# UniPHY: Unified Physical Layer Security for LPWANs

Stéphane Delbruel, Leo Mendiboure, Yannis Formery, Joachim

Bruneau-Queyreix, Laurent Réveillère

► To cite this version:

Stéphane Delbruel, Leo Mendiboure, Yannis Formery, Joachim Bruneau-Queyreix, Laurent Réveillère. UniPHY: Unified Physical Layer Security for LPWANs. 2025 55th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S), Jun 2025, Naples, France. pp.211-215, 10.1109/DSN-S65789.2025.00066. hal-05163759

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# UniPHY: Unified Physical Layer Security for LPWANs

Stéphane Delbruel<sup>1</sup>, Leo Mendiboure<sup>1,2</sup> (IEEE Member), Yannis Formery<sup>2</sup>,
Joachim Bruneau-Queyreix<sup>1</sup>, Laurent Réveillère<sup>1</sup>,

<sup>1</sup> Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, F-33400 Talence, France

<sup>2</sup>COSYS-ERENA, Univ. Gustave Eiffel, F-33067 Pessac, France

**Abstract**—Low-Power Wide Area Networks (LPWANs) play a crucial role in IoT infrastructures, enabling large-scale deployments with minimal power consumption. However, existing security mechanisms primarily operate at higher layers, relying on application-level encryption and centralized authentication schemes. These approaches introduce coverage gaps, energy inefficiencies and fail to provide continuous protection in the event of network partitions. In this paper, we propose UniPHY, a novel bottom-up security paradigm leveraging the physical layer for authentication and key management in LPWANs. UniPHY redefines LPWAN security by eliminating reliance on traditional cryptographic frameworks, enabling a self-securing IoT infrastructure that is scalable, lightweight, and resistant to quantum-era threats. We discuss key challenges, implementation considerations, and potential impact on infrastructure operators.

**Index Terms**—security, faults, wireless sensors network, physical layer security, secure stack

## I. INTRODUCTION

The widespread adoption of Internet of Things (IoT) devices has driven the growth of Low-Power Wide Area Networks (LPWANs), enabling applications in smart cities, industrial monitoring, and agriculture. These networks consist of resource-constrained, spatially distributed sensors that monitor environmental parameters (e.g., temperature, humidity, pollution) and transmit data to centralized systems. They prioritize low power consumption and long-range communication, often relying on battery-operated sensors with limited computational capabilities.

However, the rapid integration of connected devices into everyday environments has amplified security challenges, largely due to long-standing vulnerabilities in legacy architectures that have exposed users to attacks [1]. The heterogeneity of hardware, including modulation schemes, chip architectures, transmission protocols, and radio technologies, complicates the implementation of consistent security measures and increases the attack surface across the network.

Security in LPWANs relies on a fragmented stack of protection mechanisms spanning multiple layers. This complexity makes deployment difficult and inefficient. Originally designed for wired networks, these centralized, multi-layered approaches

introduce significant computational and energy overhead, a major drawback for LPWANs, where battery-powered devices must operate efficiently over long periods. Moreover, the complexity of deploying security measures at each level leads many industrial stakeholders to delay or even forgo recommended practices in favor of operational simplicity. However, this trade-off increases system vulnerability, particularly during network disconnections [1], and partly explains why the industry often neglects fully integrating security measures [2]. These limitations call for a fundamental rethinking of LPWAN security.

Recent research has shifted toward decentralized security approaches, particularly through physical layer security (PHY-sec). By leveraging intrinsic wireless channel characteristics such as fading, ambient noise, and reciprocity, PHYsec methods embed security directly into the transmission process. Techniques such as RF fingerprinting, channel-based key generation, and jamming-resistant modulation enable device-to-infrastructure protection while avoiding the computational overhead and centralized dependencies of conventional cryptography [3], [4]. Originally designed for 5G/6G cellular networks with high-end devices, these approaches have recently been adapted for deployment on resource-constrained devices, unlocking new possibilities.

