Genetic algorithm and pure random search for exosensor distribution optimisation
International Journal of Bio-Inspired Computation
| Hi-index | 0.00 |
The number of intelligent environment implementations such as smart homes is set to increase dramatically within the next 40 years. This is predicted using forecasts of demographic data which indicates an expansion of the aged population. It has also been predicted that governments will struggle to meet the demand for resources such as sensor technology due to costs. Optimisation of limited resources involves physically positioning devices to maximise pertinent data gathering potential. Currently the most utilised methodology of distributing limited spatial detection sensors such as pressure mats within smart homes is via ad-hoc deployments performed by a human being. In this study idiosyncratic inhabitant spatial-frequency data was processed using a Pure Random Search (PRS) algorithm to uncover probabilistic future regions of interest, alluding to optimal sensor distributions under resource constraint. With PRS a null hypothesis was stated: ‘using lower iteration stopping criteria produce less optimal sensor distributions than when using higher iteration stopping criteria’. A student t-test between 1000 and 5000 iterations was statistically significant at 5% (p = 0.016852) whereby the null hypothesis was rejected. Similar results were obtained between other iteration criteria. These data demonstrate that the iteration stopping criterion is not as critical as sensor size or number of sensors; and that comparable results could be obtained when lower stopping parameters are specified when using PRS.