Physical-Layer Security for Ambient Backscattering Internet-of-Things

Part of the Internet of Things book series (ITTCC)


The pursuit of tiny computing and sensor devices become a big challenge in the Internet of Things (IoT) era. The process of powering such small-size wireless nodes becomes more difficult as the battery adds extra weight, size, and cost. Additionally, batteries replacement is impractical for the expected massive IoT connectivity especially in inaccessible environments, while recharging is very difficult in multiple scenarios. Ambient backscatter communication (AmBC) solves this problem by leveraging existing radio-frequency transmissions for wirelessly powering battery-free nodes. Due to the limited computational power of such nodes, high-complexity security and authentication protocols are infeasible. Consequently, it is imperative to exploit low-complexity techniques such as physical-layer security (PLS). PLS is a key-less security technique that relies on the randomness of the communication channel between the transceiver nodes for securing the transmitted message. In this work, we consider the PLS of an ambient backscattering IoT (AmBC-IoT) system. In AmBC-IoT system, backscattering IoT devices (BDs) form a symbiotic system, in which the access point (i.e., radio frequency source) supports not only the conventional legacy receiver but also the IoT transmission. Specifically, we derive closed-form expressions for the secrecy outage probability and the ergodic secrecy rate under passive eavesdropping. Additionally, we provide asymptotic analysis for both metrics to gain insights on the effect of different parameters on the performance. The accuracy of the analytical results has been validated by extensive simulations.



This research was supported in part by the China NSFC Grant (61872248, U1736207), Guangdong NSF 2017A030312008, Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Institutions of China (Grant No.161064), GDUPS (2015), and Shenzhen Science and Technology Foundation (No. ZDSYS20190902092853047). The research is partially supported by Benha University, Egypt research fund (Project 2/1/14).


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© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)Shenzhen UniversityShenzhenChina
  2. 2.Benha UniversityBenhaEgypt
  3. 3.PCL Research Center of Networks and Communications, Peng Cheng LaboratoryShenzhenChina

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