With these recent advances, we envision a holistic security paradigm tailored to the LPWANs ecosystem. We introduce UniPHY, a disruptive bottom-up paradigm for IoT security that redefines protection by embedding security mechanisms directly at the physical layer. Unlike top-down solutions that spread security across multiple layers, UniPHY advocates a model built entirely on PHYsec primitives. This ensures end-to-end protection while minimizing energy consumption. UniPHY adapts to diverse hardware, mitigates coverage gaps, and meets the constraints of battery-powered and low-computing resources deployments, making it particularly well-suited for LPWANs. Ultimately, UniPHY strengthens security, privacy, and sustainability in the evolving IoT landscape.

## II. THE CASE FOR A BOTTOM-UP APPROACH TO SECURITY IN IOT

Infrastructure operators are responsible for ensuring security and privacy but lack adequate tools for low-power Wireless Sensor Networks (WSNs), particularly LPWANs. Traditional

This research was funded by the Agence Nationale de la Recherche through the grant ANR-23-CE25-0010 and is licensed under a Creative Commons Attribution CC BY License.
<figure><img src="image_2.png" /><figcaption>Fig. 1: Segmented IoT LPWAN architecture: homogeneous upper zone (black) vs. heterogeneous lower zones (magenta) with corresponding security mechanisms used to protect the infrastructure's stack, their origin layer, coverage and identified gaps.</figcaption></figure>

systems, whether centralized or distributed, assume the phys-
ical layer is a reliable, error-free channel, treating faults as
node-centric rather than link-centric. This abstraction persists
in modern IoT architectures, where interconnected WSNs
are provided scalability and availability through centralized
management.

A. Current IoT LPWAN architecture and security measures

As shown in Figure 1, IoT LPWANs are segmented into zones,
from homogeneous, stable environments to highly heteroge-
neous ones with varying operational conditions. The core in-
frastructure follows a cloud-centric model, managing security,
data processing, and application services via high-performance
servers and enterprise-grade networks. These gateways bridge
distinct network zones, using radio protocols (e.g., LoRa,
Sigfox) to communicate with sensors while relying on IP-
based backhaul links for broader connectivity. While cloud-
centric models meet sovereignty and cost constraints, they rely
on legacy security frameworks.

As illustrated in Figure 1, security measures operate at
multiple levels. Central servers manage Over-the-Air (OTA)
updates, while security orchestration services handle encryp-
tion from the application layer to the MAC layer. Device
authentication starts at the MAC layer using pre-implemented
device_IDs, whereas high-end devices rely on secure boot
for firmware validation. However, this fragmented, off-the-
shelf security stack lacks cohesion, leading to inefficiencies
and vulnerabilities, particularly at the sensor level. LPWANs
deploy many resource-constrained devices, making static, one-
size-fits-all security solutions inadequate. We identify two key
limitations in current security frameworks:

*Over-reliance on higher-layer cryptographic abstraction.* Traditional IoT security relies on a cloud-centric model, assuming (wrongly) that cryptographic protocols such as TLS and RSA at the application or transport layers are sufficient to protect the system. However, LPWAN end-devices, constrained by energy, memory, and computation, struggle with these computation-intensive operations and costly key-distribution schemes. Although there exist lightweight PHYsec measures, such as LoRaWAN's AES-128 encryption, it still remains vulnerable

to physical layer eavesdropping and side-channel attacks due
to static, pre-distributed cryptographic keys.

*Lack of protection at lower layers.* Top-down security designs implicitly assume lower layers (physical layer, MAC) are secure, which is a flawed assumption in LPWANs. Attacks such as jamming, replay, and sybil exploits occur at the lower layers, bypassing application-layer defenses entirely. Additionally, rigid application-layer security policies fail to adapt to dynamic LPWAN conditions, such as channel variability, mobility, and energy-constrained links. This mismatch exacerbates energy inefficiency and limits the system's ability to respond to threats, demonstrating that a one-size-fits-all approach is ill-suited for these infrastructures.

