An investigation of min-max method problems for RSSI-based indoor localization: Theoretical and experimental studies

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Apidet Booranawong
Kiattisak Sengchuai
Nattha Jindapetch
Hiroshi Saito


A study of limitations of a min-max or a bounding-box method for received signal strength indicator (RSSI)-based indoor localization is introduced in this paper. The main goal of our study is to clearly understand how the widely used min-max method determines an unknown target position, and to investigate its significant limitations. For this purpose, we provide both theoretical and experimental studies. The theoretical study first gives an understanding of min-max theoretical limitations, while an experimental study then reveals more limitations. Experiments were done in an indoor environment, a laboratory room, where we employed an LPC2103F with a CC2500 RF module as a wireless node. Our results indicate that the min-max method can be efficiently used to estimate an unknown target’s position. However, such a method has limitations in several cases. First, it produces a significantly high estimation error when the unknown target is located outside an internal zone, the area within reference node positions. Second, fluctuations of measured RSSI signals in an obstacle environment is a major problem that produces significantly more estimation errors. Various effects in this case are detailed in the paper. Our information will be useful to develop more efficient min-max methods.


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Booranawong, A., Sengchuai, K., Jindapetch, N., & Saito, H. (2020). An investigation of min-max method problems for RSSI-based indoor localization: Theoretical and experimental studies. Engineering and Applied Science Research, 47(3), 313-325. Retrieved from


[1] Patwari N, Ash JN, Kyperountas S, Hero III AO, Moses RL, Correal NS. Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Process Mag. 2005;22(4):54-69.

[2] Liu H, Darabi H, Banerjee P, Liu J. Survey of wireless indoor positioning techniques and systems. IEEE Trans Syst Man Cybern C Appl Rev. 2007;37(6):1067-80.

[3] Redondi A, Chirico M, Borsani L, Cesana M, Tagliasacchi M. An integrated system based on wireless senor networks for patient monitoring, localization and tracking. Ad Hoc Netw. 2013;11(1):39-53.

[4] He J, Geng Y, Pahlavan K. Modeling indoor TOA ranging error for body mounted sensors. Proceedings of the IEEE 23rd International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC); 2012 Sep 9-12; Sydney, Australia. USA: IEEE; 2012. p. 682-6.

[5] Severino R, Alves M. Engineering a search and rescue application with a wireless sensor network-based localization mechanism. Proceedings of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks; 2007 Jun 18-21; Espoo, Finland. USA: IEEE; 2007. p. 1-4.

[6] Mautz R. Indoor positioning technologies [thesis]. Zürich: ETH Zurich; 2012.

[7] Luo X, Brien WJ, Julien CL. Comparative evaluation of received signal-strength index (RSSI) based indoor localization techniques for construction jobsites. Adv Eng Informat. 2011;25(2):355-63.

[8] Pei Z, Deng Z, Xu S, Xu X. Anchor-free localization method for mobile targets in coal mine wireless sensor networks. Sensors. 2009;9(4):2836-50.

[9] Bjorkbom M, Nethi S, Eriksson LM, Jantti R. Wireless control system design and co-simulation. Contr Eng Pract. 2011;19(9):1075-86.

[10] Booranawong A, Teerapabkajorndet W, Limsakul C. Energy consumption and control response evaluations of AODV routing in WSANs for building-temperature control. Sensors 2013;13(7):8303-30.

[11] Booranawong A, Teerapabkajorndet W. An enhanced AODV routing protocol for wireless sensor and actuator networks. Int J Electron Comm Eng. 2013;7(12):1693-700.

[12] Zanca G, Zorzi F, Zanella A, Zorzi M. Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks.Proceedings of the Workshop on Real-word Wireless Sensor Networks; 2008 Apr 1; Glasgow, Scotland. New York: ACM press; 2008. p. 1-5.

[13] He S, Hu T, Chan SHG. Contour-based trilateration for indoor fingerprinting localization. Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems; 2015 Nov 1-4; Seoul, South Korea. New York: ACM press; 2015. p. 225-38.

[14] Hossain AKMM, Soh WS. Cramer-Rao bound analysis of localization using signal strength difference as location fingerprint. Proceedings of the IEEE INFOCOM; 2010 Mar 14-19; San Diego, USA. USA: IEEE; 2010. p. 1-9.

[15] Savvides A, Park H, Srivastava M. The bits and flops of the N-hop multilateration primitive for node localization problems. Proceedings of the First ACM International Workshop on Wireless Sensor Networks and Application; 2002 Sep 28; Atlanta, USA. New York: ACM press; 2002. p. 112-21.

[16] Goldoni E, Savioli A, Risi M, Gamba P. Experimental analysis of RSSI-based indoor localization with IEEE 802.15.4. Proceedings of the 2010 European Wireless Conference; 2010 Apr 12-15; Lucca, Italy. USA: IEEE; 2010. p. 71-7.

[17] Kaseva VA, Kohvakka M, Kuorilehto M, Hannikainen M, Hamalainen TD. A wireless sensor network for RF- based indoor localization. EURASIP J Adv Signal Process. 2008;2008:1-27.

[18] Kianoush S, Goldoni E, Savioli A, Gamba P. Low-complexity localization and tracking in hybrid wireless sensor networks. ISRN Sens Network. 2012;2012:1-7.

[19] Rattanalert B, Jindamaneepon W, Sengchuai K, Booranawong A, Jindapetch N. Problem investigation of min-max method for RSSI based indoor localization. Proceedings of the 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology; 2015 Jun 24-27; Hua Hin, Thailand. USA: IEEE; 2015. p. 1-5.

[20] Robles JJ, Pola JS, Lehnert R. Extended min-max algorithm for position estimation in sensor networks. Proceedings of the 9th Workshop on Positioning Navigation and Communication (WPNC); 2012 Mar 15-16; Dresden, Germany. USA: IEEE; 2012. p. 47-52.

[21] Shi X, Zhang L. High-precision weighted bounding box localization algorithm for wireless sensor network. Proceedings of the third on Information Science and Technology; 2013 Mar 23-25; Yangzhou, China. USA: IEEE; 2013. p. 1110-3.

[22] Langendoen K, Reijers N. Distributed localization in wireless sensor networks: a quantitative comparison. Comput Network. 2003;43(4):499-518.

[23] Mao G, Anderson BD, Fidan B. Path loss exponent estimation for wireless sensor network localization. Comput Network. 2007;51(10):2467-83.

[24] Booranawong A, Jindapetch N, Saito H. Adaptive filtering methods for RSSI signals in a device-free human detection and tracking system. IEEE Syst J. 2019;13(3):2998-3009.

[25] Texas Instrument. Instrument T CC2500 datasheet [Internet]. 2017 [cited 2017 Nov 8]. Available from:

[26] Yang LD. Implementation of a wireless sensor network with EZ430-RF2500 development tools and MSP430FG4618/F2013 experimenter boards from Texas instruments [thesis]. Louisiana: Department of Electrical & Computer Engineering, Louisiana State University and Agricultural and Mechanical College; 2011.

[27] Jindamaneepon W, Rattanalert B, Sengchuai K, Booranawong A, Jindapetch N. A novel FPGA-based
multi-channel multi-interface wireless node: implementation and preliminary test. In: Sulaiman H,
Othman M, Othman M, Rahim Y, Pee N, editors. Advanced Computer and Communication Engineering Technology, Lecture Notes in Electrical Engineering. Cham: Springer; 2016. p. 1163-73.

[28] Booranawong A, Jindapetch N, Saito H. A system for detection and tracking of human movements using RSSI signals. IEEE Sensor J. 2018;18(6):2531-44.