iisrt divya nagaraj (networks)

International Journal of Networks (IJN)

Vol. 1, Issue. 1, April 2015 ISSN (Online): 2454-1060

10

AbstractWireless Sensor Networks (WSN) consists of

wireless nodes which are either stationary or mobile with limited

energy capabilities. These wireless nodes are located randomly on a dynamically changing environment. The WSN is deployed over

a region to monitor any phenomenon. The WSN network is used

to collect and send the various kinds of data to the base station.

All the sensor nodes are battery-powered devices. The important

aspect is how to reduce the energy consumption of nodes to keep the network lifetime extended to some reasonable time. Data

acquisition being a deep seated strategy in WSN holds major part

of the consumed energy. Hence, this paper proposes a routing

scheme through which the energy in WSN will be optimally

utilized by reducing the energy loss in its transmission from sensed node to sink nodes. The consumed energy will depend on

the distance between sensor nodes and sink node.

Keywords- WSN, Data acquisition, energy efficiency, mobile node,

sink node.

I. INTRODUCTION

Wireless networks have a significant impact on the world.

The need for the battery operated devices running with energy

efficient wireless protocols increases since the wireless

networks become mobile and move in to remote locations.

Energy conservation in the wireless protocols will continue to

be a critical issue in the future because the energy densities of

the batteries have only doubled every 5 to 20 years, depending

on the particular chemistry of the battery. Prolonged

refinement of any given chemistry yields a diminishing return.

The main objective of any sensor network is to maximize

the network life time. All the sensor nodes are disposed when

they are out of battery. Hence the energy must be efficiently

utilized under these circumstances. Unlike other wireless

sensor networks, it is generally hard to charge or replace the

exhausted battery. Therefore it is essential to maximize the

network lifetime which is the most important and primary

objective leaving other metrics as secondary objective [1-2].

The three main components of the wireless sensor network

are sink node, sensor node and monitored events. The sensor

nodes are assumed to be stationary in most of the network

architecture. At the same time the mobility of sink node or

Cluster Head [CH] becomes necessary.

These sensor nodes are deployed randomly on a created

infrastructure in an ad-hoc manner. Energy efficiency and

performance are crucial based on the movement and position

of the sink node or cluster head. The sensor nodes are

deployed randomly over an area of interest and hence mult i-

hop routing becomes mandatory [3-5].

Many wireless networks have been deployed in the recent

years. The main aim of WSN that is deployed in large scale is

to have inexpensive sensor network with low power

utilization. Lot of efforts have been made to achieve this type

of inexpensive and low power utilization network. Some of

the application areas of this wireless sensor network are,

military applications such as battle field surveillance and

enemy tracking and civil applications such as habitat

monitoring, environment observation, forecast system, health

and other commercial applications [6].

The source of energy is very finite for the sensor nodes and

hence the energy efficiency is the most important

consideration. For the optimized usage of energy the sensor

must be in idle state. For energy efficient operation, clustering

approach is employed, where the cluster heads are randomly

selected based on the residual energy. The sensor nodes are

joined in to the clusters in a cost effective way. Power

optimization is well achieved by the reactive routing protocols

and sleep mode operations [7-8].

The primary task of the wireless sensor network is to

collect the data from the interested area and transmit that

information to the Base Station [BS]. A simple approach is

that each sensor node can directly transmit the information to

the BS. But, when the BS is located far away from the target

area, the sensor nodes will die quickly due to much of the

energy consumption. Therefore mobile sinks have been

proposed as a solution for the data acquisition in the WSN to

balance the energy consumption [9-10].

The overview of the related work of data acquisition is

provided in section II. System model is introduced in section

III and the proposed scheme is explained in detail. The

simulation results are analyzed in section IV. Finally, the work

is concluded in section.

