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Active Networks paradigm integrated with distributed data fusion has the potential to significantly reduce energy dissipation in WSNs, where energy conservation is the most challenging issue. This work aims to minimize energy cost when distributed data fusion is deployed for Active Networks computing paradigm. An approximate solution with much less computational complexity than optimal solution is proposed for large networks called P2lace, which includes two phases, task graph partition and task graph placement. An extensive experimental evaluation compares approximate solution with optimal solution. The results show that our approximate solution is scalable to different task graph characteristics and network size and only causes little more transmission cost than optimal solution.