Accurate prediction of power consumption in sensor networks pdf

Accurate prediction of power consumption in sensor networks pdf
Fig. 8 shows the aggregate energy consumption. To examine closely the trade-off of using an additional sensor for adaptive sampling, we separate the total energy consumption into three components: radio, sensor, and processing power consumptions.
Directional communication with movement prediction in mobile wireless sensor networks Article (PDF Available) in Personal and Ubiquitous Computing 18(8) · December 2014 with 137 Reads
International Journal of Computer Science & Engineering Survey (IJCSES) Vol.1, No.1, August 2010 NEURAL NETWORK BASED ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS: A SURVEY Neda Enami1, Reza Askari Moghadam1, Kourosh Dadashtabar2 & Mojtaba Hoseini3 1
Wireless sensor network is the first choice to complete these types of tasks. Basically, information prediction scheme is an important feature in any sensor nodes. The efficiency of the sensor network can be improved to large extent with a suitable information prediction scheme. Previously, there were several efforts to resolve this problem, but their accuracy is decreased as the prediction
Prior art keywords energy consumption building sensing device future method Prior art date 2008-07-31 Legal status (The legal status is an assumption and is not a legal conclusion.
Accurate Prediction of Power Consumption in Sensor Networks. University of Tubingen, Germany In EmNetS 2005 Presented by Han. Outline. Goal Approach to build AEON Power evaluation of TinyOS Comparison with PowerTossim.
ables highly accurate, ultra-low power DNN accelerators (in the range of tens of milliwatts), making it feasible to deploy DNNs in power-constrained IoT and mobile devices.
Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a …
POWER CONSUMPTION PREDICTION IN WIRELESS SENSOR NETWORKS Mounir ACHIR and Laurent OUVRY Electronic and Information Technology Laboratory Atomic Energy Commission
To maximize the power saving in wireless sensor network, our adopted method achieves the accuracy of 60.28 and 59.2238 for prediction threshold of 0.01 for Milne Simpson and Adams-Bashforth-Moulton algorithms, respectively.
A Wireless Sensor Network for Smart Roadbeds and Intelligent Transportation Systems by Ara N. Knaian S.B., Electrical Science and Engineering (1999) Massachusetts Institute of Technology Submitted to the Department of Electrical Engineering and Com puter Science in Partial Fulfillment of the Requirements for the Degree of Master of Engineering in Electrical Engineering and Computer …

Modeling Predicting and Reducing Energy Consumption in

A review on stochastic approach for dynamic power
Round robin cycle for predictions in wireless sensor networks. In Intelligent Sensors, Sensor Networks and Information Processing Conference (2005), 253–258. In Intelligent Sensors, Sensor Networks and Information Processing Conference (2005), 253–258.
wireless sensor networks typically rely on indirect measurement methods, such as counting the number of transmitted packets or CPU duty cycles, which pro- vide limited accuracy as pointed out in [11].
Prediction-Based Object Tracking in Visual Sensor Networks power consumption, and accuracy have to be considered in the tracking of mobile objects [3, 7, 11]. When monitoring mobile objects, visual sensors cannot remain in an active mode until the object is out of the sensing range, in order to conserve energy [16, 19, 20]. To track objects, a set of visual sensors must be active before
communication protocols for sensors and sensor networks must minimize power consumption. The existing research on energy consumption of sensors is usually based on either theoretical models or computer simulations.
Specific Energy Consumption Prediction Method Based on Machine Tool Power Measurement * Guoyong Zhao, Shunt sensor, Power measurement, Specific energy consumption, Machining, BP neural network 1. Introduction The global energy crisis and climate warming situation become more and more serious [1]. Recently, the low carbon revolution aimed to high energy efficiency and low …
Techniques for Minimizing Power Consumption in Low Data-Rate Wireless Sensor Networks Sokwoo Rhee, Deva Seetharam and Sheng Liu Millennial Net 201 Broadway, Cambridge, MA – 02139.
Tracking of Moving Object in Wireless Sensor Network 8 consumption, an assumption is made that all the sensor nodes have same energy level.
However, the aforementioned apparatus is limited for reducing power consumption of sensor where the signal to be sampled is periodic in nature and so is the prediction. Some signal are non-periodic but predictable all the same (a trivial example is a linear function). For these non-periodic signals, the aforementioned apparatus fail.

