Although evolving mobile technologies bring millions of users closer to the vision of information anywhere-anytime, device battery depletions hamper the quality of experience to a great extent. We argue that the design of energy-efficient solutions starts by energy-awareness and propose EnergyBox, a tool that provides accurate and repeatable energy consumption studies for 3G and WiFi transmissions at the user end. We recognize that the energy consumption of data transmission is highly dependable on the traffic pattern, and provide the means for trace-based iterative packet-driven simulation to derive the operation states of wireless interfaces. The strength of EnergyBox is that it allows to modularly set the 3G network parameters specified at operator level, the adaptive power save mode mechanism for a WiFi device, and the different power levels of the operation states for different handheld devices. EnergyBox enables efficient energy consumption studies using real data, which complements the device-dependent laborious physical power measurements. Using real application transmission traces, we have validated EnergyBox showing an accuracy range of 94-99% for 3G and 93-99% for WiFi compared to the real measured energy consumption by a 3G modem and a smartphone with WiFi.

Although evolving mobile technologies bring millions of users closer to the vision of information anywhere-anytime, device battery depletions hamper the quality of experience to a great extent. The massive explosion of mobile applications with the ensuing data exchange over the cellular infrastructure is not only a blessing to the mobile user, but also has a price in terms of rapid discharge of the device battery. Wireless communication is a large contributor to the energy consumption. Thus, the current call for energy economy in mobile devices poses the challenge of reducing the energy consumption of wireless data transmissions at the user end by developing energy-efficient communication. This thesis addresses the energy efficiency of data transmission at the user end in the context of cellular networks. We argue that the design of energy-efficient solutions starts by energy awareness and propose EnergyBox, a parametrised tool that enables accurate and repeatable energy quantification at the user end using real data traffic traces as input. EnergyBox abstracts the underlying states for operation of the wireless interfaces and allows to estimate the energy consumption for different operator settings and device characteristics. Next, we devise an energy-efficient algorithm that schedules the packet transmissions at the user end based on the knowledge of the network parameters that impact the handset energy consumption. The solution focuses on the characteristics of a given traffic class with the lowest quality of service requirements. The cost of running the solution itself is studied showing that the proposed cross-layer scheduler uses a small amount of energy to significantly extend the battery lifetime at the cost of some added latency.  Finally, the benefit of employing EnergyBox to systematically study the different design choices that developers face with respect to data transmissions of applications is shown in the context of location sharing services and instant messaging applications. The results show that quantifying energy consumption of communication patterns, protocols, and data formats can aid the design of tailor-made solutions with a significantly smaller energy footprint.