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2021 | OriginalPaper | Chapter

Applications of RSSI Preprocessing in Multi-Domain Wireless Networks: A Survey

Authors : Tapesh Sarsodia, Uma Rathore Bhatt, Raksha Upadhyay

Published in: Advances in Computing and Network Communications

Publisher: Springer Singapore

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Abstract

Today’s age of communication has been looking for technologies and techniques to support high data rate applications with required quality of services. Advanced communication network architectures like Internet of things (IoT), fifth generation (5G), and long term evolution (LTE) with supporting high end transmission and reception processes have evolved to meet present requirements. It is also observed that to further enhance network performance, incorporation of received signal strength indicator (RSSI)/channel state information (CSI)-based preprocessing techniques have been exhibiting substantial impact. Physical layer key generation in wireless networks, localization of nodes in wireless networks, signal identification, human activity recognition, etc., are few such applications, using RSSI/CSI preprocessing for their performance improvement in multi-domain wireless networks. Hence, this paper describes above-mentioned applications using different preprocessing techniques of RSSI, which is not investigated comprehensively in literature so far. Therefore, the purpose of this paper is to reveal the impact of RSSI preprocessing techniques in system performance enhancement as per the need of application. As an outcome, we find the possibility of applying other preprocessing techniques in existing and upcoming applications in future to achieve desired system performance.

