Many-core embedded systems will feature an extremely dynamic workload distribution where massive applications arranged as an unpredictable sequence enter and leave the system at run-time. Efficient mapping strategy is required to allocate system resources to the incoming application. Noncontiguous mapping improves system throughput by utilizing disjoint nodes, however, the increasing communication distance and external congestion lead to high power consumption and network delay. This paper thus presents an enhanced noncontiguous dynamic mapping algorithm, aiming at decreasing interprocessor communication overhead and improving both network and application performance. Communication volumes are utilized to arrange the mapping order of tasks belong to the same application. Moreover, expanding parameter of each task is developed which directs the optimized mapping decision comparing to the current neighborhood and occupancy information. Experimental results show that our modified mapping algorithm Weighted-based Neighborhood Allocation (WeNA) makes considerable improvements on Average Weighted Manhattan Distance (8.06%) and network latency (9.8%) in comparison with the state-of-the-art algorithm.