Structural Health Monitoring Using Smart Sensors - ideals ...

need to be within the coordinator's communication range. Ember (2005) employed the
mesh network to monitor water treatment plants, using their ZigBee chipset. The first
generation Intel Mote (Kling, 2003; Kling et al., 2005) employed the cluster tree topology
utilizing Bluetooth technology. Mechitov et al. (2004) employed the tree topology with
Mica2s. By refreshing the topology periodically, the sensor networks are resistant to
communication link failures. Most of the current civil engineering applications using
smart sensors utilize the star topology. For mesh or cluster tree topologies, the routing
path needs to be optimized. To collect data efficiently and reliably at the base station,
Mechitov et al. (2004) set the path length as the primary criterion for establishing the tree
structure, with link quality being the secondary criterion.
Packet loss intrinsic to wireless communication needs to be addressed. Some data
need to be delivered reliably while others need to be delivered with better than a certain
probability. Although packet loss is inevitable, the loss of data can be avoided or reduced.
Sending a packet multiple times increases the possibility of successful delivery.
Furthermore, a packet can be repeatedly sent until acknowledgment is received. This
approach guarantees successful communication unless two nodes are completely out of
the communication range of each other or one of the nodes fails. Reliable communication
with acknowledgment messages was implemented on the Mica2 platform (Mechitov et al.,
2004). Also, standards such as Bluetooth and IEEE 802.11, provide their own
specifications to deal with communication errors. Successful delivery can be guaranteed
or increased at the expense of extra packets to exchange, larger header size, and/or extra
computation.
Because RAM is one of the power-consuming components on a smart sensor, the size
of RAM is usually small, sometimes necessitating virtual memory or other solutions.
Applications such as SHM deal with large response data records and need a large amount
of memory. For example, a modal analysis method, Eigensystem Realization Algorithm
(ERA; Juang and Pappa, 1985) employs singular value decomposition of two Hankel
matrices, whose size could be much larger than the RAM size; a 50 x 50 rather small
Hankel matrix consisting of 8-byte, double precision matrix elements needs 20 kB of
RAM, while hundreds by hundreds Hankel matrices are not uncommon. Mica2's 4 kB of
RAM memory is apparently insufficient for this SHM application. Kwon et al. (2005b)
proposed ActorNet which employs virtual memory on the Mica2. In addition to the virtual
memory, large physical memory can be a solution. For example, the Imote2 possesses 32
MB of RAM at the expense of larger power consumption.
Because of efficient power usage considerations, TinyOS employs nonblocking I/O,
which makes programming code complicated. The nonblocking I/O system does not wait
for a return value after it calls a function. A called task is posted for execution, and the
main thread keeps running. Once the task is executed, the completion is signaled. Kwon et
al. (2005b) proposed converting nonblocking I/O to blocking I/O to make the program
development more efficient. This nonblocking I/O is not a critical problem to be solved,
but this middleware service will increase the productivity of the inexperienced developers.
Though many middleware services have been proposed, studied, and implemented,
their performance has not been carefully examined from the SHM perspective. Moreover,
the middleware requirements for SHM are unclear. Based on SHM application
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