Landsiedel, O., Wehrle, K., Götz, S.: Accurate prediction of power consumption in sensor networks. In: Proceedings of the Second IEEE Workshop on Embedded Networked Sensors (EmNetS-II), Sidney, Australia (May 2005) Google Scholar
A Study of Implanted and Wearable Body Sensor Networks Sana Ullah1, Henry Higgin2, M. Arif Siddiqui1, A Study of Implanted and Wearable Body Sensor Networks 465 induced episodes of myocardial ischemia and their time cannot be predicted [6]. The accurate prediction of these episodes improves the quality of life. Body Sensor Network (BSN) is a key technology to prevent the …
In this paper we performed a trade-off analysis of energy consumption vs. QoS gain in reliability, timeliness, and security for wireless sensor networks utilizing optimal sleep schedule with directional target prediction method to answer user queries. Finally, we applied our analysis results to the design of a OSS With DTP algorithm to identify and apply the best design parameter settings in
consumption in wireless sensor networks, most of the proposed approaches focus on reducing communication among nodes while maintaining some form of cooperation.
with each other, such as, Data Acquisition, Power Consumption, Wireless Sensor Networks, Radio and Mobile Communications, Data Analytic and Processing , Internet Technology. IoT takes its name from its wide spread applications from wearable fitness trackers to connected cars, spanning the industries of utilities, transportation, healthcare, con sumer electronics, and many others. The
Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol
Abstract: Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management.
FPGA Based Kalman Filter for Wireless Sensor Networks . Vikrant Vij* , Rajesh Mehra** *ME Student, Department of Electronics & Communication Engineering
power consumption at the decoder outweighs the transmitted power savings due to using ECC, then ECC would not be energy-efficient compared with an uncoded system. Previous research using ECC in wireless sensor networks focused primarily on longtime industry-standard

