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9.2.7 Communication range adjustment When the communication range of a smart sensor includes neighboring sensor communities, the communication range should be shortened to reduce RF interference. When two sensor communities are out of communication range of each other, these communities can perform communication in parallel without interfering with each other. Communication range adjustment results in faster execution of DCS and smaller power consumption. 9.2.8 Reliability of the system For the system to be used for years, the reliability of smart sensor nodes needs to be improved. In the current system, the Imote2s sometimes hangs up. The possibility of hang-ups should be reduced. Additionally, node failure tolerance should preferably be realized, because the possibility of node failure cannot be completely eliminated; for example, even a PC with better resources sometimes hangs up. Failure of a single node should not provoke failure of the entire system. Even when all of the nodes seem to be working, sensor signals may be erroneous. For example, sensors may not be firmly attached to a structure. Such Byzantine-type error can possibly be detected by checking the linearity between measured signals. When the linearity between two signals is much lower than that of other pairs, one of the two signals may come from a sensor which is not adequately installed. Implementation of such a Byzantine error detection algorithm can improve the reliability of the damage detection system. 9.2.9 Environmental hardening The Imote2 needs to be packaged before the node can be installed on full-scale structures, especially when the Imote2 is used outside. The package needs to protect the Imote2 from weather, birds, insects, etc. Attention needs to be paid to not blocking radio reception/transmission. Also, the packaging should not absorb or amplify vibration. 9.2.10 Multiple purpose usage of smart sensors Smart sensor networks can potentially serve multiple purposes, while the developed SHM framework currently serves only for SHM purposes. Smart sensors can be used for traffic monitoring, local weather monitoring, fire detection, etc. Integration of these services enhances the value of smart sensors and makes introduction of smart sensor systems more attractive from a cost-benefit perspective. 165
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NSEL Report Series Report No. NSEL-
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The Newmark Structural Engineering
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Contents Page CHAPTER 1 INTRODUCTIO
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9.2.3 Power harvesting . . . . . .
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damage/deterioration is intrinsical
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scalability to a large number of sm
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logic circuit. However, FPGAs are m
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easons, smart sensors spatially dis
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Figure 2.1. EYES project sensor pro
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Table 2.1. Smart Sensor Specificati
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While a number of sensor boards for
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need to be within the coordinator's
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eing examined (Moore et al., 2001).
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structural damage, the analysis to
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that sensor installation cost inclu
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(2004) indicated that this accelero
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different type of sensors connected
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Chapter 3 SHM ARCHITECTURE This cha
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Modal operation Several modal opera
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its spatial gradient, which is clai
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Manager node Cluster head node Leaf
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commands; execution timing cannot b
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Table 3.2. Desirable Characteristic
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Table 3.2. Desirable Characteristic
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Figure 4.1. Foil strain gage. strai
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dB A s Figure 4.4. AA filter design
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8.32F 3V 3.45F 3V Input 1K 1K 1K 1K
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Strain ( ) 80 w/o Digital Filter 60
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performance was experimentally vali
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anges from 10 to 20. If 50 percent
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Node 1 x1 i =1,2,…,ns E x 1
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Correlation function estimate (m 2
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Figure 5.5. Effect of data loss and
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Frobenius norm. Because only a limi
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90 80 70 60 50 Node1 Node2 Node3 No
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On reception of a NACK, the sender
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Sender SendLData.send() Pack parame
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If there are missing packets, the r
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Sender SendLData.bcast() Pack param
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Sender Send a packet • Sender sen
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utilized in SHM applications follow
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where R xxref is a vector consistin
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Time (sec) 80 60 40 20 0 -20 -40 -6
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Difficulties encountered in realizi
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1.8715 x10 5 #ofdatablocks 1.871 me
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Before this decimation is applied,
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where xn is the original signal,
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Timer firing every T3 110 data poin
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locks before or after the current b
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Chapter 6 ALGORITHMS In this chapte
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The eigenproblem of the system matr
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where M 0 is the mass matrix of the
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same community, eliminating the nee
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measurement or mass perturbation ca
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these algorithms are implemented on
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10 0 10 -2 10 -4 0 500 1000 1500 20
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Table 7.1. SVD Accuracy Check on Ma
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7.1.4 Complex matrix inverse A comp
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Figure 7.9. Details of the joint (G
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Figure 7.13. Amplifier (Gao, 2005).
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Page 123 and 124: 3 Imote2_2/Imote2_1 Imote2_1/Siglab
Page 125 and 126: 200 3 Phase of spectral densities (
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Page 129 and 130: input forces applied at nodes in th
Page 131 and 132: CH1 CH2 CH3 …. CHn-1 CHn Initiali
Page 133 and 134: 1 2 Node ID 102 RETAKE 1 3 4 INCONS
Page 135 and 136: Initialization: within local sensor
Page 137 and 138: PC Base station Manager sensor Clus
Page 143 and 144: Table 7.7. Instructions in DCS Code
Page 145 and 146: EXPERIMENTAL VERIFICATION Chapter 8
Page 147 and 148: 8 x10-3 Time (sec) Correlation func
Page 149 and 150: Table 8.2. Mass Normalization Const
Page 151 and 152: 1 2 Node ID 67 RETAKE 0 3 4 ID 67 #
Page 153 and 154: for a SHM strategy does not necessa
Page 155 and 156: are saved. The 420 seconds listed a
Page 157 and 158: 1 1 Normalized accumulated stress m
Page 163 and 164: Table 8.5. Summary of False Damage
Page 165 and 166: Table 8.7. Summary of False Damage
Page 167 and 168: e scalability, autonomous distribut
Page 169 and 170: The lowest frequencies of interest
Page 171: structural vibration is considered
Page 175 and 176: Casciati, F., Faravelli, L, Borghet
Page 177 and 178: Feldman, M. & Braun, S. (1995). “
Page 179 and 180: Kling, R. (2003). “Intel Mote: an
Page 181 and 182: Mufti, A. A. (2003). “Integration
Page 183 and 184: Sazonov, E., Janoyan, K., & Jha, R.
Page 185 and 186: Yi, J.-H. & Yun, C.-B. (2004). “C
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