This paper presents an integrated power management unit （PMU） for a battery-operated wireless endoscopic system. This PMU is integrated with a baseband chip in standard 0.18μm CMOS technology,promising low cost, ea...This paper presents an integrated power management unit （PMU） for a battery-operated wireless endoscopic system. This PMU is integrated with a baseband chip in standard 0.18μm CMOS technology,promising low cost, ease in PCB design, and a minimum in system size. The optimized power supply architecture is derived from comparison. Circuits of sub blocks are presented in detail. As a result, only five small off-chip capacitances are required by PMU with an overall quiet current consumption of less than 100μA. Moreover,a digital calibration method is adopted to alleviate the effect of process variation. The achieved performance is also demonstrated with corresponding measurement results.展开更多
Dynamic power management （DPM） in wireless sensor nodes is a well-known technique for reducing idle energy consumption. DPM controls a node＇s operating mode by dynamically toggling the on/off status of its units ba...Dynamic power management （DPM） in wireless sensor nodes is a well-known technique for reducing idle energy consumption. DPM controls a node＇s operating mode by dynamically toggling the on/off status of its units based on predictions of event occurrences. However, since each mode change induces some overhead in its own right, guaranteeing DPM＇s eificiency is no mean feat in environments exhibiting non-determinism and uncertainty with unknown statistics. Our solution suite in this paper, collectively referred to as cognitive power management （CPM）, is a principled attempt toward enabling DPM in statistically unknown settings and gives two different analytical guarantees. Our first design is based on learning automata and guarantees better-than-pure-chance DPM in the face of non-stationary event processes. Our second solution caters tor an even more general setting in which event occurrences may take on an adversarial character. In this case, we formulate the interaction of an individual mote with its environment in terms of a repeated zero-sum game in which the node relies on a no-external-regret procedure to learn its mini-max strategies in an online fashion. We conduct numerical experiments to measure the performance of our schemes in terms of network lifetime and event loss percentage.展开更多
基金the National Natural Science Foundation of China(No.60475018)~~
文摘This paper presents an integrated power management unit （PMU） for a battery-operated wireless endoscopic system. This PMU is integrated with a baseband chip in standard 0.18μm CMOS technology,promising low cost, ease in PCB design, and a minimum in system size. The optimized power supply architecture is derived from comparison. Circuits of sub blocks are presented in detail. As a result, only five small off-chip capacitances are required by PMU with an overall quiet current consumption of less than 100μA. Moreover,a digital calibration method is adopted to alleviate the effect of process variation. The achieved performance is also demonstrated with corresponding measurement results.
文摘Dynamic power management （DPM） in wireless sensor nodes is a well-known technique for reducing idle energy consumption. DPM controls a node＇s operating mode by dynamically toggling the on/off status of its units based on predictions of event occurrences. However, since each mode change induces some overhead in its own right, guaranteeing DPM＇s eificiency is no mean feat in environments exhibiting non-determinism and uncertainty with unknown statistics. Our solution suite in this paper, collectively referred to as cognitive power management （CPM）, is a principled attempt toward enabling DPM in statistically unknown settings and gives two different analytical guarantees. Our first design is based on learning automata and guarantees better-than-pure-chance DPM in the face of non-stationary event processes. Our second solution caters tor an even more general setting in which event occurrences may take on an adversarial character. In this case, we formulate the interaction of an individual mote with its environment in terms of a repeated zero-sum game in which the node relies on a no-external-regret procedure to learn its mini-max strategies in an online fashion. We conduct numerical experiments to measure the performance of our schemes in terms of network lifetime and event loss percentage.
