Wireless Sensor Networks : Technology, Protocols, and Applications

EXISTING MIDDLEWARE 257
tools and services to create WSN applications. Em* tools can be used to support
deployment, simulation, emulation, and visualization of live systems, and its
services include link and neighborhood estimation, time synchronization, and
routing. Em* supports flooding-based, geographical, and quad-tree-based routing
protocols, and supports many devices and a variety of radio hardware. It does not
provide information on how to adapt or manage network resources while utilizing
application knowledge.
8.4.8 Impala
Impala [8.10] is lightweight middleware and an API for sensor application
adaptation and update which can improve system reliability and energy efficiency.
It is event-driven middleware that achieves effective application adaptation.
It is intended to act as an operating system, resource manager, and event filter
on top of which specific applications can be installed and run. This WSN middleware
contains three middleware agents: an application adapter, an application
updater, and an event filter. The application adapter adapts applications to various
runtime conditions in order to improve performance, energy efficiency, and
robustness. The application updater receives and propagates software updates
through the wireless transceiver and installs them on the sensor node. The
event filter captures and dispatches events to the application adapter and updater,
and initiates chains of processing. Impala has five types of events: timer, packet,
send done, data, and device failure. Applications, the application adapter, and
the application updater are all programmed into a set of event handlers which
are invoked by the event filter when events are received. Impala supports both
parameter- and device-based adaptations. An example of the application adapter
is: When a device failure is detected, history-based protocol is switched to
flooding protocol.
8.4.9 DFuse
DFuse [8.11] is proposed for programming fusion applications, and it is middleware
only for data fusion. Data fusion focuses on decision making based on data and
information that is acquired, filtered, and correlated with other relevant information.
That process would involve information conversion into an appropriate format,
which may be acquired from one or multiple sources. Data fusion of multiple
sources usually reduces uncertainty, improves the reliability of event detection,
and enhances system tolerance and robustness. When performed systematically
with an appropriate application in mind, it reduces volume, improves QoS, and
reduces energy consumption. In WSNs, data fusion may occur in the sink or sensor
nodes. If the fusion point is closer to the geographical area where the data have been
generated, the data filtering/aggregation efficiency could be higher. If the data
fusion point is too close to the area, the fusion operation will be limited to a few
sources and therefore will be less immune to undetected errors. Therefore, the data