Energy Efficient Data Acquisition system for increasing

the lifetime for WSN 1N.Divya,

2Kovendan.AKP,

3Dr.D.Sridharan

1,2,3Department of ECE, College of Engineering, Guindy, Anna University, Chennai, INDIA



11

II. RELATED WORK

The challenging task is to design a wireless sensor network

where the sensor nodes are organised in to a multi-hop

wireless network that must be able to function properly for a

long time with a limited power supply. In order to solve this

problem, many researchers have suggested deploying different

types of nodes in to the network. The basic sensor nodes and

the sink node are the two types of nodes that are deployed in

the wireless sensor network. Sensing task is performed by the

basic sensor node. These sensing nodes are simple nodes

which have limited power supplies. The sink nodes organize

the basic sensors around them in to a cluster that only

communicates with the cluster node. The sink nodes are much

more powerful and focus on the communications and

computations. Such network helps to increase the energy

efficiency by using the cluster nodes as central media

controllers. This type of network helps to reduce the idle

listening and resending due to collisions. It also reduces the

protocol overhead used in collision avoidance. But this kind of

central controller cannot be used in the environment where the

network layout changes rapidly. Therefore the sink nodes

should be used only in the applications where the environment

and sensors are static [11-12].

Energy efficiency is one of the most important

performance measures in WSN. Over the past few decades,

considerable number of articles has been published on the

optimization of the power consumption. Optimization

framework for a WSN is proposed to determine whether a

direct transmission is preferred for a configuration of nodes on

a cooperative transmission.

In the recent years sink mobility has become an important

research topic in wireless sensor networks. Existing

methodology shows that sink mobility has a good performance

in WSN. In [13-15], mobile sinks are mounted on people or

animals which move randomly in order to collect the data

from the area of interest. The information is sensed by the

sensors where the sink trajectories are random. If the

trajectories of mobile sinks are constrained or predetermined

as in [16], then the efficient data collection problems are

concerned in order to improve the network performance. The

energy efficiency is improved by the path constrained sink

mobility of single-hop sensor network. But this may be

infeasible due to the limits of the path location and

communication power. Hence the authors propose multi-hop

sensor networks[17], [18] with the path constrained mobile

sink where the shortest path tree [SPT] method is used to

choose the cluster heads and route the data that may result in

the low energy efficiency for data collection.

III. SYSTEM MODEL

The network model is constructed with 30 sensor nodes, a

Base Station and sink node. Two different types of sinks are

considered, one is static and the other is dynamic. With each

different types of sink the performance metrics are studied.

The performance metrics are, Energy consumed by the node,

Energy remain, Packet Count, Packet Delivery Ratio and the

Delay. First case is the network architecture with static sink

and the second case is the network architecture with mobile

sink. The performance metrics for both static and dynamic

sink is studied. The sensor nodes collect the data from the

interested region and send these data to the sink nodes which

are either static or dynamic. Finally this information is sent to

the BS. An assumption is made that each sensor nodes

transmit and receive the data with the fixed transmission and

reception power respectively and also it is assumed that the

mobile sink has memory and computing resources. In case of

the dynamic sink usage in the network, each sensor node will

choose its own Cluster Head (CH) or subsink in terms of hop

distance as its destination and it transmits its own data to the

CH. The number of nodes that are connected to each cluster is

independent of its communication time. Sometimes subsinks

with very short communication time may own large number of

sensor nodes.

Fig.1. Zone Partitioning

In case of the network architecture where the static sink

node is deployed, all the sensor nodes collect the data from the

interested area and send these data to the static sink which in

turn is sent to the Base Station. The sensor nodes are

partitioned in to two zones as shown in the fig 1. Each zone is

divided in to three clusters with each cluster having a Cluster



12

Head. Here the Maximum Amount Shortest Path (MASP)

scheme is proposed to enhance the data collection. The sensor

nodes sense the data and send that information to the CH. The

CH in turn sends that data to the static sink. Through zone

partitioning the whole area to be monitored is divided in to

several zones and then the MASP scheme is executed to get

the optimal assignment of the members to the subsinks in each

zone.

The second case is the data collection using dynamic sink

as shown in fig2. These mobile sinks move towards the CH in

a shortest path and collect the data from them. The mobile

sink may be mounted on a public transport or animal or people

according to the application in which it is used. For both the

static and dynamic sink the performance metrics are studied

and the respective graphs are plotted. The energy profile and

the packet delivery ratio alone shown in the results and

discussion and other parameters are studied and the

comparison between static and dynamic sink is made through

the comparison table.

Fig.2. Network architecture with zone partitioning and clusters

Each of the sensor nodes is initially given the energy of

about 100J. The data is sensed and given to the static sink.