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Literature
1.
C. Wang et al., Cellular architecture and key technologies for 5G wireless communication networks. IEEE Commun. Mag. 52(2), 122–130 (2014)CrossRef
2.
D. Konings, Device-free localization systems utilizing wireless RSSI: a comparative practical investigation. IEEE Sens. J. 19(7), 2747–2757 (2019)CrossRef
3.
Y. Shiu et al., Physical layer security in wireless networks: a tutorial. IEEE Wirel. Commun. 18(2), 66–74 (2011)CrossRef
4.
T.L. Marzetta, B.M. Hochwald, Fast transfer of channel state information in wireless systems. IEEE Trans. Signal Process. 54(4), 1268–1278 (2006)CrossRef
5.
X. Ding, S. Dong, Improving positioning algorithm based on RSSI. Wireless Pers Commun 110, 1947–1961 (2020)CrossRef
6.
D. Li, Y. Lei, H. Zhang, A novel outdoor positioning technique using LTE network fingerprints. J. Sens. 20, 169 (2020)
7.
J.A. Santana, E. Macías, Á. Suárez et al., Adaptive estimation of WiFi RSSI and its impact over advanced wireless services. Mobile Netw. Appl. 22, 1100–1112 (2017)CrossRef
8.
B. Han, S. Peng, C. Wu, X. Wang, B. Wang, LoRa-based physical layer key generation for secure V2V/V2I communications. Sensors 20, 682 (2020)CrossRef
9.
B.S. Meena, S. Deb, K. Hemachandran, Impact of heterogeneous IoT devices for indoor localization using RSSI, in Intelligent Computing in Engineering. Advances in Intelligent Systems and Computing ed by V. Solanki, M. Hoang, Z. Lu, P. Pattnaik, 1125 (Springer, Singapore 2020), pp. 187–198
10.
S. Alasadi, W. Bhaya, Review of data preprocessing techniques in data mining. J. Eng. Appl. Sci. 12(16), 4102–4107 (2017)
11.
J. Bauer, N. Aschenbruck, Towards a Low-cost RSSI-based crop monitoring. ACM Trans. Internet Things, 1(4), 26
12.
P. Koutsakis, M. Paterakis, Highly efficient voice—data integration over medium and high capacity wireless TDMA channels. Wireless Netw. 7, 43–54 (2001)CrossRef
13.
R. Upadhyay et al., A study on principal component analysis over wireless channel. J. Telecommun. Electron. Comput. Eng. 11(4), 5–9 (2019)MathSciNet
14.
P.E. Lopez-de-Teruel et al., Using dimensionality reduction techniques for refining passive indoor positioning systems based on radio fingerprinting. Sensors 17(4), 871 (2017)CrossRef
15.
Muladi1 et al., Adaptive power management for self-powered IoT on smart shoes, in AIP Conference Proceedings. American Institute of Physics 2228 (2020), 030019.
16.
A. Guidara, et al., Impacts of temperature and humidity variations on RSSI in indoor wireless sensor networks, in 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems in Elsevier Procedia Computer Science, 126 (2018), 1072–1081
17.
S. Farahani, Location estimation methods. in ZigBee Wireless Networks and Transceivers (Chapter 7) (2008), 225–246.
18.
H. Ahmadi, R. Bouallegue, Exploiting machine learning strategies and RSSI for localization in wireless sensor networks: a survey, in 13th International Wireless Communications and Mobile Computing Conference (IWCMC), (Valencia, 2017), pp 1150–1154
19.
C. Hsieh, J. Chen, B. Nien, Deep learning-based indoor localization using received signal strength and channel state information. IEEE Access 7, 33256–33267 (2019)CrossRef
20.
S. Li, Q. Du, A review of physical layer security techniques for internet of things: challenges and solutions. J. Entropy 20, 730 (2018)
21.
Y. Zou, J. Zhu, X. Wang, L. Hanjo, A survey on wireless security: technical challenges. Recent advances and future trends. Proc IEEE 104(9):1727–1765
22.
J. Zhang et al., Key generation from wireless channels: a review. IEEE Access. 4, 614–626 (2016)CrossRef
23.
F. Zhan, N. Yao, On the using of Discrete wavelet transform for physical layer key generation. J. Adhoc Netw. 64, 22–31 (2017)CrossRef
24.
G. Li et al., High-agreement uncorrelated secret key generation based on principal component analysis preprocessing. IEEE Trans. Commun. 66(7), 3022–3303 (2018)CrossRef
25.
A. Soni, R. Upadhyay, A. Kumar, Wireless physical layer key generation with improved bit disagreement of the internet of things using moving window averaging. J. Phys. Commun. 33, 249–258 (2019)CrossRef
26.
A. Soni, R. Upadhyay, A. Kumar, RSS based phy layer key generation in wireless communication, in Proceedings of Recent Advances in Interdisciplinary Trends in Engineering & Applications (RAITEA)
27.
A. Soni, R. Upadhyay, A. Kumar, Performance improvement of wireless secret key generation with colored noise for IoT. Int. J. Commun. Syst. 32 (2019)
28.
R. Lin, L. Xu, H. Fang, et al., Efficient physical layer key generation technique in wireless communications. J. Wireless Com. Netw.13 (2020)
29.
R.T. Reza, V.M. Srivastava, Effect of GSM frequency band on received signal strength and distance estimation from cell tower. in10th International Conference on Developments in eSystems Engineering (DeSE) (Paris, 2017), pp. 151–154
30.
J. Kuriakose S. Joshi R. Vikram Raju A. Kilaru A, A review on localization in wireless sensor networks. in Advances in Signal Processing and Intelligent Recognition Systems, Advances in Intelligent Systems and Computing, 264 (2014)
