IoT için blok zinciri tabanlı ve enerji farkındalıklı uyarlanabilir güven mekanizmaları

Modern Internet of Things (IoT) increasingly rely on distributed decision-making, computation, and communication between independent nodes. As these systems expand and diversify, particularly when central coordination is not possible or not acceptable, decentralization becomes the primary concept. Decentralized systems eliminate dependence on central authority or trusted third parties by distributing decision-making and verification tasks among independent members of a network. While this shift promises greater scalability and fault tolerance, it introduces a critical challenge: establishing trust among resource-constrained and anonymous devices with no prior relationship or shared context. Blockchain technology has emerged to create a fundamental infrastructure to address this challenge by enabling transparent, auditable, and tamper-resistant management of data and consensus management in decentralized environments. Its distributed ledger mechanism ensures that transactions and state changes are immutably recorded, ensuring accountability in trustless networks. However, the presence of blockchain alone does not guarantee trustworthiness, especially when consensus mechanisms fail to account for the behavior, honesty, or sustainability of participating nodes. In this context, trust management becomes essential to maintaining the security and reliability of decentralized IoT networks. Trust can generally be broadly defined as the confidence in an entity's behavior based on past interactions, feedback, and reliability in service delivery. Modern trust models, ranging from reputation-based to computational and voting-based schemes, attempt to quantify this notion, yet most fall short in dynamic and malicious conditions. Despite extensive research, many existing trust models rely on static scores, stake-based voting, or unweighted historical data, making them vulnerable to adaptive adversaries and resource exhaustion. Especially for large-scale and heterogeneous IoT networks, lack of adaptive and behavior-aware trust evaluation diminishes the overall security and reliability. To address these limitations, this thesis introduces a series of progressively enhanced trust-aware models, each developed based on the insights gained from its predecessors: TrustVista introduces a layered trust evaluation model that calculates trust at the node, group, and community perspectives. It emphasizes recent behavior by using a weighted average that gives more importance to recent interactions, and applies statistical techniques to detect abnormal or potentially malicious behavior. By evaluating trust not just individually, but in a collaborative and structural context, TrustVista improves the speed and accuracy of identifying harmful participants. During this stage, it was observed that mechanisms like Delegated Proof of Stake (DPoS) could be a viable consensus mechanism for IoT, yet lacked any awareness of nodes' energy capacity. Based on this insight, EDPoS was developed to extend classical DPoS by incorporating energy-awareness. EDPoS enhances the classical DPoS mechanism by introducing energy-awareness into the consensus process. It dynamically adjusts how many nodes can become validators, how much data is included in each block, and which nodes are eligible to participate, based on how much energy nodes have and how efficiently they've used it in the past. This reduces the likelihood of block validation failures due to node dropouts and extends the overall system lifetime in IoT environments where resources are limited. Building on EDPoS, TEDPoS further integrates behavioral trust into the energy-aware DPoS framework. TEDPoS proposes a dynamic validator scoring method that combines three key factors: a node's trust level based on its behavior, its available energy, and the level of community support it receives through voting. Unlike traditional DPoS, TEDPoS adapts its selection process based on real-time network conditions, favoring nodes that are both trustworthy and resource-sufficient. This enables robust, context-aware, and attack-resilient delegate selection in decentralized IoT environments. As a supplementary use-case, TEMBUV, presented in Appendix~\ref{app:tembuv}, presents how trust and blockchain can jointly support secure operations in post-disaster communication networks. While not simulated in OMNeT++, this model offers a mathematical abstraction of trust-based decision-making in UAV and VANET-integrated domains. All proposed models are implemented and and tested against various adversarial conditions, including camouflage, bad-mouthing, and self-promotion attacks. Experimental results demonstrate clear improvements in malicious node detection, energy efficiency, and consensus reliability compared to baseline models. Collectively, the frameworks presented in this thesis contribute to the development of adaptive, secure, and scalable trust mechanisms for decentralized and heterogeneous IoT systems.