Exploiting Energy Awareness in Mobile Communication

Abstract

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-e cient communication.This thesis addresses the energy e ciency of data transmission at the user end in the context of cellular networks. We argue that the design of energy-e cient 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 tra c traces as input. EnergyBox abstracts the underlying states for operation of the wireless interfaces and allows to estimate the energy consumption for di↵erent operator settings and device characteristics.Next, we devise an energy-e cient 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 tra c 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 di↵erent 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.

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