## 1 Introduction  
IoT has become an important technology across multiple sectors, impacting industries such as healthcare, smart cities, agriculture, environmental monitoring, and more. IoT, as defined by Ref. [1], is a complex system of entities--comprising cyber-physical devices, information resources, and people--that exchange information and interact with the physical world through sensing, processing, and actuating. In recent years, the integration of Artificial Intelligence (AI) with IoT has further accelerated technological advancements, created new use cases, and enhanced the capabilities of next-generation connected systems [2]. According to Ref. [3], combining AI and IoT will transform industries by creating smarter and more flexible networks and infrastructure. An essential component within the IoT ecosystem is WSNs, consisting of numerous sensor devices deployed to gather environmental data such as temperature and humidity [4]. Based on Ref. [5], WSNs are experiencing rapid growth, with the global market expected to expand at a CompoundAnnual Growth Rate (CAGR) of 17.64%, reaching a valuation of USD 148.67 billion. WSNs play a crucial role in monitoring systems across multiple domains, such as healthcare, agriculture, and environmental protection, including forest fire detection. However, the security challenges posed by the large-scale deployment of IoT and WSN devices are significant. The heterogeneity of IoT devices, particularly in WSNs, introduces vulnerabilities due to resource constraints such as limited battery life, processing power, and storage capacity. These constraints make it difficult to implement robust software- and hardware-based security measures, exposing the network to external attacks [6]. Security in WSNs is particularly critical given the real-time, sensitive nature of the data transmitted. Unauthorized access, data manipulation, and network breaches could have devastating consequences, especially in applications such as healthcare and environmental monitoring, where WSNs are often deployed in open, unattended environments. For instance, in healthcare, WSNs can monitor patients' vital signs, while in military operations, they can detect enemy intrusion. Both scenarios rely on the authenticity, integrity, and confidentiality of the data transmitted over the WSNs [7]. Given the broadcasting nature of LoRaWAN, networks using this protocol remain particularly susceptible to attacks like eavesdropping, data forgery, and spoofing. Spoofing occurs when a malicious actor impersonates a legitimate sensor by falsifying attributes such as MAC addresses, signal strength, or location. These attacks compromise the authenticity and reliability of transmitted data, potentially leading to unauthorized access and the disruption of critical applications [8; 9]. In addition to spoofing, jamming attacks can disrupt communication by introducing intentional interference, rendering the network unusable and causing significant reliability issues [10]. LoRaWAN's OTAA join procedure aggravates these vulnerabilities by transmitting plaintext device identifiers during initial communication, making it a critical risk area for spoofing attacks. This lack of encryption enables attackers to intercept and manipulate transmitted data, highlighting the need for stronger physical-layer authentication mechanisms to secure communication. To address these security challenges, this paper presents a novel multi-attribute PLA scheme for WSNs. PLA offers a proactive approach to security by authenticating devices based on their unique physical characteristics, such as RSSI, BL, and GPS-reported altitude. Unlike traditional cryptographic techniques, which may be too resource-intensive for WSNs, PLA is better suited for low-power devices, providing an efficient means of ensuring device authenticity at the physical layer. This research focuses on applying the proposed PLA scheme in a forest fire detection scenario, where static sensors are deployed to monitor environmental conditions.