Hostile and harsh environments may preclude the possibility of manual redeployment of new sensor nodes, especially in the areas suffering from widespread damage and unbalanced node deployments. Distributed local relocations of currently deployed nodes is one promising solution to this problem. By using expected global node density and force-based movement algorithms inspired by the laws of nature, it is possible to address the aforementioned challenge. Force-based movement algorithms steer nodes towards their new locations based on the aggregation of exerted virtual forces on the node from their neighborhood. Some implicit assumptions about nodes' global status such as expected global node density are not realistic in dynamic and harsh environments. Thus, to conform to the uncertain nature and local interactions of nodes, a combination of radial-angular force fuzzy movement algorithms is suggested. The performance of the proposed model in terms of percentage of coverage, uniformity and average movement under three different boundary conditions are evaluated and compared with distributed self-spreading algorithms (DSSA). The results show that the simple fuzzy movement algorithm either outperforms or matches DSSA even if nodes don't benefit from expected global node density as in DSSA. © 2012 IEEE.