Battery Modeling

The use of mobile devices is often limited by the capacity of the employed batteries. The battery lifetime determines how long one can use a device. Battery modeling can help to predict, and possibly extend this lifetime. Many different battery models have been developed over the years. However, with these models one can only compute lifetimes for specific discharge profiles, and not for workloads in general. In this paper, we give an overview of the different battery models that are available, and evaluate these models in their suitability to combine them with a workload model to create a more powerful battery model. \u

Energy-Efficient Streaming Using Non-volatile Memory

The disk and the DRAM in a typical mobile system consume a significant fraction (up to 30%) of the total system energy. To save on storage energy, the DRAM should be small and the disk should be spun down for long periods of time. We show that this can be achieved for predominantly streaming workloads by connecting the disk to the DRAM via a large non-volatile memory (NVM). We refer to this as the NVM-based architecture (NVMBA); the conventional architecture with only a DRAM and a disk is referred to as DRAMBA. The NVM in the NVMBA acts as a traffic reshaper from the disk to the DRAM. The total system costs are balanced, since the cost increase due to adding the NVM is compensated by the decrease in DRAM cost. We analyze the energy saving of NVMBA, with NAND flash memory serving as NVM, relative to DRAMBA with respect to (1) the streaming demand, (2) the disk form factor, (3) the best-effort provision, and (4) the stream location on the disk. We present a worst-case analysis of the reliability of the disk drive and the flash memory, and show that a small flash capacity is sufficient to operate the system over a year at negligible cost. Disk lifetime is superior to flash, so that is of no concern

Stochastic Learning Feedback Hybrid Automata for Power Management in Embedded Systems

In this paper we show that stochastic learning automata based feedback control switching strategy can be used for dynamic power management (DPM) employed at the system level. DPM strategies are usually incorporated at the operating systems of embedded devices to exploit multiple power states available in today\u27s ACPI compliant devices. The idea is to switch between power states depending on the device usage, and since device usage times are not deterministic, probabilistic techniques are often used to create stochastic strategies, or strategies that make decisions based on probabilities of device usage spans. Previous work (Irani et al., 2001) has shown how to approximate the probability distribution of device idle times and dynamically update them, and then use such knowledge in controlling power states. Here, we use stochastic learning automata (SLA) which interacts with the environment to update such probabilities, and then apply techniques similar to (Irani et al., 2001) to optimize power usage with minimal effect on response time for the devices

Stochastic Learning Feedback Hybrid Automata for Dynamic Power Management in Embedded Systems

Dynamic power management (DPM) refers to the strategies employed at system level to reduce energy expenditure (i.e. to prolong battery life) in embedded systems. The trade-off involved in DPM techniques is between the reductions of energy consumption and latency suffered by the tasks. Such trade-offs need to be decided at runtime, making DPM an on-line problem. We formulate DPM as a hybrid automaton control problem and integrate stochastic control. The control strategy is learnt dynamically using stochastic learning hybrid automata (SLHA) with feedback learning algorithms. Simulation-based experiments show the expediency of the feedback systems in stationary environments. Further experiments reveal that SLHA attains better trade-offs than several former predictive algorithms under certain trace data

Power management algorithms for IoT platforms

The Internet of Things (IoT) is a platform that connects various electronic systems such as home appliances, vehicles, and medical devices through wired or wireless communications. Without recharging the battery of sensors and mobile systems in IoT networks, their usage time is limited. In order to improve performance with finite battery energy, power management is used to conserve the energy dissipation of sensor networks and mobile systems. This dissertation addresses power management in two categories of systems within IoT: wireless sensor networks (WSNs) and electric vehicles (EVs). For power management in WSNs, this dissertation develops an algorithm using network coding (NC). When one sender transmits multiple packets to different receivers in a WSN, an NC algorithm reduces transmissions between the sender and the receivers by encoding many packets into one packet. Consequently, the total communication energy between the sender and the receivers is decreased. For further study about real energy gains generated by NC algorithms, we develop a wireless testbed by using mobile devices. Consequently, by varying different network variables such as transmission range of a sender and the number of receivers in the testbed network, we discover network conditions where communication energy saved by NC algorithms is increased. However, NC algorithms spend operational energy overheads for algorithm execution, encoding, and decoding. Hence, our research also shows the threshold conditions where the energy saved by the NC algorithms are larger than the energy overheads with consideration of communication variables or algorithm complexity in order to identify opportunities for energy savings. For power management of EVs, this dissertation develops an energy-efficient algorithm using neural networks which can be used for power management of EVs\u27 electronic control system. Power management saves energy consumption of the electronic control system by selectively activating electronic control units (ECUs) in the system. However, the energy savings generated by the power management could be less than the energy overheads used for the selective ECU activation and deactivation. Our algorithm experiences events where energy overheads were greater than energy savings and trains neural networks for the experienced events. The neural networks forecast energy-inefficient events and conserve energy overheads based on the predicted events. Our simulation study using real driving datasets shows that the algorithm improves the energy dissipation of the electronic control system by 5% to 7%