Quick and Automatic Selection of POMDP Implementations on Mobile Platform Based on Battery Consumption Estimation

Partially Observable Markov Decision Process (POMDP) is widely used to model sequential decision making process under uncertainty and incomplete knowledge of the environment. It requires strong computation capability and is thus usually deployed on powerful machine. However, as mobile platforms become more advanced and more popular, the potential has been studied to combine POMDP and mobile in order to provide a broader range of services. And yet a question comes with this trend: how should we implement POMDP on mobile platform so that we can take advantages of mobile features while at the same time avoid being restricted by mobile limitations, such as short battery life, weak CPU, unstable networking connection, and other limited resources. In response to the above question, we first point out that the cases vary by problem nature, accuracy requirements and mobile device models. Rather than pure mathematical analysis, our approach is to run experiments on a mobile device and concentrate on a more specific question: which POMDP implementation is the ``best'' for a particular problem on a particular kind of device. Second, we propose and justify a POMDP implementation criterion mainly based on battery consumption that quantifies ``goodness'' of POMDP implementations in terms of mobile battery depletion rate. Then, we present a mobile battery consumption model that translates CPU and WIFI usage into part of the battery depletion rate in order to greatly accelerate the experiment process. With our mobile battery consumption model, we combine a set of simple benchmark experiments with CPU and WIFI usage data from each POMDP implementation candidate to generate estimated battery depletion rates, as opposed to conducting hours of real battery experiments for each implementation individually. The final result is a ranking of POMDP implementations based on their estimated battery depletion rates. It serves as a guidance for on POMDP implementation selection for mobile developers. We develop a mobile software toolkit to automate the above process. Given basic POMDP problem specifications, a set of POMDP implementation candidates and a simple press on the ``start'' button, the toolkit automatically performs benchmark experiments on the target device on which it is installed, and records CPU and WIFI statistics for each POMDP implementation candidate. It then feeds the data to its embedded mobile battery consumption model and produces an estimated battery depletion rate for each candidate. Finally, the toolkit visualizes the ranking of POMDP implementations for mobile developers' reference. Evaluation is assessed through comparsion between the ranking from estimated battery depletion rate and that from real experimental battery depletion rate. We observe the same ranking out of both, which is also our expectation. What's more, the similarity between estimated battery depletion rate and experimental battery depletion rate measured by cosine-similarity is almost 0.999 where 1 indicates they are exactly the same

Performance Analysis of Battery Power Management Schemes in Wireless Mobile Devices

In this paper, we analyze the performance of battery power management schemes in wireless mobile devices using a queueing theory approach. We model the battery as a server with finite service capacity and data packets as customers to be served. With an intent to exploit the recharging capability of the battery when left idle, we allow the battery to go on intentional vacations during which the battery can recharge itself. The recharge thus built up can effectively increase the number of customers served (in other words, battery life can be extended). Such improved battery life performance would, however, come at the expense of increased packet delay performance. We quantify the battery life gain versus delay performance trade-off in this approach through analysis and simulations. By considering a continuous recharge model of the battery, we derive expressions for the number of customers served and the mean delay for an M=GI=1 queueing system without and with server vacations. We show that allowing intentional vacations during busy periods helps to increase battery life, and that this approach can be beneficial when applied on traffic of delay-tolerant applications. We also propose a packet delay constrained power saving algorithm that will exploit the recharge phenomenon when packet delay constraints are imposed. I