forwarding scheme to route
data among roadside sensors using reinforcement learning, and a reverse
forwarding scheme to deliver road condition information or warning messages
from static roadside sensors to vehicles approaching a designated
region-of-interest.