B. Closing LPWAN security gaps with PHYsec

Given these limitations, we need to rethink security mod-
els from the ground up. Physical layer security (PHYsec)
is a promising approach grounded in information-theoretic
principles. Operating at the lowest communication level, it
leverages inherent wireless channel properties to establish trust
and mitigate threats without relying solely on computationally
intensive cryptography.

**PHYsec promises.** In IoT environments with resource-
constrained devices, long-range connectivity, and strict energy
limitations, PHYsec provides lightweight, dynamic safeguards
that enhance traditional protocol-layer security. Its core prin-
ciples include:

*   Channel-dependent security: Wireless channel characteristics (e.g., fading, noise, reciprocity) are unique to each transmitter-receiver pair and vary over time. These properties enable secure key generation and authentication based on channel state information (CSI).
*   Device-specific RF fingerprints: Hardware imperfections (e.g., oscillator offsets, nonlinearities) combined with distinct CSI create unique RF signatures, allowing device identification and spoofing detection at the physical layer.
*   Energy-efficient security: By integrating security into signal processing (e.g., adaptive modulation, noise shaping), PHYsec reduces reliance on energy-intensive cryptographic operations.

* Device-specific RF fingerprints: Hardware imperfections (e.g., oscillator offsets, nonlinearities) combined with distinct CSI create unique RF signatures, allowing device identification and spoofing detection at the physical layer.
* Energy-efficient security: By integrating security into signal processing (e.g., adaptive modulation, noise shaping), PHYsec reduces reliance on energy-intensive cryptographic operations.

* Energy-efficient security: By integrating security into signal processing (e.g., adaptive modulation, noise shaping), PHYsec reduces reliance on energy-intensive cryptographic operations.
* Resilience to eavesdropping and jamming: physical layer techniques exploit channel randomness and directional signal properties (e.g., beamforming) to obscure transmissions from unauthorized receivers and detect jamming through anomalous signal distortions.

**PHYsec open challenges.** While existing PHYsec mechanisms showed promises in high-resource 3GPP ecosystems, leveraging multi-antenna systems and powerful platforms, adaptation to low-power, resource-constrained environments remains in its early stages. To develop efficient PHYsec mechanisms for LPWAN end-devices, two key research directions must be pursued:

*Adapting PHYsec to LPWANs.* Early efforts in applying PHYsec key generation and lightweight authentication existing mechanisms to LPWANs face challenges, such as high computational overhead and excessive communication requirements. Existing adaptations [5]–[11] often rely on resource-intensive operations or frequent message exchanges making them ill-suited for IoT end-devices. Developing novel and efficient, LPWAN-suited approaches is essential, particularly in the reconciliation phase of the key generation [6], [7].

*Integrating security into energy management.* Balancing security with energy efficiency is critical in LPWANs, which operate under severe power constraints [12], [13]. Communication is particularly energy-intensive, and while advances in efficient decoding [14] and novel physical layer techniques [15], [16] have been made, a holistic energy-security strategy is lacking. The shift toward battery-less and energy-harvesting platforms [17]–[21] further underscores the need for architectures that dynamically align security parameters (e.g., encryption strength, attestation frequency) with devices’ fluctuating energy availability.

### C. Leveraging PHYsec for a novel unified secure stack model

After analyzing IoT LPWAN limitations and PHYsec's potential, we advocate for a shift from the current top-down security model. Centralized, application-layer mechanisms are inefficient for resource-constrained LPWANs, increasing energy consumption and leaving edge devices vulnerable, especially during network disruptions.

