Download A Distributive Polling Based Mobile Data Gathering in WSN

International Journal of
Emerging Research in Management &Technology
ISSN: 2278-9359 (Volume-5, Issue-5)
Research Article
May
2016
Special Issue on International Conference on Advances in Engineering (ICAE) -2016
Conference Held at Hotel Magaji Orchid, Sheshadripuram, Bengaluru, India.
A Distributive Polling Based Mobile Data Gathering in WSN
Sunita Patil*
Research Scholar, Department of CSE,
ACS College of Engineering Bengaluru
VTU University, Karnataka, India
Dr.Senthil Kumaran
Associate Professor, Department of CSE,,
ACS College of Engineering Bengalur
VTU University, Karnataka, India
Abstract— Data gathering is the biggest issue in the Wireless sensor networks(WSNs). Due to the data collection in
MWSNs, scalability and life time of the network is reduced. In the existing system provided two approaches for
collecting the data: the first approach collected the data via multi-hop relay by data packets; the second approach
using a mobile element for collecting the data. But this approaches had some limitations such as reduced the energy
consumption, increase of delay, lose their data due to overflow while waiting for the mobile element. In the proposed
system, the wireless sensor network has a three-layer framework for mobile data collection, which includes multiplecluster head for load balancing. Multiple cluster heads within a cluster cooperate with each other to perform energysaving inter-cluster communications. Cluster head data is forwarded to SenCar through inter-cluster transmissions.
SenCar is equipped with two antennas, which enables two cluster heads to simultaneously upload data to SenCar in
each time by utilizing multi-user multiple-input and multiple-output (MU-MIMO) technique. SenCar can efficiently
gather data from cluster heads and transport the data to the static data sink by visiting selected polling point. The
objective is to achieve energy saving per node and energy saving on cluster heads comparing with data collection
through multi-hop relay to the static data sink, and shorter data collection time compared to traditional mobile data
gathering.
Keywords— MU-MIMO, SenCar, MWSN, WSN, selected polling point
I. INTRODUCTION
Most of the wireless sensor networks (WSN) consist of static sensors, which can be deployed in a wide environment
for monitoring applications. While transmitting the data from source to static sink, the amount of energy consumption of
the sensor node is high. It results in reduced lifetime of the network. A WSN is usually deployed with static sensor nodes
to perform monitoring missions in the region of interest. However, due to the dynamic changes of events and hostile
environment, a pure static WSN could face the problems like covering the whole area of interest, holes in the coverage
area. Some of the WSN architectures have been proposed based on Mobile Elements. There is large number of
approaches to resolve the above problem.
Clustering is a method used to overcome traditional WSNs related issues. In clustered networks, some sensors are
elected as cluster heads (CHs) for each cluster created. Sensor nodes in each cluster transmit their data to the respective
CHs and CH aggregates data and forwards them to a central base station. Clustering facilitates efficient utilization of
limited energy of sensor nodes and hence extends network lifetime.
The entire sensor network can be divided into several clusters, where sensors in each cluster must be connected to
SenCar while it is moving through the cluster. When SenCar moves close to the cluster, all sensors belonging to the
cluster will be woken up and will prepare to send packets. Sensed data can be collected by SenCar while it is traversing
the cluster. To make this scheme work, two issues must be resolved here. The first issue is how the user can wake up and
turn off sensors only when needed. A radio wakeup scheme allows the transceivers of sensors to be deactivated when
they are idle.
A. Problem statement
To collect data as fast as possible in a cluster, the following two requirements should be satisfied:
 The two cluster heads in a scheduling pair both should be covered by SenCar with the same transmission range
as a sensor, when SenCar is at the selected polling point specific for this scheduling pair.
 By visiting the selected polling points in a cluster, SenCar should achieve maximum sum of the uplink MIMO
capacities in the cluster.
B. Objective
 Polling point selection and cluster head pairing operations are not integrated.
 Polling point selection is not optimized.
 Spatial coverage properties are not considered.
 Multiple cluster based MIMO scheduling is not provided
II. RELATED WORK
Kai Li and Kien A. Hua Department of Electrical Engineering and Computer Science, University of Central Florida
Orlando, Florida 32816, U.S.A. “Mobile Data Collection Networks for Wireless Sensors” [1] have proposed that a
© 2016, IJERMT All Rights Reserved
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Patil et al., International Journal of Emerging Research in Management &Technology
ISSN: 2278-9359 (Volume-5, Issue-5)
mobile data collection network (MDCNet) as a new paradigm for wireless sensing applications. MDCNet is a fully selfdeployed mesh network with virtual mesh nodes (mobile relay nodes), through which sensor data can be collected in a
single hop and transmitted to the sinks.
