Stopping criterion impact on pure random search optimisation for intelligent device distribution

The number of intelligent environmentimplementations such as smart homes is set to increasedramatically within the next 40 years. This is predicted usingforecasts of demographic data which indicates an expansion ofthe aged population. It has also been predicted that governmentswill struggle to meet the demand for resources such as sensortechnology due to costs. Optimisation of limited resourcesinvolves physically positioning devices to maximise pertinent-data gathering potential. Currently the most utilisedmethodology of distributing limited spatial detection sensors suchas pressure mats within smart homes is via ad-hoc deploymentsperformed by a human being. In this study idiosyncraticinhabitant spatial-frequency data was processed using a PureRandom Search (PRS) algorithm to uncover probabilistic futureregions of interest, alluding to optimal sensor distributions underresource constraint. With PRS a null hypothesis was stated:‘using lower iteration stopping criteria produce less optimalsensor distributions than when using higher iteration stoppingcriteria’. A student t-test between 1000 and 5000 iterations wasstatistically significant at 5% (p = 0.016852) whereby the nullhypothesis was rejected. Similar results were obtained betweenother iteration criteria. These data demonstrate that the iterationstopping criterion is not as critical as sensor size or number ofsensors; and that comparable results could be obtained whenlower stopping parameters are specified when using PRS.