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In the context of service provisioning, the integration of Network Functions Virtualization (NFV) enhances the flexibility, scalability, and programmability of telecommunication networks. However, this integration introduces challenges, particularly in optimizing the placement of Virtualized Network Functions (VNFs) within the NFV Infrastructure (NFVI). Existing studies have predominantly focused on well-connected, mains-powered ecosystems like datacentres and cloud networks. In contrast, the aim of this paper is to identify a solution that distributes and deploys a Wireless Mesh Network (WMN) as the backbone for a disaster management communication and service infrastructure. Given the mobility of mesh routers in such scenarios, these devices are often battery-powered. Consequently, the placement of VNFs directly impacts the energy consumption in the network and, subsequently, its lifetime. The proposed solution for the energy-efficient placement of VNF is formulated as a multi-objective optimization problem. This context introduces different approaches and proposes a heuristic algorithm to optimize the placement of VNFs. The evaluation results indicate that the proposed algorithm outperforms prior alternatives in various scenarios. Notably, it surpasses established methods like the Nondominated Sorting Genetic Algorithm II (NSGA-II), commonly used to solve similar problems. This research signifies a significant advancement in addressing the specific challenges associated with NFV integration in wireless mesh networks, particularly in disaster management contexts.

Building trust relationships between different decentralized entities in the IoT ecosystem is essential. Hereof, the combination of blockchain technology and trust evaluation techniques is recently considered as an efficient measure. However, both technologies within the IoT are still facing some limitations which are addressed in this research. First, this publication reviews various blockchain-based trust approaches and depicts their strengths and limitations regarding their usage in decentralized IoT communities. Then, an optimized trust model with a multi-layer adaptive and trust-based weighting system is proposed. Additionally, different trust metric parameters and their mathematical models used for trust evaluation are presented. Moreover, this publication presents a novel approach for incentivization processes in the IoT marketplace using control loops and smart contracts. Thereby, participants are motivated to continuously improve their behavior. Finally, the proposed trust model is proved to be reliable. The experimental results conducted from different scenarios show that the presented approach provides more resiliency against various attacks than existing ones.