Minimum Variance Energy Allocation for a Solar-Powered Sensor System.
ABSTRACT Using solar power in wireless sensor networks (WSNs) requires adaptation to a highly varying energy supply. From an application’s
perspective, however, it is often preferred to operate at a constant quality level as opposed to changing application behavior
frequently. Reconciling the varying supply with the fixed demand requires good tools for predicting supply such that its average
is computed and demand is fixed accordingly. In this paper, we describe a probabilistic observation-based model for harvested
solar energy, which accounts for both long-term tendencies and temporary environmental conditions. Based on this model, we
develop a time-slot-based energy allocation scheme to use the periodically harvested solar energy optimally, while minimizing
the variance in energy allocation. Our algorithm is tested on both outdoor and indoor testbeds, demonstrating the efficacy
of the approach.
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Conference Proceeding: Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks[show abstract] [hide abstract]
ABSTRACT: Increasingly many wireless sensor network deployments are using harvested environmental energy to extend system lifetime. Because the temporal profiles of such energy sources exhibit great variability due to dynamic weather patterns, an important problem is designing an adaptive duty-cycling mechanism that allows sensor nodes to maintain their power supply at sufficient levels (energy neutral operation) by adapting to changing environmental conditions. Existing techniques to address this problem are minimally adaptive and assume a priori knowledge of the energy profile. While such approaches are reasonable in environments that exhibit low variance, we find that it is highly inefficient in more variable scenarios. We introduce a new technique for solving this problem based on results from adaptive control theory and show that we achieve better performance than previous approaches on a broader class of energy source data sets. Additionally, we include a tunable mechanism for reducing the variance of the node's duty cycle over time, which is an important feature in tasks such as event monitoring. We obtain reductions in variance as great as two-thirds without compromising task performance or ability to maintain energy neutral operation.Sensor, Mesh and Ad Hoc Communications and Networks, 2007. SECON '07. 4th Annual IEEE Communications Society Conference on; 07/2007
Conference Proceeding: Design, Modeling, and Capacity Planning for Micro-solar Power Sensor Networks[show abstract] [hide abstract]
ABSTRACT: This paper describes a systematic approach to building micro-solar power subsystems for wireless sensor network nodes. Our approach composes models of the basic pieces - solar panels, regulators, energy storage elements, and application loads - to appropriately select and size the components. We demonstrate our approach in the context of a microclimate monitoring project through the design of the node, micro-solar subsystem, and network, which is deployed in a challenging, deep forest setting. We evaluate our deployment by analyzing the effects of the range of solar profiles experienced across the network.Information Processing in Sensor Networks, 2008. IPSN '08. International Conference on; 05/2008
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ABSTRACT: 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.ACM Trans. Embedded Comput. Syst. 01/2007; 6.