Jason C. Hsu

Power management in energy harvesting sensor networks

Power management is an important concern in sensor networks, because a tethered energy infrastructure is usually not available and an obvious concern is to use the available battery energy efficiently. However, in some of the sensor networking applications, an additional facility is available to ameliorate the energy problem: harvesting energy from the environment. Certain considerations in using an energy harvesting source are fundamentally different from that in using a battery, because, rather than a limit on the maximum energy, it has a limit on the maximum rate at which the energy can be used. Further, the harvested energy availability typically varies with time in a nondeterministic manner. While a deterministic metric, such as residual battery, suffices to characterize the energy availability in the case of batteries, a more sophisticated characterization may be required for a harvesting source. Another issue that becomes important in networked systems with multiple harvesting nodes is that different nodes may have different harvesting opportunity. In a distributed application, the same end-user performance may be achieved using different workload allocations, and resultant energy consumptions at multiple nodes. In this case, it is important to align the workload allocation with the energy availability at the harvesting nodes. We consider the above issues in power management for energy-harvesting sensor networks. We develop abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues. We also develop distributed methods to efficiently use harvested energy and test these both in simulation and experimentally on an energy-harvesting sensor network, prototyped for this work.

Design considerations for solar energy harvesting wireless embedded systems

Sustainable operation of battery powered wireless embedded systems (such as sensor nodes) is a key challenge, and considerable research effort has been devoted to energy optimization of such systems. Environmental energy harvesting, in particular solar based, has emerged as a viable technique to supplement battery supplies. However, designing an efficient solar harvesting system to realize the potential benefits of energy harvesting requires an in-depth understanding of several factors. For example, solar energy supply is highly time varying and may not always be sufficient to power the embedded system. Harvesting components, such as solar panels, and energy storage elements, such as batteries or ultracapacitors, have different voltage-current characteristics, which must be matched to each other as well as the energy requirements of the system to maximize harvesting efficiency. Further, battery non-idealities, such as self-discharge and round trip efficiency, directly affect energy usage and storage decisions. The ability of the system to modulate its power consumption by selectively deactivating its sub-components also impacts the overall power management architecture. This paper describes key issues and tradeoffs which arise in the design of solar energy harvesting, wireless embedded systems and presents the design, implementation, and performance evaluation of Heliomote, our prototype that addresses several of these issues. Experimental results demonstrate that Heliomote, which behaves as a plug-in to the Berkeley/Crossbow motes and autonomously manages energy harvesting and storage, enables near-perpetual, harvesting aware operation of the sensor node.

Adaptive duty cycling for energy harvesting systems

Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks. In this paper, we present an adaptive duty cycling algorithm that allows energy harvesting sensor nodes to autonomously adjust their duty cycle according to the energy availability in the environment. The algorithm has three objectives, namely: (a) achieving energy neutral operation, i.e., energy consumption should not be more than the energy provided by the environment; (b) maximizing the system performance based on an application utility model subject to the above energy-neutrality constraint; and (c) adapting to the dynamics of the energy source at run-time. We present a model that enables harvesting sensor nodes to predict future energy opportunities based on historical data. We also derive an upper bound on the maximum achievable performance assuming perfect knowledge about the future behavior of the energy source. Our methods are evaluated using data gathered from a prototype solar energy harvesting platform and we show that our algorithm can utilize up to 58% more environmental energy compared to the case when harvesting-aware power management is not used

Harvesting aware power management for sensor networks

Energy harvesting offers a promising alternative to solve the sustainability limitations arising from battery size constraints in sensor networks. Several considerations in using an environmental energy source are fundamentally different from using batteries. Rather than a limit on the total energy, harvesting transducers impose a limit on the instantaneous power available. Further, environmental energy availability is often highly variable and a deterministic metric such as residual battery capacity is not available to characterize the energy source. The different nodes in a sensor network may also have different energy harvesting opportunities. Since the same end-user performance may be achieved using different workload allocations at multiple nodes, it is important to adapt the workload allocation to the spatio-temporal energy availability profile in order to enable energy-neutral operation of the network. This paper describes power management techniques for such energy harvesting sensor networks. Platform design considerations as well as power scaling techniques at the node-level and network-level are described

Heliomote: enabling long-lived sensor networks through solar energy harvesting

The crucial need for long-lived and autonomous operation has elevated power and energy consumption to primary optimization metrics during wireless sensor network design. While most work in the field of power management and low-power design has focused on optimizing the energy consumer (i.e., the sensor node, including its hardware, software, applications, and network protocols), very little work has targeted the energy supply system itself. A practical approach to alleviating the problem of limited battery resources in sensor nodes is the use of environmental energy harvesting. Solar energy harvesting, in particular, holds significant promise since photovoltaic conversion techniques are now mature enough to permit the development of cheap and small, yet reasonably efficient, solar panels. Our demonstration showcases our recent research in designing solar energy harvesting systems, as well as harvesting aware performance scaling algorithms and network protocols [1].

