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Location Optimization Of Wireless Sensor Networks In Specific to Structural Health Monitoring Systems S.Surya1, Dr.D.C.Joy Winnie Wise2 P.G. Scholars, Department of CSE, Francis Xavier Engineering College, Tirunelveli 1 Prof and Head, Department of CSE, Francis Xavier Engineering College, Tirunelveli2

Abstract—there has been a rapid advancement in wireless sensor network (WSN) technology in the past few years and its applicationin structural monitoring has been the focus of several research projects. Starting from petroleum exploration, mining, weather and even battle operations, all of theserequire sensor applications. One reason behind the growing popularity of wireless sensors is thatthey can work in remote areas without manual intervention. All the user needs to do is to gatherthe data sent by the sensors, and with certain analysis extract meaningful information from them.Usually sensor applications involve many sensors deployed together. These sensors form anetwork and collaborate with each other to gather data and send it to the base station. The basestation acts as the control centre where the data from the sensors are gathered for further analysisand processing. In a nutshell, a wireless sensor network (WSN) is a wireless network consistingof spatially distributed nodes which use sensors to monitor physical or environmental conditions.These nodes combine with routers and gateways to create a WSN system. The evaluation of the newly developed sensor system is an important aspect of such research efforts. Although much of this evaluation is donein the laboratories and using generic signal processing techniques, it is important to validate the system for its intendedapplication as well. In this paper the performance of a newly developed sensor is evaluated by usingthe data specimen with a local damage detection algorithm.According to deployment methodsfrom civil/structural/mechanicalengineering, wired sensors are usually deployed at strategic locations to achieve the best Estimates of structural health status. To prolong the WSN lifetime, the energy cost of each sensor for monitoring must be carefully considered. An energy-efficient SHM (Structural Health Monitoring) algorithm, called DamageIndicator is proposed; it runs on each sensor and then provides a light-weighted indication of damage in a cluster in a decentralized manner. If there is no indication found in the cluster, the “uninteresting” data transmission toward the BS can be reduced. Also, it is used to prevent the path in WSN by calculating residual energy of the nodes.The collected data from the sensors is then used to estimate two sets of system influence coefficients with the wired one as thereference baseline. The performance of the WSN is evaluated by comparing the quality of the influence coefficients andthe rate of convergence of the estimated parameters. Keywords—SHM(Structural Health Monitoring), Wireless Sensor Networks, Energy Optimization, Damage Detection

III.

INTRODUCTION

The deterioration of our civil infrastructure is a growing Problem both around the world. For example, during their lifetimes, bridges suffer from environmentalcorrosion, persistent traffic and wind loading, extreme earthquakeevents, material aging, etc., which inevitably result in structural deficiencies. According to the American Societyfor Civil Engineers 2009 Report Card for America's Infrastructure, more than 26%, or one in four, of the American nation'sbridges were either structurally deficient or functionally obsolete". Our damage facing civil infrastructure faces the critical challenge of long-term structural health monitoring for damage detection and localization. In contrast to existing research that often separates the designs of wireless sensor networksand structural engineering algorithms; this paper proposes a co-design approach to structural health monitoring based on wireless sensor networks. Our approach closely integrates (1) flexible-based damage localizationmethods that allow a tradeoff between the number of sensors and the resolution of damage localization, and (2) an energy-efficient, multi-level computing architecture specially designed to leverage the multi-resolution feature of the flexible-based approach. The proposed approach has been simulated and the simulations demonstrate the system's efficiency in damage localization and energy efficiency. What is needed is a fundamentally different approach which considers both the constraints of the underlyingWSN system (the cyber components) and the SHMrequirements (the physical components) in its numerical approach.This can be achieved by leveraging the increasinglypowerful processing capability of wireless sensor \motes" topartially process locally-collected data, extracting (and subsequentlyexchanging) only the important features relevantfor SHM. Several recent studies demonstrate the potentialfor distributed SHM approaches to significantly reduce energycost through localized data processing. In this paper, we present a hierarchical decentralized SHM system that implements a exibile-based damage identification and localization method. In contrast to previousdecentralized algorithms like DLAC, exible-based methods explicitly

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correlate data across multiple sensors, allowing them to accurately identify and localize damage ona wider rangeof

Figure 1.1: The Traditional method based on flexible structures. Our hierarchical system organizesnodes into clusters using a novel multi-level search approachthat incrementally activates sensors in the damagedtake advantage of the platform's computationalpower to perform in-network processing whereverpossible; thus, nodes further save energy and bandwidth byonly transmitting the intermediate results related to the exible calculation. In this paper, we make the following contributions and wepropose a architecture which efficiently maps exibile-based damage identification and localization methodsonto a distributed WSN, demonstrating that our approach can successfullylocalize damage on both structures to the resolutionof a single element. Latency and power consumption datacollected during these experiments also demonstrate the energy efficiency of our approach.Wedescribes related SHM systems in literature and also we discuss the basic numerical methods used by ourexible-based damage localization. Then we present ourmapping of these methods into an efficient distributed architecture. I.

