Secrecy Limits of Energy Harvesting IoT Networks under Channel Imperfections

TL;DR

This paper analyzes the physical layer security limits of energy harvesting IoT networks using SWIPT under realistic channel estimation imperfections, providing closed-form secrecy capacity expressions for Rician and Nakagami-m fading channels.

Contribution

It introduces a novel analysis of ergodic secrecy capacity considering channel estimation errors in SWIPT-based IoT systems with Rician and Nakagami-m fading.

Findings

Secrecy capacity saturates at high SNR due to channel estimation errors.

Error ceiling appears in secrecy capacity at high SNR levels.

Secrecy-energy trade-off is characterized under imperfect channel estimation.

Abstract

Simultaneous wireless information and power transfer (SWIPT) has recently gathered much research interest from both academia and industry as a key enabler of energy harvesting Internet-of-things (IoT) networks. Due to a number of growing use cases of such networks, it is important to study their performance limits from the perspective of physical layer security (PLS). With this intent, this work aims to provide a novel analysis of the ergodic secrecy capacity of a SWIPT system is provided for Rician and Nakagami-m faded communication links. For a realistic evaluation of the system, the imperfections of channel estimations for different receiver designs of the SWIPT-based IoT systems have been taken into account. Subsequently, the closedform expressions of the ergodic secrecy capacities for the considered scenario are provided and, then, validated through extensive simulations. The results indicate that an error ceiling appears due to imperfect channel estimation at high values of signal-to-noise ratio (SNR). More importantly, the secrecy capacity under different channel conditions stops increasing beyond a certain limit, despite an increase of the main link SNR. The in-depth analysis of secrecy-energy trade-off has also been performed and a comparison has been provided for imperfect and perfect channel estimation cases. As part of the continuous evolution of IoT networks, the results provided in this work can help in identifying the secrecy limits of IoT networks in the presence of multiple eavesdroppers.