Energy Efficient Residual Energy Monitoring in Wireless Sensor Networks EDWARD CHAN1 and SONG HAN2 1Department of Computer Science, City University of Hong Kong, Kowloon,
The performance evaluation is based on the prediction accuracy, the achieved sensor node throughput, and the execution time. The latter is a good measure of the implementation complexity of the algorithm whereas the achieved throughput is a good measure of the effectiveness of the prediction policy when integrated in an actual sensor network. We also develop a new energy management …
Aeon: Accurate Prediction of Power Consumption in Sensor Networks Olaf Landsiedel Klaus Wehrle Protocol Engineering and Distributed Systems Group University of Tübingen, Germany firstname.lastname@uni-tuebingen.de Abstract— Due to limited resources, power consumption is a crucial characteristic of sensor node hardware and applications.
A Generic Structure for Modeling Time and Energy Consumption in Abstract Virtual Prototypes of Embedded Systems Florence Maraninch and Catherine Parent-Vigouroux and …
and the design of low power consumption communication protocols [3] One solution is clustering-based localized prediction[10], where a cluster head also a sensor node maintains a set of history data of each sensor node within a cluster. They expect the use of localized prediction techniques is highly energy efficient due to the reduced length of routing path for transmitting sensor data. On
PPT Accurate Prediction of Power Consumption in Sensor
UNIVERSITY OF CALIFORNIA Santa Barbara Modeling, Predicting and Reducing Energy Consumption in Resource Restricted Computers A Dissertation submitted in partial satisfaction
Power consumption is a critical consideration in a wireless sensor network. The limited amount of energy stored The limited amount of energy stored at each node must support multiple functions, including sensor operations, on-board signal processing, and
Article A prediction-error-based method for data transmission and damage detection in wireless sensor networks for structural health monitoring Umut Yildirim1, Onur Oguz2 and Nikola Bogdanovic3
Simulating the Power Consumption of Large-Scale Sensor Network Applications Victor Shnayder, Mark Hempstead, Bor-rong Chen, Geoff Werner Allen, and Matt Welsh Division of Engineering and Applied Sciences Harvard University {shnayder,mhempste,brchen,werner,mdw}@eecs.harvard.edu ABSTRACT Developing sensor network applications demands a new set of tools to aid … – 1999 chevy tracker repair manual pdf A novel target important research issues in wireless sensor networks tracking protocol using sensor networks was proposed by since it determines the lifetime of the sensor network TSAI et al [16] for mobile users. It was assumed that a deployed for the intended applications, such as mobile target may move in any way, so in all the ways environmental monitoring, area surveillance and target the
consumption and lifetime of wireless sensor networks. In general two main enabling techniques In general two main enabling techniques are identified i.e. duty cycling and data- driven approaches.
In this paper, we present AEON (Accurate Prediction of Power Consumption), a novel evaluation tool to quantitatively v predict energy consumption of sensor nodes and whole sensor networks. Our
Accurate Modeling and Prediction of Energy Availability in Energy Harvesting Real-Time Embedded Systems Jun Lu, Shaobo Liu, Qing Wu and Qinru Qiu Department of Electrical and Computer Engineering Binghamton University, State University of New York Binghamton, New York, USA {jlu5, sliu5, qwu, qqiu}@binghamton.edu Abstract — Energy availability is the primary subject that drives …
prediction period, characteristic of time series, and size of time series [2]. In this research, we are interest in time series analysis with the most popular method, that is, the Box and Jenkins method [3]. The result model of this method is quite accurate compared to other methods and can be applied to all types of data movement. There were two forecasting techniques that were used in this
The emerging technologies in low-power micro-sensors, actuators, embedded processors, and RF radios have facil- itated the deployment of large scale sensor networks.
as mobile applications usually require real-time, low power consumption and fully embeddable. As a result, there is much interest in research and development of dedicated hardware for deep neural networks (DNNs).
Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions. This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings.
for Wireless Sensor Networks Qingquan Zhang, Yu Gu, Tian He and Gerald E. Sobelman Abstract—Dynamic scheduling management in wireless sensor networks is one of the most challenging problems in long lifetime monitoring applications. In this paper, we propose and evaluate a novel data correlation-based stochastic scheduling algorithm, called Cscan. Our system architecture integrates …
1 Energy Conservation in Wireless Sensor Networks Giuseppe Anastasi Pervasive Computing & Networking Lab (PerLab) Dept. of Information Engineering, University of Pisa
2) Prediction Model Update: Once the sensor is in the re- sampling phase, the system will not only get precise readings but can also refresh the empirical model parameters.
Transmission power control using state estimation-based received signal strength prediction for energy efficiency in wireless sensor networks. Turkish Journal of Electrical Engineering and Computer Sciences , 25 (1), 591–604.
this basic approach for solar power prediction in Section V that adapt to seasonal variations in sunlight [9], [10], [20] or sudden changes in cloud cover [15].
Aeon: Accurate Prediction of Power Consumption in Sensor Networks Olaf Landsiedel Klaus Wehrle Protocol Engineering and Distributed Systems Group
@MISC{Landsiedel05accurateprediction, author = {Olaf Landsiedel and Klaus Wehrle and Stefan Götz}, title = {Accurate Prediction of Power Consumption in Sensor Networks}, year = {2005}} Energy consumption is a crucial characteristic of sensor networks and their applications as sensor nodes are
Towards Accurate Binary Convolutional Neural Network.
A tradeoff between accuracy of the prediction model and optimum choice of the nonlinear parameters is required for minimized power consumption. The implementation of time-series forecasting techniques in WSN is simple and lightweight since they provide considerable accuracy with simpler models [2] .
Energy model of sensor nodes in WSN factors are the size, cost and energy. Computational power and communication in general are considered to be of acceptable
sions, such as sensor sample rate, on overall application power consumption, and (5) describing how data produced by SPOT can be used to refine and o ptimize application power consumption.
Estimation of energy demand is used as an important tool for decision makers determining company strategies and policies. Apart from this, the fact that the actual consumption differs from the forecast is harmful for the economy of the company and even for the economy of the big scale.
Accurate Prediction of Power Consumption in Sensor