LOCATING DAMAGES

A. Damage Identification Approach In this section, we introduce the physical (structural engineering)aspects of our decentralized damage localizationsystem. Our system is based on a family of damage localizationtechniques collectively known as exible based algorithms. The intuition behind these methods is that structureswill ex slightly when a force is applied, as shown in Figure 2. As a structure weakens, its sti_ness decreases, and thus its exible changes. Changes in structural exible over a structure's lifetime can be used to identify and localize damage. We have chosen this family of methodsbecause they address the aforementioned limitations inDLAC. Moreover, they enableus to develop a multi-level system architecture specifically optimized for this method. We will provide here a brief background on two particular

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Exible-based methods used within our decentralizedsystem. While exible-based methods are well-known instructural engineering literature, the existing research generallydeals with algorithmic issues (i.e., selecting the bestnumerical methods for damage identification and localization) rather than efficiently deploying these methods on adistributed architecture for WSNs. We will focus here onthe details of these algorithms that are most relevant toour system design; more mathematical details can be foundin Flexible-based methods are executed in two stages. When the system is first Turned on, a baseline structural modal identification is performed. The sensors simultaneously collect vibration data. Multiple sensors' data are correlated to identify the structure's modal parameters (naturalfrequencies and mode shapes). The modal parameters arethen further processed to compute the structure's exible matrix. Online, the data collection and processing phaseâ&#x20AC;&#x2122;s aboveare repeated, and the base station produces a new exible matrix. By subtracting the new exible matrix from the stored one, the base station can determine if the structureis damaged (and if so, identify the damaged region). We will now summarize the main components of exibile based methods, as shown in Figure 1. The structure's modal parameters are identified using Frequency Domain Decomposition (FDD), an existing structural engineering techniquewhich can be decomposed into several stages. Traditionally, FDD is executed as follows. (1) All the nodes in a clustersimultaneously collect D vibration samples using theironboard accelerometers. The size of D depends on structuralproperties (like its complexity and material) as well asthe modes we are interested in, and is typically hundreds or thousands of samples. (2,3) Each node independently performs an FFT and power spectrum analysis on the vibrationdata, transforming it into magnitudes in the frequency domain.(4) D magnitudes collected from each node are correlatedto compute a Cross Spectral Density (CSD) matrix.(5) A Singular Value Decomposition (SVD) is performed onthe CSD matrix at each of D discrete frequencies. The singular value in each singular value matrix is collected to forma vector, and the structure's P lowest natural frequencies areidentified as the peaks in this vector. The mode shapes correspondingto the natural frequencies can be estimated from the first column of the corresponding left SVD matrix. The FDD output is then input into a exible-based method. System uses two specific exible-based methods: the Angles-Between-String-and-Horizon exible based method (ASHFM) and the Axial Strain exible based method (ASFM) . We are particularly interestedin these two methods because they can localize damage downto a resolution of a specific element on beam-like and trusslikestructures, respectively. Most other exible-based methods localize damage only to less specific regions of the structure, while it achieves similar

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damage localization resolutionat a much higher computational cost. II.

