Indoor Localization Algorithm and Appropriate Implementation Using Wireless Sensor Networks
The relationship dependence between RSS and distance
in an enclosed environment is an important consideration because it is
a factor that can influence the reliability of any localization algorithm
founded on RSS. Several algorithms effectively reduce the variance of
RSS to improve localization or accuracy performance. Our proposed
algorithm essentially avoids this pitfall and consequently, its high
adaptability in the face of erratic radio signal. Using 3 anchors in
close proximity of each other, we are able to establish that RSS can be
used as reliable indicator for localization with an acceptable degree of
accuracy. Inherent in this concept, is the ability for each prospective
anchor to validate (guarantee) the position or the proximity of the
other 2 anchors involved in the localization and vice versa. This
procedure ensures that the uncertainties of radio signals due to
multipath effects in enclosed environments are minimized. A major
driver of this idea is the implicit topological relationship among
sensors due to raw radio signal strength. The algorithm is an area
based algorithm; however, it does not trade accuracy for precision
(i.e the size of the returned area).
[1] YoungMin Kwon, Kirill Mechitov, Sameer Sundresh, Wooyoung Kim,
and Gul Agha. Resilient localization for sensor networks in outdoor
environments. In Distributed Computing Systems, 2005. ICDCS 2005.
Proceedings. 25th IEEE International Conference on, pages 643–652.
IEEE, 2005.
[2] Neal Patwari, Alfred O Hero III, Matt Perkins, Neiyer S Correal, and
Robert J O’dea. Relative location estimation in wireless sensor networks.
Signal Processing, IEEE Transactions on, 51(8):2137–2148, 2003.
[3] H¨useyin Akcan and Cem Evrendilek. Gps-free directional localization
via dual wireless radios. Computer Communications, 35(9):1151–1163,
2012.
[4] Mussa Bshara, Umut Orguner, Fredrik Gustafsson, and Leo
Van Biesen. Fingerprinting localization in wireless networks based
on received-signal-strength measurements: A case study on wimax
networks. Vehicular Technology, IEEE Transactions on, 59(1):283–294,
2010.
[5] Guoqiang Mao and Barıs¸ Fidan. Introduction to wireless sensor network
localization. Localization Algorithms and Strategies for Wireless Sensor
Networks, pages 1–32, 2009.
[6] Parameshwaran Krishnan, AS Krishnakumar, Wen-Hua Ju, Colin
Mallows, and SN Gamt. A system for lease: Location estimation assisted
by stationary emitters for indoor rf wireless networks. In INFOCOM
2004. Twenty-third AnnualJoint Conference of the IEEE Computer and
Communications Societies, volume 2, pages 1001–1011. IEEE, 2004.
[7] Phongsak Prasithsangaree, Prashant Krishnamurthy, and Panos K
Chrysanthis. On indoor position location with wireless lans. In
Personal, Indoor and Mobile Radio Communications, 2002. The 13th
IEEE International Symposium on, volume 2, pages 720–724. IEEE,
2002.
[8] Teemu Roos, Petri Myllym¨aki, and Henry Tirri. A statistical modeling
approach to location estimation. Mobile Computing, IEEE Transactions
on, 1(1):59–69, 2002.
[9] Nirupama Bulusu, Vladimir Bychkovskiy, Deborah Estrin, and John
Heidemann. Scalable, ad hoc deployable rf-based localization. In
Proceedings of the Grace Hopper Conference on Celebration of Women
in Computing, volume 31, 2002.
[10] Guoqiang Mao. Localization Algorithms and Strategies for Wireless
Sensor Networks: Monitoring and Surveillance Techniques for Target
Tracking: Monitoring and Surveillance Techniques for Target Tracking.
IGI Global, 2009.
[11] Vaidyanathan Ramadurai and Mihail L Sichitiu. Simulation-based
analysis of a localization algorithm for wireless ad-hoc sensor networks.
In Proceedings of the International Conference on Wireless Networks,
Las Vegas, NV, 2003.
[12] Michael Allen, Sebnem Baydere, Elena Gaura, and Gurhan Kucuk.
Evaluation of localization algorithms. Localization Algorithms and
Strategies for Wireless Sensor Networks. IGI Global, 2009.
[13] Ambili Thottam Parameswaran, Mohammad Iftekhar Husain, Shambhu
Upadhyaya, et al. Is rssi a reliable parameter in sensor localization
algorithms: An experimental study. In Field Failure Data Analysis
Workshop (F2DA09), page 5, 2009. [14] Qian Dong and Waltenegus Dargie. Evaluation of the reliability of rssi
for indoor localization. In Wireless Communications in Unusual and
Confined Areas (ICWCUCA), 2012 International Conference on, pages
1–6. IEEE, 2012.
[15] Zaher Merhi, Michel Nahas, Samih Abdul-Nabi, Amin Haj-Ali, and
M Bayoumi. Rssi range estimation for indoor anchor based localization
for wireless sensor networks. In Microelectronics (ICM), 2013 25th
International Conference on, pages 1–4. IEEE, 2013.
[1] YoungMin Kwon, Kirill Mechitov, Sameer Sundresh, Wooyoung Kim,
and Gul Agha. Resilient localization for sensor networks in outdoor
environments. In Distributed Computing Systems, 2005. ICDCS 2005.
