## 1 Introduction
### WSN Vulnerabilities and Trust Models  
WSNs are commonly deployed to support Internet of Things (IoT) services and capabilities. However, WSNs are vulnerable to node failure, to internal attacks, such as tampering with WSB nodes, and to Internet-based denial-of-service (DoS) attacks. This poses a serious risk to systems relying on data collected by WSNs. For instance, a faulty leaf wetness sensor's reading in a humidity lab remained stuck at the maximum value of the measurement range for several days. As a consequence, the temperature and humidity sensors housed in the same box in the lab short-circuited, causing erroneous temperature readings [5]. The high failure rate of sensors deployed in harsh environments means that faulty nodes can produce incorrect readings, potentially leading to errors in the overall measurement results. Low-cost, non-tamper-resistant nodes can be captured by a malicious actor, who can then exploit them to obtain access to the cryptographic keys used and launch an internal attack from the compromised node. Furthermore, WSN nodes can be compromised by an externally launched DoS attack. According to [PERSON] et al. [6], a DoS attack is an event that diminishes or eliminates a network's capacity to perform its expected function. This is especially important in critical  
Figure 1: The network architecture of a flat WSN. Figure 3: The network architecture of an SDWSN. Figure 2: The network architecture of a cluster-based WSN. applications, for example, a WSN that gathers and transmits signals from medical implants. In this case, a DoS attack can compromise the availability of the data and threaten the well-being of a human patient [7]. Some typical DoS attacks on sensor networks are described in [6]. While most of them are similar to DoS attacks affecting the Internet, the security solutions for detecting and preventing Internet-based DoS attacks are not always applicable to DoS attacks threatening WSNs. This is due to reasons such as resource and energy constraints, the open nature of wireless communications, and the dynamic topology of WSNs [1; 8; 9]. In addition, attackers continue to create new DoS attacks that exploit the structural and protocol vulnerabilities of evolving WSNs, including SDWSNs. [PERSON] et al. [10] described the most typical threats to SDWSNs as shown in Figure 4. In the 'flood' scenario, the malicious node (MLN) sends a large number of packets to its own cluster nodes as well as to the upstream nodes. This consumes the corresponding link bandwidth and node power, negatively affecting network performance. In the 'drop scenario', the MLN discards packets passing through it, preventing these packets from reaching the sink node. The data received by the DB server are incomplete, which may negatively affect the data processing output. [PERSON] et al. [5] suggested that establishing a reputation-based system within a WSN (similar to reputation-based communication systems in human societies) can be used as an effective protection mechanism. The reputation-based framework proposed in their study provides security controls to counter the impact of compromised nodes (malicious or faulty). A trust model establishes reputation-based trust relationships among the entities (nodes) of a network. This enables an entity to predict the future behavior of other entities based on knowledge about their reputation and supports decision-making in otherwise uncertain situations. Trust models are inspired by human society, where a person develops perceptions about the reputation of every other person they interact with. A reputation is built gradually through continuous observation and interaction [5]. [PERSON] et al. [11] defined trust in WSNs as the subjective assessment by one node of another node for reliability and accuracy in receiving or transmitting information under specific circumstances. In this study, we define trust as the subjective assessment of whether one node's behavior is normal (and therefore can be trusted) or not by another node, based on previous behavioral observations.