FAULT DETECTION AND RECOVERY

Detection of faulty sensor nodes can be achieved by two mechanisms i.e. self-detection (orpassive-detection) and active-detection. In self-detection, sensor nodes are required to Periodically monitor their residual energy, and identify the potential failure. In this scheme, weconsider the battery depletion as a main cause of node sudden death. A node is termed as failingwhen its energy drops below the threshold value. When a common node is failing due to energydepletion, it sends a message to its cell manager that it is going to sleep mode due to energy below the threshold value. This requires no recovery steps. Self-detection is considered as alocal computational process of sensor nodes, and requires less innetwork communication toconserve the node energy. In addition, it also reduces the response delay of the management System towards the potential failure of sensor nodes. To efficiently detect the node sudden death, our fault management system employed an activedetection mode. In this approach, the message of updating the node residual battery is applied totrack the existence of sensor nodes. In active detection, cell manager asks its cell members onregular basis to send their updates. Such as the cell manager sends â&#x20AC;&#x153;getâ&#x20AC;? messages to theassociated common nodes on regular basis and in return nodes send their updates. This is calledincell update cycle. The update message consists of node ID, energy and location information. Asshown in figure 2.1, exchange of update messages takes place between cell manager and its cell members. If the cell manager does not receive an update from any node then it sends an instantmessage to the node acquiring about its status. If cell manager does not receive the acknowledgement in a given time, it then declares the node faulty and passes this information tothe remaining nodes in the cell. Cell managers only concentrate on its cell members and onlyinform the group manager for further assistant if the network performance of its small region hasbeen in a critical level. A cell manager also employs the self-detection approach and regularly monitors its residualenergy status. All sensor nodes start with the same residual energy. After going through varioustransmissions, the node energy decreases. If the node energy becomes less than or equal to 20%of battery life, the node is ranked as low energy node and becomes liable to put to sleep. If thenode energy is greater or equal to 50% of the battery life, it is ranked as high and becomes thepromising candidate for the cell manager. Thus, if a cell manager residual energy becomes lessthan or equal to 20% of battery life, it then triggers the alarm and notifies its cell members and the group manager of its low energy status and appoints a new cell manager to replace it.

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Every cell manager sends health status information to its group manager. This is called out-cell update cycle and are less frequent than in-cell update cycle. If a group manager does not hearfrom a particular cell manager during out-cell update cycle, it then sends a quick reminder to the Cell manager and enquires about its status. If the group manager does not hear from the same cellmanager again during second update cycle, it then declares the cell manager faulty and informs its cell members. This approach is used to detect the sudden death of a cell manager. Groupmanager also monitor its health status regularly and respond when its residual energy dropsbelow the threshold value. It notifies its cell members and neighboring group managers of its lowenergy status and an indication to appoint a new group manager. Sudden death of a groupmanager can be detected by the base station. If the bases station does not receive any traffic froma particular group manager, it then consults the group manager and asks for its current status. Ifthe base station does not receive any acknowledgement; it then considers the group managerfaulty (sudden death) and propagates this information to its cell managers. The base stationprimarily focuses on the existence of the group managers from their sudden death. Meanwhile,the group managers and cell managers take most parts in passive and active detection in thenetwork. A. Fault Recovery Process After nodes failure detection (as a result of self-detection or active detection), sleeping nodes canbe awaked to cover the required cell density or mobile nodes can be moved to fill the coveragehole. A cell manager also appoints a secondary cell manager within its cell to acts as a backupcell manager. Cell manager and secondary cell manager are known to their cell members. If the cell manager energy drops below the threshold value (i.e. less than or equal to 20% of batterylife), it then sends a message to its cell members including secondary cell manager. It also informs its group manager of its residual energy status and about the candidate secondary cellmanager. This is an indication for secondary cell manager to stand up as a new cell manager andthe existing cell manager becomes common node and goes to a low computational mode.Common nodes will automatically start treating the secondary cell manager as their new cellmanager and the new cell manager upon receiving updates from its cell members; choose a new secondary cell manager. The failure recovery mechanisms are performed locally by each cell. In let us assume that cell 1 cell manager is failing due to energy depletion andnode 3 is chosen as secondary cell manager. Cell manager will send a message to node 1, 2, 3and 4 and this will initiate the recovery mechanism by invoking node 3 to stand up as a new cellmanager. In a scenario, where the residual battery energy of a particular cell manager is not sufficientenough to support its

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management role, and the secondary cell manager also does not havesufficient energy to replace its cell manager. Thus, common nodes exchange energy messageswithin the cell to appoint a new cell manager with residual energy greater or equal to 50% ofbattery life. In addition, if there is no candidate node within the cell that has sufficient energy toreplace the cell manager. The event cell manager sends a request to its group manager to mergethe remaining nodes with the neighboring cells.When a group manager detects the sudden death of a cell manager, it then informs the cellmembers of that faulty cell manager (including the secondary cell manager). This is an indication

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operation. Therefore, a group manager caneasily avoid using cells with low health status or alternatively, instruct the low health status cellto join the neighboring cell. B. REASONS OF FAILURE OF SENSOR NODE IN WSN: In Wireless Sensor Networks, Sensor nodes get fail due to their deployment in harsh environment. Sensor nodes get fail due to hardware failure, energy utilization, and malicious attacks. In Wireless sensor networks, sensing unit and transceiver directly interact with the environment which is subject to variety ofphysical, chemical and biological factors which further affect the reliability of sensor nodes. In WSN, Whether the hardware condition is good, but the communication between sensor nodes get affected by signal strength, interferences, obstacles. Sensor nodes have limited battery power that cannot be replenished. When sensor node depletes their energy then that nodebecome the fault node and cannot relay the data to the base station and network functionality get affected. C. Proposed Model For Fault Tolerance

