Towards Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks

Towards Energy Efficient Big Data Gathering

in Densely Distributed Sensor Networks

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.  

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 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.

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