Modeling of Node Energy Consumption for Wireless Sensor
An Environment for Runtime Power Monitoring Of Wireless Sensor Network Piatforms AIeksandar Milenkovic, Power Consumption, Wireless Sensor Networks, Real-time Monitoring, Measurements Abstract-Wireless sensor networks emerged as a key technology for prolonged, unsupervised monitoring in a wide spectrum of applications, from biological and environmental to civil and military. The sensor
Wireless sensor networks (WSNs) demand low power and energy efficient hardware and software. Dynamic Power Management (DPM) technique reduces the maximum possible active states of a wireless sensor node by controlling the switching of the low power manageable components in power down or off states.
Data fusion techniques reduce total network traffic in a wireless sensor network, since data fusion can integrate multiple raw data sets into one fused data set. However, the security or assurance of the data requires more processing power and is an important issue. Increasing the security of the fusion data increases factors such as power consumption, and packet overhead. Therefore any data
Wireless sensor networks (WSNs) present a new way of data-stream sources in which data is received periodically from different sensors; resulting in a large amount of data accumulated over a short period. WSNs have limited resources in which fine-detailed data streams lead to an exhaustive energy consumption of the sensor nodes. In this paper
A Distributed Approach for Prediction in Sensor Networks Sabine M. McConnell and David B. Skillicorn School of Computing Queen’s University fmcconnell,skillg@cs.queensu.ca
A key problem in sensor networks is to decide which sensors to query when, in order to obtain the most useful information (e.g., for performing accurate prediction), subject to con-
Object Tracking Sensor Networks impacts on the energy consumption in OTSN: Object tracking sensor networks (OTSNs) have • Network workload is related to the number of mo- widespread use in applications such as security surveil- bile objects inside the network, which has an im- lance and wildlife habitat monitoring [18]. OTSNs pact on the overall energy dissipation of OTSNs face severe energy
localized prediction for power efficient object tracking in sensor networks by harsh-484388 localized prediction for power efficient object tracking in sensor networks Search Search
Minerva Enabling Low-Power Highly-Accurate Deep Neural

US9642086B1 Method and system for reducing power
Accurate Prediction of Power Consumption in Sensor Networks Olaf Landsiedel, Klaus Wehrle, Stefan Gotz¨ Protocol Engineering and Distributed Systems Group
Sensors 2008, 8 2605 1. Introduction Wireless sensor networks (WSNs) have become a growing research field.
Energies Free Full-Text An Application of Non-Linear

Prediction-based Strategies for Energy Saving in Object

Analyzing Mobile Application Software Power Consumption
2001 chevy tracker service manual pdf – NEURAL NETWORK BASED ENERGY EFFICIENCY IN WIRELESS SENSOR
Trust based data prediction aggregation and

US20100025483A1 Sensor-Based Occupancy and Behavior

Dual Prediction-Based Reporting for Object Tracking Sensor

Optimal Sleep Schedule And Directional Target Prediction
Cloudy Computing Leveraging Weather Forecasts in Energy

Energy Efficient Residual Energy Monitoring in Wireless Sensor Networks EDWARD CHAN1 and SONG HAN2 1Department of Computer Science, City University of Hong Kong, Kowloon,
An Environment for Runtime Power Monitoring Of Wireless Sensor Network Piatforms AIeksandar Milenkovic, Power Consumption, Wireless Sensor Networks, Real-time Monitoring, Measurements Abstract-Wireless sensor networks emerged as a key technology for prolonged, unsupervised monitoring in a wide spectrum of applications, from biological and environmental to civil and military. The sensor
as mobile applications usually require real-time, low power consumption and fully embeddable. As a result, there is much interest in research and development of dedicated hardware for deep neural networks (DNNs).
consumption and lifetime of wireless sensor networks. In general two main enabling techniques In general two main enabling techniques are identified i.e. duty cycling and data- driven approaches.
Wireless sensor network is the first choice to complete these types of tasks. Basically, information prediction scheme is an important feature in any sensor nodes. The efficiency of the sensor network can be improved to large extent with a suitable information prediction scheme. Previously, there were several efforts to resolve this problem, but their accuracy is decreased as the prediction
Data fusion techniques reduce total network traffic in a wireless sensor network, since data fusion can integrate multiple raw data sets into one fused data set. However, the security or assurance of the data requires more processing power and is an important issue. Increasing the security of the fusion data increases factors such as power consumption, and packet overhead. Therefore any data
with each other, such as, Data Acquisition, Power Consumption, Wireless Sensor Networks, Radio and Mobile Communications, Data Analytic and Processing , Internet Technology. IoT takes its name from its wide spread applications from wearable fitness trackers to connected cars, spanning the industries of utilities, transportation, healthcare, con sumer electronics, and many others. The
POWER CONSUMPTION PREDICTION IN WIRELESS SENSOR NETWORKS Mounir ACHIR and Laurent OUVRY Electronic and Information Technology Laboratory Atomic Energy Commission
Power consumption is a critical consideration in a wireless sensor network. The limited amount of energy stored The limited amount of energy stored at each node must support multiple functions, including sensor operations, on-board signal processing, and
A Study of Implanted and Wearable Body Sensor Networks Sana Ullah1, Henry Higgin2, M. Arif Siddiqui1, A Study of Implanted and Wearable Body Sensor Networks 465 induced episodes of myocardial ischemia and their time cannot be predicted [6]. The accurate prediction of these episodes improves the quality of life. Body Sensor Network (BSN) is a key technology to prevent the …
Article A prediction-error-based method for data transmission and damage detection in wireless sensor networks for structural health monitoring Umut Yildirim1, Onur Oguz2 and Nikola Bogdanovic3
consumption in wireless sensor networks, most of the proposed approaches focus on reducing communication among nodes while maintaining some form of cooperation.
ables highly accurate, ultra-low power DNN accelerators (in the range of tens of milliwatts), making it feasible to deploy DNNs in power-constrained IoT and mobile devices.

