# Trusted Energy-Aware Hierarchical Routing (TEAHR) for Wireless Sensor Networks
## 1 Introduction  
Trust, a fundamental component of human interactions, serves as a conceptual keystone for establishing secure and reliable communication among sensor nodes. In WSN, Trust assesses node behavior and encompasses a multifaceted evaluation of communication patterns, data integrity, and overall trustworthiness. Integrating trust models provides an upward route to defend WSNs from malicious features, generates fidelity of transmitted messages, and grows the life span of the network in operation. Abstract With the dramatic proliferation of wireless sensor networks, much is expected to provide a broad range of services and applications that come up with an overwhelming need for cost-effective, robust security solutions. Conventional WSNs, despite being irreplaceable in terms of their data-acquisition potential, are subject to limitations such as node compromise, which is tempered with data and communication interruptions. The vulnerabilities, escalated by the inherent resource constraints of sensor nodes, point towards a dire need for new ways to enhance the dependability and efficiency of wireless sensor networks. WSNs are naturally vulnerable because wireless communication is open, so we see many security threats. The concept of trust management for WSNs provides an easy and straightforward solution to couple the security and cooperation problem, which measures the degree/level by which sensor nodes in improving network performance are being trusted or distrusted. WSNs are capable of providing multi-tiered detection capability, as they can gather information wirelessly and communicate. However, there are also security flaws in their open messaging, unattended deployment, and limited resources. Malicious nodes (compromised or faulty) can inject erroneous data, disrupt routing protocols, and consume energy, thereby jeopardizing the network integrity and application performance. Recent studies have proposed various trust evaluation models for assessing the trustworthiness of sensor nodes. [PERSON] et al. [1] have proposed a dynamic trust evaluation model (DTEM), considering several factors of communication, called communication trust, data storage, and processing demands for adaptively evaluating data flow quality, which is referred to here for DTEM as energy-aware reputation-based scheduling in CSPs. This model calculates direct Trust and evaluates indirect Trust using trusted third-party recommendations. The integration of direct and indirect Trust was achieved by assigning and combining dynamic weights. Trust-based security mechanisms involve the development of lightweight security protocols tailored to the resource-constrained environments of WSNs. These are trust-based cluster head election mechanisms, location-aware trust-based mechanisms for detecting and isolating compromised nodes, and scalable enforcement algorithms using many-to-one balanced evolutionary game theory. Mathematical models underpin these techniques and provide a theoretical foundation for trust management systems. Trust management systems are comprehensive frameworks that include the collection, storage, modeling, transferring, and decision-making of trust-related information [2]. They are designed to detect and defend against internal attacks such as DoS, bad-mouthing, and on-off attacks, among others. Trust models offer an appropriate mechanism for formally setting up and handling trust relationships among sensor nodes. The Trust of sensor nodes is evaluated in many dimensions, including data trust, behavior trust, comprehensive Trust, and historical trust [3]. Relay nodes are also subjected to trust evaluation to ensure the integrity of the data transmission. A trust list was maintained to keep track of the trustworthiness of nodes within the network. Trust in wireless sensor networks is always considered a broader concept than just the evaluation of single-node behavior. This refers to communication patterns, data correctness verification, and collaborative decision-making.