Now the energy consumed by the 30 sensor nodes and the

energy that remains after data collection is plotted in a graph

with the energy in Y axis and the number of nodes in the X

axis. During this data acquisition the total packet count that

has been transmitted and received are calculated. This data is

plotted in a graph with the nodes in X axis and number of

packets in Y axis. Another performance metric called the

packet delay is plotted between average packet received time

along Y axis and the number of packets along X axis. Finally

the packet delivery ratio is calculated and plotted in a graph.

IV. RESULTS AND DISCUSSION

A. STATIC SINK PERFORMANCE METRICS

The performance metrics are plotted in a graph for both the

static and dynamic sink. Fig 3 shows the energy consumed

graph for the static sink node. This graph depicts the amount

of that is consumed by all the sensor nodes when a static sink

is employed. Fig 5 shows the Packet Delivery Ratio. This

graph shows the percentage of packets received.

Fig 3. Energy consumed by all sensor nodes for static sink

The total amount of energy consumed by all the sensor

nodes is shown in the form of graph. In case of the static sink,

the energy consumed by 10 sensor nodes is 7J. The energy

consumption increases as the number of sensor nodes

increases. For 30 sensor nodes the amount of energy

consumed is 20J.



13

Fig.4. Packet delivery ratio for static sink

The above graph shows the packet delivery ratio for

the static sink node architecture. For the transmission of 20

packets the percentage of packet delivered is about 97%. As

the number of packets increases the percentage decreases. For

30 nodes the static sink is able to collect only 75 packets. So

for 75 packets the percentage of packet delivered is about

80%. Other parameter like delay is also calculated for the

static sink node. The delay is about 925 S for the static sink

node.

B. DYNAMIC SINK RESULTS

Fig 5. Energy consumed for mobile sink

The total amount of energy consumed by all the sensor

nodes is shown in the form of graph. In case of the dynamic

sink, the energy consumed by 10 sensor nodes is 1.8J. The

energy consumption increases as the number of sensor nodes

increases. For 30 sensor nodes the amount of energy

consumed is 12.5J. when compared to static sink the mobile

sink consumes less energy.

Fig.6. Packet delivery ratio for mobile sink.

The above graph shows the packet delivery ratio for the

mobile sink node architecture. For the transmission of 30

packets the percentage of packet delivered is about 98.5%. As

the number of packets increases the percentage decreases. For

30 nodes the mobile sink is able to collect nearly 90 packets.

So for 75 packets the percentage of packet delivered is about

91%. For 90 packets the percentage of packet received is

87.5% which is high compared to static sink performance.

Other parameter like delay is also calculated for the static sink

node. The delay is about 755 S for the mobile sink node.

Compared to static sink this delay is less.

The comparison between the static and dynamic sink is

studied from the table below. This table is depicted from the

graphs above.

Table 1. comparison between static and dynamic sink

PERFORMANCE

METRICS STATIC SINK

DYNAMIC

SINK

ENERGY

CONSUMED 20 J 12.5 J

ENERGY

REMAIN 80 J 87.5 J

TOTAL

PACKET

COUNT

75 91

PACKET

DELIVERY

RATIO

82 % 92.3 %

PACKET 925 S 755 S



14

DELAY

The comparison table clearly shows that mobile sink

increases the performance compared to the static sink.

V. CONCLUSION AND FUTURE SCOPE

In this paper a wireless Sensor Network has been designed

and tested for ensuring energy efficient data acquisition

system. The energy consumption has been reduced to about

30%. This will enhance the lifetime and it doesnt compromise the network performance. The performance metrics has been

clearly measured and it symbolizes that the proposed system is

delivering data packets with more throughput compared to

static sink network. In future, the energy efficiency parameter

has to be considered with enhanced security of this network

for increasing the reliability along with lifetime of the

network.

REFERENCES [1] Ilker Demirkol, Cem Ersoy, and Fatih Alagz, MAC Protocols for

wireless Sensor Networks: a Survey

[2] Wei Ye, John Heidemann, Deborah Estrin, An Energy -Efficient MAC Protocol for Wireless Sensor Networks, 0-7803-7476-2/02/$17.00 (c) 2002 IEEE.