Figure 2.1 : Fault Detection and Diagnostics Process For the secondary cell manager to start acting as a new cell manager. A group manager alsomaintains a backup node within the group to replace it when required. If the group managerresidual energy drops below the threshold value (i.e. greater or equal to 50% of battery life), itmay downgrade itself to a common node or enter into a sleep mode, and notify its backup nodeto replace it. The information of this change is propagated to neighboring group managerâ&#x20AC;&#x2122;s andcell managers within the group. As a result of group manager sudden death, the backup node willreceive a message from the base station to start acting as the new group manager. If the backupnode does not have enough energy to replace the group manager, cell managers within a groupco-ordinate to appoint a new group manager for themselves based on residual energy. Each cell maintains its health status in terms of energy. It can be High, Medium or Low.These health statuses are then sent out to their associate group managers periodically during out cell update cycle. Upon receiving these health statuses, group manager predict and avoid futurefaults. For example; if a cell has health status high then group manager always recommends thatcell for any operation or routing but if the health status is medium then group manager willoccasionally recommend it for any operation. Health status Low means that the cell hasinsufficient energy and should be avoided for any

We assume that sensors are randomly deployed in the interested area which is very dense and allthe sensors have a common transmission range. The dark circles in the figure represent faultysensors and the gray circles are good sensors. There might be a failure occurring in a certain areaas illustrated in the figure 2.1. All sensors in this area go out of service. As we are depending on majority voting among the sensors, we assume that each sensor nodehas at least 3 neighboring nodes. Because a large amount of sensors are deployed into theinterested area to form a wireless network, this condition can be easily obtained. Each sensornode is able to locate its neighbors within its transmission range via a broadcast/ acknowledgeprotocol. Faults can occur at different levels of the sensor network, such as system software, hardware, physical layer, and middleware. In this mechanism, we focus on hardware level faults by assuming all system software as well asthe application software is always fault tolerant. We can categorize the hardware components ofsensor nodes into two groups. The first group of hardware level components consists of a storagesubsystem, computation engine and power supply infrastructure. The second groups ofcomponents are sensors and actuators. The second group is most prone to malfunctioning. Weonly consider the sensor faults which occur in the second group. Sensor nodes are stillcapable of receiving, sending, and processing when they are faulty in the algorithm.Sensors are considered as neighboring sensors if they are within the transmission range of eachother. Each node regularly sends its measured value to all its neighbors. We are interested in the history data if more

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than half of the sensorâ&#x20AC;&#x2122;s neighbors have a significantly different value fromit. We can find the current measurement is different from previous measurement. If themeasurements change over the time significantly, it is more likely the sensor is faulty.

Figure 3.1 Role of various sensors in the system III.

ARCHITECHTURE OF THE SYSTEM

The numerical methods discussed above have been designedwith centralized networks in mind, where sensors are used as simple data collection devices that can stream largedata sets to a central server over a wired backbone. Under a WSN, this approach is inappropriate because of the nodes' limited network and energy resources. However, in order to design an efficient decentralized architecture, we can leverage a particularly powerful feature of these exible based Methods. Specifically, they enable a tradeoff between energy consumption and localization resolution: the more nodes that are activated, the finer-grained the damage localization.We leverage this feature to construct an energy-efficient,multilevel damage localization system which selectively activatesadditional sensors at each level in order to more preciselylocalize structural damage. In the common case thatthe structure is not damaged at all, only a minimal subsetof nodes are enabled, considerably reducing the system'senergy and bandwidth consumption. This approach naturallymaps to a hierarchical, cluster-based distributed network architecture. In addition, to promote a more efficientmapping onto our distributed system, we leverage an existingpeak picking technique to reduce the data low amongsensors participating in each cluster. After the text edit has been completed, the paper is ready for the template. Duplicate the template file by using the Save As command, and use the naming convention prescribed by your conference for the name of your paper. In this newly created file, highlight all of the contents and import your prepared text file. You are now ready to style your paper; use the scroll down window on the left of the MS Word Formatting toolbar.

