Three Tier Indoor Localization System for Digital Forensics
Mobile localization has attracted a great deal of attention recently due to the introduction of wireless networks. Although several localization algorithms and systems have been implemented and discussed in the literature, very few researchers have exploited the gap that exists between indoor localization, tracking, external storage of location information and outdoor localization for the purpose of digital forensics during and after a disaster. The contribution of this paper lies in the implementation of a robust system that is capable of locating, tracking mobile device users and store location information for both indoor and partially outdoor the cloud. The system can be used during disaster to track and locate mobile phone users. The developed system is a mobile application built based on Android, Hypertext Preprocessor (PHP), Cascading Style Sheets (CSS), JavaScript and MATLAB for the Android mobile users. Using Waterfall model of software development, we have implemented a three level system that is able to track, locate and store mobile device information in secure database (cloud) on almost a real time basis. The outcome of the study showed that the developed system is efficient with regard to the tracking and locating mobile devices. The system is also flexible, i.e. can be used in any building with fewer adjustments. Finally, the system is accurate for both indoor and outdoor in terms of locating and tracking mobile devices.
[1] Hofmann-Wellenhof, B., Lichtenegger, H., and Collins, J.: ‘Global positioning system: theory and practice’ (Springer Science & Business Media).
[2] He, T., Huang, C., Blum, B. M., Stankovic, J. A., and Abdelzaher, T.: ‘Range-free localization schemes for large scale sensor networks’, in Editor (Ed.)^(Eds.): ‘Book Range-free localization schemes for large scale sensor networks’ (ACM, 2003, edn.), pp. 81-95.
[3] Chan, Y.-T., Tsui, W.-Y., So, H.-C., and Ching, P.-C.: ‘Time-of-arrival based localization under NLOS conditions’, IEEE Transactions on Vehicular Technology, 2006, 55, (1), pp. 17-24.
[4] Khodayari, S., Maleki, M., and Hamedi, E.: ‘A RSS-based fingerprinting method for positioning based on historical data’, in Editor (Ed.)^(Eds.): ‘Book A RSS-based fingerprinting method for positioning based on historical data’ (IEEE, edn.), pp. 306-310.
[5] Xiao, J., Wu, K., Yi, Y., and Ni, L. M.: ‘FIFS: Fine-grained indoor fingerprinting system’, in Editor (Ed.)^(Eds.): ‘Book FIFS: Fine-grained indoor fingerprinting system’ (IEEE, edn.), pp. 1-7.
[6] Han, Y., Stuntebeck, E. P., Stasko, J. T., and Abowd, G. D.: ‘A visual analytics system for radio frequency fingerprinting-based localization’, in Editor (Ed.)^(Eds.): ‘Book A visual analytics system for radio frequency fingerprinting-based localization’ (IEEE, 2009, edn.), pp. 35-42.
[7] Pereira, C., Guenda, L., and Carvalho, N. B.: ‘A smart-phone indoor/outdoor localization system’, in Editor (Ed.)^(Eds.): ‘Book A smart-phone indoor/outdoor localization system’ (edn.), pp. 21-23.
[8] Chen, Y., Lymberopoulos, D., Liu, J., and Priyantha, B.: ‘FM-based indoor localization’, in Editor (Ed.)^(Eds.): ‘Book FM-based indoor localization’ (ACM, edn.), pp. 169-182.
[9] Wu, C., Yang, Z., Liu, Y., and Xi, W.: ‘WILL: Wireless indoor localization without site survey’, IEEE Transactions on Parallel and Distributed Systems, 24, (4), pp. 839-848.
[10] Campos, R. S., and Lovisolo, L.: ‘RF Positioning: Fundamentals, Applications, and Tools’ (Artech House).
[11] Jiang, P., Zhang, Y., Fu, W., Liu, H., and Su, X.: ‘Indoor mobile localization based on Wi-Fi fingerprint's important access point’, International Journal of Distributed Sensor Networks.
[12] Van de Goor, M.: ‘Indoor localization in wireless sensor networks’, Master’s thesis, Radboud University Nijmegen, 2009.
[13] Yang, Z., Wu, C., and Liu, Y.: ‘Locating in fingerprint space: wireless indoor localization with little human intervention’, in Editor (Ed.)^(Eds.): ‘Book Locating in fingerprint space: wireless indoor localization with little human intervention’ (ACM, edn.), pp. 269-280.
