Energy consumption and mobile data cost are two key factors affecting users' willingness to participate in crowdsensing tasks. While data-plan users are mostly concerned about the energy consumption, non-data-plan users are more sensitive to data transmission cost incurred. Traditional ways of data collection in mobile crowdsensing often go to two extremes: either uploading the sensed data online in real-time or fully offline after the whole sensing task is finished. In this paper, we propose effSense - a novel energy-efficient and cost-effective data uploading framework leveraging the delay-tolerant mechanisms. Specifically, effSense reduces the data cost of non-data-plan users by maximally offloading the data to Bluetooth/WiFi gateways or data-plan users encountered to relay the data to the server; it reduces energy consumption of data-plan users by uploading data in parallel with a call or using less-energy demand networks (e.g. Bluetooth). By leveraging the prediction of critical events such as user's future calls or encounters, effSense selects the optimal uploading scheme for both types of users. Our evaluation with MIT Reality Mining and Nodobo datasets show that effSense can save 55%~65% energy and 45%~50% data cost for the two types of users, respectively, compared with the traditional uploading schemes