Within the last few years mobile phones have evolved from traditional communication devices to powerful computational platforms that are equipped with a variety of sensors. For instance, a modern phone comes with a barometer, a light sensor, a magnetic field sensor and a microphone. These sensors turn mobile phones into powerful networked sensor platforms that can be used for capturing sensor data. The captured data can be aggregated and analyzed to monitor environmental phenomena such as noise pollution, for instance. Using mobile phones for sensing is termed in the literature as Public Sensing (PS) and has recently attracted increasing interest in the research community. One big challenge of PS is the high energy consumption that it imposes on mobile devices. Running a PS system can quickly drain the battery of a device, which is a major concern that prevents people from participating in PS. To tackle this issue, this thesis provides energy efficiency algorithms for two of the most energy-intensive operations in PS. First, it introduces a novel query distribution approach that significantly reduces the communication energy of a device when receiving queries for sensor data from the infrastructure. This approach limits the set of devices that receive a sensing query, in contrast to existing approaches, which distribute queries to all available devices. As part of this contribution, a novel position update protocol is presented that increases the efficiency of existing protocols by sending updates opportunistically together with other messages. Secondly, this thesis presents an efficient position sensing approach that controls the positioning system of a mobile device such that the energy for positioning is minimized. While existing PS systems assume that a device's positioning system is always-on, this approach temporarily disables the positioning system of a device without reducing its sensing effectiveness.