The problem of stolen and poorly managed gas well covers in cities has become increasingly prominent, and the existing urban manhole cover monitoring system has single monitoring parameters, immature technology and insufficient comprehensive analysis capabilities.To solve these problems, this paper designs an intelligent manhole cover monitoring system based on narrow band Internet of things (NB-IoT) technology.The system consists of perception layer, network layer and application layer.The sensing layer is based on the embedded ARM microprocessor and combines sensor technology to collect the manhole cover data.The network layer takes NB-IoT technology as the core and is responsible for the communication connection between the access device and the background.The application layer mainly completes the functions of data storage, display and alarm.The test results show that the system works stably and data transmission is reliable, which is helpful to realize intelligent management of smart city and create a harmonious and safe smart city environment.

Due to particle filter SLAM algorithm has particle weight degradation and particle depletion, it affects the positioning accuracy of mobile robot SLAM (simultaneous localization and mapping) algorithm. In order to effectively improve the positioning accuracy of SLAM algorithm, this paper combines the operating mechanism of particle filtering in SLAM to improve the firefly brightness formula, use the firefly position update formula, the global optimization of the dynamic balance algorithm and the local optimization ability. The simulation results show that compared with the original firefly particle filtering SLAM algorithm, the proposed method makes the particle representation more reasonable and further improves the positioning accuracy of the SLAM algorithm.

This paper develops a new lower bound method for POMDPs that approximates the update of a belief by the update of its non-zero states. It uses the underlying MDP to explore the optimal reachable state space from initial belief and select actions during value iterations, which significantly accelerates the convergence speed. Also, an algorithm which collects and prunes belief points based on the upper and lower bounds is presented, and experimental results show that it outperforms some of the state-of-art point-based algorithms.