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Institute
Implicit Sleep Mode Determination in Power Management of Event-driven Deeply Embedded Systems
(2009)
Implicit Sleep Mode Determination in Power Management of Event-driven Deeply Embedded Systems
(2008)
The battery dictates the lifetime of many embedded systems, especially wireless sensor networks.This makes it necessary to deal with battery management.In this paper we present an approach for a battery management which enables a sensor node to reach a defined lifetime.The presented approach gives feedback to an energy manager if the current power consumption must be lowered or can be increased to reach the runtime goal.In contrast to other systems the battery is handled as black box to keep the system independent from the battery type and brand.First experiments yield promising results of this concept to reach a certain lifetime goal while maintaining a high application quality.
Energy is the crucial factor for the lifetime of wireless sensor networks. Nonlinear battery effects and nonuniform workload distribution can lead to early node failures. This makes it necessary to manage energy consumption. But to manage energy it is essential to know how much energy is spent by the system. Additionally, for a more fine-grained management it is necessary, to know where the energy is spent. This can be a complicated task, since nodes are not identical due to device variations and the consumption can change over time.
In this paper we present an online energy accounting approach which focuses on simplicity instead on fine granularity and timing accuracy. We argue that the efficacy of an energy accounting model depends more on the input consumption data than on exact timing, especially when the real consumption varies between nodes and in time. Results show that this approach is capable of correctly accounting the energy that nodes spend in scenarios with deviating environment conditions.
Energy and run time are mayor concerns in wireless sensor networks. Reliable information about the energy consumption is needed to be able to build a network and tune its application. In this paper we take a look on the energy consumption of the Texas Instruments eZ430-Chronos, an MSP430 based wireless sensor node, and compare it to the manufacturers datasheet. The measurements show how reliable these specifications are and which consequences should be taken.
Nodes within sensor networks often have tight bound goals for the lifetime while running from a non-renewable energy source. Variations within the hardware or induced by the software complicate the prediction of the energy consumption. Additionally, batteries are vulnerable to temperature and non-linear effects. To reach certain lifetime goals under these influences without sacrificing energy due to pessimistic estimations, online energy management is necessary. At Sensorcomm 2015 we presented policies to control the behavior of applications and devices using energy budgets. This paper is an extended version which adds further details and the evaluation of the proposed dynamic energy management in a real-world scenario.
In landscapes with heterogeneous vegetation structure, interception and throughfall patterns produce spatiotemporal
variability of soil moisture. This variability is important for eco-hydrological processes, in particular on
small spatial scales up to the catchment scale. Throughfall depends on vegetation structure, whereas vegetation
development is presumably co-determined by the spatio-temporal distribution of throughfall itself. In addition
to vegetation structure, meteorological factors like wind speed and rainfall intensity also have an impact on
throughfall.
The objective of this study is to quantify the influence of vegetation structure and meteorological variables
on spatial (and in the long run the temporal) variability of throughfall. For that purpose, we developed an
approach combining field methods, image analysis and multivariate statistics. The 6-ha constructed catchment
‚Hühnerwasser‘ (aka Chicken Creek, southern Brandenburg, Germany) offers ideal conditions for the investigation
of eco-hydrological feedback processes. After more than 10 years of development, vegetation structure on the
catchment is spatially heterogeneous and evolves through natural succession. Furthermore, complementary
meteorological data are available on-site.
Throughfall was measured using 50 tipping-bucket rain gauges, which are aligned along two transects in 0.5
and 1 m heights, covering the dominating vegetation types on the catchment (e.g., robinia, sallow thorn, reed,
reedgrass, herbs). The spatial distribution of vegetation structures around each measurement site was recorded
with hemispheric photographs, which were subsequently analyzed using image processing techniques. Two
weather stations provide reference values for precipitation and relevant meteorological variables for wind speed
and direction, air humidity, temperature and irradiation.
The amount and distribution of precipitation measured in scarcely vegetated areas of the catchment widely
correspond with values from the reference weather stations. Under dense vegetation, very heterogeneous values
were recorded, which can be explained by i) canopy interception, and ii) fetching effects. The results of this study
can serve as basis for interception models and may also contribute to complex eco-hydrological models.
Embedded systems, e.g. nodes within sensor networks, often have tight bound goals for lifetime while running from a not renewable energy source. Mostly batteries are used, which are vulnerable to temperature and non-linear effects. Additionally, variations within the hardware or induced by the software make the prediction of the available and consumed energy a complicated task. To reach certain lifetime goals under these influences, online energy management is necessary. For a fine-grained management on the level of individual sub-tasks, it is necessary to know where in the system the energy is consumed.
In this work, we extend our online energy accounting approach to enable online energy management. We present ways to control application and device behavior, and, thus, energy using energy budgets. First experiments yield promising results, reaching their lifetime goals while maintaining a high application quality.
Nodes within sensor networks often have tight bound goals for the lifetime while running from a non-renewable energy source. Variations within the hardware or induced by the software complicate the prediction of the energy consumption. Additionally, batteries are vulnerable to temperature and non-linear effects. To reach certain lifetime goals under these influences without sacrificing energy due to pessimistic estimations, online energy management is necessary. In this paper, we present policies to control the behavior of applications and devices using energy budgets. First experiments yield promising results, with nodes reaching their lifetime goals while maintaining a high application quality.