We strongly believe that a bottom-up approach rooted in PHYsec offers a more robust alternative. By embedding security at the physical layer, it reduces computational overhead and strengthens resilience against eavesdropping and spoofing. A unified physical layer based framework can also accommodate IoT device heterogeneity and diverse application needs, enabling more secure and sustainable LPWAN deployments. For infrastructure operators, this paradigm shift transforms LPWANs into self-securing ecosystems, where security emerges from the physical layer rather than fragile centralized models.

## III. PROPOSED APPROACH

We observe that, to the best of our knowledge, no security paradigm leveraging PHYsec to secure in a continuous and

coherent fashion the entire OSI stack has been proposed for IoT LPWANs, despite its transformative potential. To address this gap, we introduce UniPHY, a novel bottom-up, physical layer centric security architecture designed to overcome existing limitations. Unlike top-down application-layer security models, which are ill-suited for low-power, large-scale deployments, UniPHY anchors trust in physical-layer primitives and propagates security across the entire stack. This shift enhances resilience, energy efficiency, and scalability.

### A. Core principles

UniPHY redefines IoT security by enforcing security across all layers, starting at the physical layer, through three core principles:

1. **Intrinsic physical layer security:** UniPHY leverages wireless properties (e.g., channel reciprocity, RF fingerprints) to establish trust before data reaches higher layers. Physical layer key generation derives symmetric keys from shared CSI, eliminating risks and costs associated with pre-shared keys. RF fingerprinting authenticates devices using unique hardware signal features, preventing MAC-layer spoofing.

2. **Cross-layer defense propagation:** Physical layer security strengthens higher-layer protections via adaptive modulation and cross-layer attestation. Adaptive modulation obfuscates data at the physical layer (e.g., dynamic LoRa spreading factors), complicating eavesdropping while preserving energy efficiency. Cross-layer attestation correlates physical layer anomalies with application-layer behavior to detect compromised nodes.

3. **Dynamic, context-aware security:** Physical layer metrics (e.g., RSSI, SNR) enable real-time and context-aware security adjustments. For example, a LoRaWAN gateway could escalate encryption strength when detecting jamming attacks at the physical layer.

The direct benefits of such a bottom-up approach are a reduced attack surface. By securing the physical layer, it neutralizes threats before they propagate upward the stack, it also enhances scalability as these protocol-agnostic physical layer protections are independent of higher-layer protocols, enabling uniform security across heterogeneous LPWANs (e.g., LoRaWAN, NB-IoT). Moreover, as these mechanisms are information-theory based, they prevail a future-proofing aspect with post-quantum readiness as physical layer key exchange, using channel randomness, is inherently resistant to quantum computing attacks [22], unlike traditional PKI systems.

### B. UniPHY architecture design

A secure, bottom-up IoT LPWAN stack must be anchored in PHY-layer security mechanisms, which serve as the foundational root of trust while abstracting complexity for usability and scalability. UniPHY achieves this through three interdependent tiers: hardware-centric security primitives, a unifying middleware layer, and a decoupled core infrastructure. Each
tier propagates trust upward, ensuring systemic resilience without compromising efficiency.

The first tier integrates physical layer security directly into hardware through specialized libraries interfacing with the Hardware Abstraction Layer (HAL). These libraries, tailored for targeted platforms, exploit immutable physical properties of devices and wireless channels to establish trust. For instance, RF fingerprinting extracts unique hardware imperfections such as Carrier Frequency Offset or nonlinear power amplifier signatures to generate unforgeable device identifiers. Simultaneously, channel reciprocity enables symmetric key derivation from shared wireless CSI, bypassing traditional key exchange protocols. Such mechanisms are implemented as low-level primitives within the HAL, ensuring minimal computational overhead and resistance to software-based tampering. By rooting security in hardware and signal physics, this tier neutralizes threats at their origin, such as spoofing or eavesdropping, while preserving energy efficiency for resource-constrained LPWAN devices.

