Instruction-Level Power Estimator for Sensor Networks

Joe, Hyunwoo; Park, Jae-Bok; Lim, Chaedeok; Woo, Duk-Kyun; Kim, Hyungshin

doi:10.4218/etrij.08.0106.0240

Cited by 13 publications

(5 citation statements)

References 17 publications

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“…Energy consumption simulation is basic part in several simulators such as SensorSim, senQ, SENSE, SENSIM amd J-Sim with detailed energy models and in some of them battery discharge rate and relaxation is considered as well [5]. Another software simulator, which is based on instruction level power simulation (IPEN) rather than state transition energy simulator [6], was proposed. Instruction level simulation is slow, compared to state-based simulation trading off the increased estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Energy consumption measurement and analysis in wireless sensor networks for biomedical applications

Karagiannis

Vouyioukas

Constantinou

2011

Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments

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Energy Consumption in Wireless Sensor Networks is a fundamental issue in terms of functionality and network lifetime. Minimization of energy consumption by applying optimization techniques enables pervasive computing especially in the field of biomedical engineering. A framework for energy consumption measurement and analysis is proposed which combines a theoretical approach, a simulation procedure based on a widely used software simulator and the validation by means of a high sensitivity measurement setup. Application driven profiling of energy consumption at the node level is a useful tool for optimal function of energy consumable node components in order to improve total energy efficiency.

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Section: Introductionmentioning

confidence: 99%

Energy consumption measurement and analysis in wireless sensor networks for biomedical applications

Karagiannis

Vouyioukas

Constantinou

2011

Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments

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“…However, in a large number of practical applications, this assumption does not hold, especially in the case when the power consumption of active (not passive) sensor element can be comparable to that of the communication subsystem. Similar problem was considered in reference [42], [43]. In order to cope with this challenge in an effective way, we propose to implement the power management concept into two levels, system and component level, respectively.…”

Section: Energy Profilementioning

confidence: 97%

Wireless sensor node with low-power sensing

et al. 2014

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Wireless sensor network consists of a large number of simple sensor nodes that collect information from external environment with sensors, then process the information, and communicate with other neighboring nodes in the network. Usually, sensor nodes operate with exhaustible batteries unattended. Since manual replacement or recharging of the batteries is not an easy, desirable or always possible task, the power consumption becomes a very important issue in the development of these networks. The total power consumption of a node is a result of all steps of the operation: sensing, data processing and radio transmission. In most published papers in literature it is assumed that the sensing subsystem consumes significantly less energy than a radio block. However, this assumption does not apply in numerous applications, especially in the case when power consumption of the sensing activity is comparably bigger than that of a radio. In that context, in this work we focus on the impact of the sensing hardware on the total power consumption of a sensor node. Firstly, we describe the structure of the sensor node architecture, identify its key energy consumption sources, and introduce an energy model for the sensing subsystem as a building block of a node. Secondly, with the aim to reduce energy consumption we investigate joint effectiveness of two common power-saving techniques in a specific sensor node: duty-cycling and power-gating. Duty-cycling is effective at the system level. It is used for switching a node between active and sleep mode (with the dutycycle factor of 1%, the reduction of in dynamic energy consumption is achieved). Power-gating is used at the circuit level with the goal to decrease the power loss due to the leakage current (in our design, the reduction of dynamic and static energy consumption of off-chip sensor elements as constituents of sensing hardware within a node of is achieved). Compared to a sensor node architecture in which both energy saving techniques are omitted, the conducted MATLAB simulation results suggest that in total, thanks to involving duty-cycling and power-gating techniques, a three order of magnitude reduction for sensing activities in energy consumption can be achieved.

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“…NQEM is high-fidelity instruction-level simulator, and it can be extended to the power estimator [21]. It has greatly reduced the simulation overheads by using a discrete-event based SMPL simulation engine which contains an event queue to maintain conservative manner.…”

Section: Related Workmentioning

confidence: 99%

Enhancing the Simulation Speed of Sensor Network Applications by Asynchronization of Interrupt Service Routines

Joe

Woo

Kim

2013

Sensors

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Sensor network simulations require high fidelity and timing accuracy to be used as an implementation and evaluation tool. The cycle-accurate and instruction-level simulator is the known solution for these purposes. However, this type of simulation incurs a high computation cost since it has to model not only the instruction level behavior but also the synchronization between multiple sensors for their causality. This paper presents a novel technique that exploits asynchronous simulations of interrupt service routines (ISR). We can avoid the synchronization overheads when the interrupt service routines are simulated without preemption. If the causality errors occur, we devise a rollback procedure to restore the original synchronized simulation. This concept can be extended to any instruction-level sensor network simulator. Evaluation results show our method can enhance the simulation speed up to 52% in the case of our experiments. For applications with longer interrupt service routines and smaller number of preemptions, the speedup becomes greater. In addition, our simulator is 2 to 11 times faster than the well-known sensor network simulator.

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Instruction-Level Power Estimator for Sensor Networks

Cited by 13 publications

References 17 publications

Energy consumption measurement and analysis in wireless sensor networks for biomedical applications

Energy consumption measurement and analysis in wireless sensor networks for biomedical applications

Wireless sensor node with low-power sensing

Enhancing the Simulation Speed of Sensor Network Applications by Asynchronization of Interrupt Service Routines

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