## 2 Background Work
### Analysis of the State of the Art  
Opportunistic communication networks have become a viable option in recent decades for situations where standard connectivity is either nonexistent or severely constrained [21]. These systems make use of connection opportunities in dynamic and irregular connectivity environments by enabling direct communications between mobile devices and other nodes. As technology develops, a variety of applications in crucial fields like disaster relief [5], environmental monitoring [4], and people tracking [22] are made possible by the combination of opportunistic networks with cutting-edge technologies like IoT, mobile Edge Computing (MEC), or 6G networks. In the past, some researchers have focused on the design and optimization of communication protocols and routing techniques for opportunistic networks. Specifically, some researchers analyzed how routing protocols and communication approaches might be improved to respond to the changing conditions of opportunistic networks. For instance, in [23], the authors describe a forwarding factor routing protocol that enhances data forwarding by dynamically choosing nodes based on their forwarding capabilities, which are influenced by various parameters including node mobility and network structure. Compared with static routing systems, the authors' techniques improve data delivery rates and reduce latency, which makes them suitable for IoT scenarios with high mobility and fluctuating node density. Recent work on information freshness (Age of Information, AoI) optimization, such as [24], demonstrates how time-modulated arrays can prioritize time-sensitive data in covert communications, a concept relevant to opportunistic networks where minimizing AoI is critical for applications like disaster response or real-time monitoring. In addition, in [25], a technique is presented that considers node energy levels and connection stability to make a dynamic selection of forwarding nodes, decreasing energy usage, extending network useful life, and assuring reliable data transmission. Another interesting work is described in [4], where a fuzzy Q-learning algorithm is applied to vehicular crowdsensing networks, enhanced by MEC. Integrating fuzzy logic with Q-learning allows for more sophisticated routing and data dissemination decisions, taking into account elements such as vehicle speed, direction, and network connectivity. Finally, it is also worth mentioning that there are emerging frameworks for integrated sensing and communications, like the tensor-based approach described in [26], which unifies channel estimation and target detection in massive MIMO systems, providing insights into how opportunistic networks could dynamically adapt RF parameters (e.g., transmit power, beamforming) to improve reliability in unstable environments. Other articles examine the integration of opportunistic communication systems with advanced technologies such as 6G, MEC, or blockchain to enhance their capabilities and applications. For example, in [5], the authors utilize 6G networks to enhance the responsiveness and effectiveness of rescue operations for drone-based disaster response. In [27], blockchain technology is integrated into IoT networks using opportunistic block validation to enhance security and trust, utilizing the decentralized and immutable nature of blockchain. Moreover, in [28], the authors explore how opportunistic networking principles can be integrated into IoT systems for smart city applications to improve connectivity and data sharing. Such a study highlights the potential of opportunistic communications to improve network scalability, to reduce infrastructure costs, and to enhance the overall performance of IoT deployments. Other articles provide comprehensive evaluations, highlighting difficulties, significance, and prospective applications for opportunistic networks. For instance, in [29], important challenges such as irregular connectivity and the need for effective routing strategies are discussed.