Ms.Rubia.R, Mr.SivanArulSelvan “A Survey on Mobile Data Gathering in Wireless Sensor Networks - Bounded
Relay” [2] have proposed two approaches, namely Single Hop Data Gathering problem (SHDGP) and mobile Data
Gathering, which is used to increase the lifetime of the network. Single Hop Data Gathering Problem is used to achieve
the uniform energy consumption. The mobile Data Gathering algorithm is used to find the minimal set of points in the
sensor network, which serves as data gathering points for mobile network. Even after so many decades of research, there
are some unresolved problems like non uniform energy consumption, increased latency, which needs to be resolved.
Abdul Waheed Khan, Abdul Hanan Abdullah *, Mohammad Hossein Anisi and Javed Iqbal Bangash A
“Comprehensive Study of Data Collection Schemes Using Mobile Sinks in Wireless Sensor Networks” [3] have
proposed that taxonomy of various data collection/dissemination schemes that exploit sink mobility. Based on how sink
mobility is exploited in the sensor field, we classify existing schemes into three classes, namely path constrained, path
unconstrained, and controlled sink mobility-based schemes. We also organize existing schemes based on their primary
goals and provide a comparative study to aid readers in selecting the appropriate scheme in accordance with their
particular intended applications and network dynamics. Finally, we conclude our discussion with the identification of
some unresolved issues in pursuit of data delivery to a mobile sink.
Javad Rezazadeh, Marjan Moradi, Abdul Samad Ismail “Mobile Wireless Sensor Networks Overview” [4] have
proposed that Mobile wireless sensor networks (MWSNs) have recently launched a growing popular class of WSN in
which mobility plays a key role in the execution of the application. In recent years, mobility has become an important
area of research for the WSN community. The increasing capabilities and the decreasing costs of mobile sensors make
mobile sensor networks possible and practical. Although WSN deployments were never envisioned to be fully static,
mobility was initially regarded as having several challenges that needed to be overcome, including connectivity,
coverage, and energy consumption, among others.
III. PROPOSED SYSTEM
In the proposed system, the wireless sensor network has a three-layer framework for mobile data collection, which
includes multiple-cluster head for load balancing. Multiple cluster heads within a cluster cooperate with each other to
perform energy-saving inter-cluster communications. Cluster head data is forwarded to SenCar through inter-cluster
transmissions. SenCar is equipped with two antennas, which enables two cluster heads to simultaneously upload data to
SenCar in each time by utilizing multi-user multiple-input and multiple-output (MU-MIMO) technique. SenCar can
efficiently gather data from cluster heads and transport the data to the static data sink by visiting selected polling point.
Figure 1 Efficient data collection mechanisms in wireless sensor networks
This proposed system consists of the three layers such as Sensor Layer, Cluster Head Layer and Sencar Layer as
shown in figure1.
A. Sensor Layer
The essential operation of clustering is the selection of cluster heads. To prolong network lifetime, we naturally
expect the selected cluster heads are the ones with higher residual energy. The LBC algorithm is comprised of four
phases: (1) Initialization; (2) Status claim; (3) Cluster forming and (4) Cluster head synchronization.
1. Initialization Phase
In the initialization phase, each sensor acquaints itself with all the neighbors in its proximity. If a sensor is an
isolated node (i.e., no neighbor exists), it claims itself to be a cluster head and the cluster only contains itself. Otherwise,
a sensor, say, si, first sets its status as “tentative” and its initial priority by the percentage of residual energy.
2. Status Claim
In the second phase, each sensor determines its status by iteratively updating its local information, refraining from
promptly claiming to be a cluster head. We use the node degree to control the maximum number of iterations for each
© 2016, IJERMT All Rights Reserved
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Patil et al., International Journal of Emerging Research in Management &Technology
ISSN: 2278-9359 (Volume-5, Issue-5)
sensor. Whether a sensor can finally become a cluster head primarily depends on its priority. Specifically, we partition
the priority into three zones by two thresholds, t h and tm (th > tm), which enable a sensor to declare itself to be a cluster
head or member, respectively, before reaching its maximum number of iterations. During the iterations, in some cases, if
the priority of a sensor is greater than th or less than tm compared with its neighbours, it can immediately decide its final
status and quit from the iteration.
3. Cluster Forming
The third phase is cluster forming that decides which cluster head a sensor should be associated with. The criteria
can be described as follows: for a sensor with tentative status or being a cluster member, it would randomly affiliate itself
with a cluster head among its candidate peers for load balance purpose. In the rare case that there is no cluster head
among the candidate peers of a sensor with tentative status, the sensor would claim itself and its current candidate peers
as the cluster heads.
4. Synchronization among Cluster Heads
To perform data collection by TDMA techniques, intra cluster time synchronization among established cluster heads
should be considered. The fourth phase is to synchronize local clocks among cluster heads in a CHG by beacon messages.
