CAN SENSORS COLLECT BIG DATA? AN ENERGY EFFICIENT BIG DATA GATHERING ALGORITHM FOR WSN

CONTACT: PRAVEEN KUMAR. L (,+91 – 9791938249)
MAIL ID: , praveen@nexgenproject.com
Web: www.nexgenproject.com,
CAN SENSORS COLLECT BIG DATA? AN ENERGY EFFICIENT BIG
DATA GATHERING ALGORITHM FOR WSN
ABSTRACT
Recently, incredible growth in communication technology has given rise to the hot topic, Big
Data. Distributed wireless sensor networks (WSNs) are the key provider of Big Data and can
generate a significant amount of data. Various technical challenges exist in gathering the real
time data. Energy efficient routing algorithms can overcome these challenges. The signal
transmission features have been obtained by analyzing the experiments. According to these
experiments, an energy efficient Big Data algorithm (BDEG) for WSN is proposed for real time
data collection. Clustering communication is established on the basis of RSSI and residual
energy of sensor nodes. Experimental simulations show that BDEG is stable in terms of network
lifetime and data transmission time because of load balancing scheme. The effectiveness of the
proposed scheme is verified through numerical results obtained in MATLAB.
EXISTING SYSTEM:
Sequentially clustered energy efficient algorithm (S-EERP)and randomly energy efficient
algorithm (R-EERP) are presented in [18]. In S-EERP, nodes are arranged sequentially in indoor
environment, whereas in R-EERP, nodes are deployed randomly. Both of these are based on
LEACH. Authors of [19]have shown the experiments over cooperative routing schemes and
analyze the results for industrial production plant. A routes election algorithm has been proposed
in [20]. Neighbor nodes share information with each other for industrial operations. Local
information is disseminated among the local nodes with less overhead. Its performance is
optimized in different environments, but real time data gathering risk and constraints of

industrial applications are not analyzed, therefore it connote used for Big Data applications and
industrial applications without modification. A routing algorithm for large scale network has
been proposed in [21] which is based on link quality and hop count. However, routing on the
basis of hop count does not present an optimal solution for energy efficiency in terrestrial
applications. A large number of sensors generate huge amount of data for the service providers
which is steadily monitored for quality improvement and maintenance. To monitor the
conditions of the atmosphere, huge data is gathered by the electronic sensors. Electronic and
mechanical sensors used for the health care services are a big source of data. But how to manage
this data in an energy efficient mannerism still an important issue.
PROPOSED SYSTEM:
Our main objective is to minimize wireless communication distance as energy depletion is
directly proportional to the traveled distance [22]. To avoid the flooding of data (when all the
sensor nodes requests for the data or transmit the data to the base station), we follow the static
approach to determine the number of clusters. Flooding causes the problem in gathering accurate
data from all the nodes. Earlier research supports the idea of increasing the number of clusters to
conserve energy. But in this paper, we advocate the idea, that large number of cluster heads
degrades the performance in terms of time and energy. We use the received signal strength
(RSSI), to transmit and receive the data. In the proposed scheme for Big Data, we have devised
an energy efficient algorithm in which energy is conserved in the process of forwarding and
receiving data to and from the datacenter. This algorithm uses the information of received signal
strength (RSSI) computed through the practical experiments on Mica motes. To analyze the
signal strength of the nodes, different experiments have been conducted [9]. Radio
frequency(RF) propagation feature of Mica motes antenna has been evaluated with ground level
effect, from various elevation angles and distances. Link quality indicator (LQI) and received
signal strength (RSSI) are significant parameters of RF link which are delivered by CC2420 (A

true single-chip 2.4 GHzIEEE 802.15.4 compliant RF transceiver designed for low power and
low-voltage wireless).
CONCLUSION
In the proposed scheme, deployment of a large number of nodes is considered to identify the
stringent requirements of WSN. Experimental evaluation, to recognize the various features of
WSN has been taken into consideration like ground level effects on WSN signals which affect
the data transmission among nodes. The signal transmission features of WSN are primed from
experimental analysis and an energy efﬁcient Big Data gathering algorithm called BDEG has
been proposed for real time Big Data application like border surveillance, environmental
monitoring and industrial operations. In the proposed scheme, RSSI values are used to determine
the director indirect communication of SNs with BS. Constraint of energy and time are solved
via BDEG and discussed in the section IV. Cluster formation and re-structuring makes thebe
more reliable and optimized. The performance of novel scheme is compared with traditional and
well known algorithms. Experimental simulations show that BDEG can attain high enactment in
energy conservation and time for gathering Big Data for critical operations. Total distance in
multi-hop communication is higher than single hop data transmission. Future work will
concentrate on reducing the total distance for data transmission and will consider the other
parameters like signal to noise ratio and bit error rate to more optimize the BDEG algorithm.
REFERENCES
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CAN SENSORS COLLECT BIG DATA? AN ENERGY EFFICIENT BIG DATA GATHERING ALGORITHM FOR WSN

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CAN SENSORS COLLECT BIG DATA? AN ENERGY EFFICIENT BIG DATA GATHERING ALGORITHM FOR WSN