31.
G. Deak, K. Curran, J. Condell, A survey of active and passive indoor localisation systems. Comput. Commun. 35, 1939–1954 (2012)
32.
R. Niu, A. Vempaty, P.K. Varshney, Received-signal-strength-based localization in wireless sensor networks. Proc. IEEE 106(7), 1166–1182 (2018)CrossRef
33.
A. Abusara, M.S. Hassan, M.H. Ismail, RSS fingerprints dimensionality reduction in WLAN-based indoor positioning. in Wireless Telecommunications Symposium (WTS) (London, 2016), pp. 1–6
34.
X. Hou, T. Arslan, J. GU, Indoor localization for Bluetooth low energy using wavelet and smoothing filter. in International Conference on Localization and GNSS (ICL-GNSS), (Nottingham, 2017), pp. 1–6
35.
P. Roy, M. Kundu, C. Chowdhury, Indoor Localization using Stable Set of Wireless Access Points Subject to Varying Granularity Levels, in International Conference on Wireless Communications Signal Processing and Networking (WiSPNET). (Chennai, India, 2019), pp. 491–496CrossRef
36.
K. Wu, M. Yang, C. Ma, J. Yan, CSI-based wireless localization and activity recognition using support vector machine, in IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) (Dalian, China, 2019), pp. 1–5
37.
W. Liu, Q, Cheng, Z. Deng, X. Fu, X. Zheng, C-Map: hyper-resolution adaptive preprocessing system for CSI amplitude-based fingerprint localization. IEEE Access7,135063–135075
38.
G.G. Anagnostopoulos, A. Kalousis, A reproducible analysis of RSSI fingerprinting for outdoor localization using sigfox: preprocessing and hyperparameter tuning. in International Conference on Indoor Positioning and Indoor Navigation (IPIN) (Pisa, Italy, 2019), pp. 1–8
39.
G. Anastasi et al., Energy conservation in wireless sensor networks: a survey. J. Ad Hoc Netw. 7(3), 537–568 (2008)CrossRef
40.
J. Ogbebor, et al., Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions (2020), 1-19
41.
A. Soni, R. Upadhyay, A. Kumar, AvDR—based wireless secure key generation with colored noise for IoT. Fluct. Noise Lett. World Sci. 19(2), 1–18
42.
G. Margelis, et al., Physical layer secret-key generation with discreet cosine transform for the Internet of Things. in IEEE International Conference on Communications (ICC) (Paris, 2017), pp. 1–6
43.
M. Xu, Research and design of data preprocessing of wireless sensor networks based on Multi-Agents. in IEEE International Conference on Network Infrastructure and Digital Content (Beijing, 2009), pp. 50–53
44.
U.N. Nisha, A.M. Basha, Triangular fuzzy-based spectral clustering for energy-efficient routing in wireless sensor network. J. Supercomput. 76, 4302–4327 (2020)CrossRef
45.
C. Jobanputra et al., Human activity recognition: a survey. Procedia Comput. Sci. 155, 698–703 (2019)CrossRef
46.
V. Michalis, et al., A review of human activity recognition methods. J. Frontiers Robot. AI.2 28 (2015)
47.
S. Sigg, U. Blanke, G. Tröster, The telepathic phone: Frictionless activity recognition from WiFi-RSSI, in IEEE International Conference on Pervasive Computing and Communications (PerCom) (Budapest, 2014), pp 148–155
48.
M. Mukherjee, A.B. Bhattacharya, RSSI based indoor human activity recognition system. J. Techno. Int. J. Health Eng. Manage. Sci. 2(5), 185–190 (2018)
49.
A. Booranawong, N. Jindapetch, H. Saito, Adaptive filtering methods for RSSI signals in a device-free human detection and tracking system. IEEE Syst. J. 13(3), 2998–3009 (2019)CrossRef
50.
W. Su, Wearable antennas for cross-body communication and human activity recognition. IEEE Access 8, 58575–58584 (2020)CrossRef
51.
Y.T Wang, et al., Wireless signal identification in 230 MHz band based on interference cleaning and convolutional neural network. in Proceedings of the 9th International Conference on Communication and Network Security (ICCNS 2019) (Association for Computing Machinery, New York, NY, USA, 2019), pp. 133–136.
52.
F. Kaleem, et al., A fuzzy preprocessing module for optimizing the access network selection in wireless networks. J Adv. Fuzzy Syst. Hindawi Publishing Corporation 1687–7101 (2013)
53.
H.K. Boyapati, et al., Implementation of RSSI indexed look up table based AGC for improved dynamic range of DSSS based wireless RF transceivers. in 2nd International Conference on Next Generation Computing Technologies (NGCT) (Dehradun, 2016), pp. 373–377
54.
F. Carpi et al., RSSI-based Methods for LOS/NLOS Channel Identification in Indoor Scenarios, in 16th International Symposium on Wireless Communication Systems (ISWCS). (Oulu, Finland, 2019), pp. 171–175CrossRef
55.
H. Hojatian, et al., Unsupervised deep learning for massive MIMO hybrid beamforming. J. Electr. Eng. and Syst. Sci. Signal Process. arXiv.org, eess.2.
Metadata
Title
Applications of RSSI Preprocessing in Multi-Domain Wireless Networks: A Survey
Authors
Tapesh Sarsodia
Uma Rathore Bhatt
Raksha Upadhyay
Copyright Year
2021
Publisher
Springer Singapore
Book
Advances in Computing and Network Communications

Print ISBN: 978-981-336-976-4

Electronic ISBN: 978-981-336-977-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-33-6977-1_30