First, each cluster head will send out a beacon message with its initial priority and local clock information to other nodes
in the CHG. Then it examines the received beacon messages to see if the priority of a beacon message is higher. If yes, it
adjusts its local clock according to the timestamp of the beacon message. In our framework, such synchronization among
cluster heads is only performed while SenCar is collecting data.
B. Cluster Head Layer: Connectivity among CHG s
The multiple cluster heads in a CHG coordinate among cluster members and collaborate to communicate with other
CHGs. Hence, the inter-cluster communication in LBCDDU is essentially the communication among CHGs. By
employing the mobile collector, cluster heads in a CHG need not to forward data packets from other clusters. Instead, the
inter-cluster transmissions are only used to forward the information of each CHG to SenCar. The CHG information will
be used to optimize the moving trajectory of SenCar, which will be discussed in the next section. For CHG information
forwarding, the main issue at the cluster head layer is the inter-cluster organization to ensure the connectivity among
CHGs.
1. Connectivity among CHGs
The inter-cluster organization is determined by the relationship between the inter-cluster transmission range Rt and
the sensor transmission range Rs. Clearly, Rt is much larger than Rs. It implies that in a traditional single-head cluster,
each cluster head must greatly enhance its output power to reach other cluster heads
2. Inter-Cluster Communications
How cluster heads in a CHG collaborate for energy-efficient inter-cluster communication. We treat cluster heads in a
CHG as multiple antennas both in the transmitting and receiving sides such that an equivalent MIMO system can be
constructed [27]. The self-driven cluster head in a CHG can either coordinate the local information sharing at the
transmitting side or act as the destination for the cooperative reception at the receiving side. Each collaborative cluster
head as the transmitter encodes the transmission sequence according to a specified space-time block code (STBC) [36] to
achieve spatial diversity. Compared to the single-input single-output system, it has been shown in [37] that a MIMO
system with spatial diversity leads to higher reliability given the same power budget. An alternative view is that for the
same receive sensitivity; MIMO systems require less transmission energy than SISO systems for the same transmission
distance.
C. Sencar Layer: Trajectory Planning
How to optimize the trajectory of SenCar for the data collection tour with the CHG information, which is referred to
as the mobility control at the Sen-Car layer.
1. Selection of polling point
If the mobile collector is available then the data collection is partitioned in two ways :
i.
The sensors which are selected as PPs are efficiently distributed and are close to the data sink.
ii.
The number of PPs is smallest under the constraint of the relay hop bound.
2. MU-MIMO Uploading
Schedule pattern and selected polling points for the corresponding scheduling pairs, aiming at achieving the
maximum sum of MIMO uplink capacity in a cluster. We assume that SenCar utilizes the minimum mean square error
receiver with successive interference cancellation (MMSE-SIC) as the receiving structure for each MIMO data uploading.
3. Data Collection with Time Constraints
When there are time constraints on data messages. In their solution, the mobile collector would visit the nodes with
messages of the earliest deadline. Here, we extend and adapt their solutions to the clustered network.
i.
The cluster heads collect data messages and calculate a deadline by averaging all the deadlines from messages in
the cluster. All the clusters then forward their deadline information to SenCar. The SenCar selects the cluster
with the earliest average deadline and moves to the polling point to collect data via MU-MIMO transmissions.
ii.
After SenCar finishes data gathering, it checks to see whether collecting data from the next polling point would
cause any violations of deadline in its buffer. If yes, it immediately moves back to the data sink to upload
buffered data and resumes data collection in the same way. By prioritizing messages with earlier deadlines,
SenCar would do its best to avoid missing deadlines.
© 2016, IJERMT All Rights Reserved
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Patil et al., International Journal of Emerging Research in Management &Technology
ISSN: 2278-9359 (Volume-5, Issue-5)
IV. EXPERIMENTAL RESULT
We evaluate the performance of our frame-work and compare it with other schemes. The simulation software used
and evaluation of results is done by using MATLAB 2013b. Figure 2 is described about the energy consumption and data
latency through the graph.
Figure 2 Performance comparisons of different data gathering schemes when M ¼ 2
V. CONCLUSION
Sensor data gathering is performed using mobile collectors. The Distributed Load Balanced Clustering with Dual Data
Upload (LBC-DDU) scheme is employed for the data collection process. LBC-DDU scheme is enhanced with optimal
polling point selection and spatial coverage management features. The Multiple Input and Multiple Output (MIMO)
scheduling is improved to support multiple cluster model. Wireless sensor network data collection process is handled
with energy and network lifetime management factors. Traffic level and mobile collector movement are controlled with
Optimal polling point selection mechanism. Spatial coverage analysis is carried out to verify the network coverage
achievement. The system reduces the computational and communication load in the data collection process.
ACKNOWLEDGEMENTS
Thanks to ACS College of Engineering, Bangalore; family and friends.
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