Proceedings. 25th IEEE International Conference on, pages 643–652.
IEEE, 2005.
[2] Neal Patwari, Alfred O Hero III, Matt Perkins, Neiyer S Correal, and
Robert J O’dea. Relative location estimation in wireless sensor networks.
Signal Processing, IEEE Transactions on, 51(8):2137–2148, 2003.
[3] H¨useyin Akcan and Cem Evrendilek. Gps-free directional localization
via dual wireless radios. Computer Communications, 35(9):1151–1163,
2012.
[4] Mussa Bshara, Umut Orguner, Fredrik Gustafsson, and Leo
Van Biesen. Fingerprinting localization in wireless networks based
on received-signal-strength measurements: A case study on wimax
networks. Vehicular Technology, IEEE Transactions on, 59(1):283–294,
2010.
[5] Guoqiang Mao and Barıs¸ Fidan. Introduction to wireless sensor network
localization. Localization Algorithms and Strategies for Wireless Sensor
Networks, pages 1–32, 2009.
[6] Parameshwaran Krishnan, AS Krishnakumar, Wen-Hua Ju, Colin
Mallows, and SN Gamt. A system for lease: Location estimation assisted
by stationary emitters for indoor rf wireless networks. In INFOCOM
2004. Twenty-third AnnualJoint Conference of the IEEE Computer and
Communications Societies, volume 2, pages 1001–1011. IEEE, 2004.
[7] Phongsak Prasithsangaree, Prashant Krishnamurthy, and Panos K
Chrysanthis. On indoor position location with wireless lans. In
Personal, Indoor and Mobile Radio Communications, 2002. The 13th
IEEE International Symposium on, volume 2, pages 720–724. IEEE,
2002.
[8] Teemu Roos, Petri Myllym¨aki, and Henry Tirri. A statistical modeling
approach to location estimation. Mobile Computing, IEEE Transactions
on, 1(1):59–69, 2002.
[9] Nirupama Bulusu, Vladimir Bychkovskiy, Deborah Estrin, and John
Heidemann. Scalable, ad hoc deployable rf-based localization. In
Proceedings of the Grace Hopper Conference on Celebration of Women
in Computing, volume 31, 2002.
[10] Guoqiang Mao. Localization Algorithms and Strategies for Wireless
Sensor Networks: Monitoring and Surveillance Techniques for Target
Tracking: Monitoring and Surveillance Techniques for Target Tracking.
IGI Global, 2009.
[11] Vaidyanathan Ramadurai and Mihail L Sichitiu. Simulation-based
analysis of a localization algorithm for wireless ad-hoc sensor networks.
In Proceedings of the International Conference on Wireless Networks,
Las Vegas, NV, 2003.
[12] Michael Allen, Sebnem Baydere, Elena Gaura, and Gurhan Kucuk.
Evaluation of localization algorithms. Localization Algorithms and
Strategies for Wireless Sensor Networks. IGI Global, 2009.
[13] Ambili Thottam Parameswaran, Mohammad Iftekhar Husain, Shambhu
Upadhyaya, et al. Is rssi a reliable parameter in sensor localization
algorithms: An experimental study. In Field Failure Data Analysis
Workshop (F2DA09), page 5, 2009. [14] Qian Dong and Waltenegus Dargie. Evaluation of the reliability of rssi
for indoor localization. In Wireless Communications in Unusual and
Confined Areas (ICWCUCA), 2012 International Conference on, pages
1–6. IEEE, 2012.
[15] Zaher Merhi, Michel Nahas, Samih Abdul-Nabi, Amin Haj-Ali, and
M Bayoumi. Rssi range estimation for indoor anchor based localization
for wireless sensor networks. In Microelectronics (ICM), 2013 25th
International Conference on, pages 1–4. IEEE, 2013.
@article{"International Journal of Electrical, Electronic and Communication Sciences:71287", author = "Adeniran Ademuwagun and Alastair Allen", title = "Indoor Localization Algorithm and Appropriate Implementation Using Wireless Sensor Networks", abstract = "The relationship dependence between RSS and distance
in an enclosed environment is an important consideration because it is
a factor that can influence the reliability of any localization algorithm
founded on RSS. Several algorithms effectively reduce the variance of
RSS to improve localization or accuracy performance. Our proposed
algorithm essentially avoids this pitfall and consequently, its high
adaptability in the face of erratic radio signal. Using 3 anchors in
close proximity of each other, we are able to establish that RSS can be
used as reliable indicator for localization with an acceptable degree of
accuracy. Inherent in this concept, is the ability for each prospective
anchor to validate (guarantee) the position or the proximity of the
other 2 anchors involved in the localization and vice versa. This
procedure ensures that the uncertainties of radio signals due to
multipath effects in enclosed environments are minimized. A major
driver of this idea is the implicit topological relationship among
sensors due to raw radio signal strength. The algorithm is an area
based algorithm; however, it does not trade accuracy for precision
(i.e the size of the returned area).", keywords = "Anchor nodes, centroid algorithm, communication
graph, received signal strength (RSS).", volume = "9", number = "10", pages = "1215-5", }
