A voting median base algorithm for approximate performance monitoring of wireless sensor networks
2008
Wireless Sensor Networks (WSNs) are expected to be a new, revolutionary
technology in the same manner as the Internet. This is due to their special
characteristics such as low power consumption, ad hoc operation, self-maintenance
and many other features. These special characteristics help in reducing the costs of
network manufacture and implementation which extends their applications in a
number of areas such as health and military services. Unfortunately, network
resources such as memory, power and processing capacity constitute a serious
constraint. In addition, they reduce the immunity of the network against external and
internal impacts (such as electromagnetic interference) which make sensor node
operations frequently deviate from the norm, degrading the WSN's functionality. In
some cases the data collected by the network becomes unreliable; the monitoring of
the phenomenon may even fail. To ensure the reliability of the network, several tools
have been proposed to detect and isolate these deviations but most use relatively
high levels of resources. In certain circumstances these state-of-the-art tools are
unable to avoid the instant impact of data deviations on the accuracy of the collected
data and on the network's functionality.
This thesis overcomes these drawbacks by proposing a new, real-time, low
resources usage, distributed performance algorithm that will monitor the accuracy of
collected data and network functionality in large scale dense deployed WSNs. In
order to achieve this, we have used the spatio-temporal correlation between the
measurements of the neighbour nodes in large scale dense deployed WSNs. This
correlation arises due to near proximity (of the nodes) and/or the slow characteristics'
change of monitored phenomenon.
The proposed algorithm has been tested via simulation experiments using different
simulated and real world application data sets. Moreover, it has been tested on a real
network testbed with Mote sensors using continuous reporting and event-driven
applications. The results from these experiments showed a high rate of detection of
changes in the reliability levels of data and in network performance. They also
showed a high level of accuracy in terms of the detection of sensor faults. This,
however, comes alongside certain limitations because of the use of simple passive
analysis with the proposed algorithm.
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