ENERGY MODEL OF SENSOR NODES IN WSN afahc.ro
Aeon Accurate Prediction of Power Consumption in Sensor

localized prediction for power efficient object tracking in sensor networks by harsh-484388 localized prediction for power efficient object tracking in sensor networks Search Search
Accurate Prediction of Power Consumption in Sensor Networks. University of Tubingen, Germany In EmNetS 2005 Presented by Han. Outline. Goal Approach to build AEON Power evaluation of TinyOS Comparison with PowerTossim.
Sensors 2008, 8 2605 1. Introduction Wireless sensor networks (WSNs) have become a growing research field.
A novel target important research issues in wireless sensor networks tracking protocol using sensor networks was proposed by since it determines the lifetime of the sensor network TSAI et al [16] for mobile users. It was assumed that a deployed for the intended applications, such as mobile target may move in any way, so in all the ways environmental monitoring, area surveillance and target the
Landsiedel, O., Wehrle, K., Götz, S.: Accurate prediction of power consumption in sensor networks. In: Proceedings of the Second IEEE Workshop on Embedded Networked Sensors (EmNetS-II), Sidney, Australia (May 2005) Google Scholar
In this paper, we present AEON (Accurate Prediction of Power Consumption), a novel evaluation tool to quantitatively v predict energy consumption of sensor nodes and whole sensor networks. Our

ESTIMATION OF UNBALANCE COST DUE TO DEMAND PREDICTION
Cloudy Computing Leveraging Weather Forecasts in Energy

The performance evaluation is based on the prediction accuracy, the achieved sensor node throughput, and the execution time. The latter is a good measure of the implementation complexity of the algorithm whereas the achieved throughput is a good measure of the effectiveness of the prediction policy when integrated in an actual sensor network. We also develop a new energy management …
Specific Energy Consumption Prediction Method Based on Machine Tool Power Measurement * Guoyong Zhao, Shunt sensor, Power measurement, Specific energy consumption, Machining, BP neural network 1. Introduction The global energy crisis and climate warming situation become more and more serious [1]. Recently, the low carbon revolution aimed to high energy efficiency and low …
ables highly accurate, ultra-low power DNN accelerators (in the range of tens of milliwatts), making it feasible to deploy DNNs in power-constrained IoT and mobile devices.
FPGA Based Kalman Filter for Wireless Sensor Networks . Vikrant Vij* , Rajesh Mehra** *ME Student, Department of Electronics & Communication Engineering
Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol
prediction period, characteristic of time series, and size of time series [2]. In this research, we are interest in time series analysis with the most popular method, that is, the Box and Jenkins method [3]. The result model of this method is quite accurate compared to other methods and can be applied to all types of data movement. There were two forecasting techniques that were used in this
Wireless sensor networks (WSNs) present a new way of data-stream sources in which data is received periodically from different sensors; resulting in a large amount of data accumulated over a short period. WSNs have limited resources in which fine-detailed data streams lead to an exhaustive energy consumption of the sensor nodes. In this paper
Techniques for Minimizing Power Consumption in Low Data-Rate Wireless Sensor Networks Sokwoo Rhee, Deva Seetharam and Sheng Liu Millennial Net 201 Broadway, Cambridge, MA – 02139.
Transmission power control using state estimation-based received signal strength prediction for energy efficiency in wireless sensor networks. Turkish Journal of Electrical Engineering and Computer Sciences , 25 (1), 591–604.
Accurate Modeling and Prediction of Energy Availability in Energy Harvesting Real-Time Embedded Systems Jun Lu, Shaobo Liu, Qing Wu and Qinru Qiu Department of Electrical and Computer Engineering Binghamton University, State University of New York Binghamton, New York, USA {jlu5, sliu5, qwu, qqiu}@binghamton.edu Abstract — Energy availability is the primary subject that drives …
Wireless sensor networks (WSNs) demand low power and energy efficient hardware and software. Dynamic Power Management (DPM) technique reduces the maximum possible active states of a wireless sensor node by controlling the switching of the low power manageable components in power down or off states.
1 Energy Conservation in Wireless Sensor Networks Giuseppe Anastasi Pervasive Computing & Networking Lab (PerLab) Dept. of Information Engineering, University of Pisa

