An Energy Efficient Algorithmic Rule for Audio Compression of Wireless Sensor Networks (WSNs)

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 9, Issue 1, January 2019)

75

An Energy Efficient Algorithmic Rule for Audio Compression

of Wireless Sensor Networks (WSNs)

Srismrita Basu

, Sayani Sarkar

, Subhodip Maulik

_{West Bengal University of Technology}

Abstract— In this paper, a complete endeavor has been made to develop an algorithm to limit the energy required to transmit an audio signal from the nodes under the system design to the sink. For remote sensor systems to exploit signal, information must be gathered at different sensors and must be shared among the sensors. The tremendous sharing of information among the sensors repudiates the prerequisites, for example, vitality proficiency, low idleness and high exactness of remote sensor systems.

This paper describes the design and implementation of the methodology for two lossy data compression algorithm. To approve and assess our work, we applied it to various datasets from real-world deployments and demonstrate that our methodologies can decrease energy utilization.

Keywords— Wireless sensor networks. Compression, energy consumption.

I.

I

A. Networking and wireless communication

Networking is referred to as data communication which

allows sharing of resources and information among

interconnected devices. Wireless communication can be

used to transfer information both over short distances and

long distance. Radio frequency (RF), infrared light etc. are

utilized to exchange data over long distances. Headway in

wireless communications and electronics has developed

low cost, low power, multifunctional sensor hubs that are

small in size and convey inside short separation. These tiny

sensor hubs (nodes) can be used to develop sensor

networks. Sensor networks are huge enhancement over

conventional sensors. A sensor network is made out of an

expansive number of sensor hubs that are densely deployed

where the location of the sensor hub is not foreordained.

B. Wireless sensor networks

Wireless sensor network (WSN) is a network of sensors.

Ongoing headways in wireless sensor networks empower

the arrangement of low cost, low power multifunctional

sensor nodes. These sensor nodes are tiny in size and

communicate within a short distance. These are commonly

made out of one or more sinks (or base stations) and tens or

thousands of tiny embedded devices-sensor nodes scattered

over an area.

Sensor nodes have the capability of information sensing,

processing and communicating over wireless links. There is

a processor fitted in every sensor by which sensor nodes

sense physical data, process unrefined data, and report

required information to the sink. The sink or base station

gathers

information

transfers

it

to

different

frameworks/systems and once in a while inquires the

sensor hubs for data and control. It is vital to take note that

the topology of WSNs are dynamic basically brought about

by time-varying link, node number and condition. Fig.1

below shows a basic WSN arrangement over an area.

Fig1: Sensor nodes scattered in physical space

C. Sensor network topology

Diverse network topologies are used in WSNs. Sensor

nodes commonly composed in either multi-hop or

single-hop network. In single-single-hop network, each sensor is in direct

transmission range with all the other sensor nodes. The

types of topologies are star-tree topology, flat or

hierarchical, cluster with cluster-head etc. In the tree

topology sink of the tree is responsible for information

collection and transferring it to the outside systems, though

in cluster the cluster-head aggregates the information data.

The traffic attributes in WSNs are principally of two kinds-

upstream direction and downstream direction. In the

upstream traffic, data flows from the sensors nodes to the

sink. In down-stream traffic, the sink may occasionally

generate traffic of data packets. This is usually done for the

purposes of query and control.

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 9, Issue 1, January 2019)

76

Fig 2: Basic Sensor Architecture

The energy of the signal is given by:

E

= E

× k + E

× k× D× D

E

= E

× k --- (a)

Where Eelec =50 nj/bit and Eamp=

100pj/bit/square meter

respectively. k is the number of bits obtained from binary

conversion and D=1000m is the distance of the sink from

the node. The intermodal distance is “d” which is assumed

to be 5 meter. For simplicity, the network is considered to

be linear. Here d is negligible compared to D, hence the

internally transmitted energy has been neglected here.

II.

P

D

A. Calculation of uncompressed energy

Real time data with the help of an API program in the

form of a text file has been taken and this file has been

decoded with a C++ code. The output gives the real time

data that has been collected. These data has numeric values.

Again utilizing C++ coding, the data is distributed amongst

number of sensor nodes. Then the values of each node are

converted into its equivalent binary code in signed

magnitude and then the total number of bits (k) in each

node is calculated. The energy in each node is calculated

using the equation (a). The energy thus obtained will give

the uncompressed energy of each node.

Fig.3 shows the approach to calculate uncompressed

energy and Fig.4 shows the flowchart to calculate

uncompressed energy.

Fig 3: Block diagram to calculate uncompressed Energy

Fig 4: Flow chart to calculate uncompressed Energy

B. Calculation of compressed energy

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 9, Issue 1, January 2019)

77

These data has numeric values. Again utilizing C++

coding, the data is distributed amongst number of sensor

nodes. Then all the elements of each node are added and

was converted into its equivalent binary code in signed

magnitude after addition to calculate the total number of

bits (k) in each node. Then equation (a) was used to

calculate the energy in each node.

Fig.5 shows the approach to calculate compressed

energy and Fig.6 shows the flowchart to calculate

compressed energy.

Fig 5: Block diagram to calculate compressed Energy

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 9, Issue 1, January 2019)

78

III.

R

From the graphs below (Fig.7 and Fig.8), we see the

variation of SNR for each node. Comparative study of SNR

Vs number of bits between the compressed and

uncompressed Energy is shown in Fig 9.

Fig 7: Graph of SNR Vs number of bits per node when the signal is uncompressed

Fig 8: Graph of SNR Vs number of bits per node when the signal is compressed

Fig 9: A Comparison of SNR Vs number of bits between the compressed and uncompressed energy

IV.

C

An energy efficient audio coding for wireless sensor

network is proposed in this work. The performance of the

WSN for compression scheme has been analyzed in this

paper. A compression technique which works well for a

given WSN may not suit another WSN with different

requirement. As the architecture of the network may get

changed, the algorithm may require some modification. If

we try the same proposed algorithm for a video signal then

the algorithm may not work properly due to mismatch of

dimensions. This scheme shows that the energy required to

transmit an audio signal from the network to the sink is less

compared to the one when the signal is being transfer

without any compression logic, it also shows the plot of

signal to noise ratio with better result.

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Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 9, Issue 1, January 2019)

79

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