The second tier introduces a unifying middleware that translates physical layer features into system-wide security primitives. This middleware operates at the edge, interfacing with sensors and gateways to abstract physical layer specific details into standardized commands. For example, a gateway invokes middleware functions like `authenticate/device_id)`, which internally validates RF fingerprints or channel-derived keys without exposing raw physical layer data to higher layers. The middleware also orchestrates adaptive responses to dynamic threats: physical layer jamming detection triggers MAC-layer frequency hopping, while abrupt signal attenuation at the physical layer prompts application-layer data rate throttling. Crucially, this tier enforces policy consistency across heterogeneous devices, enabling over-the-air updates to deploy new physical layer security protocols, such as post-quantum key generation, without disrupting legacy systems. By decentralizing security logic to the edge, the middleware eliminates single points of failure and ensures context-aware protection.

The third tier comprises a core infrastructure entirely decoupled from security implementation details. Developers and operators interact solely with high-level directives such as `encrypt(sensor_data)` or `retrieve(authenticated metrics)`, while the underlying physical and middleware layers autonomously execute these commands. For instance, a request to encrypt might activate channel-based encryption seeded by physical layer entropy, while `retrieve` could transparently verify data provenance using RF fingerprinting. This abstraction not only simplifies compliance and auditing but also future-proofs the infrastructure: upgrades to physical layer mechanisms, for example adopting ultra-wideband signaling for enhanced spoofing resistance, require no modifications to application logic. The core remains agnostic to whether authentication relies on PKI-distributed or physical layer generated keys, treating security as an intrinsic, self-managing service.

The efficacy of this architecture lies in its seamless integration

of physical layer mechanisms across all tiers. Security policies propagate upward, with physical layer derived trust reinforcing MAC layer access control and application layer data integrity. For instance, a LoRaWAN sensor's RF fingerprint, validated at the HAL, can be associated with a session key for encrypting payloads at the MAC layer, while the middleware ensures end-to-end protection without exposing keys to the application. This vertical integration eliminates siloed defenses, reducing attack surfaces and operational overhead. Efficiency is achieved by offloading computationally intensive tasks, such as key generation, to physical layer entropy, which consumes significantly less energy than digital alternatives. Resilience is further strengthened by neutralizing threats at their origin: a rogue device attempting MAC-layer spoofing is rejected at the physical layer tier via RF fingerprint mismatches, preventing escalation.

## IV. RELATED WORKS

Research on PHYsec initially focused on complex systems, particularly 3GPP technologies. Early studies demonstrated that PHYsec mechanisms, leveraging abundant power and advanced multi-antenna systems, effectively secure communications [1], [23], [24]. Building on these insights, later research explored the application of PHYsec to IoT. Studies found that these mechanisms benefit IoT networks, especially beyond traditional 5G/6G targets, by adapting to resource-constrained environments [25].

At the same time, extensive research has examined the security of IoT LPWAN architectures. Numerous studies have systematically analyzed vulnerabilities, attacks, and countermeasures in these networks, leading to comprehensive surveys that map the LPWAN security landscape [24], [26], [27].

Despite these securitisation efforts, current approaches have significant limitations. Some solutions address specific flaws in existing security mechanisms [28], while others adopt a broader infrastructure-level approach but fail to account for the severe resource constraints of LPWAN devices [29]. This underscores the need for innovative, holistic security solutions tailored to the unique requirements of IoT LPWANs.

To the best of our knowledge, no existing work provides a comprehensive security framework leveraging physical-layer security for the entire LPWAN architecture stack. Filling this gap is essential to developing robust, end-to-end security solutions.

## V. CONCLUSION

We introduced UniPHY, a disruptive security paradigm that embeds protection at the physical layer, reducing reliance on energy-intensive cryptography and mitigating threats before they propagate. By leveraging physical layer properties such as RF fingerprinting and channel reciprocity, UniPHY enables self-securing LPWANs, enhancing resilience under strict resource constraints.
This bottom-up approach challenges traditional security models by unifying protection across diverse IoT deployments and neutralizing threats at their origin, emphasizing fault isolation and self-stabilization. Additionally, its decoupled core infrastructure ensures long-term adaptability, making it well-suited for evolving IoT ecosystems.

