An Adaptive Energy Saving Algorithm for an RSSI-Based Localization System in Mobile Radio Sensors

In localization systems based on the emission of reference radio signals, an important issue related to the reliability of sensor operation is the problem of operating time and power of the emitted reference radio signal. There are many localization methods that have proven useful in practice and that use a reference radio signal for this purpose. In the issue of determining the location of radio emitters, various radio signal propagation models are used to determine the effective range and distance of the sensor-receiver from the radio emitter. This paper presents an adaptive power control algorithm for a transmitter, as a reference emitter, operating in power-saving mode. An important advantage of the presented solution is the adjustment of the localization system accuracy at the assumed level of energy radiated by radio emitters based on the RSSI signal received power estimation.

[1]  A. Singh,et al.  Real time RSSI error reduction in distance estimation using RLS algorithm , 2013, 2013 3rd IEEE International Advance Computing Conference (IACC).

[2]  Noor Azurati Ahmad,et al.  Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation , 2019, Sensors.

[3]  Osama Moselhi,et al.  Enhanced Localization for Indoor Construction , 2015 .

[4]  Jan Macheta Synchroniczna, niskomocowa bezprzewodowa sieć sensorowa do monitorowania przemysłowych procesów produkcyjnych , 2019 .

[5]  Sang Hyuk Son,et al.  ATPC: Adaptive Transmission Power Control for Wireless Sensor Networks , 2016, TOSN.

[6]  Hussein M. ElAttar,et al.  An Enhanced Indoor Positioning Technique Based on a Novel Received Signal Strength Indicator Distance Prediction and Correction Model , 2021, Sensors.

[7]  Shengqi Yang,et al.  Improved localization algorithm based on RSSI in low power Bluetooth network , 2016, 2016 2nd International Conference on Cloud Computing and Internet of Things (CCIOT).

[8]  Kuan-Ching Li,et al.  Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs , 2019, Sensors.

[9]  Łukasz Chruszczyk,et al.  Comparison of indoor/outdoor, RSSI-based positioning using 433, 868 or 2400 MHz ISM bands , 2016 .

[10]  Yu Pang,et al.  Indoor Positioning Algorithm Based on the Improved RSSI Distance Model , 2018, Sensors.

[11]  I. Rasool,et al.  Indoor RSSI-based ranging consistency and error factors in wireless sensor networks , 2012, 2012 20th Telecommunications Forum (TELFOR).

[12]  Albert Heuberger,et al.  Impact of antenna pattern modeling errors on RSSI-based DOA estimation , 2017, 2017 International Workshop on Antenna Technology: Small Antennas, Innovative Structures, and Applications (iWAT).

[13]  Gregory M. P. O'Hare,et al.  Reliable multihop intra-cluster communication for Wireless Sensor Networks , 2014, 2014 International Conference on Computing, Networking and Communications (ICNC).

[14]  Gianfranco Manes,et al.  Impact of the dihedral angle of Switched Beam Antennas in indoor positioning based on RSSI , 2014, 2014 44th European Microwave Conference.

[15]  Xin Li,et al.  An RSSI-based Wireless Sensor Network Localization Algorithm with Error Checking and Correction , 2017, Int. J. Online Eng..

[16]  Łukasz Chruszczyk,et al.  Statistical analysis of indoor RSSI read-outs for 433 MHz, 868 MHz, 2.4 GHz and 5 GHz ISM bands , 2017 .

[17]  Joonhyuk Kang,et al.  Joint AOA/RSSI based multi-user location system for military mobile base-station , 2008, MILCOM 2008 - 2008 IEEE Military Communications Conference.

[18]  Violet R. Syrotiuk,et al.  Fair Variable Transmission Power Control , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[19]  Zbigniew Piotrowski,et al.  A Reconfigurable Prototyping Platform for iBeacon Service , 2015, MCSS.

[20]  Ingrid Moerman,et al.  Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices , 2017, Sensors.

[21]  Sangjoon Lee,et al.  Hybrid RSSI-AoA Positioning System with Single Time-Modulated Array Receiver for LoRa IoT , 2018, 2018 48th European Microwave Conference (EuMC).

[22]  Biplab Sikdar,et al.  Distinguishing between channel errors and collisions in IEEE 802.11 , 2012, 2012 46th Annual Conference on Information Sciences and Systems (CISS).

[23]  Jun Luo Range error correction in RSSI-based wireless sensor node localization , 2014, 2014 IEEE International Conference on Mechatronics and Automation.

[24]  V Thanikaiselvan,et al.  Improving the Performance of RSSI Based Indoor Localization Techniques Using Neural Networks , 2018, 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA).

[25]  Drago Zagar,et al.  Power consumption and RF propagation analysis on ZigBee XBee modules for ATPC , 2012, 2012 35th International Conference on Telecommunications and Signal Processing (TSP).

[26]  Armin Wittneben,et al.  On the Crucial Impact of Antennas and Diversity on BLE RSSI-Based Indoor Localization , 2019, 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring).

[27]  Sharma Sanjeev,et al.  Enhanced Transmission Power Control Mechanism based on RSSI for MANET , 2011 .

[28]  Nattha Jindapetch,et al.  A Comparative Study of RSSI-Based Localization Methods: RSSI Variation Caused by Human Presence and Movement , 2020 .

[29]  John A. Stankovic,et al.  Adaptive Transmission Power Control for Wireless Sensor Networks , 2012 .

[30]  Erhu Wei,et al.  Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO , 2021, Sensors.

[31]  Eryk Dutkiewicz,et al.  Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks , 2020, Sensors.

[32]  Bo Wang,et al.  A MLE-PSO indoor localization algorithm based on RSSI , 2017, 2017 36th Chinese Control Conference (CCC).

[33]  Zimu Yuan,et al.  Error data analytics on RSS range-based localization , 2020, Big Data Min. Anal..

[34]  Mohamed H. Abd El Azeem,et al.  An Enhanced Indoor Positioning Technique Based on a Novel Received Signal Strength Indicator Distance Prediction and Correction Model , 2021, Sensors.

[35]  Brennan E. Yamamoto,et al.  Received Signal Strength Indication (RSSI) of 2.4 GHz and 5 GHz Wireless Local Area Network Systems Projected over Land and Sea for Near-Shore Maritime Robot Operations , 2019, Journal of Marine Science and Engineering.