Various types of sensors have been embedded in smartphones such that a mobile user can easily conduct some sensing tasks. The mobile users conducting the sensing task with their sensor- equipped smartphones have their own unique features, thus can be efficiently complementary to stationary sensors which are deployed at specific locations. In this paper, we consider a heterogenous sensor network composed of stationary sensors and mobile sensors (i.e., mobile users with sensorequipped smartphones), in which a key question is how the service provider selects the sensors to conduct the sensing task considering the heterogeneity of sensors in terms of location, mobility pattern, energy constraint, and sensing cost. We propose a greedy algorithm GSSA to reduce the computational complexity. Simulation results show the nice performance of the proposed algorithms compared with the optimal sensor selection algorithm using dynamic programming. In specific, the proposed GSSA improves the achieved social welfare by 32.3% and 35.6% with the time period T=20 for high mobility and low mobility patterns, respectively, compared with the random selection.