# DTN Trustworthiness for Permafrost Telemetry IoT Network
## 2 Related Work
### Trustworthiness in Cyber Physical Systems  
A cyber physical system (CPS) is defined as a system with integrated computational and physical capabilities. Wireless sensor networks, smart grids, and some IoT devices are examples of CPSs [47]. Even though there is no consensus in the literature to define the trustworthiness property and its scope [48], we can define a CPS's trustworthiness, in general terms, as the property of behaving as expected under adversarial conditions [47]. Network malfunction, Byzantine errors, and faulty nodes are examples of adverse conditions that can affect a system's trustworthiness. Some authors limit this definition to system security issues only [49], while others propose a broader scope and relate trustworthiness with other terms such as resilience, availability, reliability, scalability, maintainability, heterogeneity, data quality, hardware resources, and fault management policies [48]. We can find many approaches to measuring or providing trustworthiness in literature, referring to different elements. We classify them into four main categories [7]:  
1. Data trustworthiness: It is defined as the possibility to ascertain the correctness of the data provided by the source [50]. Many methods use different approaches that try to detect faulty nodes, false alarms, and sensor misreading using. For instance, authors in [51] use a fog computing architecture to detect, filter, and correct abnormal sensed data. In addition, authors in [52] present a data intrusion detection system to trigger false data from malicious attacks. 2. Network trustworthiness: Defined as the likelihood of a packet to reach its destination unaltered despite the adversities (e.g., link failure, link saturation, or malicious attacks, among others), it is a relevant aspect to consider in challenging networks [53], such as the use case we propose. The network's performance and trustworthiness have been addressed from several perspectives, such as channel coding [54], transport protocols [11], dynamic routing and topology control protocols [55; 56], and DTN architectures and protocols [8]. 3. Social trustworthiness: This field has become more popular since the appearance of the Social Internet of Things (SIoT) [57; 58]. In SIoT trustworthiness, objects or network nodes interact and establish social relationships, which are used to define trust and reputation models that take into account several input parameters. Authors in [59] present a model that considers factors as the computational capabilities of the nodes, the type of relationship between them, the total number of transactions, the credibility of a node, and the feedback provided by other nodes, among others. Figure 2: Results of average throughput (%) vs. packet loss ratio (%) of the transport protocols tested in previous work [11]. To represent the graph in semilogarithmic scale, the packet loss ratio values of 0% are represented as 0.001% in the graph. Each transport protocol was tested in three LFN scenarios: London to Iowa (L–I), Sidney to Iowa (S–I), and Sidney to London (S–L). Authors in [60] present an evolution of the aforementioned trust management model, which applies a machine learning algorithm to calculate novel parameters such as the goodness, usefulness, and perseverance of a node. Thanks to these parameters, this upgraded trust model is resilient to more types of malicious node attacks. Authors in [61] propose another model that defines the input parameters as the expected gain on success, the expected damage on a failure, the expected cost, the expected result, and the goal. Authors in [62] define a decentralized self-enforcing trust management system which is based on a feedback system and reputational secure multiparty calculations to ensure the privacy of each party's provided data. 4. Consensus: It represents a state where all the participants of the same distributed system agree on the same data values [63]. Consensus protocols can be classified into two major groups: proof-based consensus and Byzantine consensus.