Mobile node localization is one of the key technologies in wireless sensor network applications. Aiming at the shortcomings of Monte Carlo algorithm (MCL) in mobile wireless sensor network node localization in nonideal environments, such as low accuracy and low sampling rate, in the prediction phase and filtering phase of MCL, the communication radius of the unknown node is determined according to the size of the irregularity of the node. Perform layering, assign adaptive weights to anchor nodes of different layers according to the area where they are located, and propose an adaptive improved Monte Carlo algorithm. After simulation analysis, the algorithm has an average localization error of nodes under different regularity conditions. It has dropped by about 12%, and the localization error has dropped by about 10% on average under different speed conditions. Aiming at the shortcomings of the MCL algorithm in mobile wireless sensor mobile networks such as low localization accuracy, large sample demand, and long localization time, the communication radius of the node is fuzzified to reduce the sampling area of the node, and an improved Monte Carlo localization algorithm based on fuzzy theory is proposed. The improved Monte Carlo localization algorithm, after simulation analysis, is about 50% shorter than the traditional MCL algorithm in localization time, and the localization accuracy is up to about 30% higher than the traditional MCL algorithm.

One of the key research topics to extend the network lifetime is choosing the suitable routing algorithm to increase the energy efficiency of nodes in information transfer. An improved EEABR algorithm called N-EEABR is proposed to address the problems that the current ant colony routing algorithm is easily prone to: increasing the communication cost when the long ant packet is used; the routing loop is easily formed when the short ant packet is used; and the uneven distribution of node energy consumption. The pheromone update formula of the backward ant packet is redefined and the deviation value of node energy and path energy is added. This effectively weakens the loop effect of EEABR in short ant packets and balances the residual energy of nodes in the network. "pkt_src" (ant packet source address) and "sq_num" (ant packet sequence number) are added together in the neighbor list of nodes in the EEABR algorithm. The N-EEABR algorithm extracts the RSSI value from the "hello" packet to determine the best transmission power of the nearby node, which helps to further reduce the energy consumption of the node when sending packets. To conserve transmission energy and prevent energy waste, a PCABR-based ant colony routing algorithm is presented. The simulation results demonstrate that these algorithms have some degree of increase in node energy consumption efficiency and network energy balance, with the PCABR's path optimization accuracy being more obvious than that of N-EEABR and EEABR.