The document reviews the advancements in structural health monitoring (SHM) systems using wireless sensor networks (WSNs), highlighting their benefits like ease of installation and low cost. It discusses the importance of SHM across various structures, including bridges and buildings, and presents different types of sensors employed in monitoring. Key challenges such as data quality, reliability, scalability, and real-time response within WSNs are also addressed.

1. Structural Health Monitoring System Using
Wireless Sensor Network
Kavita Kumari
Student, Dept. of IT
UIET,PU, Chandigarh
Email: kavita06it16@gmail.com
Inderdeep Kaur Aulakh
Asst. Professor, Dept.Of IT
UIET, PU, Chandigarh
Email: ikaulakh@yahoo.com
Amol P Bhondekar
Principal Scientst
Agrionics,CSIO, Chandigarh
Email:amol.bhondekar@gmail.com
Abstract— The longevity and health monitoring of structure are
important for their lifespan optimization and preservation. WSN
technology has proven to be a boon for structural health monitor-
ing in recent year due to its ease of installation, minimal struc-
tural intervention/damage and low cost. This paper provides a re-
view on the recent developments in the area of SHM using WSNs.
Keywords: wireless sensor network; structural health monitoring;
scheduling approach; energy efficiency
I. INTRODUCTION
Structural Health Monitoring (SHM) is referred as the process
of implementing damage detection and characterization
strategy for engineering structures. The changes to the
material and/or geometric properties of a structural system,
including changes to the boundary conditions and system
connectivity which adversely affect the system’s performance,
is defined as damage. In SHM process we observe system
using periodically sampled dynamic response measurements
from an array of sensors. Then the extraction of damage,
damage-sensitive features from these measurements are
carried out. To determine the current state of system health,
the statistical analysis of the features is performed.
There will be inevitable aging and degradation in the structure
resulting from operational environment. Long term SHM is
defined as output of this process that is periodically updated
regarding the ability of the structure to perform its intended
function. Regarding the integrity of the structure, SHM is used
for rapid condition screening and to provide near real time
reliable information, for example in case of extreme events
such as earthquakes or blast loading [1]. To estimate the state
of structure health, SHM detects the changes in structure that
effects its performance. Time- scale of change and severity of
change are two major factors. How quickly the change occurs
is time- scale of change, and degree of change is severity of
change. SHM has two major categories: disaster response
(earthquake, explosion, etc.) and continuous health monitoring
(ambient vibration, etc.). SHM has two approaches: direct
damage detection (visual inspection, and X- ray, etc) and
indirect damage detection (change in structural
properties/behavior). A typical SHM system, in general,
includes three major categories: a sensor system, a data
processing system (including data acquisition, transmission,
and storage), and health evaluation system(including
diagnostic algorithms and information managements).
II. IMPORTANCE OF STRUCTURAL HEALTH
MONITORING
There is a significant development in SHM due to major
construction projects, such as large dams, long- span cable
supported bridges and offshore gas/oil production installation.
SHM infrastructure provides the means for society to function.
It also includes buildings, pedestrian and vehicular bridges,
tunnels, factories, conventional and nuclear power plants,
offshore petroleum installations and heritage structures.
A. Bridges
For the purpose of understanding and eventually calibrating
models of the load-structure-response chain, bridge
monitoring programmes have historically been implemented.
B. Buildings and towers
The need to understand building performance during
earthquakes and storms, the developments in monitoring of
buildings werehistorically motivated. Originally, from
vibration testing, the understanding of low-amplitude dynamic
response was obtained. [3]
C. Nuclear installations
For one of the UK's civil nuclear reactors, Smith (1996)
provided an overview of the inspection and monitoring
regime. To validate and calibrate designs during performance
testing, the safety- critical structural components of nuclear
reactors, instrumentation were used. It also contributed to the
condition monitoring during normal operation. [4]
D. Tunnels and excavations
In terms of stability and effects on or from adjacent structures,
tunnel monitoring is aimed to ensure whether tunnel
deformation is within limits. Hence, the emphasis is on
deflections, while stresses and strains may also be measured.
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2. During tunneling or mining, monitoring of heritage and other
structures is a major concern. [5]
III WIRELESS SENSOR NETWORKS (WSNS)
The development of Wireless Sensor Network(WSNs) has
originated from the need to continuously monitoring the
physical phenomena coupled with the recent advances in
sensing, computing and communication technologies. WSN
consist of four main components: sensors, a processor, a
radio, and a battery. In an application area, the WSN is formed
through densely deployed sensors nodes. To collaboratively
perform a particular task, in most deployments the sensor
nodes have self- organizing capabilities to form an appropriate
structure. WSNs are found suitable for applications such as
surveillance, precision agriculture, smart homes, automation,
vehicular traffic managements, habitat monitoring, and
disaster detection. To revolutionize information and
communication technology, WSN has great enabling
technology. WSN connects the physical world to the Internet
at fine granularity.WSN has power of creating a pervasive
environment capable of remote sensing, monitoring and
control. As a benefit, this technology offers fine granular
tracking of actions in far away or inaccessible locations. WSN
can also enable remote monitoring of components responsible
for global warming.
IV. SENSORS FOR SHM
The sensing system in the SHM is formed by smart
materials/sensors;Fibre optic sensors (FOS), piezoelectric
sensors, magnetoresistive sensors, and self - diagnosing fibre
reinforced structural composites. Thesesensorsare
characterized with very important capabilities of sensing
various physical and chemical parameters related to the health
of the structures.
A.FIBRE OPTIC SENSORS (FOSS)
FOS can be classified by several methods. FOS can be
classified based on the modulation of light characteristics
(intensity, wavelength, phase, or polarization etc.) by the
parameters to be sensed. It can also be classified by the
methodthrough which the light in the sensing segments is
modified inside or outside the fibre (intrinsic or extrinsic).
FOS can also be classified based on the sensing range; local
(Fabry- Perot FOS or long - gauge FOS etc.), quasi-
distributed(fibre Bragg grating) and distributed sensors
(Brillouin-scattering-based distributed FOS). FOS are
