Abstract:

To achieve effective coverage of key monitoring points in an elongated structural space, a heterogeneous wireless sensor network(HWSN) deployment optimization method combining the virtual force algorithm(VFA) and multi-strategy improved whale optimization algorithm(MSIWOA), namely HVF-MSIWOA, was proposed. A dynamic adaptive weight mechanism and a t-distribution perturbation operator with heterogeneous degrees of freedom were designed, enabling the whale optimization algorithm(WOA) to balance global exploration and local exploitation and jump out of local optima; combining the topological characteristics of the elongated space and node density distribution, an adaptive virtual force distance threshold between heterogeneous nodes was constructed; the mapping relationship between network node density and optimal virtual force threshold was established, and nodes were guided to move toward coverage gaps by virtual forces, reducing the invalid movement distance; an optimal allocation strategy was applied to nodes of the same type to reduce the total movement distance. Simulation results show that in the HWSN coverage optimization scenario of the three-dimensional elongated structural space, compared with WOA, three-dimensional virtual force algorithm(3DVFA), virtual force-directed improved sand cat swarm optimization algorithm(VF-ISCSO), and adaptive coverage-aware deployment based on deep reinforcement learning(ACDRL) algorithms, the coverage rate of this algorithm increases by 21.5%, 15.6%, 12.3%, and 5.2%, respectively. The HVF-MSIWOA algorithm shows significant advantages in redundancy rate, movement distance, and robustness.

Key words: structural monitoring, heterogeneous wireless sensor network, whale optimization algorithm, virtual force algorithm, deployment optimization