[14] Liu, H., Yang, J., Sidhom, S., Wang, Y., Chen, Y., and Ye, F.: ‘Accurate WiFi based localization for smartphones using peer assistance’, IEEE Transactions on Mobile Computing, 13, (10), pp. 2199-2214.
[15] Husen, M. N., and Lee, S.: ‘Indoor Location Sensing with Invariant Wi-Fi Received Signal Strength Fingerprinting’, Sensors, 16, (11), pp. 1898
[16] Kwapisz, J. R., Weiss, G. M., and Moore, S. A.: ‘Activity recognition using cell phone accelerometers’, ACM SigKDD Explorations Newsletter, 12, (2), pp. 74-82.
[17] Shala, U., and Rodriguez, A.: ‘Indoor positioning using sensor-fusion in Android devices’.
[18] Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., and Campbell, A. T.: ‘A survey of mobile phone sensing’, IEEE Communications magazine, 48, (9), pp. 140-150.
[19] Barthold, C., Subbu, K.P., and Dantu, R.: ‘Evaluation of gyroscope-embedded mobile phones’, in Editor (Ed.)^(Eds.): ‘Book Evaluation of gyroscope-embedded mobile phones’ (IEEE, edn.), pp. 1632-1638.
[20] Ying, H., Silex, C., Schnitzer, A., Leonhardt, S., and Schiek, M.: ‘Automatic step detection in the accelerometer signal’, in Editor (Ed.)^(Eds.): ‘Book Automatic step detection in the accelerometer signal’ (Springer, 2007, edn.), pp. 80-85.
[21] Han, D., Andersen, D. G., Kaminsky, M., Papagiannaki, K., and Seshan, S.: ‘Access point localization using local signal strength gradient’, in Editor (Ed.)^(Eds.): ‘Book Access point localization using local signal strength gradient’ (Springer, 2009, edn.), pp. 99-108.
[1] Hofmann-Wellenhof, B., Lichtenegger, H., and Collins, J.: ‘Global positioning system: theory and practice’ (Springer Science & Business Media).
[2] He, T., Huang, C., Blum, B. M., Stankovic, J. A., and Abdelzaher, T.: ‘Range-free localization schemes for large scale sensor networks’, in Editor (Ed.)^(Eds.): ‘Book Range-free localization schemes for large scale sensor networks’ (ACM, 2003, edn.), pp. 81-95.
[3] Chan, Y.-T., Tsui, W.-Y., So, H.-C., and Ching, P.-C.: ‘Time-of-arrival based localization under NLOS conditions’, IEEE Transactions on Vehicular Technology, 2006, 55, (1), pp. 17-24.
[4] Khodayari, S., Maleki, M., and Hamedi, E.: ‘A RSS-based fingerprinting method for positioning based on historical data’, in Editor (Ed.)^(Eds.): ‘Book A RSS-based fingerprinting method for positioning based on historical data’ (IEEE, edn.), pp. 306-310.
[5] Xiao, J., Wu, K., Yi, Y., and Ni, L. M.: ‘FIFS: Fine-grained indoor fingerprinting system’, in Editor (Ed.)^(Eds.): ‘Book FIFS: Fine-grained indoor fingerprinting system’ (IEEE, edn.), pp. 1-7.
[6] Han, Y., Stuntebeck, E. P., Stasko, J. T., and Abowd, G. D.: ‘A visual analytics system for radio frequency fingerprinting-based localization’, in Editor (Ed.)^(Eds.): ‘Book A visual analytics system for radio frequency fingerprinting-based localization’ (IEEE, 2009, edn.), pp. 35-42.
[7] Pereira, C., Guenda, L., and Carvalho, N. B.: ‘A smart-phone indoor/outdoor localization system’, in Editor (Ed.)^(Eds.): ‘Book A smart-phone indoor/outdoor localization system’ (edn.), pp. 21-23.
[8] Chen, Y., Lymberopoulos, D., Liu, J., and Priyantha, B.: ‘FM-based indoor localization’, in Editor (Ed.)^(Eds.): ‘Book FM-based indoor localization’ (ACM, edn.), pp. 169-182.
