A Bi-Objective Optimization for Sensor Path Planning and Communication Node Deployment

Efficient data processing and signal acquisition are becoming increasingly critical. Pipeline networks present unique topological constraints that complicate the balance between signal sampling efficiency and data-transmission reliability. In this paper, we propose a bi-objective optimization model for the urban pipeline network (UPN). The model optimizes autonomous mobile sensor (AMS) path planning using an Euler path scheme and communication node (CN) deployment using a deterministic deployment scheme. The model aims to minimize both monitoring time (MMT) and data delay (MDD). These two indicators are used as quality of service (QoS) metrics for communication and sensing. By representing the UPN as a graph structure, we establish two mathematical models for the MMT and MDD problems. Then, we introduce a topology-guided heuristic virtual-edge strategy to construct an Euler traversal for the MMT problem. An adaptive simulated annealing (ASA) algorithm is designed to solve the MMT problem. On this basis, the MDD problem is solved using an enhanced ant colony optimization (EACO) algorithm. Simulation results show that the proposed scheme achieves shorter monitoring times and lower data delays. Specifically, the Euler path scheme for the AMS reduces MMT by more than 43.26%, and the deterministic CN-deployment scheme reduces MDD by more than 44.10%.