Abstract. This work considers a time domain inverse acoustic obstacle scattering problem due to passive data. Motivated by the Helmholtz–Kirchhoff identity in the frequency domain, we propose to relate the time domain measurement data in passive imaging to an approximate data set given by the subtraction of two scattered wave fields. We propose a time domain linear sampling method for the approximate data set and show how to tackle the measurement data in passive imaging. An imaging functional is built based on the linear sampling method, which reconstructs the support of the unknown scattering object using directly the time domain measurements. The functional framework is based on the Laplace transform, which relates the mapping properties of Laplace domain factorized operators to their counterparts in the time domain. Numerical examples are provided to illustrate the capability of the proposed method.

Routing-driven timely data collection in Underwater Acoustic Sensor Networks (UASNs) is crucial for marine environmental monitoring, disaster warning, and underwater resource exploration, etc. However, harsh underwater conditions, including high delays, limited bandwidth, and dynamic topologies, make efficient routing decisions challenging in UASNs. In this paper, we propose a smart interrupted routing scheme for UASNs to address dynamic underwater challenges. We first model underwater noise influences from real underwater routing features, e.g., turbulence and storms. We then propose a Software-Defined Networking (SDN)-based Interrupted Software-defined UASNs Reinforcement Learning (ISURL) framework, which ensures adaptive routing through dynamical failure handling (e.g., energy depletion of sensor nodes or link instability) and real-time interrupted recovery. Based on ISURL, we propose the MA-MAPPO algorithm, integrating multi-head attention mask mechanism with MAPPO to filter out infeasible actions and streamline training. Furthermore, to support interrupted data routing in UASNs, we introduce MA-MAPPO_i, MA-MAPPO with interrupted policy, to enable smart interrupted routing decisions in UASNs. The evaluations demonstrate that our proposed routing scheme achieves exact underwater data routing decisions with faster convergence speed and lower routing delays than existing approaches.

With the development of virtualization technology, serverless computing has been gaining significant attention in recent years, primarily due to its advantages in scalability and a pay-as-you-go pricing model. In edge networks, the deployment of fine-grained function instances to handle massive request data makes it more difficult to optimize the quality of user experience. This paper addresses the scheduling of heterogeneous function processing requests generated by users in serverless edge computing scenarios, considering the constraints of resources on edge nodes, and making decisions regarding the warm and cold start during the scheduling process. The problem is modeled as a constrained multi-objective optimization issue aimed at minimizing latency and energy consumption. A deep reinforcement learning strategy, grounded in Multi-Agent Proximal Policy Optimization (MAPPO), is introduced to address this challenge, with each user being represented as an autonomous agent. Simulations were conducted to assess the impacts of the learning rate, the request volume, and the size of the input data of the function. The experimental results indicate that, compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), under different scenarios of request scales, the average system delay is reduced by at most 31%, and the average energy consumption is reduced by at most 24%.