# Trusted Energy-Aware Hierarchical Routing (TEAHR) for Wireless Sensor Networks
## 1 Introduction
Through the amalgamation of trust metrics, anomaly detection algorithms, and cryptographic techniques, a comprehensive trust-based framework has emerged, which is promising for significantly enhancing the security posture of wireless sensor networks. In WSNs, trust management requires customization of applications as trust management (TM) is challenging owing to the nature of WSNs, deployment of sensor nodes, and how the dynamic characteristics of sensor nodes change over time. The deployed sensor nodes create numerous complications in TM [4]. Hostile surroundings can harm or physically impact a sensor node, causing it to function poorly. In addition, sensors are vulnerable to physical capture and temptation because of the distant and unsupervised nature of the environment. This results in a node behaving in an intelligent but incorrect manner, further complicating this issue. The network's topology is dynamic, causing the neighborhood to vary dynamically. It is crucial to manufacture sensors at a low cost, which results in limitations in their computational power, communication range, and battery capacity. Therefore, in light of these aspects, it is essential to develop trust estimations (TEs) that are effective in terms of energy and processing resources to align with the limitations of sensor nodes while also ensuring that they are strong enough to fulfil the security needs of TM. Furthermore, exchanging trust values between nodes may be restricted because of the increased energy consumption and congestion in the limited bandwidth caused by the message overhead. Proposing a trademark while considering the aforementioned considerations can be challenging. Executing TE and managing each Node in a large number of neighboring nodes is a tedious task. Despite numerous proposed studies, current computational techniques need a comprehensive analysis of all essential design factors for TM. Furthermore, while TM is still in its early stages, numerous research difficulties require attention, particularly the confusion between TM, reputation management, and trust establishment. To ensure secure communication in WSNs, it is essential to ensure that the intermediate nodes responsible for forwarding data packets are trustworthy within the network. Hence, an effective trust model needs to be created. Each sensor node should evaluate the trustworthiness of its neighboring nodes based on model parameters. The trust model is one of the critical issues in WSNs. Various studies have also shown that a trust management system is a beneficial approach to identification and protection from threats [5]. Moreover, to avoid captured/compromised nodes from internal attacks, the trust model used here, the author has designed a trust management system. Trust management systems are employed to assess the attributes of information, provide network trustworthiness services such as authentication and sensing the rogue nodes, and facilitate secure resource sharing. This is a more profound research area in understanding Trust Management technology mechanisms, designs, and objectives of Trust management for WSNs, along with related study and open challenges. To find Trust, we need to cover the fundamental concepts of Trust, i.e., ideas and design elements. To construct a Trust Establishment (TE) scheme appropriately, it is crucial to have a thorough understanding of the fundamentals, i.e., the definition, values, and properties of Trust. Furthermore, TM-related attacks, protection methods, and applications are discussed to provide a comprehensive understanding of TM. Understanding the concept, values, and features of Trust is crucial for estimating the trust levels of nodes. The trust model establishes and manages trust connections between two nodes to ensure that legitimate nodes may be trusted to participate in the information transmission process [2]. Trust is the level of belief in the future actions of other nodes as determined by prior experiences and observations of their behavior.