embedded in newly constructed civil structures, including
bridges, buildings, and dams to yield information about strain
( static and dynamic), temperature, defects (delamination,
cracks, and corrosion ) and concentration of chloride ions. On
existing structures, FOSsare generally surface mounted. The
data collected by FOSs is used to evaluate the safety of both
the new-built structures and repaired structures, and diagnose
the location and degree of damage.
B. Piezoelectric Sensors
Piezoelectric materials exhibit simultaneous actuator/sensor
behavior based on electrical-mechanical transformation. There
are many types of piezoelectric materials: piezoelectric
ceramics, piezoelectric polymers, and piezoelectric
composites. Based on the measurement of electrical
impedance and elastic waves piezoelectric sensors were
recently introduced into SHM of civil engineering structures
as an active sensing technology.
C. Magnetostrictive Sensors
Ferromagnetic materials are the materials which are
mechanically deformed when placed in magnetic field. This
phenomenon is known as the magnetostrictive effect. Inthe
inverse magnetostrictive effect, the magnetic induction of the
material changes when the material is mechanically
deformed.Based on the above phenomena, Kwun and Bartels
[10] invented a type of magnetostrictive sensor (MsS) without
direct physical contact to the material surface which could
generate and detect guided waves in the ferromagnetic
materials under testing. Khazem et al. [11] also utilized MsS
to inspect suspender ropes on the George Washington Bridge
in New York. A pulse of 10 kHz longitudinal guided wave
along the length of the suspenderdetected the reflected signals
from geometric features and defects in the suspender.
V. ROUTING ALGORITHMS FOR WIRELESS SENSOR
NETWORKS
A. Data-centric protocols
Due to the sheer number of nodes deployed, it is not feasible
to assign global identifiers to each node in many applications
of sensor networks. It is hard to select a specific set of sensor
nodes to be queried due to lack of global identification and
random deployment of sensor. Therefore, from every sensor
node, the data is usually transmittedwithin the deployment
region with significant redundancy. Since this is very
inefficient in terms of energy consumption, routing protocols
have been considered that select a set of sensor nodes and
utilize data aggregation during the relaying of data. This
consideration is known as data-centric routing. In data-centric
routing, the sink sends queries to certain regionsand waits for
data from the sensors located in the selected regions.
Attribute-based naming is necessary to specify the properties
of data, since the data is being requested through queries. In
the first data-centric protocol SPIN [14],data negotiation
between nodes is considered in order to eliminate redundant
data and save energy. Later, a breakthrough Directed Diffusion
[18] data-centric routing has been developed. Based on
Directed Diffusion [17–18], many similar concepts and
protocolshave been proposed [16,15,19,20].
B. Flooding and gossiping
To relay data insensor networks without the need for any
routing algorithms and topology maintenance, there are two
classical mechanisms: : Flooding and gossiping [21].
Inflooding, each sensor receives a data packet, broadcasts it to
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3. all of its neighbors and this processcontinues until the packet
arrives at the destination or the maximum number of hops for
the packetis reached. Gossiping is a slightly enhanced version
of flooding, where thereceiving node sends the packet to a
randomly selected neighbor, and that neighbor picks another
randomneighbor to forward the packet to and so on.
C. Sensor protocols for information via negotiation
SPIN technique was amongst the early works to pursue adata-
centric routing mechanism [15]. SPIN uses high-level
descriptors or meta-data. A dataadvertisement mechanism is
used to exchange the data among the sensorsbefore
thetransmission which is the key feature of SPIN. After
receiving new data, each node advertises it to its neighbors
and interested neighbors.
D. Directed Diffusion
In the data-centric routingresearch of sensor networks,
Directed Diffusion [18,19] is an important milestone. The idea
is to diffuse data through sensor nodes by using anaming
scheme for the data.
E. Energy-aware routing
To increase the lifetime of the network, Shah and Rabaey [19]
proposed the occasional use of set of sub-optimal paths.
Depending on the energy consumption, these paths are chosen
by means of a probability function. The approach is concerned
with network survivability as the main metric. Energy-aware
routingapproach argues that using the minimum energy path
all the time will deplete the energy of nodes on that path. The
assumption of the protocol that each node is addressable
through a class-based addressing which includes thelocation
and types of the nodes.
F. Rumor routing
Directed Diffusion has another variation ‘Rumor routing’ [16].
It is used in contextswhere geographic routing criteria are not
applicable. Generally, in the entire network, Directed
Diffusion floods the query when there is no geographic
criterion to diffuse tasks. The use of flooding is unnecessary in
some cases where only a little amount of data is requested
from the nodes. When the number of queries is large and the
number of events is small, an alternative approach floods the
events.
G. Gradient-based routing
A slightly changed version of Directed Diffusion, called
Gradient-based routing (GBR) was proposed by Schurgers et
al. [17]. Here each node can discover the minimum number of
hops to the sink, which is called height of the node. The
gradient on the link is considered as the difference between a
node'sheight and that of its neighbor on that link. A packet is
forwarded on alink with the largest gradient.
H. CADR
Constrained anisotropic diffusion routing (CADR) is ageneral
form of Directed Diffusion [18]protocol. Two techniques are
proposed:information driven sensor querying(IDSQ) and
constrained anisotropic diffusion routing. The idea is to query
sensorsand route data in a networkin order to maximize the
information gain while minimizing the latency and
bandwidth.This is achieved by activating only the sensors that
are close to a particular eventand dynamically adjusting data
routes.
I. COUGAR
The network as a huge distributed database system in data-
centric protocol has beenproposed [14]. The main idea is to
use declarative queries in order to abstract query
processingfrom the network layer functions such as selection
of relevant sensors etc. and utilize in-networkdata aggregation
to save energy. The abstraction is supported through a new
query layer betweenthe network and application layers.
J. ACQUIRE
Active Queryforwarding in sensor networks (ACQUIRE) [20]
is a fairly new data-centric mechanism for querying sensor
networks.The approach views the sensornetwork as a
distributed database and is well-suited for complex queries
which consist of severalsub queries.
TABLE 1 COMPARISON OF PROTOCOLS AND FEATURES
Routing
Protocol
Data-
Centric
Hierarch
ical
Location
based
QoS Networ
k flow
Data
aggregatio
n
SPIN  
Directed
Diffusion
 
