Accurate predictions of surgical case lengths are useful for patient scheduling in hospitals. In pediatric hospitals, this prediction problem is particularly difficult. Predictions are typically provided by highly trained medical staff, but these predictions are not necessarily accurate. We present a novel decision support tool that detects when expert predictions are inaccurate so that these predictions can be re-evaluated. We explore several different algorithms. We provide methodological insights and suggest directions of future work.

In this paper, we consider a basic issue in wireless computing, where mobile devices (with relatively limited memory) fetch data (text, images, multimedia, etc.) from access points over wireless channels of fluctuating quality. When the channel quality is low, slow data downloads can contribute to application latency, and degradation of the user experience. To mitigate this issue, mobile devices can prefetch data during good quality channel periods preemptively, in anticipation of using them during low quality channel epochs. In other words, under favorable wireless channel conditions, the mobile terminal can prefetch data aggressively to reduce application latency when channel conditions have degraded. Considering channel fluctuations, memory constraints, and application latency, the issue at each point in time is whether to prefetch or not. A dynamic programming approach is taken here to determine the optimal prefetching policy. The latter is leveraged to develop a prefetching algorithm “Fetch-or-Not” (FON). We then design a randomized “Fetch-or-Not” (RFON) prefetching algorithm, which uses a randomized approximation of FON, thus lifting the need for online optimization and substantially reducing the computational complexity. Simulations are used to demonstrate that our low-complexity schemes perform well when compared to the optimal. In addition, our schemes outperform benchmark design techniques in realistic channel conditions.

In this paper, we develop a general-use autonomous control strategy for managing the trade-off between processing throughput and power consumption in data centers. This approach relies on the concept that the delay in completing computational tasks can be reduced at the cost of more power. The scheme's generality allows it to be applied at multiple hierarchical levels within a data center, or another system with similar architecture. In particular, we show that our scheme converges asynchronously to a unique solution. This property allows the control strategy to be implemented in a low-complexity, yet robust and scalable manner. These properties are particularly important when considering data center power control system architectures, which can involve a wide variety of distributed computing resources performing diverse tasks. The presented scheme is mostly decentralized, except for a single global power stress signal provided by a redundant central authority. Based on this power stress, computing resources independently and autonomously manage power consumption to optimally balance power versus delay.

We consider the problem of streaming media packets from a transmitter buffer to a receiver over a wireless channel, controlling the transmitter power. When each packet comes to the head-of-line (HOL) in the buffer, it is a given a deadline D, which is the maximum number of times it can attempt retransmission in order to get successfully transmitted to the receiver. If that fails to happen, the packet is dropped and the next in line packet becomes the HOL one. Cost is incurred in each time slot for keeping packets in the transmitter buffer, transmitting power, and dropping HOL packets exceeding their deadlines. We investigate how transmission power should be chosen efficiently given the remaining backlog and residual transmission attempts of the HOL packet, so as to minimize the overall cost to transmit the buffer content. We formulate the optimal power control problem and study properties of the optimal control, proving that it is monotone under certain conditions. We then develop an approximate power control, which increases logarithmically in the transmitter backlog, and is demonstrated to perform quite closely to the optimal one.

Inexpensive wireless communication and emerging energy storage technologies are creating a new future for the power grid. These tools are enabling a “smart grid” which can utilize novel control strategies, such as real-time pricing, to achieve efficiency, fairness, and stability. We propose a decentralized smart grid architecture which uses minimal communication to meet these objectives. Then, inspired by queueing models and distributed control in wireless networks, we develop an algorithm for allocating power which is sensitive to both delay as well as price. The delay sensitivity allows for load shifting while the price sensitivity is a natural consumer objective. We show that the algorithm quickly converges even when homes operate asynchronously which makes the architecture practical for implementation. In addition, we show convergence using techniques from lattice theory. Although similar ideas have been used in standard power control algorithms for wireless systems, we demonstrate that the underlying ideas are more general. This provides a theoretical contribution along with a practical contribution to smart grid power management systems.

Heating, ventilation, and air conditioning (HVAC) systems are an important target for efficiency improvements through new equipment and retrofitting because of their large energy footprint. One type of equipment that is common in homes and some offices is an electrical, single-stage heat pump air conditioner (AC). To study this setup, we have built the Berkeley Retrofitted and Inexpensive HVAC Testbed for Energy Efficiency (BRITE) platform. This platform allows us to actuate an AC unit that controls the room temperature of a computer laboratory on the Berkeley campus that is actively used by students, while sensors record room temperature and AC energy consumption. We build a mathematical model of the temperature dynamics of the room, and combining this model with statistical methods allows us to compute the heating load due to occupants and equipment using only a single temperature sensor. Next, we implement a control strategy that uses learning-based model-predictive control (MPC) to learn and compensate for the amount of heating due to occupancy as it varies throughout the day and year. Experiments on BRITE show that our techniques result in a 30%-70% reduction in energy consumption as compared to two-position control, while still maintaining a comfortable room temperature. The energy savings are due to our control scheme compensating for varying occupancy, while considering the transient and steady state electrical consumption of the AC. Our techniques can likely be generalized to other HVAC systems while still maintaining these energy saving features.

Epithelia are sheets of connected cells that are essential across the animal kingdom. Experimental observations suggest that the dynamical behavior of many single-layered epithelial tissues has strong analogies with that of specific mechanical systems, namely large networks consisting of point masses connected through spring-damper elements and undergoing the influence of active and dissipating forces. Based on this analogy, this work develops a modeling framework to enable the study of the mechanical properties and of the dynamic behavior of large epithelial cellular networks. The model is built first by creating a network topology that is extracted from the actual cellular geometry as obtained from experiments, then by associating a mechanical structure and dynamics to the network via spring-damper elements. This scalable approach enables running simulations of large network dynamics: the derived modeling framework in particular is predisposed to be tailored to study general dynamics (for example, morphogenesis) of various classes of single-layered epithelial cellular networks. In this contribution, we test the model on a case study of the dorsal epithelium of the Drosophila melanogaster embryo during early dorsal closure (and, less conspicuously, germband retraction).