A p-Persistent Frequent Itemsets with 1-RHS Based Correction Algorithm for Improving the Performance of WiFi-Based Occupant Detection Method
Considering that existing device-based occupant detection methods cannot count those who do not carry a device, in this paper, for buildings where the behaviour of the occupants tends to be regular, taking the WiFi-based occupant detection method as a basis, we propose a p-persistent frequent itemsets with 1-right-hand-side (RHS)-based occupant detection algorithm to improve the occupant detection performance in terms of accuracy. Association analysis using apriori algorithm is utilized to predict the occupancy of buildings through mining the relationships among occupants. We mathematically prove the reasonability of frequent itemsets with 1-RHS chosen in our algorithm and show the experimental results of applying this approach with different p. The results show that our proposed method can improve the accuracy performance in that it can see the occupant in buildings that the WiFi-based occupant detection method cannot see.
KeywordsOccupant detection Association analysis Apriori algorithm Frequent itemsets p-persistent
This work was supported by National Key Research and Development Project of China, No. 2017YFC0704100 (entitled New generation intelligent building platform techniques), National Experimental Teaching Demonstration Center (entitled Building Control and Energy Saving Optimization Experiment Center, Anhui Jianzhu University), National Natural Science Foundation of China (Grant No. 11471304), and Ph.D. Research Startup Foundation of Anhui Jianzhu University (Grant No. 2017QD07).
- 1.Akkaya, K., Guvenc, I., Aygun, R., Pala, N., Kadri, A.: IoT-based occupancy monitoring techniques for energy-efficient smart buildings. In: Proceedings of IEEE Wireless Communications Networking Conference Workshops, pp. 58–63 (2015)Google Scholar
- 2.Huang, Q., Cox, R., Shaurette, M., Wang, J.: Intelligent building hazard detection using wireless sensor network and machine learning techniques. In: International Conference on Computing in Civil Engineering, pp. 485–492 (2012)Google Scholar
- 4.Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)Google Scholar
- 5.Ryan, C., Brown, K.N.: Predicting occupant locations using association rule mining. In: 33rd SGAI International Conference on Artificial Intelligence, Cambridge, England, pp. 63–77 (2013)Google Scholar
- 6.Musa, A.B.M., Eriksson, J.: Tracking unmodified smartphones using Wi-Fi monitors. In: Proceedings of ACM Conference on Embedded Network Sensor Systems, ser. SenSys ’ 12, New York, NY, USA, pp. 281–294. ACM (2012)Google Scholar
- 7.Kropeit, T.: Don’t trust open hotspots: Wi-Fi hacker detection and privacy protection via smartphone, BS Thesis (2015)Google Scholar
- 8.Vattapparamban, E., Ciftler, B.S., Guvenc, I.G., Akkaya, K., et al.: Indoor occupancy tracking in smart buildings using passive sniffing of probe requests. In: IEEE International Conference on Communications Workshops. IEEE, pp. 38–44 (2016)Google Scholar
- 9.Ciftler, B.S., Dikmese, S., Guvenc, I.G., et al.: Occupancy counting with burst and intermittent signals in smart buildings. IEEE Internet Things J. 1–11 (2017)Google Scholar
- 10.Qolomany, B., Al-Fuqaha, A., Benhaddou, D., Gupta, A.: Role of deep LSTM neural networks and Wi-Fi networks in support of occupancy prediction in smart buildings. In: The 15th IEEE International Conference on Smart City (SmartCity 2017), Bangkok, Thailand, 18–20 Dec 2017Google Scholar
- 11.Nguyen, C.L., Khan, A.: WiLAD: wireless localisation through anomaly detection (2018). https://www.researchgate.net/publication/319416168_WiLAD_Wireless_Localisation_through_Anomaly_Detection