Artificial Noise Management for Securing Backscatter Communication Networks: Current Advances, Open Challenges, and Future Directions

Backscatter Communication Networks (BCNets) hold significant promise for advancing the ubiquitous Internet of Things (IoT) due to their capabilities in energy harvesting and ultra-low-power consumption. However, their broadcast and open nature expose them to serious security risks and privacy breaches that hinder their widespread applications. Artificial Noise Management (ANM) has recently emerged as a solution to these vulnerabilities. By deploying artificial noise to disrupt potential attackers while mitigating its negative impact on legitimate receivers through advanced signal processing techniques, ANM creates a significant disparity in decoding capability between the attackers and the legitimate receivers. This disparity makes attackers unable to accurately recover the complete content of the communication, thus preventing information leakage. Despite substantial recent research on ANM within BCNets, a comprehensive review is notably absent from the literature. To fill this gap, this paper presents a systematic survey on ANM in BCNets. We begin by proposing a set of evaluation criteria that a sound ANM scheme should satisfy. Then, we introduce a taxonomy of ANM techniques in BCNets and conduct a thorough review of the current state-of-the-art by employing these criteria to analyze their pros and cons. Finally, we outline open challenges and suggest future research directions based on our serious review.