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Journal of Communications and Information Networks, Vol.3, No.1, Mar. 2018

DOI: 10.1007/s41650-018-0011-8 Research paper

Performance Improvement of Wireless

Multimedia Sensor Networks Using MIMO

and Compressive Sensing

Arjav Bavarva, Preetida Vinayakray Jani, Komal Ghetiya

Abstract—Wireless Multimedia Sensor Networks

(WMSN) are designed to transmit audio and video

streams, still images, and scalar data. Multimedia

transmission over wireless sensor networks has many

applications, such as video surveillance, object tracking,

telemedicine, theft control systems, and trafﬁc monitor-

ing. Researchers face many challenges, such as higher

data rates, lower energy consumption, reliability, signal

detection and estimation, uncertainty in network topology,

quality of service (QoS), and security- and privacy-related

issues to accomplish various applications of WMSN. This

paper presents multiple input multiple output (MIMO)

along with compressive sensing (CS) properties to improve

system performance in terms of energy consumption and

QoS in deep fade environments. The CS theory model has

been proposed to reduce energy consumption by taking

fewer measurements of the original signal or information

and reconstructing it with acceptable image quality at the

receiver side. The transmission and processing energy can

be reduced by transmitting fewer measurements from the

sensor side itself. The MIMO model and CS algorithm

have been simulated, and results show that CS performs

well on images.

Keywords—MIMO, wireless multimedia sensor net-

works, compressive sensing, energy-efﬁcient WMSN

I. INTRODUCTION

he ﬁeld of wireless sensor networks (WSNs) has re-

ceived much attention in the networking research com-

munity, and is also in huge demand because of the expansion

of the Internet of Things (IoT). WSNs are widely used in vari-

ous applications such as health monitoring, battleﬁeld surveil-

Manuscript received Dec. 20, 2016; accepted Jun 30, 2017.

A. Bavarva, K. Ghetiya. Department of Electronics and Communication,

School of Engineering, RK University, Rajkot 360020, India.

P. V. Jani. Department of Electronics and Telecommunication, Sardar Patel

Institute of Technology, Mumbai 400058, India.

lance, industrial monitoring and control, etc. Researchers

have explored the area of WSNs, and as a result, the imple-

mentation of efﬁcient WSNs with low cost and low power

consumption has become possible. WMSN is a special cat-

egory of WSNs. It has been introduced to send multimedia

information, such as images, audio, and video, along with

scalar information like temperature, humidity, and pressure.

Rapid development and progress of embedded systems, sen-

sors, source coding and channel coding techniques, comple-

mentary metal oxide semiconductor, cameras, and signal pro-

cessing techniques have given a new horizon to WMSN. Var-

ious wireless multimedia sensor nodes are compared with its

hardware speciﬁcations

[1]

Networks can be broadly designed as either cluster base

or backbone base, and various methods can be used for the

design of WMSN architecture

[2]

. Single-tier, ﬂat, central-

ized storage-cluster-based network design has been taken into

consideration throughout the paper. Hardware, software, and

protocols on various layers are considered major components

for an efﬁcient WMSN. Transmission, reception, and process-

ing are energy-consuming processes. MIMO properties

[3]

and

CS techniques

[4,5]

are used to reduce the energy consump-

tion for transmission and processing processes, respectively.

Researchers face many challenges in the implementation of

efﬁcient WMSN

[2]

. A MIMO-based scheduling algorithm

[5]

adopts diverse transmission strategies based on channel con-

ditions and node type. A multi-node MIMO system improves

the ergodic capacity of the WMSN

[6]

. A CS algorithm is ap-

plied to the WMSN because of its ﬁve unique properties. First,

it is asymmetrical: a dumb encoder and a smart decoder work

together satisfactorily, which means most processing is at the

gateway side. Second, it is democratic: it takes fewer samples

than the Nyquist rate, each measurement carrying the same

amount of information. It is robust to measure loss and quan-

tization. The energy consumption can be reduced by sending

fewer measurements. Third, it is encrypted; random measure-

ments are taken, and fourth, the acquisition/recovery process

is stable. Finally, it is universal; the same random projec-

tions/hardware can be used for any spare signal class. The

Performance Improvement of Wireless Multimedia Sensor Networks Using MIMO and Compressive Sensing 85

…

wireless multimedia sensor nodes S

data

acquisition

compressive

sensing

MIMO

transmitter

data

acquisition

compressive

sensing

MIMO

transmitter

data

acquisition

compressive

sensing

MIMO

transmitter

MIMO

receiver

data recovery

receiver/gateway

multipath fading

channels

total

Figure 1 System model

energy consumption can be remarkably reduced because of

ﬁrst and second property.

II. SYSTEM MODEL

Fig. 1 represents system model of the proposed work. Mul-

timedia information has been captured by a multimedia sen-

sor node. CS takes measurements and transmits via a MIMO

transmitter. A 2 × 4 MIMO performs better than a 3 ×3

MIMO

[3]

. In this paper, a 2 ×4 MIMO is considered. The

multipath fading channel between the transmitter and receiver

adds noise and delay spread. A mathematical model of MIMO

has been described in detail

[3]

. The CS algorithm performs

a complex decoding process at the receiver side and recon-

structs data that is similar to the original information trans-

mitted by the transmitter.

III. COMPRESSIVE SENSING ALGORITHM AND

MATHEMATICAL ANALYSIS

CS techniques are classiﬁed into two main categories: dis-

tributed compressive sensing (DCS) and jointly compressive

sensing (JCS). In DCS, sampling is performed separately and

recovery is done jointly. In other words, the measurements are

acquired independently by each node using a separate sens-

ing matrix, and the same reconstruction algorithm is applied

jointly to recover the signal at the receiver side. In JCS, mea-

surements and reconstruction processes are done using the

same procedure with the same sensing matrix, which allows

joint reconstruction of sparse signals

[5]

CS is further classiﬁed on the bases of various measure-

ment, sensing, or sampling matrices. The discrete cosine

transform (DCT) matrix technique is simple and easy to im-

plement, but requires more measurements

[6]

. A random con-

volution matrix is also simple, easy to implement, and uni-

versal, but it can only recover noiseless signals and also re-

quires more measurements

[7,8]

. Universal means the signal

can be sparse in any domain or any basis. The determinis-

tic matrix technique is fast and needs less memory, but it also

takes a high number of measurements

[9]

. The Toeplitz ma-

trix method is also fast, but is not universal

[10,11]

. The random

demodulation matrix method is universal and easy to imple-

ment, but is not accurate and highly uncertain

[7,8]

. The Gaus-

sian or Bernoulli matrix method is simple, easy to implement,

and universal, but hardware implementation is costly

[11,12]

The Fourier transform technique is easy to implement and has

fast recovery, but is highly uncertain, not universal, and not

accurate

[13,14]

. Random matrix technique has random projec-

tions and it is universal, easy to implement and recover the in-

formation with high efﬁciency

[8]

. This method requires more

memory.

Any signal that is sparse (most elements are zero or approx-

imately zero) or compressible can be recovered using linear

equations depending upon the number of measurements that

have been taken. If the signal is not sparse, it can be trans-

formed into an appropriate sparse domain without losing any

information

[4]

. The CS technique consists of signal acquisi-

tion with fewer samples followed by reconstruction. The en-

tire procedure has been explained by the ﬂow chart shown in

Fig. 2.

Here, let us take any signal, X, with ﬁnite length, and a

large number of samples, N. The signal is sparse in the time

or frequency domain, represented by K, where K  N. For

a non-sparse signal, we take the number of projections over a

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