Rumor
routing
 
Shah and
Rabey
 
GBR  
CADR 
COUGAR  
ACQUIRE  
LEACH  
TEEN and
APTEEN
  
PEGASIS  
MECN and
SMECN

GAF  
GEAR  
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4. Chang and
Tassiulas
 
SAR 
SPEED  
Fe et al. 
VI. ISSUE RELATED TO WSN
Depending on the application scenario and specific structure,
issues related to WSN for monitoring structural health systems
may impose different requirements. The following issue are
the base of the building structure.
A. Quality of Data
Data is the essential evidence. Quality of data is more
important because it carries structural health information. Any
missing data is an error result of the analysis. Other parameter
related to signal processing must be accurately specified during
signal synchronization. To continue error free analysis, lossless
data transmission is required and packet/symbol/bit error must
be avoided.
B. Reliability and Scalability
It seems that wireless communication could be unreliable
because is uses a share transmission media and information
error is also calculated on probability base. Increases in the
transmission node in the network lead collision and packet loss.
Unknown errors and lack of reliability may also occur while
analyzing the results.
One of the most important issues is to cover the large
geographical civil infrastructure. Scalability of the WSN will
provide adjustment flexibility with infrastructure for
monitoring structural health by adding new transmission node
in the network with higher precision of damage detection. The
sensor coverage area defines the complexity of the scalability
to cover the whole service area.
C. Real-time Response and Lifetime of the Overall
The measurement of the overall system should be a real time
response. Efficient design of the fault management solution of
the wireless sensor network is an another important challenge
based on real time environment. Every system is defined by
real time response. The faster real time system response may
provide more accurate data and such system helps in correct
decision for better result.
The lifetime of the overall monitoring system should be
increased to reduce the overall system cost. Limited
maintenance and power efficiency are also the important
parameters.
CONCLUSION
This paper presents a review of recent research and
development activities in SHM of civil structures and discusses
several techniques that evaluate structural damage and issue
related to the WSN. Traditionally, wired system is used for
collecting sensor data periodically, but the SHM system has
several disadvantages. The main issues in the use of WSN in
SHM are the scalability, accuracy, reliability and data
precision.
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