Abstract: Solar power plants(SPPs) have shown a lot of good outcomes
in providing a various functions depending on industrial expectations by
deploying ad-hoc networking with helps of light loaded and battery powered
sensor nodes. In particular, it is strongly requested to develop an algorithm to
deriver the sensing data from the end node of solar power plants to the sink node
on time. In this paper, based on the above observation we have proposed an
IEEE802.15.4 based self routing scheme for solar power plants. The proposed
beacon based priority routing Algorithm (BPRA) scheme utilizes beacon
periods in sending message with embedding the high priority data and thus
provides high quality of service(QoS) in the given criteria. The performance
measures are the packet Throughput, delivery, latency, total energy
consumption. Simulation results under TinyOS Simulator(TOSSIM) have
shown the proposed scheme outcome the conventional Ad hoc On-Demand
Distance Vector(AODV) Routing in solar power plants.
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: Sensor network applications are often data centric and
involve collecting data from a set of sensor nodes to be delivered
to various consumers. Typically, nodes in a sensor network are
resource-constrained, and hence the algorithms operating in these
networks must be efficient. There may be several algorithms available
implementing the same service, and efficient considerations may
require a sensor application to choose the best suited algorithm. In
this paper, we present a systematic evaluation of a set of algorithms
implementing the data gathering service. We propose a modular
infrastructure for implementing such algorithms in TOSSIM with
separate configurable modules for various tasks such as interest
propagation, data propagation, aggregation, and path maintenance.
By appropriately configuring these modules, we propose a number
of data gathering algorithms, each of which incorporates a different
set of heuristics for optimizing performance. We have performed
comprehensive experiments to evaluate the effectiveness of these
heuristics, and we present results from our experimentation efforts.