Towards energy efficient big data gathering
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This document discusses energy efficient big data gathering in densely distributed sensor networks. It proposes a new mobile sink routing and data gathering method using network clustering based on a modified Expectation-Maximization technique. This aims to minimize energy consumption by deriving an optimal number of clusters to reduce energy used for data requests and wireless transmissions between sensor nodes. Numerical results are presented to validate that the proposed method can efficiently gather big data from sensor networks in an energy efficient manner.
![No: 6, 2nd Floor, 11th Main, Jaya Nager 4th Block, (Above raymond showrooms ), blr-11
M:9611582234, 9945657526
INTRODUCTION
RECENT development of various areas of Information and Communication Technology
(ICT) has contributed to an explosive growth in the volume of data. According to a
report published by IBM in 2012 [1] , 90 percent of the data in the world was generated
in the previous two years. As a consequence, the concept of the big data has emerged
as a widely recognized trend, which is currently attracting much attention from
government, industry, and academia [2]. As shown in Fig. 1, the big data comprises
high volume, high velocity, and high variety information assets [3], which are difficult to
gather, store, and process by using the available technologies.
The variety indicates that the data is of highly varied structures (e.g. data
generated by a wide range of sources such as Machine-to-Machine (M2M), Radio
Frequency Identification (RFID), and sensors) while the velocity refers to the high
speed processing/analysis (e.g., click-streaming, fast database transactions, and so
forth). On the other hand, the volume refers to the fact that a lot of data needs to be
gathered for processing and analysis. Although currently used services (e.g. social
networks, cloud storage, network switches, and so forth) are already generating much
volume of the big data. it is anticipated that more and more data will be generated
by sensors/RFID devices such as thermometric sensors, atmospheric sensors, motion
sensors, accelerometers, and so on. In fact, according to a report by ORACLE [4], the
volume of data generated by sensors and RFID devices is expected to reach the order
of petabytes. Interestingly as shown in Fig. 1, the sensors are responsible for
generation of big data in big volume and also in a wide variety](https://image.slidesharecdn.com/towardsenergyefficientbigdatagathering-141014004456-conversion-gate02/85/Towards-energy-efficient-big-data-gathering-2-320.jpg)
![No: 6, 2nd Floor, 11th Main, Jaya Nager 4th Block, (Above raymond showrooms ), blr-11
M:9611582234, 9945657526
Literature Survey
2. Problem Statement :
First, the network is divided to some sub-networks because of the limited
wireless communication range. For example, sensors deployed in a building may not be
able to communicate with the sensors which are distributed in the neighboring buildings.
Therefore, limited communication range may pose a challenge for data collection from
all sensor nodes.
Second, the wireless transmission consumes the energy of the sensors. Even
though the volume of data generated by an individual sensor is not significant, each
sensor requires a lot of energy to relay the data generated by surrounding sensors.
Especially in dense WSNs, the life time of sensors will be very short because each
sensor node relays a lot of data generated by tremendous number of surrounding
sensors. In order to solve these problems, we need an energy-efficient method to
gather huge volume of data from a large number of sensors in the densely distributed
WSNs.
Analysis on Existing Networks
The data compression technology [7] is capable of shrinking the volume of the
transmitted data. Although it is easy to be implemented, the data compression
technology requires the nodes to be equipped with a big volume of storage and high
computational power. In addition, the topology control technology can evaluate the best
logical topology and reduce redundant wireless transmissions [8], [9]. When the
redundant wireless transmissions are reduced, the required energy for wireless
transmissions can be also reduced. Furthermore flow control and routing can choose](https://image.slidesharecdn.com/towardsenergyefficientbigdatagathering-141014004456-conversion-gate02/85/Towards-energy-efficient-big-data-gathering-3-320.jpg)
![the path which consists of nodes having high remaining energy [10], [11]. However,
these techn technologies are not able to deal with the divided networks problem.
