@inproceedings{316ad909d24e4f92b3d464facdbfdb55,
title = "Two High-Performance Antenna Selection Schemes for Secure SSK Modulation in Intelligent Industrial Wireless Sensing Systems",
abstract = "Industrial wireless sensor networks (IWSNs) provide intelligent factory management with a new dimension. Reliable intelligent industrial wireless sensing systems play a vital role in ensuring the reliability and security of IWSNs. Therefore, improving the confidentiality of real-time communication of intelligent industrial wireless sensing systems and making IWSNs more intelligent has attracted widespread attention. This paper proposes two high-performance antenna selection (AS) schemes along with friendly jamming to protect the confidentiality of real-time communication of intelligent industrial wireless sensing systems based on space shift keying (SSK). Additionally, two AS schemes named the generalized Euclidean distance antenna selection (GEDAS) and the leakage-based antenna selection (LBAS) are proposed for maximizing the difference in average mutual information (AMI) between D and E. In this way, the physical layer security (PLS) of the system is further improved. Moreover, the algorithmic complexity of the two proposed AS schemes is thoroughly analyzed in this paper. The simulation results demonstrate that the performance of the proposed joint schemes is superior to the traditional random selection scheme in terms of secrecy rate (SR), secrecy capacity (SC), and bit error rate (BER). Thus, the security enhancement benefits the real-time communication of the intelligent industrial wireless sensing systems and prevents data transmission leakage.",
keywords = "Antenna selection, Intelligent industrial wireless sensing systems, Jamming signal, Security, Space shift keying",
author = "Hui Xu and Ng, {Benjamin K.} and Huibin Wang and Boyu Yang and Lam, {Chan Tong}",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Big Data Intelligence and Computing, DataCom 2022 ; Conference date: 08-12-2022 Through 10-12-2022",
year = "2023",
doi = "10.1007/978-981-99-2233-8_33",
language = "English",
isbn = "9789819922321",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "463--474",
editor = "Ching-Hsien Hsu and Mengwei Xu and Hung Cao and Hojjat Baghban and {Shawkat Ali}, {A. B.}",
booktitle = "Big Data Intelligence and Computing - International Conference, DataCom 2022, Proceedings",
address = "Germany",
}