Purpose Waste management for end-of-life (EoL) smartphones is a growing problem due to their high turnover rate and concentration of toxic chemicals. The versatility of modern smartphones presents an interesting alternative waste management strategy: repurposing. This paper investigates the environmental impact of smartphone repurposing as compared to traditional refurbishing using Life Cycle Assessment (LCA). Methods A case study of repurposing was conducted by creating a smartphone "app" that replicates the functionality of an in-car parking meter. The environmental impacts of this prototype were quantified using waste management LCA methodology. Studied systems included three waste management options: traditional refurbishment, repurposing using battery power, and repurposing using a portable solar charger. The functional unit was defined as the EoL management of a used smartphone. Consequential system expansion was employed to account for secondary functions provided; avoided impacts from displaced primary products were included. Impacts were calculated in five impact categories. Break-even displacement rates were calculated and sensitivity to standby power consumption were assessed.Results and discussion LCA results showed that refurbishing creates the highest environmental impacts of the three reuse routes in every impact category except ODP. High break-even displacement rates suggest that this finding is robust within a reasonable range of primary cell phone displacement. The repurposed smartphone in-car parking meter had lower impacts than the primary production parking meter. Impacts for battery-powered devices were dominated by use-phase charging electricity, whereas solar-power impacts were concentrated in manufacturing. Repurposed phones using battery power had lower impacts than those using solar power, however, standby power sensitivity analysis revealed that solar power is preferred if the battery charger is left plugged-in more than 20 % of the use period. Conclusions Our analysis concludes that repurposing represents an environmentally preferable EoL option to refurbishing for used smartphones. The results suggest two generalizable findings. First, primary product displacement is a major factor affecting whether any EoL strategy is environmentally beneficial. The benefit depends not only on what is displaced, but also on how much displacement occurs; in general, repurposing allows freedom to target reuse opportunities with high "displacement potential." Second, the notion that solar power is preferable to batteries is not always correct; here, the rank-order is sensitive to assumptions about user behavior.Keywords Avoided burden . End of life . E-waste . Reuse . Smartphone . System expansion . Waste management LCA IntroductionMillions of smartphones reach the end of their lives each year, making their responsible management an urgent environmental goal. Cell phone e-waste will continue to be a growing
We present a framework based on Markov decision process to optimize software on mobile phones. Unlike previous approaches in literature that focus on energy optimization while meeting a specific task-related time constraint, we model the desired talk-time as an explicit user given parameter and formulate the optimization of resources such as battery-life on a mobile phone as a decision processes that maximizes a user specified application specific reward or utility metric while meeting the talk-time constraint. We propose efficient techniques to solve the optimization problem based on dynamic programming and illustrate how it can be used in the context of realistic applications such as WiFi radio power optimization and email synchronization. We present a design methodology to use the proposed technique and experimental results using the Android platform from Google running on the HTC mobile phone.
The advent of smart phones, along with the paradigm shift towards cloud-based services, presents new challenges to the cellular backbone infrastructure. Cisco predicts that mobile data traffic will double every year through 2014, with a CAGR of 108% from 2009 to 2014, reaching 3.6 exabytes per month. We propose to exploit the potential of smart phones in proximity cooperatively, using their resources to reduce the demand on the cellular infrastructure, through a decision framework called RACE (Resource Aware Collaborative Execution). RACE enables the use of other mobile devices in the promixity as mobile data relays. RACE is a Markov Decision Process (MDP) optimization framework that takes user profiles and user preferences to determine the degree of collaboration. Both centralized and decentralized policies are developed and validated through simulation using real mobile usage traces. We implemented a simple prototype on a network of HTC G1 phones running the Android 1.5 operating system to demonstrate the viability of the system.
We present an optimization framework for delay-tolerant data applications on mobile phones based on the Markov decision process (MDP). This process maximizes an application specific reward or utility metric, specified by the user, while still meeting a talktime constraint, under limited resources such as battery life. This approach is novel for two reasons. First, it is user profile driven, which means that the user's history is an input to help predict and reserve resources for future talk-time. It is also dynamic: an application will adapt its behavior to current phone conditions such as battery level or time before the next recharge period. We propose efficient techniques to solve the optimization problem based on dynamic programming and illustrate how it can be used to optimize realistic applications. We also present a heuristic based on the MDP framework that performs well and is highly scalable for multiple applications. This approach is demonstrated using two applications: Email and Twitter synchronization with different priorities. We present experimental results based on Google's Android platform running on an Android Develepor Phone 1 (HTC Dream) mobile phone.
Abstract-Smartphones offer sophisticated features (e.g., WiFi, GPS, etc.) that require significant energy and limit battery life. Offline smartphone power modeling with benchtop equipment is cumbersome for software developers and takes substantial time to perform on multiple devices. By running on the device itself, online modeling can be performed dynamically and is scalable to many different smartphones. Previous online modeling work used existing battery management unit (BMU) current sensors with a high internal sample rate (18.6 kHz), but very low (softwarereadable) output register update rates (0.28 Hz). We propose allowing the register update rate to be dynamically adjusted to decrease online modeling time and energy cost. In this work we consider the benefits and evaluate the trade-offs of this approach.
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