The microtremor survey method (MSM) holds great potential for obtaining subsurface shear wave velocity structures in exploration geophysics. However, the lack of an instant imaging mechanism with local fast computation and processing has become a significant bottleneck hindering the development of MSM. In instant imaging tasks, the computational resources of ordinary nodes employed for imaging are often limited. In this article, we consider a single-point microtremor array network with time-varying wireless channels and stochastic imaging task data arrivals in sequential time frames. In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability and optimize service quality subject to the long-term data queue stability and average power constraints. We formulate the problem as a the minimum delay problem that jointly determines the binary offloading and system resource allocation decisions in sequential time frames. To address the coupling in the decisions of different time frames, we propose a novel framework named LyECCO that combines the Lyapunov optimization and energy consumption optimization, solve the binary offloading problems with very low computational complexity. Simulation results show the feasibility of the LyECCO, which achieves optimal computation performance while stabilizing all queues in the system.

Due to the numerous advantages of dual-band infrared radiation (DBIR) attitude measurement (AM) technology, it has garnered significant attention from industry and academia. However, geometric errors caused by sensor measurement noise, assembly positions, motor interference and other sensor-related commonalities, along with random errors induced by the sensitivity of DBIR characteristics to infrared radiation interference, collectively undermine the reliability of the estimated attitude information. To address this issue, the bidirectional gated recurrent unit (Bi-GRU) and Transformer were combined to assist extended kalman filter (EKF) for DBIR attitude estimation (CBGTE). The core concept involves two aspects: 1) utilizing EKF to mitigate geometric errors during the process of DBIR AM, and 2) combining Bi-GRU and Transformer to aid EKF in compensating for random errors in the measurement process. A semi-physical experimental platform is established to validate the performance of CBGTE. Through experimental validation with real-world data, the proposed CBGTE algorithm has demonstrated significant improvements in accuracy when compared with several state-of-the-art algorithms, achieving roll angle error of ±0.4° and pitch angle error of ±0.2°.

The large-scale microtremor profiling method (LSMPM) operates under a “distributed data-collection and centralized data-recovery” framework, which has led to significant delays in retrieving shear wave velocity structural information, diminished detection efficacy, and the absence of prompt local computation and processing mechanisms for real-time imaging. Effectively harnessing the computational capabilities of acquisition nodes to facilitate wireless, multinode, low-latency collaborative computing remains a significant challenge. We present a task delay optimization and scheduling algorithm based on modified particle swarm optimization (MPSO) to achieve real-time imaging of shear wave velocity structures at the edge of the sensor network. It enhances the mutation process of the standard particle swarm optimization (PSO), utilizing the delay improvement coefficient as a metric to optimize the delay within computing task queues, thereby avoiding local convergence, augmenting global search capabilities, and consequently reducing the maximum completion time $(\textrm {Makespan})$ of tasks. The MPSO algorithm has undergone extensive simulation testing within the CloudSim environment, and the results indicate that, relative to the PSO task scheduling algorithm, the proposed MPSO task scheduling algorithm reduces the standard deviation of the Makespan by approximately 90%, and when the number of edge servers ranges from 10 to 30, the delay improvement coefficient of the proposed MPSO algorithm can surpass 60%, which underscores the superiority of the MPSO in terms of delay optimization efficacy and algorithmic stability.

In addition to the physical field research on the Earth’s magnetic and gravity fields, the infrared radiation field has garnered significant attention in recent years. The inherent infrared radiation characteristics of the Earth are essential for military, meteorological, aerospace, and other fields. The attitude measurement method based on infrared radiation has the advantages of low cost, good performance, and all-weather. This article reviews the researches of missile-borne infrared attitude measurement technology in recent years. These researches can be summarized into four perspectives: missile-borne infrared attitude measurement theoretical models, infrared attitude calculation method, infrared interference error compensation method, and multi-sensor fusion attitude estimation algorithm. Finally, the current challenges and future prospects of infrared attitude measurement are discussed.