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centralized power-aware multicasting, and localized power-aware multicasting.
Location-based multicasting mechanisms include scalable energy-efficient asynchro-
nous dissemination, geographic multicast routing, and two-tier data dissemination.
1.9.1.3 Geocasting
In geocasting, dissemination of packets is done in a set of nodes belonging to a speci-
fied area. For example, the area might be a rectangle, square, circle, or a polygon. This
scheme assumes prior knowledge of the location coordinates of each node before dis-
semination of packets. Geocasting can be further divided into nonguaranteed geocast-
ing and guaranteed geocasting, depending on whether the sensor nodes can receive
geocast packets or not. Examples of nonguaranteed geocasting mechanisms include
unicast routing with area delivery and directed flooding-based geocasting. Guaranteed
geocasting mechanisms include simple flooding, geocasting via efficient broadcasting,
and geocasting via face routing.
1.9.2 Network Discovery
The a-priori information about the location of nodes can play a major role in proper
functioning of the network. However, due to the dynamic environment of WSN, it
may not be possible to have prior knowledge of the topology. Hence, in such ad-hoc,
decentralized architectures, the topology has to be constructed in real time. In addi-
tion, periodic updates about the topology should be disseminated to all the nodes in
the network, as there could be deletion or addition of new sensors in the network.
While dynamic topology discovery in WSN has been an ongoing research topic, self-
discovery of each node with reference to its respective sink nodes is an equally impor-
tant research topic in WSN. Although the Global Positioning System (GPS) has been
used extensively in MANET, its direct application in WSN is not feasible due to the
resource-constrained environment of WSN.
1.9.3 Data Aggregation in WSN
The high-density deployment of sensor nodes in a particular region can lead to a phe-
nomenon called spatial correlation. In this event, identical sensing of data can occur
by nodes in close proximity to each other. Hence, there is a need to minimize redun-
dancy in data received from the node's immediate neighbors, before transmitting it to
the sink node. It is during such scenarios that data aggregation plays a critical role in
reducing redundancy and minimizing retransmissions, and in doing so, increasing the
overall energy efficiency of the network. Furthermore, the adoption of a data-centric
approach for networking over an address-centric approach has provided more emphasis
for data aggregation.
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