Over the past three centuries, spinning technology has been getting continuously
improved through thousands of minor innovations, and occasional major advances
that have collectively increased the quality and lowered the cost of producing yarn.
In spinning mills, the most important parameter to be monitored while yarn is being
formed is the yarn breakage. Hence, detecting the yarn breakage in a spinning machine
in a textile industry is one of the important tasks as it directly affects the production
efficiency. In current sensing technique, a wired network is used in the spinning
machine. Wireless communication, if used, can however remedy the cabling issues
with the traditional monitoring system usage and thereby significantly reduce the
maintenance costs (Stuart et al., 2009).
Wireless Sensor Network (WSN) finds ameliorating popularity in industrial sensing
applications due to its comparatively low cost and simplicity for improvement into
existing infrastructure (Baker et al., 2009). Progressive improvements in the hardware
technology have resulted in manufacturing miniature sensor nodes on single chips
with embedded memory, processor, transceiver and a companion battery. As it is difficult
to replace or charge the exhausted battery, this aspect gives way to the primary objective
of increasing the lifetime of the battery, leaving the optimization of other performance
metrics as the second objective. Since the communication aspect involved in the
sensor node is more energy-consuming than their involvement in computational aspects
(Linnyeret al., 2004; and Mohammad and Imad, 2005), it becomes mandatory to keep
the communication duration as minimum as possible, so it would be better to collect
the desired data from the sensor networks directly rather than communicating through
the nearby sink. In this context, yarn breakage data collection using an appropriate
WSN, in addition to supporting real-time dynamic configuration of collection of tasks,
seems to be a challenging research. The key performance metrics in WSN are both
network lifetime and an average time required to report an event reliably (Fatma
et al., 2008). Taking the two aspects, viz., yarn break detection and the subsequent
data collection, it is also felt that a mobile agent-based framework if used for collecting
data on yarn breakages through the use of an appropriate sensor network would also
be beneficial. With this in view, in the proposed approach, the sensed information has
been collected using mobile agents, and it has been found that the energy spent on
sensor nodes getting minimized.
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