As LPWANs become integral to next-generation infrastructures, UniPHY provides a scalable, efficient, and future-proof security foundation, redefining IoT security as an emergent property of the physical layer.

## REFERENCES

[1] I. Ahmad, T. Kumar, M. Liyanage, J. Okwuibe, M. Ylianttila, and A. Gurtov, "Overview of 5g security challenges and solutions," *IEEE Communications Standards Magazine*, vol. 2, no. 1, pp. 36–43, 2018.

[2] K. S. team, "Kaspersky iot security report," *Online Report*, 2022, https://www.kaspersky.com/blog/iot-report-2022/.

[3] D. Altolini, V. Lakkundi, N. Bui, C. Tapparello, and M. Rossi, "Low power link layer security for iot: Implementation and performance analysis," in *2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC)*. IEEE, 2013, pp. 919–925.

[4] Z. Wei, C. Masouros, F. Liu, S. Chatzinotas, and B. Ottersten, "Energy- and cost-efficient physical layer security in the era of iot: The role of interference," *IEEE Communications Magazine*, vol. 58, no. 4, pp. 81–87, 2020.

[5] A. Weinand, A. G. de la Fuente, C. Lipps, and M. Karrenbauer, "Physical layer security based key management for lorawan," in *Workshop on Next Generation Networks and Applications*, 2021.

[6] W. Xu, S. Jha, and W. Hu, "Lora-key: Secure key generation system for lora-based network," *IEEE Internet of Things Journal*, vol. 6, no. 4, pp. 6404–6416, 2018.

[7] B. Han, Y. Li, X. Wang, H. Li, and J. Huang, "Flora: Sequential fuzzy extractor based physical layer key generation for lpwan," *Future Generation Computer Systems*, vol. 140, pp. 253–265, 2023.

[8] A. K. Junejo, F. Benkhelifa, B. Wong, and J. A. Mccann, "Lora-lisk: A lightweight shared secret key generation scheme for lora networks," *IEEE Internet of Things Journal*, vol. 9, no. 6, pp. 4110–4124, 2022.

[9] X. Wang, L. Kong, Z. Wu, L. Cheng, C. Xu, and G. Chen, "Slora: Towards secure lora communications with fine-grained physical layer features," in *Proceedings of the 18th Conference on Embedded Networked Sensor Systems*, 2020, pp. 258–270.

[10] Y. Jiang, L. Peng, A. Hu, S. Wang, Y. Huang, and L. Zhang, "Physical layer identification of lora devices using constellation trace figure," *EURASIP Journal on Wireless Communications and Networking*, vol. 2019, pp. 1–11, 2019.

[11] P. Robyns, E. Marin, W. Lamotte, P. Quax, D. Singelée, and B. Preneel, "Physical-layer fingerprinting of lora devices using zero-shot learning," in *Proceedings of the 10th ACM Conference on Security and Privacy in Wireless and Mobile Networks*, 2017.

[12] J. P. S. Sundaram, W. Du, and Z. Zhao, "A survey on lora networking: Research problems, current solutions, and open issues," *IEEE Communications Surveys & Tutorials*, vol. 22, no. 1, pp. 371–388, 2019.

[13] R. K. Singh, P. P. Puluckul, R. Berkvens, and M. Weyn, "Energy consumption analysis of lpwan technologies and lifetime estimation for iot application," *Sensors*, vol. 20, no. 17, p. 4794, 2020.

[14] J. Liu, W. Xu, and W. Hu, "Energy efficient lpwan decoding via joint sparse approximation," in *Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems*, 2018, pp. 325–326.