No: 6, 2nd Floor, 11th Main, Jaya Nager 4th Block, (Above raymond showrooms ), blr-11
M:9611582234, 9945657526
3.Idea on proposed System:
we propose an energy minimized clustering algorithm by using the Expectation-
Maximization (EM) algorithm for 2-dimensional Gaussian mixture distribution.
Our proposal aims to minimize the sum of square of wireless communication distance
since the energy consumption is proportional to the square of the wireless
communication distance. Moreover, we first focus on the “data request flooding
problem” to decide the optimal number of clusters. The data request flooding problem
refers to the energy inefficiency that occurs when all the nodes broadcast data request
messages to their respective neighboring nodes. This problem wastes energy,
particularly in the high density WSNs. Previous research work advocates increasing the
number of clusters to reduce the data transmission energy. However, in this paper, we
point out that an excessive number of clusters can result in performance degradation,
and therefore, we propose an adequate method for deriving the optimal number of
clusters](https://image.slidesharecdn.com/towardsenergyefficientbigdatagathering-141014004456-conversion-gate02/85/Towards-energy-efficient-big-data-gathering-4-320.jpg)
![Expectation- Maximization technique. Furthermore, an optimal number of clusters to
minimize the energy consumption was evaluated. Numerical results were presented to
verify the effectiveness of our proposal
No: 6, 2nd Floor, 11th Main, Jaya Nager 4th Block, (Above raymond showrooms ), blr-11
M:9611582234, 9945657526
REFERENCES
[1] IBM, “ Four vendor views on big Data and big data analytics: IBM,”
http://www-01.ibm.com/software/in/data/bigdata/, Jan. 2012.
[2] A. Divyakant, B. Philip, and et al., “ Challenges and opportunities
with Big Data,” 2012, a community white paper developed
by leading researchers across the United States. [Online]. Available:
http://cra.org/ccc/docs/init/bigdatawhitepaper.pdf.
[3] S. Sagiroglu and D. Sinanc, “ Big data: A review,” in International
Conference on Collaboration Technologies and Systems (CTS), 2013.
[4] Oracle, “ Big data: Business opportunities, requirements and oracle’s
approach,” pp. 1–8, 2011.
[5] I. Bisio and M. Marchese, “ Efficient satellite-based sensor networks for
information retrieval,” IEEE Systems Journal, vol. 2, no. 4, pp. 464–475,
Dec. 2008.
[6] I. Bisio, M. Cello, M. Davoil, and et al, “ A survey of architectures
and scenarios in satellite-based wireless sensor networks: System design
aspects,” International Journal of Satellite Communications and
Networking (IJSC), vol. 30, no. 6, 2012.
[7] S. Katti, H. Rahul, W. Hu, D. Katabi, M. Medard, and J. Crowcroft,
“ XORs in the air: Practical wireless network coding,” IEEE/ACM
Transactions on Networking, vol. 16, no. 3, pp. 497–510, Jun. 2008.
[8] K. Miyao, H. Nakayama, N. Ansari, and N. Kato, “ LT RT: An efficient
and reliable topology control algorithm for ad-hoc networks,” IEEE
Transactions on Wireless Communications, vol. 8, no. 12, pp. 6050–
6058, Dec. 2009.