[3] R.C. Shah, S. Roy, S. Jain, and W. Brunette, Data MULEs: Modeling a Three-Tier Architecture for Sparse Sensor Networks, Proc. First IEEE Intl Workshop Sensor Network Protocols and Applications, pp. 30-41, 2003.

[4] S. Jain, R.C. Shah, W. Brunette, G. Borriello , and S. Roy, Exploiting Mobility for Energy Efficient Data Collection in Sensor Networks, Mobile Networks and Applications, vol. 11, no. 3, pp. 327-339, 2006.

[5] Kazi Chandrima Rahman, A Survey on Sensor Network, Copyright 2010 JCIT, ISSN 2078-5828 (PRINT), ISSN 2218-5224 (ONLINE), Volume 01, Issue 01, Manuscript Code: 100715

[6] Ning Xu, Computer Science Department ,University of Southern California, A Survey of Sensor Net work Applications.

[7] Hung-Ta Pai, Yung Hsiang S. Han, "Energy-Efficient Multihop Polling in Clusters of Two-Layered Heterogeneous Sensor Networks" Source: IEEE Transactions on Computers v.57 no.2 (February 2008) p. 231-245

[8] Jalal Habibi, Ali Ghrayeb, and Amir G. Aghdam, Energy-Efficient Cooperative Routing in Wireless Sensor Networks: A Mixed-Integer Optimization Framework and Explicit Solution, IEEE transactions on communications, vol. 61, no. 8, august 2013.

[9] Shuai Gao, Hongke Zhang, Sajal K. Das, Efficient Data Collection in Wireless Sensor Networks with Path-Constrained Mobile Sinks, IEEE Transactions on Mobile Computing, vol. 10, no. 5, pp. 592-608, 2011.

[10] Xinxin Liu, Han Zhao, et al, Trailing Mobile Sinks: A Proactive Data Report ing Protocol for Wireless Sensor Networks, IEEE Transactions on Computers, pp. 214-223, 2011.

[11] Kartik N. Shah, Shantanu Santoki, Himanshu Ghetia and Abdul Gaffar H., Energy Optimization in Wireless Sensor Networks, International Journal of Applied Engineering Research, ISSN 0973-4562, Vol. 8, No. 19 (2013) Research India Publications.

[12] Zhao Han, Jie Wu, Member, IEEE, Jie Zhang, Liefeng Liu, and Kaiyun Tian, A General Self-Organized Tree-Based Energy-Balance Routing Protocol for Wireless Sensor Network, IEEE Transactions On Nuclear Science, Vol. 61, No. 2, April 2014.

[13] Fei Yin, Zhenhong Li, Haifeng Wang Renesas Mobile Corporation, Shanghai Branch, China , Energy-Efficient Data Collection in Multiple Mobile Gateways WSN-MCN Convergence System, The 10th Annual IEEE- CCNC Smart Spaces and Sensor Networks

[14] A.Rajeswari, P.T.Kalaivaani, Energy Efficient Routing Protocol for Wireless Sensor Networks Using Spatial Correlation Based Medium Access Control Protocol Compared with IEEE 802.11.

[15] L. Song and D. Hatzinakos, Architecture of Wireless Sensor Networks with Mobile Sinks: Sparsely Deployed Sensors, IEEE Trans. Vehicular Technology, vol. 56, no. 4, pp. 1826-1836, July 2007.

[16] J. Luo, J. Panchard, M. Piorkowski, M. Grossglauser, and J. Hubaux, MobiRoute: Routing towards a Mobile Sink for Improving Lifetime in Sensor Networks, Proc. Second IEEE/ ACM Intl Conf. Distributed Computing in Sensor Systems (DCOSS), pp. 480-497, 2006.

[17]A. Kansal, A. Somasundara, D. Jea, M. Srivastava, and D. Estrin, Intelligent Fluid Infrastructure for Embedded Networks, Proc.ACM MobiSys, pp. 111-124, 2004.

[18] A. Somasundara, A. Kansal, D. Jea, D. Estrin, and M. Srivastava, Controllably Mobile Infrastructure for Low Energy Embedded Networks, IEEE Trans. Mobile Computing, vol. 5, no. 8, pp. 958 -973, Aug. 2006.

iisrt divya nagaraj (networks)

Documents