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With the continuous development of 5G network technology, the amount of data has surged. Network data transmission technology needs improvement. To ensure the reliable transmission of data, reduce network congestion and network energy consumption, the paper proposed an effective edge cache collaboration routing strategy of opportunistic social networks in the trust community, namely the edge collaboration cache trust community routing algorithm (ECTC). The algorithm proposes that the nodes are used to carry information through the cache. Based on the node trust degree, the efficient trust relay node is identified. Build a trusted network based on the trust degree of nodes. Divide local trust community modules into clusters to realize efficient community retrieval. Under the concept of edge nodes, trust nodes are found in the trust community. Edge caches cooperate to share transmitted data, reducing backbone network congestion. The data update efficiency is greatly improved. Through edge nodes in trust community cooperation and communication, efficient data packet delivery is realized, thereby improving the community trust network’s routing performance. The simulation experiment results show that the edge collaboration cache trust community routing algorithm (ECTC) can significantly improve delivery effectiveness and reduce network overhead.

Current FastSLAM algorithms face challenges such as heavy computing requirements and difficulty in enhancing estimation accuracy. This paper presents a fast algorithm of simultaneous localization and mapping (SLAM) based on combinatorial interval filters coupled with an improved box particle filter (IBPF) and extended interval Kalman filter (EIKF). First, strategies for improving box contracting and resampling are studied in depth via the linear programming contractor and dimension selection subdivision resampling methods. Then, we propose a weighted average based on a time-varying Markov model to increase the estimation accuracy of the EIKF. In this way, a kind of fast SLAM algorithm is designed through combinatorial synthetic integration, in which the IBPF algorithm is employed to realize simultaneous localization and the EIKF is utilized to build a map. A series of simulations and experiments demonstrate the superior performance of our interval filters based SLAM algorithm.

The FastSLAM algorithm has become an effective way to solve the simultaneous localization and mapping (SLAM) problem. However, measured in terms of the number of particles required to build an accurate map, currently, its accuracy cannot be easily enhanced because of particle degeneracy. In view of these problems, in this paper, we present a fast algorithm of SLAM based on the ball particle filter (Ball-PF), which originates from the modification of the box particle filter (Box-PF). First, the transform relationship between Box-PF and Ball-PF are studied in depth so as to show the advantages of Ball-PF with respect to solving the interval constraints satisfaction problem and prevent from breaking down effectively. Then, a new fast algorithm of SLAM is designed with Ball-PF, in which the firefly algorithm is used to maintain the diversity of the ball particles to increase the consistency of the pose estimation effectually. Furthermore, the map matching technique is used to compute the weight of the ball particles and learn the grid maps incrementally. The simulation and experimental results demonstrate the performance superiority of the proposed algorithm.