[9] N. Li, J. Hou, and L. Sha, “ Design and analysis of an MST -based
topology control algorithm,” INFOCOM 2003. Twenty-Second Annual
Joint Conference of the IEEE Computer and Communications, vol. 4,
no. 3, pp. 1195–1206, May 2005.](https://image.slidesharecdn.com/towardsenergyefficientbigdatagathering-141014004456-conversion-gate02/85/Towards-energy-efficient-big-data-gathering-6-320.jpg)
Towards energy efficient big data gathering
- 1. Towards Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks No: 6, 2nd Floor, 11th Main, Jaya Nager 4th Block, (Above raymond showrooms ), blr-11 M:9611582234, 9945657526 ABSTRACT Recently, the “big dat” emerged as a hot topic because of the tremendous growth of the Information and Communication Technology (ICT). One of the highly anticipated key contributors of the big data in the future networks is the distributed Wireless Sensor Networks (WSNs). Although the data generated by an individual sensor may not appear to be significant, the overall data generated across numerous sensors in the densely distributed WSNs can produce a significant portion of the big data. Energy-efficient big data gathering in the densely distributed sensor networks is, therefore, a challenging research area. One of the most effective solutions to address this challenge is to utilize the sink node’s mobility to facilitate the data gathering. While this technique can reduce energy consumption of the sensor nodes, the use of mobile sink presents additional challenges such as determining the sink node’s trajectory and cluster formation prior to data collection. In this paper, we propose a new mobile sink routing and data gathering method through network clustering based on modified Expectation- Maximization (EM) technique. In addition, we derive an optimal number of clusters to minimize the energy consumption. The effectiveness of our proposal is verified through numerical results. Index Terms—Big data, Wireless Sensor Networks (WSNs), clusterin g, optimization, data gathering, and energy efficiency
- 2. No: 6, 2nd Floor, 11th Main, Jaya Nager 4th Block, (Above raymond showrooms ), blr-11 M:9611582234, 9945657526 INTRODUCTION RECENT development of various areas of Information and Communication Technology (ICT) has contributed to an explosive growth in the volume of data. According to a report published by IBM in 2012 [1] , 90 percent of the data in the world was generated in the previous two years. As a consequence, the concept of the big data has emerged as a widely recognized trend, which is currently attracting much attention from government, industry, and academia [2]. As shown in Fig. 1, the big data comprises high volume, high velocity, and high variety information assets [3], which are difficult to gather, store, and process by using the available technologies. The variety indicates that the data is of highly varied structures (e.g. data generated by a wide range of sources such as Machine-to-Machine (M2M), Radio Frequency Identification (RFID), and sensors) while the velocity refers to the high speed processing/analysis (e.g., click-streaming, fast database transactions, and so forth). On the other hand, the volume refers to the fact that a lot of data needs to be gathered for processing and analysis. Although currently used services (e.g. social networks, cloud storage, network switches, and so forth) are already generating much volume of the big data. it is anticipated that more and more data will be generated by sensors/RFID devices such as thermometric sensors, atmospheric sensors, motion sensors, accelerometers, and so on. In fact, according to a report by ORACLE [4], the volume of data generated by sensors and RFID devices is expected to reach the order of petabytes. Interestingly as shown in Fig. 1, the sensors are responsible for generation of big data in big volume and also in a wide variety
- 3. No: 6, 2nd Floor, 11th Main, Jaya Nager 4th Block, (Above raymond showrooms ), blr-11 M:9611582234, 9945657526 Literature Survey 2. Problem Statement : First, the network is divided to some sub-networks because of the limited wireless communication range. For example, sensors deployed in a building may not be able to communicate with the sensors which are distributed in the neighboring buildings. Therefore, limited communication range may pose a challenge for data collection from all sensor nodes. Second, the wireless transmission consumes the energy of the sensors. Even though the volume of data generated by an individual sensor is not significant, each sensor requires a lot of energy to relay the data generated by surrounding sensors. Especially in dense WSNs, the life time of sensors will be very short because each sensor node relays a lot of data generated by tremendous number of surrounding sensors. In