Towards a Realistic Energy Model for Wireless Sensor Networks

Kellner, Simon; Pink, Mario; Meier, Detlev; Blaß, Erik-Oliver

doi:10.1109/wons.2008.4459362

Cited by 34 publications

(21 citation statements)

References 9 publications

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“…This categorizes into only two states for the microcontroller and the radio chip, respectively. Obviously, these do not considered all nodes for conceivable energy which stated in [8].…”

Section: Background and Related Workmentioning

confidence: 99%

Proficient Energy Consumption Aware Model in Wireless Sensor Network

Bhadoria

Tomar

Kang³

2014

IJMUE

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“…This categorizes into only two states for the microcontroller and the radio chip, respectively. Obviously, these do not considered all nodes for conceivable energy which stated in [8].…”

Section: Background and Related Workmentioning

confidence: 99%

Proficient Energy Consumption Aware Model in Wireless Sensor Network

Bhadoria

Tomar

Kang³

2014

IJMUE

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“…Kellner [19] also used a state machine to model hardware components in a sensor-network platform. The states are those that have distinguishable power draws, and they also assign a fixed amount of energy per transition.…”

Section: Related Workmentioning

confidence: 99%

On the Effectiveness of Energy Metering on Every Node

Martins

Gnawali

et al. 2013

2013 IEEE International Conference on Distributed Computing in Sensor Systems

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Making wireless sensor node platforms energyefficient is one of the major research thrusts in the sensor network community. Energy metering lies at the foundation of this research, either by providing direct measurements for profiling, or by serving as the base for the formulation and fitting of energyusage models. Most of the literature and tools, however, make their measurements on a very small subset of the node population, and usually at a single point in time, before deployment. In this paper we set out to evaluate the cost, in loss of precision, of not having constant and ubiquitous measurement. Through experiments on a 240-node sensor-network testbed, we find that the variations in energy consumption due to temperature change are small, and we establish a model between environmental temperature changes and power consumption of Quanto testbed motes. We also find that different nodes of the same kind can have up to 15% variation in power draw, suggesting a need to deploy instrumentation on a subset of nodes. We quantify the energy estimation error of different metering techniques and characterize the conditions in which the errors disappear. Overall, we find that a small number of measurements in time and across nodes is adequate for accurate estimation of network-wide energy use.

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“…Models based on finite state machines have been proposed in [8]. Although [11] discuss the use of Petri nets to model a single processor utilizing real-time scheduling, we have not found any literature that discusses the use of Petri nets to model energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Energy Modeling of Processors in Wireless Sensor Networks Based on Petri Nets

Shareef

Zhu

2008

2008 International Conference on Parallel Processing - Workshops

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Power minimization is a serious issue in wireless sensor networks to extend the lifetime and minimize costs. However, in MotivationsWireless sensor networks are becoming increasingly prevalent in a wide range of areas from surveillance [6] to monitoring temperature, humidity, and other environmental parameters [12,10].Wireless sensor network are usually comprised of nodes powered by batteries in locations where maintenance access may be difficult. Minimizing energy consumption in these networks would go a long ways toward extending the lifetime of the network and increasing the usability. However, in order to obtain a thorough understanding of the energy consumption characteristics in these networks, accurate modeling methods need to be developed.Although the primary energy consumption in wireless sensor networks is for communication [2], the processor also serves a key role and there is a need to examine the energy characteristics of the embedded processors of these sensor nodes. A sound method of modeling provides a stable platform upon which the energy characteristics of innovative technology can be analyzed. One example of this is the capability of processors to power down to a low power mode after some time of no activity, which we will refer to as the Power Down Threshold. However, once powered down, the processor requires time Power Up Delay to reach operating mode again.In this paper, we compare two different processor models: a Markov chain and a Petri net against software simulation. We show that the Petri net model is more accurate than the Markov chain. Section 2 introduces the use of both Markov models and Petri nets. Section 3 presents related work. Section 4 develops a model of a processor using a Markov model and a Petri net. Section 5 compares the results obtained by the simulation, Markov model, and Petri net, and Section 6 concludes this paper. Introduction to the use of Markov Models, Petri Nets, and Software SimulationMarkov models have long been used to model systems dealing with exponentially distributed arrival and service rates. Markov models are composed of event chains where the likelihood of a system being in a particular state is independent of being in any other state. We can see how this restriction limits the usefulness of this method. Petri nets, on the other hand, are far more useful than Markov models. Initially Petri nets were based on Markov models, but since then, features of Petri nets' have been extended over the years that has blurred these similarities. Unlike Markov models, Petri nets are a simulation based approach. Petri nets can be thought of as a directed graph of nodes and arcs which are called places and transitions respectively. With Markov models, closed form equations can be derived that can be used to provide analysis about the modeled systems. Petri nets require that the modeled system be simulated for extended periods of time so that the steady state probability of the system is reached. Computer program such as TimeNet 4.0 [13] are available that...

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Towards a Realistic Energy Model for Wireless Sensor Networks

Cited by 34 publications

References 9 publications

Proficient Energy Consumption Aware Model in Wireless Sensor Network

Proficient Energy Consumption Aware Model in Wireless Sensor Network

On the Effectiveness of Energy Metering on Every Node

Energy Modeling of Processors in Wireless Sensor Networks Based on Petri Nets

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