Quantitative evaluation in embedded system design: predicting battery lifetime in mobile devices
Proceedings of the conference on Design, automation and test in Europe
Compositional Modeling and Minimization of Time-Inhomogeneous Markov Chains
HSCC '08 Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control
LTL Model Checking of Time-Inhomogeneous Markov Chains
ATVA '09 Proceedings of the 7th International Symposium on Automated Technology for Verification and Analysis
Dependability analysis of wireless sensor networks with active-sleep cycles and redundant nodes
Proceedings of the First Workshop on DYnamic Aspects in DEpendability Models for Fault-Tolerant Systems
Performability assessment by model checking of Markov reward models
Formal Methods in System Design
Dependable, efficient, scalable architecture for management of large-scale batteries
Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems
On the Numerical Analysis of Inhomogeneous Continuous-Time Markov Chains
INFORMS Journal on Computing
Evaluating wireless sensor node longevity through Markovian techniques
Computer Networks: The International Journal of Computer and Telecommunications Networking
Architecture-Driven reliability and energy optimization for complex embedded systems
QoSA'10 Proceedings of the 6th international conference on Quality of Software Architectures: research into Practice - Reality and Gaps
Lifetime improvement by battery scheduling
MMB'12/DFT'12 Proceedings of the 16th international GI/ITG conference on Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance
Wireless Body Area Network (WBAN) Design Techniques and Performance Evaluation
Journal of Medical Systems
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The usage of mobile devices like cell phones, navigation systems, or laptop computers, is limited by the lifetime of the included batteries. This lifetime depends naturally on the rate at which energy is consumed, however, it also depends on the usage pattern of the battery. Continuous drawing of a high current results in an excessive drop of residual capacity. However, during intervals with no or very small currents, batteries do recover to a certain extend. We model this complex behaviour with an inhomogeneousMarkov reward model, following the approach of the so-called Kinetic battery Model (KiBaM). The state-dependent reward rates thereby correspond to the power consumption of the attached device and to the available charge, respectively. We develop a tailored numerical algorithm for the computation of the distribution of the consumed energy and show how different workload patterns influence the overall lifetime of a battery.