The existing selfish node detection algorithms for opportunistic networks do not consider the impact of node energy and cache on node selfishness, resulting in inaccuracies and high latency in the detection algorithm. Therefore, this paper proposes a selfish node detection algorithm based on credibility combined with energy and cache (CDEC). Firstly, a new credibility model is presented. Then, through an analysis of the residual energy and cache usage ratio of nodes over time, selfish nodes have greater differences compared to cooperative nodes. Finally, the overall service quality of nodes is combined with their credibility, residual energy, and cache usage ratio to detect selfish nodes based on their service quality. Simulation results show that compared with existing Watchdog and 2-ACK detection algorithms, the CDEC algorithm can effectively improve the accuracy of selfish node detection and message delivery rates while reducing network latency.

The movement of nodes in opportunistic networks exhibits characteristics of clustering and regularity. Consequently, routing algorithms based on communities have become a current research hotspot. However, existing community-based routing algorithms do not comprehensively analyze both the overlap of node communities and the impact of neighboring nodes on message transmission. To address this issue, this paper proposes a novel opportunistic routing algorithm called CWON, based on overlapping communities and communication willingness. First, we utilize the PercoMCV method to partition overlapping communities. Subsequently, we define the concept of communication willingness and design the CWON algorithm based on this concept. The CWON algorithm effectively addresses the problem of overlapping community partitioning and measures the importance of different neighboring nodes in message transmission. Our simulation results demonstrate that the CWON algorithm significantly enhances the success rate of message delivery while reducing routing overhead.

Opportunistic network nodes exhibit social attributes, and existing community routing algorithms are currently designed for situations where the community structure remains fixed and do not comprehensively analyze the impact of node location information on data forwarding. Over time, the community division results do not match the current network topology structure, and it becomes challenging to select appropriate relay nodes for forwarding. In order to solve this problem, this paper proposes a community routing based on node location information-CRLI. Firstly, the communities are partitioned based on node interaction information, and then the regional affiliation and regional connectivity are defined. Based on these, the CRLI algorithm is designed to comprehensively analyze the influence of node location, movement direction, and dynamic changes in community structure on data forwarding. The experimental results show that the CRLI algorithm can effectively improve the message delivery rate and reduce overhead.

Yanhe Fu, Yanan Cao, Qingyue Wang, Yi Liu. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Opportunistic networks are mobile self-organizing networks that use the encounter opportunities brought by node movement to achieve communication. However, existing opportunistic routing algorithms rarely consider node context information and cache management at the same time, which leads to network congestion and high energy consumption problems in opportunistic networks. To solve the above problems, this paper defines the node historical activity degree and encounter duration based on the context information of nodes, and designs the AD-AC (historical Activity degree and encounter Duration of nodes-Acknowledgment deletion mechanism) opportunistic routing algorithm based on the context information of nodes by incorporating ACK (Acknowledgment) deletion mechanism. The simulation results indicate that AD-AC can substantially improve the message delivery rate while reducing the network overhead as well as the average hop count of messages.