PPT Accurate Prediction of Power Consumption in Sensor
A Survey About Prediction-Based Data Reduction in Wireless

A Study of Implanted and Wearable Body Sensor Networks Sana Ullah1, Henry Higgin2, M. Arif Siddiqui1, A Study of Implanted and Wearable Body Sensor Networks 465 induced episodes of myocardial ischemia and their time cannot be predicted [6]. The accurate prediction of these episodes improves the quality of life. Body Sensor Network (BSN) is a key technology to prevent the …
Directional communication with movement prediction in mobile wireless sensor networks Article (PDF Available) in Personal and Ubiquitous Computing 18(8) · December 2014 with 137 Reads
A tradeoff between accuracy of the prediction model and optimum choice of the nonlinear parameters is required for minimized power consumption. The implementation of time-series forecasting techniques in WSN is simple and lightweight since they provide considerable accuracy with simpler models [2] .
as mobile applications usually require real-time, low power consumption and fully embeddable. As a result, there is much interest in research and development of dedicated hardware for deep neural networks (DNNs).
Specific Energy Consumption Prediction Method Based on Machine Tool Power Measurement * Guoyong Zhao, Shunt sensor, Power measurement, Specific energy consumption, Machining, BP neural network 1. Introduction The global energy crisis and climate warming situation become more and more serious [1]. Recently, the low carbon revolution aimed to high energy efficiency and low …
Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a …
A Distributed Approach for Prediction in Sensor Networks Sabine M. McConnell and David B. Skillicorn School of Computing Queen’s University fmcconnell,skillg@cs.queensu.ca
this basic approach for solar power prediction in Section V that adapt to seasonal variations in sunlight [9], [10], [20] or sudden changes in cloud cover [15].
communication protocols for sensors and sensor networks must minimize power consumption. The existing research on energy consumption of sensors is usually based on either theoretical models or computer simulations.
consumption and lifetime of wireless sensor networks. In general two main enabling techniques In general two main enabling techniques are identified i.e. duty cycling and data- driven approaches.
ables highly accurate, ultra-low power DNN accelerators (in the range of tens of milliwatts), making it feasible to deploy DNNs in power-constrained IoT and mobile devices.
Landsiedel, O., Wehrle, K., Götz, S.: Accurate prediction of power consumption in sensor networks. In: Proceedings of the Second IEEE Workshop on Embedded Networked Sensors (EmNetS-II), Sidney, Australia (May 2005) Google Scholar
Power consumption is a critical consideration in a wireless sensor network. The limited amount of energy stored The limited amount of energy stored at each node must support multiple functions, including sensor operations, on-board signal processing, and
However, the aforementioned apparatus is limited for reducing power consumption of sensor where the signal to be sampled is periodic in nature and so is the prediction. Some signal are non-periodic but predictable all the same (a trivial example is a linear function). For these non-periodic signals, the aforementioned apparatus fail.
@MISC{Landsiedel05accurateprediction, author = {Olaf Landsiedel and Klaus Wehrle and Stefan Götz}, title = {Accurate Prediction of Power Consumption in Sensor Networks}, year = {2005}} Energy consumption is a crucial characteristic of sensor networks and their applications as sensor nodes are

