Abstract: The Sensor Network consists of densely deployed
sensor nodes. Energy optimization is one of the most important
aspects of sensor application design. Data acquisition and aggregation
techniques for processing data in-network should be energy efficient.
Due to the cross-layer design, resource-limited and noisy nature
of Wireless Sensor Networks(WSNs), it is challenging to study
the performance of these systems in a realistic setting. In this
paper, we propose optimizing queries by aggregation of data and
data redundancy to reduce energy consumption without requiring
all sensed data and directed diffusion communication paradigm to
achieve power savings, robust communication and processing data
in-network. To estimate the per-node power consumption POWERTossim
mica2 energy model is used, which provides scalable and
accurate results. The performance analysis shows that the proposed
methods overcomes the existing methods in the aspects of energy
consumption in wireless sensor networks.
Abstract: Computing and maintaining network structures for efficient
data aggregation incurs high overhead for dynamic events
where the set of nodes sensing an event changes with time. Moreover,
structured approaches are sensitive to the waiting time that is used
by nodes to wait for packets from their children before forwarding
the packet to the sink. An optimal routing and data aggregation
scheme for wireless sensor networks is proposed in this paper. We
propose Tree on DAG (ToD), a semistructured approach that uses
Dynamic Forwarding on an implicitly constructed structure composed
of multiple shortest path trees to support network scalability. The key
principle behind ToD is that adjacent nodes in a graph will have
low stretch in one of these trees in ToD, thus resulting in early
aggregation of packets. Based on simulations on a 2,000-node Mica2-
based network, we conclude that efficient aggregation in large-scale
networks can be achieved by our semistructured approach.