Bio-inspired Sensors for Structural Health Monitoring - Biotechnologies and Biomimetics for Civil Engineering - page 253

system (AIS) to improve the reliability and security of sensor networks for SHM
(Drozda et al. 2011a ; Twycross and Aickelin 2009 ; Dasgupta 2006 ).
One of the interesting features of the BIS is homeostasis, which helps maintain a
normal operation level in environments where errors and changes could occur
(Drozda et al. 2011a ). One should know that BIS consists of two components (i.e.,
innate and adaptive immune systems) to protect the biological host from threat-
ening pathogens. Here, the innate immune system immediately responds to the
known pathogen, and the system does not change. On the other hand, the adaptive
immune system involves learning and memory that take place over relatively longer
periods of time (e.g., over several days). When unknown pathogens affect the host,
the adaptive immune system recognizes and memorizes the new threat or change.
The detailed mechanism that enables the adaptive immune system response is
that lymphocytes experience either positive or negative selection processes with a
minimum self-reactivity or autoimmunity. When autoimmunity happens, the host
cells undergo self-attack. It should be noted that first generation AIS considered
only one type cell (i.e., B cell) for stabilization of the network (Hunt et al. 1999 ).
When antigens were presented to B cells, and if affinities between B cells were
strong, the B cells were cloned and contributed to stabilization of the network.
A second generation AIS was proposed by introducing different cell types (e.g.,
B and T cells) to the conventional AIS to enhance performance of error detection
(Twycross and Aickelin 2009 ). Research focused on the communication between
the different cell types. The innate immune system took part in error detection,
which involves classifying the error and providing context on errors triggering
damage. AIS' error detection structure was composed of adaptive and context
classification modules. The two modules interacted in a feedback loop. By mim-
icking biological adaptive immunity, the structure offered functions of selection,
memory, and learning for detecting new errors and changes in existing errors in the
monitored sensor network.
The context classification module, inspired by innate immunity, evaluates the
quality of service, and provides feedback to the adaptive module after error
detection. Using the immune system-inspired error detection algorithm, it was
shown that various objectives, such as energy efficiency, final false positives rate,
adaptivity, and novel error detection, could be taken into account. By simulating
the experiments conducted by Drozda et al. ( 2011b ), improvements in the adaptive
strategy-based error detection method were validated with minimum impact on
energy efficiency and intact false positives control (Drozda et al. 2011a ).
Bio-inspiration has also been adopted to improve the performance of compli-
cated sensor networks for SHM. In addition, as these sensor networks evolve, they
become even more complicated, with increasing number of sensors and dimen-
sions. In particular, complex sensor networks with large number of sensors require
the use of novel algorithms and strategies for optimum sensor placement to avoid
coverage holes and unbalanced data flows.
For instance, genetic algorithms (GA) were investigated by numerous
researchers for fault detection, as well as for monitoring spatial lattice structures and
large space structures (Worden and Burrows 2001 ; Liu et al. 2008 ; Yao et al. 1993 ).