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A. Multi-Level Damage Localization Although adding more sensors can improve a exibility-based method's localization results, only a handful of sensors are needed to accurately identify damage. In the first stage of the multi-level search, this minimal number of sensors is enabled, forming a single cluster. Damage identification and localization is performed using this small subset of sensors. In the common case that no damage is identified, the search ends and all the nodes return to sleep. In the event that damage is identified, the exibility-based method will also output coarse-grained damage localization. For example, ASHFM will identify two adjacent sensors surrounding each damage location on the structure. In the next round of the multi-level search, the system activates additional sensors in the region of interest and repeats the entire procedure, including collecting new vibration data. This second round subsequently localizes the damage to a smaller region than the first round. The system may re- peat this drilldown procedure to achieve even finer grained results until the desired resolution is reached.The key feature of this approach is that it does not activate the entire sensor network at once. Instead, relatively few sensors are used to identify damage; and when damage is identified, only those sensors in the area of interest are incrementally added to the search. As a result, many nodes are able to remain asleep for part or all of the multi-level search.This approach will also scale to larger structures, since thecost of the search is no longer proportional to the size ofthe structure. The reduced energy burden can also be distributed across the network by activating different subsets of the network at different times. B. Network Hierarchy Once the nodes participating in this multi-level search areselected, they are each assigned one of three different roles:cluster member, cluster head, and base station. A node'srole determines what data it handles as well as its level inthe network hierarchy, as shown in Figure 4. To allow thesystem to better scale to large structures, the nodes maybe organized into multiple independent clusters. Each cluster operates as an independent unit, with the cluster headcoordinating nodes within its cluster and ultimately transmitting the cluster's (relatively small) mode shape data to the base station for final processing.Based on these roles, the system operates as follows. Thecluster members collect raw vibration samples from their on-board accelerometers. They then carry out an FFT to transform the vibration response into frequency domain data, followed by a power spectrum analysis.The cluster head nodes aggregate the extracted powerspectrum data from the cluster members beneath them inthe hierarchy. There, the CSD and SVD are carried out toextract the structure's mode shape vector.The cluster heads

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then transmit the mode shapes to asingle base station node, which calculates the structure’s exibility. The exibility is then used to identify and localizeany structural damage. IV.

PROPOSED MECHANISM

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modal analysis at the cluster level. In the WSNperspective, CSHM is resource-consuming, where a CHneeds a lot of computation, delay, and transmission, due to such modal analysis. When we deploy a homogeneous WSN, such Ach may be the bottleneck, and may fail before a period of monitoring is over. We overcome these drawbacks. Section 5 further elaborates on these. C. Cluster as a Subgraph We consider each cluster as a sub graph of the WSN (where is a set of deployed sensors and isthe set of edges) as to detect repairing points (RPs) on thedeployed WSN, since each cluster with the primary sensorsmay be fault-prone or weakly connected ( ). Some sensorsmay not become parts of the cluster. After clustering, ourobjective is to detect the RPs in the WSN, and provide faulttolerance for the RPs and for the data packet-loss in eachcluster. The level of fault tolerance is the failure of up tosensors, which means to achieve a connected cluster. Thesensor fault tolerance has to be achieved by all independentCHs and sensors (i.e., cluster members). Algorithm for Backup Sensor Placement

Sensors are consideredas neighboringsensors if theyare within the transmission rangeof each other. Each node regularly sends its measured value to all its neighbors. We are interested in the history data if more than half of the sensor’s neighbors have a significantly different value from it. We can find the current measurement is different from previous measurement.If themeasurements changeover the time significantly, it is more likely the sensor is faulty. A. Backup Sensor Placement In this section, we first briefly describe an SHM applicationspecificclustering. Then, we propose our BS algorithm, including several sub-algorithms. B. Clustering In FTSHM, we detect possible repairing (failure) points in theWSN, and repair them by placing backup sensors throughclusters. We improve an existing clustering approach suggestedfor SHM, called C-SHM, which is specificallydesigned for SHM application. It obtains dynamic vibration characteristics of each cluster area, and then carries outstructural modal analysis (e.g., mode shape). It proves thatthe clustering for WSN-based SHM should meet some extra requirements for modal analysts. In the SHM perspective, although C-SHM is distributedand shown to outperform centralized approaches, it carriesout excessive

The placement of backup sensors is performed through each cluster. BSP algorithm is relatively simple: finding locationsto places the backup sensors, and improving unstable orweak connected clusters into strongly -connected clusters. First, BSP algorithm detects all of the repairing (or failure) points (RPs) step by step. The possible RPs are separablepoints, critical middle points, and isolated points in the WSN. Then, the algorithm places backup sensors until all RPs are found, or all backup sensors are placed through three sub algorithms, namely, BSP1, BSP2, and BSP3. All three algorithms call another algorithm, Search-and-Place, for finding locations around each RP. We will subsequently describeBSP1, BSP2, and BSP3.In the BSP algorithm (see Algorithm 1), there is a set B of R backup sensors, is a degree of connectivity, and all clusters ofthe network are inputs. The output is the placement of R backup sensors, and a strongly –connected network.

