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Heterogeneous multi-processor system-on-chip (MPSoC) smartphones are required to offer increasing performance and user quality-of-experience (QoE), despite comparatively slow advances in battery technology. Approaches to balance instantaneous power consumption, performance and QoE have been reported, but little research has considered how to perform longer-term budgeting of resources across a complete battery discharge cycle. Approaches that have considered this are oblivious to the daily variability in the user’s desired charging time-of-day (plug-in time), resulting in a failure to meet the user’s battery life expectations, or else an unnecessarily over-constrained QoE. This paper proposes QUAREM, an adaptive resource management approach in mobile MPSoC platforms that maximises QoE while meeting battery life expectations. The proposed approach utilises a model that learns and then predicts the dynamics of the energy usage pattern and plug-in times. Unlike state-of-the-art approaches, we maximise the QoE through the adaptive balancing of the battery life and the quality of service (QoS) for the duration of the battery discharge. Our model achieves a good degree of accuracy with a mean absolute percentage error of 3.47 % and 2.48 % for the energy demand and plug-in times respectively. Experimental evaluation on an off-the-shelf commercial smartphone shows that QUAREM achieves the expected battery life of the user within \(20-25\%\) energy demand variation with little or no QoE degradation.

As the Internet of Things grows, the number of wireless sensor networks deployed in close proximity will continue to increase. By nature, these networks are limited by the battery supply that determines their lifetime and system utility. To counter such a shortcoming, energy harvesting technologies have become increasingly investigated to provide a perpetual energy source; however, new problems arise as a result of their wide spatio-temporal variation. In this paper, we propose opportunistic energy trading, which enables otherwise independent networks to be sustained by sharing resources. Our goal is to provide a novel cooperation model based on negotiation to solve coordination conflicts between energy harvesting wireless sensor networks. Results show that networks are able to satisfy their loads when they agree to cooperate.

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