forwarding this information to advertising networks like Admob (2011) or to analytics companies such as Flurry (2011), again without the consent. This demonstrates how users’ reality mining is becoming widespread and the future of predictive technologies is bright. However, many problems still need to be overcome and whilst there is much scope for further research, the majority of it is likely to continue to be done outside of the academic world. Table 3.4 show a comparison of predictive localisation research. No consistent approach exists and many technologies and techniques are being applied with varying degrees of success. Table 3.4: Predictive Localisation Research Researcher/ Accuracy Constraints/ Category Technology Technique Company Reported Comments Simmons et Road Only 1 option GPS HMM 99% al. (2006) traffic 95% of the time Requires a high Krumm Road GPS Markov model 98% order Markov (2008) traffic model Road Inrix (2011) GPS Unknown Unknown Crowd Sourcing Traffic Ashbrook Pedestrian Small number of and Starner GPS Markov model 70-80% Outdoor nodes (2002) Sang-Jun Pedestrian No figures GPS SOM Acceptable (2004) Outdoor available Minimum Sense Volume Pedestrian GPS/Mobile Networks Embedding Unknown Crowd Sourcing Outdoor phone (2011) (MVE)/Bayesian out trees Only looked for Gonzales et Mobile Statistical Pedestrian N/A repeating al. (2008) phone Analysis patterns Bahl and Viterbi and For immediate Pedestrian 802.11 2.4 m Padmanabhan Nearest location estimate Indoor improvement (2000) Neighbour only Journeys from Vintan et al. Pedestrian 92% (pre RFID Neural Network base node not (2004) Indoor training) considered 59% Branch Journeys from Petzold et al. Pedestrian (without) RFID prediction base node not (2006) Indoor and 98% techniques considered (with pre
forwarding this information to advertising networks like Admob (2011) or to analytics companies such as Flurry (2011), again without the users’ consent. This demonstrates how reality mining is becoming widespread and the future of predictive technologies is bright. However, many problems still need to be overcome and whilst there is much scope for further research, the majority of it is likely to continue to be done outside of the academic world. Table 3.4 show a comparison of predictive localisation research. No consistent approach exists and many technologies and techniques are being applied with varying degrees of success. Table 3.4: Predictive Localisation Research Researcher/ Company Category Technology Technique Accuracy Reported Constraints/ Comments Simmons et al. (2006) Road traffic GPS HMM 99% Only 1 option 95% of the time Krumm (2008) Road traffic GPS Markov model 98% Requires a high order Markov model Inrix (2011) Road Traffic GPS Unknown Unknown Crowd Sourcing Ashbrook and Starner (2002) Pedestrian Outdoor GPS Markov model 70-80% Small number of nodes Sang-Jun (2004) Pedestrian Outdoor GPS SOM Acceptable No figures available Sense Networks (2011) Pedestrian Outdoor GPS/Mobile phone Minimum Volume Embedding (MVE)/Bayesian out trees Unknown Crowd Sourcing Gonzales et al. (2008) Pedestrian Mobile phone Statistical Analysis N/A Only looked for repeating patterns Bahl and Padmanabhan (2000) Pedestrian Indoor 802.11 Viterbi and Nearest Neighbour 2.4 m improvement For immediate location estimate only Vintan et al. (2004) Pedestrian Indoor RFID Neural Network 92% (pre training) Journeys from base node not considered Petzold et al. (2006) Pedestrian Indoor RFID Branch prediction techniques 59% (without) and 98% (with pre Journeys from base node not considered