## 2 IoT and Opportunistic Networks--State of the Art
### Paper Contribution  
Our contribution lies in addressing the unique challenges posed by opportunistic networks, which represent an evolution of MANETs where communication is independent of preinstalled infrastructure, relying instead on routing packets through various mobile nodes. In these networks, the absence of a complete path from source to destination and rapidly changing or breaking paths necessitate nodes to store and forward data opportunistically when connectivity permits. This differs fundamentally from conventional internet routing, as routes are dynamically constructed, and any reachable node can serve as a potential relay towards the destination. To tackle these challenges, we propose a novel probabilistic routing model that leverages nodes' encounter probabilities to guide packet forwarding. Our approach capitalizes on the likelihood of a node encountering the destination based on past interactions, alongside an acknowledgment table mechanism to alleviate network overload. Additionally, to mitigate high overhead, we introduce an efficient message detection scheme, facilitating the removal of selected messages from buffers during congestion. Through extensive large-scale simulations, our model demonstrates remarkable improvements in message delivery probability, while maintaining reasonable overhead and latency, thus offering a promising solution to the complexities of opportunistic networks. The model described incorporates elements of cross-layer optimization through its consideration of both mobility patterns and communication protocols:  
1. Incorporation of Mobility Patterns: The model acknowledges that in real opportunistic networks, human mobility is not entirely random but follows patterns influenced by specific activities. By considering these mobility patterns, the routing approach can adapt to the dynamic movement of nodes, enhancing the efficiency of message delivery. Mobility patterns influence the likelihood of encounters between nodes, which directly impacts routing decisions. By incorporating this information, the routing approach can optimize message forwarding based on the probability of nodes encountering each other.
2. Integration of Communication Protocols: Communication protocols govern how nodes interact and exchange messages within the network. By considering communication protocols alongside mobility patterns, the model ensures that routing decisions are not only based on node encounters but also on the underlying communication mechanisms. This integration allows for a holistic approach to routing optimization,considering both the physical movement of nodes and the communication constraints imposed by protocols.
3. Probability-Based Routing Approach: The proposed routing approach incorporates the probability of a current node encountering the destination node based on their last meeting time. By utilizing probability-based routing, the model optimizes message delivery by prioritizing routes with higher probabilities of successful transmission, thus leveraging both mobility patterns and communication protocols. This approach exemplifies the cross-layer optimization by integrating mobility information (meeting time probabilities) with communication protocols (routing decisions).
4. Acknowledgment Table for Data Management: The model integrates an acknowledgment table to facilitate the removal of acknowledged messages from the network. By managing acknowledged messages, the model ensures efficient data transmission across layers, optimizing resource utilization and minimizing overhead. This acknowledgment mechanism operates across layers, bridging the gap between the data link and network layers, and enhances the overall performance of the network.