Trust based data prediction aggregation and
Simulating the Power Consumption of Large-Scale Sensor

power consumption at the decoder outweighs the transmitted power savings due to using ECC, then ECC would not be energy-efficient compared with an uncoded system. Previous research using ECC in wireless sensor networks focused primarily on longtime industry-standard
Specific Energy Consumption Prediction Method Based on Machine Tool Power Measurement * Guoyong Zhao, Shunt sensor, Power measurement, Specific energy consumption, Machining, BP neural network 1. Introduction The global energy crisis and climate warming situation become more and more serious [1]. Recently, the low carbon revolution aimed to high energy efficiency and low …
communication protocols for sensors and sensor networks must minimize power consumption. The existing research on energy consumption of sensors is usually based on either theoretical models or computer simulations.
Energy model of sensor nodes in WSN factors are the size, cost and energy. Computational power and communication in general are considered to be of acceptable
Wireless sensor networks (WSNs) present a new way of data-stream sources in which data is received periodically from different sensors; resulting in a large amount of data accumulated over a short period. WSNs have limited resources in which fine-detailed data streams lead to an exhaustive energy consumption of the sensor nodes. In this paper
The emerging technologies in low-power micro-sensors, actuators, embedded processors, and RF radios have facil- itated the deployment of large scale sensor networks.
A Generic Structure for Modeling Time and Energy Consumption in Abstract Virtual Prototypes of Embedded Systems Florence Maraninch and Catherine Parent-Vigouroux and …

Energy-efficient Organization of Wireless Sensor Networks
Cscan A Correlation-based Scheduling Algorithm for

Simulating the Power Consumption of Large-Scale Sensor Network Applications Victor Shnayder, Mark Hempstead, Bor-rong Chen, Geoff Werner Allen, and Matt Welsh Division of Engineering and Applied Sciences Harvard University {shnayder,mhempste,brchen,werner,mdw}@eecs.harvard.edu ABSTRACT Developing sensor network applications demands a new set of tools to aid …
Aeon: Accurate Prediction of Power Consumption in Sensor Networks Olaf Landsiedel Klaus Wehrle Protocol Engineering and Distributed Systems Group
Accurate Prediction of Power Consumption in Sensor Networks Olaf Landsiedel, Klaus Wehrle, Stefan Gotz¨ Protocol Engineering and Distributed Systems Group
wireless sensor networks typically rely on indirect measurement methods, such as counting the number of transmitted packets or CPU duty cycles, which pro- vide limited accuracy as pointed out in [11].
To maximize the power saving in wireless sensor network, our adopted method achieves the accuracy of 60.28 and 59.2238 for prediction threshold of 0.01 for Milne Simpson and Adams-Bashforth-Moulton algorithms, respectively.
Estimation of energy demand is used as an important tool for decision makers determining company strategies and policies. Apart from this, the fact that the actual consumption differs from the forecast is harmful for the economy of the company and even for the economy of the big scale.
Energy Efficient Residual Energy Monitoring in Wireless Sensor Networks EDWARD CHAN1 and SONG HAN2 1Department of Computer Science, City University of Hong Kong, Kowloon,
Accurate Modeling and Prediction of Energy Availability in Energy Harvesting Real-Time Embedded Systems Jun Lu, Shaobo Liu, Qing Wu and Qinru Qiu Department of Electrical and Computer Engineering Binghamton University, State University of New York Binghamton, New York, USA {jlu5, sliu5, qwu, qqiu}@binghamton.edu Abstract — Energy availability is the primary subject that drives …
@MISC{Landsiedel05accurateprediction, author = {Olaf Landsiedel and Klaus Wehrle and Stefan Götz}, title = {Accurate Prediction of Power Consumption in Sensor Networks}, year = {2005}} Energy consumption is a crucial characteristic of sensor networks and their applications as sensor nodes are
as mobile applications usually require real-time, low power consumption and fully embeddable. As a result, there is much interest in research and development of dedicated hardware for deep neural networks (DNNs).
Round robin cycle for predictions in wireless sensor networks. In Intelligent Sensors, Sensor Networks and Information Processing Conference (2005), 253–258. In Intelligent Sensors, Sensor Networks and Information Processing Conference (2005), 253–258.
A Generic Structure for Modeling Time and Energy Consumption in Abstract Virtual Prototypes of Embedded Systems Florence Maraninch and Catherine Parent-Vigouroux and …
Sensors 2008, 8 2605 1. Introduction Wireless sensor networks (WSNs) have become a growing research field.
Directional communication with movement prediction in mobile wireless sensor networks Article (PDF Available) in Personal and Ubiquitous Computing 18(8) · December 2014 with 137 Reads