order to solve these problems, we need an energy-efficient method to gather huge volume of data from a large number of sensors in the densely distributed WSNs. Analysis on Existing Networks The data compression technology [7] is capable of shrinking the volume of the transmitted data. Although it is easy to be implemented, the data compression technology requires the nodes to be equipped with a big volume of storage and high computational power. In addition, the topology control technology can evaluate the best logical topology and reduce redundant wireless transmissions [8], [9]. When the redundant wireless transmissions are reduced, the required energy for wireless transmissions can be also reduced. Furthermore flow control and routing can choose
- 4. the path which consists of nodes having high remaining energy [10], [11]. However, these techn technologies are not able to deal with the divided networks problem. No: 6, 2nd Floor, 11th Main, Jaya Nager 4th Block, (Above raymond showrooms ), blr-11 M:9611582234, 9945657526 3.Idea on proposed System: we propose an energy minimized clustering algorithm by using the Expectation- Maximization (EM) algorithm for 2-dimensional Gaussian mixture distribution. Our proposal aims to minimize the sum of square of wireless communication distance since the energy consumption is proportional to the square of the wireless communication distance. Moreover, we first focus on the “data request flooding problem” to decide the optimal number of clusters. The data request flooding problem refers to the energy inefficiency that occurs when all the nodes broadcast data request messages to their respective neighboring nodes. This problem wastes energy, particularly in the high density WSNs. Previous research work advocates increasing the number of clusters to reduce the data transmission energy. However, in this paper, we point out that an excessive number of clusters can result in performance degradation, and therefore, we propose an adequate method for deriving the optimal number of clusters
- 5. No: 6, 2nd Floor, 11th Main, Jaya Nager 4th Block, (Above raymond showrooms ), blr-11 M:9611582234, 9945657526 CONCLUSION we investigated the challenging issues pertaining to the collection of the “big data” generated by densely distributed WSNs. Our investigation suggested that energyefficient big data gathering in such networks is, indeed, necessary. While the conventional mobile sink schemes can reduce energy consumption of the sensor nodes, they lead to a number of additional challenges such as determining the sink node’s trajectory and cluster formation prior to data collection. To address these challenges, we proposed a mobile sink based data collection method by introducing a new clustering method. Our clustering method is based upon a modified
- 6. Expectation- Maximization technique. Furthermore, an optimal number of clusters to minimize the energy consumption was evaluated. Numerical results were presented to verify the effectiveness of our proposal No: 6, 2nd Floor, 11th Main, Jaya Nager 4th Block, (Above raymond showrooms ), blr-11 M:9611582234, 9945657526 REFERENCES [1] IBM, “ Four vendor views on big Data and big data analytics: IBM,” http://www-01.ibm.com/software/in/data/bigdata/, Jan. 2012. [2] A. Divyakant, B. Philip, and et al., “ Challenges and opportunities with Big Data,” 2012, a community white paper developed by leading researchers across the United States. [Online]. Available: http://cra.org/ccc/docs/init/bigdatawhitepaper.pdf. [3] S. Sagiroglu and D. Sinanc, “ Big data: A review,” in International Conference on Collaboration Technologies and Systems (CTS), 2013. [4] Oracle, “ Big data: Business opportunities, requirements and oracle’s approach,” pp. 1–8, 2011. [5] I. Bisio and M. Marchese, “ Efficient satellite-based sensor networks for information retrieval,” IEEE Systems Journal, vol. 2, no. 4, pp. 464–475, Dec. 2008. [6] I. Bisio, M. Cello, M. Davoil, and et al, “ A survey of architectures and scenarios in satellite-based wireless sensor networks: System design aspects,” International Journal of Satellite Communications and Networking (IJSC), vol. 30, no. 6, 2012. [7] S. Katti, H. Rahul, W. Hu, D. Katabi, M. Medard, and J. Crowcroft, “ XORs in the air: Practical wireless network coding,” IEEE/ACM Transactions on Networking, vol. 16, no. 3, pp. 497–510, Jun. 2008. [8] K. Miyao, H. Nakayama, N. Ansari, and N. Kato, “ LT RT: An efficient and reliable topology control algorithm for ad-hoc networks,” IEEE Transactions on Wireless Communications, vol. 8, no. 12, pp. 6050– 6058, Dec. 2009. [9] N. Li, J. Hou, and L. Sha, “ Design and analysis of an MST -based topology control algorithm,” INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications, vol. 4, no. 3, pp. 1195–1206, May 2005.