[15] A. Saifullah, M. Rahman, D. Ismail, C. Lu, J. Liu, and R. Chandra, "Enabling reliable, asynchronous, and bidirectional communication in sensor networks over white spaces," in *Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems*, 2017, pp. 1–14.

[16] M. Rahman, D. Ismail, V. P. Modekurthy, and A. Saifullah, "Implementation of lpwan over white spaces for practical deployment," in *Proceedings of the International Conference on Internet of Things Design and Implementation*, 2019, pp. 178–189.

[17] A. I. Petrariu, A. Lavric, E. Coca, and V. Popa, "Hybrid power management system for lora communication using renewable energy," *IEEE Internet of Things Journal*, vol. 8, no. 10, pp. 8423–8436, 2020.

[18] M. Capuzzo, C. Delgado, A. K. Sultania, J. Famaey, and A. Zanella, "Enabling green iot: Energy-aware communication protocols for batteryless lorawan devices," in *Proceedings of the 24th international ACM conference on modeling, analysis and simulation of wireless and mobile systems*, 2021, pp. 95–98.

[19] G. Peruzzi and A. Pozzebon, "A review of energy harvesting techniques for low power wide area networks (lpwans)," *Energies*, vol. 13, no. 13, p. 3433, 2020.

[20] C. Delgado, J. M. Sanz, C. Blondia, and J. Famaey, "Batteryless lorawan communications using energy harvesting: Modeling and characterization," *IEEE Internet of Things Journal*, vol. 8, no. 4, pp. 2694–2711, 2020.

[21] F. Yang, A. S. Thangarajan, G. S. Ramachandran, W. Joosen, and D. Hughes, "Astar: Sustainable energy harvesting for the internet of things through adaptive task scheduling," *ACM Transactions on Sensor Networks (TOSN)*, vol. 18, no. 1, pp. 1–34, 2021.

[22] M. Shakiba-Herfeh, A. Chorti, and H. Vincent Poor, "Physical layer security: Authentication, integrity, and confidentiality," *Physical layer security*, pp. 129–150, 2021.

[23] N. Yang, L. Wang, G. Geraci, M. Elkashlan, J. Yuan, and M. Di Renzo, "Safeguarding 5g wireless communication networks using physical layer security," *IEEE communications magazine*, vol. 53, no. 4, pp. 20–27, 2015.

[24] Y. Wu, A. Khisti, C. Xiao, G. Caire, K.-K. Wong, and X. Gao, "A survey of physical layer security techniques for 5g wireless networks and challenges ahead," *IEEE Journal on selected areas in communications*, vol. 36, no. 4, pp. 679–695, 2018.

[25] E. Illi, M. K. Qaraqe, S. Althunibat, A. Alhasanat, M. Alsafasfeh, M. de Ree, G. Mantas, J. Rodriguez, W. Aman, and S. Al-kuwari, "Physical layer security for authentication, confidentiality, and malicious node detection: A paradigm shift in securing iot networks," *IEEE Communications Surveys & Tutorials*, vol. 26, pp. 347–388, 2024.

[26] F. Hessel, L. Almon, and M. Hollick, "Lorawan security: an evolvable survey on vulnerabilities, attacks and their systematic mitigation," *ACM Transactions on Sensor Networks*, vol. 18, no. 4, pp. 1–55, 2023.

[27] C. Li and Z. Cao, "Lora networking techniques for large-scale and long-term iot: A down-to-top survey," *ACM Computing Surveys (CSUR)*, vol. 55, pp. 1–36, 2022.

[28] R. McPherson and J. Irvine, "Secure decentralised deployment of lorawan sensors," *IEEE Sensors Journal*, vol. 21, pp. 725–732, 2021.

[29] J. M. Hamamreh, H. M. Furqan, and H. Arslan, "Classifications and applications of physical layer security techniques for confidentiality: A comprehensive survey," *IEEE Communications Surveys & Tutorials*, vol. 21, no. 2, pp. 1773–1828, 2019.