Cap-Weighted Portfolios are Sub-Optimal Portfolios

In this paper, we show that under a fairly innocuous assumption on price inefficiency, market capitalization weighted portfolios are sub-optimal. If market prices are more volatile than is warranted by changes in firm fundamentals, then cap-weighted portfolios do not capture the full premium commensurate their risk. The sub-optimality arises because cap-weighting tends to overweight stocks whose prices are high relative to their fundamentals and underweight stocks whose prices are low relative to their fundamentals. The size of the cap-weighted portfolio underperformance is increasing in the magnitude of price inefficiency and is roughly equal to the variance of the noise in prices. However, portfolios constructed from weights, which do not depend on prices, do not exhibit the same underperformance observed for cap-weighted portfolios. We illustrate this cap-weighting underperformance empirically by comparing returns from cap-weighted portfolio vs. non-cap-weighted portfolios with similar characteristics. We also derive testable implications from our model assumption and find empirical support.

A Survey of Alternative Equity Index Strategies

A number of quantitative investment strategies are offered to investors as passive equity indexes. We review methodologies behind the more popular ones and provide an integrated evaluation framework. In our comparison the strategies outperform their cap-weighted counterparts largely due to exposure to value and size factors. Given this insight, these strategies are similar to each other and one can be mimicked by combinations of others. Therefore, implementation cost should be an important evaluation criterion.

Risk Parity Portfolio vs. Other Asset Allocation Heuristic Portfolios

In this article, the authors conduct a horse race between representative risk parity portfolios and other asset allocation strategies, including equal weighting, minimum variance, mean–variance optimization, and the classic 60/40 equity/ bond portfolio. They find that the traditional risk parity portfolio construction does not consistently outperform (in terms of risk-adjusted return) equal weighting or a model pension fund portfolio anchored to the 60/40 equity/bond portfolio structure. However, it does significantly outperform such optimized allocation strategies as minimum variance and mean–variance efficient portfolios. Over the last 30 years, the Sharpe ratios of the risk parity and the equal-weighting portfolios have been much more stable across decade-long subperiods than either the 60/40 portfolio or the optimized portfolios. Although risk parity performs on par with equal weighting, it does provide better diversification in terms of risk allocation and thus warrants further consideration as an asset allocation strategy. The authors show, however, that the performance of the risk parity strategy can be highly dependent on the investment universe. Thus, to execute risk parity successfully, the careful selection of asset classes is critical, which, for the time being, remains an art rather than a formulaic exercise based on theory.

High-throughput screening of small molecules identifies hepcidin antagonists.

Anemia of inflammation (AI) is common in patients with infection, autoimmune diseases, cancer, and chronic kidney disease. Unless the underlying condition can be reversed, treatment options are limited to erythropoiesis-stimulating agents with or without intravenous iron therapy, modalities that are not always effective and can cause serious adverse effects. Hepcidin, the iron regulatory hormone, has been identified as a pathogenic factor in the development of AI. To explore new therapeutic options for AI and other iron-related disorders caused by hepcidin excess, we developed a cell-based screen to identify hepcidin antagonists. Of the 70,000 small molecules in the library, we identified 14 compounds that antagonized the hepcidin effect on ferroportin. One of these was fursultiamine, a Food and Drug Administration (FDA)–approved thiamine derivative. Fursultiamine directly interfered with hepcidin binding to its receptor, ferroportin, by blocking ferroportin C326 thiol residue essential for hepcidin binding. Consequently, fursultiamine prevented hepcidin-induced ferroportin ubiquitination, endocytosis, and degradation in vitro and allowed continuous cellular iron export despite the presence of hepcidin, with IC50 in the submicromolar range. Thiamine, the fursultiamine metabolite, and benfotiamine, another thiamine derivative, did not interfere with the effect of hepcidin on ferroportin. Other FDA-approved thiol-reactive compounds were at least 1000-fold less potent than fursultiamine in antagonizing hepcidin. In vivo, fursultiamine did not reproducibly antagonize the effect of hepcidin on serum iron, likely because of its rapid conversion to inactive metabolites. Fursultiamine is a unique antagonist of hepcidin in vitro that could serve as a template for the development of drug candidates that inhibit the hepcidin-ferroportin interaction.

The Surprising 'Alpha' from Malkiel's Monkey and Upside-Down Strategies

Recent index literature is replete with innovations that are based on quantitative strategies and predicated on sensible investment beliefs. Empirical studies confirm that these strategies deliver economically large and statistically significant excess returns over cap-weighted market benchmarks in nearly all regions and countries, over long periods of time. In this article, the authors show that inverting these portfolio-construction algorithms does not reverse the out-performance. Indeed, the upside-down strategies often outperform the originals. This paradoxical result is driven by the phenomenon that seemingly unrelated, non-value-based strategies and their inverted counterparts often have unintended and almost unavoidable value and small-cap tilts. Even Burt Malkiel’s legendary blindfolded monkey, throwing darts at the Wall Street Journal’s stock page, would produce a portfolio with a substantial value- and small-cap bias that would have historically outperformed the S&P 500. The value and small-cap tilts stem from the fact that non-price-based weighting schemes sever the link between a company’s share price and its weight in the portfolio. Clearly, the inverted strategy of a non-price-weighted strategy is still a non-price-weighted strategy, would con-sequently have a value and small-cap tilt, and would there-fore have outperformed historically.