| dc.description.abstract | This paper moves forward the key idea as proposed in past research works
- a self-healing deployment approach for sensor networks, where a small
percentage of mobile sensors are deployed along with the static sensors
into a field of concern. Mobile sensors can move to make-up for a coverage
holes or sensor failure and significantly boost network performance.
However, since there are energy constraints on each individual mobile
sensor, potentially receiving multiple requests from network holes, the
decision to move a mobile sensor has to be optimum, one that maximizes
network benefit. In this paper, I propose a hybrid distributed & central
decision making algorithm to facilitate optimal moves by each mobile
sensor. The algorithm uses several layered techniques like Rough Set
analysis, sorting & multi-level auction to provide the best possible
decision, given the network scenario and the approach is robust to
incompleteness of information. The proposed solution also safeguards
against network deadlocks and extensive simulations & statistical analysis
have demonstrated superior performance of the algorithm when compared
to its peers. Some traits of the algorithm proposed derive inspiration for
decision support from Ants' swarm intelligence. | en_US |