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The BSP algorithm has three steps. InStep1, BSP algorithm first calls the three sub-algorithms (i.e., BSP1, BSP2, and BSP3) that are used to detect RPs. Each of them calls an algorithm for placing backup sensors at the RPs. When running any of the three algorithms, if there is no RP in any cluster, the algorithm stops searching and goes to the next algorithm. When the three algorithms are executed, Step 2continues.When Step 2is over, BSP algorithm goes to the next cluster. When placement of all of the given backup sensors is over, the BSP algorithm terminates. Let and be the number of backup sensors to be placedduringtheBSP1,BSP2,andBSP3algorithms,runtimes, respectively. We consider Step 2asan option. Ifthere are stillsome backupsensors available tobeplaced, i.e., > ,we can placethemorsavethem.We thinkthattheWSNcanbe sufﬁcientlyconnectedafterplacementofthebackupsensors. However, there may remain some backupsensors to be placed, since the total numberofRPscan belessormore than .If islessthanthenumberofRPs(afterplacing the backup sensors),wediscardﬁndingRPs.Step2checks whethertherearestillbackupsensors available ornot.Ifan SHM user does not wish to place more backup sensors, Step2can 2inour beskipped.However,we did not skip Step evaluation.Wethink that the same physical sensor dependingonthe canfeedintotwoormore sensors, availabilityofbackupsensors. Step2checks all the sensor locations througheachcluster onebyone,and counts how manybackupsensorsareplacedateachlocation. If a location is still with only one sensor (i.e., no backup sensor is placed yet), a backup sensor is placed at the location. If two sensors are already placed at a location, including one backup sensor, we skip the location. allthe backupsensors Theplacementcontinuesuntil areplaced, i.e., -ConnectivityMaintenance: InStep3, BSP algorithm calls a connectivity maintenance algorithm, Connectivity-Recovery, which starts with a cluster (all the clusters are static, but the number of sensors may change due to sensor faults).The purpose of this algorithm is to improve connectivity of the WSN in an event of sensor failure, orconnectivitydegradation.Ifalltheconnectionsbelongingtoaf ailed(orremoved) sensor fail, we still require the improvement of the current connectivity to -connectivity. The value of can be fixed, such as For the case the minimum weight-connected cluster is known to be NPhard. Related connectivity algorithms and theorems can be found in the literature. Algorithm 2. Damage indicator algorithm

damage, a CH If there is an indication of a possible transmits the indication to the BS; otherwise, it just maintains connectivity with the BS and the sensors. The BS receives indications from all of the CHs an disable to know the health of the whole structure. After analyzing, if the BS determines that there is possibly damage, the BS can query the corresponding cluster or the sensors for detailed mode shapes, even for all sets of frequencies. In this way, if there is no damage indication(because we think that damage is a kind of event that rarely occurs in a structure), the amount of data is reduced before transmission CONCLUSION In this paper, our intention was to demonstrate a new way of incorporating the requirements of both WSN and SHM, and to make use of traditional engineering methods in the WSN. We found that it is worthwhile to place a small number of backup sensors around the repair points in the WSN to have a better performance. We believe that such an idea (of the backup sensor placement) can also be used in generic WSN applications. Besides, we proposed an SHM algorithm exploiting sensor-decentralized computing in there source-constrained WSN. Through extensive

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simulations and area implementation using integrated Imote2sensors, we validated the effectiveness of our approach. The validation shows that structural health monitoring using WSN scan be meaningless, if the requirements of WSNs (e.g., fault tolerance, energyefficiency) are not seriously considered. Thiswork leaves atleast two open issues in the multidomain research area. One issue is to develop algorithms for SHM application- specific sensor fault detection and recovery. Another issue is to develop a SHM specific scheduling technique for the backup sensors that will wake up one or more backup sensors in the areas of interest (e.g., damaged area)in the case of a sensor fault/failure. This and connectivity may help to meet both coverage requirements in a WSN-based SHMsystem.

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