[9] Wu, C., Yang, Z., Liu, Y., and Xi, W.: ‘WILL: Wireless indoor localization without site survey’, IEEE Transactions on Parallel and Distributed Systems, 24, (4), pp. 839-848.
[10] Campos, R. S., and Lovisolo, L.: ‘RF Positioning: Fundamentals, Applications, and Tools’ (Artech House).
[11] Jiang, P., Zhang, Y., Fu, W., Liu, H., and Su, X.: ‘Indoor mobile localization based on Wi-Fi fingerprint's important access point’, International Journal of Distributed Sensor Networks.
[12] Van de Goor, M.: ‘Indoor localization in wireless sensor networks’, Master’s thesis, Radboud University Nijmegen, 2009.
[13] Yang, Z., Wu, C., and Liu, Y.: ‘Locating in fingerprint space: wireless indoor localization with little human intervention’, in Editor (Ed.)^(Eds.): ‘Book Locating in fingerprint space: wireless indoor localization with little human intervention’ (ACM, edn.), pp. 269-280.
[14] Liu, H., Yang, J., Sidhom, S., Wang, Y., Chen, Y., and Ye, F.: ‘Accurate WiFi based localization for smartphones using peer assistance’, IEEE Transactions on Mobile Computing, 13, (10), pp. 2199-2214.
[15] Husen, M. N., and Lee, S.: ‘Indoor Location Sensing with Invariant Wi-Fi Received Signal Strength Fingerprinting’, Sensors, 16, (11), pp. 1898
[16] Kwapisz, J. R., Weiss, G. M., and Moore, S. A.: ‘Activity recognition using cell phone accelerometers’, ACM SigKDD Explorations Newsletter, 12, (2), pp. 74-82.
[17] Shala, U., and Rodriguez, A.: ‘Indoor positioning using sensor-fusion in Android devices’.
[18] Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., and Campbell, A. T.: ‘A survey of mobile phone sensing’, IEEE Communications magazine, 48, (9), pp. 140-150.
[19] Barthold, C., Subbu, K.P., and Dantu, R.: ‘Evaluation of gyroscope-embedded mobile phones’, in Editor (Ed.)^(Eds.): ‘Book Evaluation of gyroscope-embedded mobile phones’ (IEEE, edn.), pp. 1632-1638.
[20] Ying, H., Silex, C., Schnitzer, A., Leonhardt, S., and Schiek, M.: ‘Automatic step detection in the accelerometer signal’, in Editor (Ed.)^(Eds.): ‘Book Automatic step detection in the accelerometer signal’ (Springer, 2007, edn.), pp. 80-85.
[21] Han, D., Andersen, D. G., Kaminsky, M., Papagiannaki, K., and Seshan, S.: ‘Access point localization using local signal strength gradient’, in Editor (Ed.)^(Eds.): ‘Book Access point localization using local signal strength gradient’ (Springer, 2009, edn.), pp. 99-108.
@article{"International Journal of Electrical, Electronic and Communication Sciences:75658", author = "Dennis L. Owuor and Okuthe P. Kogeda and Johnson I. Agbinya", title = "Three Tier Indoor Localization System for Digital Forensics", abstract = "Mobile localization has attracted a great deal of attention recently due to the introduction of wireless networks. Although several localization algorithms and systems have been implemented and discussed in the literature, very few researchers have exploited the gap that exists between indoor localization, tracking, external storage of location information and outdoor localization for the purpose of digital forensics during and after a disaster. The contribution of this paper lies in the implementation of a robust system that is capable of locating, tracking mobile device users and store location information for both indoor and partially outdoor the cloud. The system can be used during disaster to track and locate mobile phone users. The developed system is a mobile application built based on Android, Hypertext Preprocessor (PHP), Cascading Style Sheets (CSS), JavaScript and MATLAB for the Android mobile users. Using Waterfall model of software development, we have implemented a three level system that is able to track, locate and store mobile device information in secure database (cloud) on almost a real time basis. The outcome of the study showed that the developed system is efficient with regard to the tracking and locating mobile devices. The system is also flexible, i.e. can be used in any building with fewer adjustments. Finally, the system is accurate for both indoor and outdoor in terms of locating and tracking mobile devices.
", keywords = "Indoor localization, waterfall, digital forensics, tracking and cloud.", volume = "11", number = "6", pages = "651-9", }