POWER CONSUMPTION PREDICTION IN WIRELESS SENSOR
Energy-efficient Organization of Wireless Sensor Networks

UNIVERSITY OF CALIFORNIA Santa Barbara Modeling, Predicting and Reducing Energy Consumption in Resource Restricted Computers A Dissertation submitted in partial satisfaction
A novel target important research issues in wireless sensor networks tracking protocol using sensor networks was proposed by since it determines the lifetime of the sensor network TSAI et al [16] for mobile users. It was assumed that a deployed for the intended applications, such as mobile target may move in any way, so in all the ways environmental monitoring, area surveillance and target the
International Journal of Computer Science & Engineering Survey (IJCSES) Vol.1, No.1, August 2010 NEURAL NETWORK BASED ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS: A SURVEY Neda Enami1, Reza Askari Moghadam1, Kourosh Dadashtabar2 & Mojtaba Hoseini3 1
Energy model of sensor nodes in WSN factors are the size, cost and energy. Computational power and communication in general are considered to be of acceptable
1 Energy Conservation in Wireless Sensor Networks Giuseppe Anastasi Pervasive Computing & Networking Lab (PerLab) Dept. of Information Engineering, University of Pisa

(PDF) Directional communication with movement prediction
A review on stochastic approach for dynamic power

Aeon: Accurate Prediction of Power Consumption in Sensor Networks Olaf Landsiedel Klaus Wehrle Protocol Engineering and Distributed Systems Group
POWER CONSUMPTION PREDICTION IN WIRELESS SENSOR NETWORKS Mounir ACHIR and Laurent OUVRY Electronic and Information Technology Laboratory Atomic Energy Commission
Prior art keywords energy consumption building sensing device future method Prior art date 2008-07-31 Legal status (The legal status is an assumption and is not a legal conclusion.
sions, such as sensor sample rate, on overall application power consumption, and (5) describing how data produced by SPOT can be used to refine and o ptimize application power consumption.
Specific Energy Consumption Prediction Method Based on Machine Tool Power Measurement * Guoyong Zhao, Shunt sensor, Power measurement, Specific energy consumption, Machining, BP neural network 1. Introduction The global energy crisis and climate warming situation become more and more serious [1]. Recently, the low carbon revolution aimed to high energy efficiency and low …
2) Prediction Model Update: Once the sensor is in the re- sampling phase, the system will not only get precise readings but can also refresh the empirical model parameters.
Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions. This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings.
However, the aforementioned apparatus is limited for reducing power consumption of sensor where the signal to be sampled is periodic in nature and so is the prediction. Some signal are non-periodic but predictable all the same (a trivial example is a linear function). For these non-periodic signals, the aforementioned apparatus fail.
The performance evaluation is based on the prediction accuracy, the achieved sensor node throughput, and the execution time. The latter is a good measure of the implementation complexity of the algorithm whereas the achieved throughput is a good measure of the effectiveness of the prediction policy when integrated in an actual sensor network. We also develop a new energy management …
@MISC{Landsiedel05accurateprediction, author = {Olaf Landsiedel and Klaus Wehrle and Stefan Götz}, title = {Accurate Prediction of Power Consumption in Sensor Networks}, year = {2005}} Energy consumption is a crucial characteristic of sensor networks and their applications as sensor nodes are

Cscan A Correlation-based Scheduling Algorithm for
Accurate Prediction of Power Consumption in Sensor

Sensors 2008, 8 2605 1. Introduction Wireless sensor networks (WSNs) have become a growing research field.
An Environment for Runtime Power Monitoring Of Wireless Sensor Network Piatforms AIeksandar Milenkovic, Power Consumption, Wireless Sensor Networks, Real-time Monitoring, Measurements Abstract-Wireless sensor networks emerged as a key technology for prolonged, unsupervised monitoring in a wide spectrum of applications, from biological and environmental to civil and military. The sensor
To maximize the power saving in wireless sensor network, our adopted method achieves the accuracy of 60.28 and 59.2238 for prediction threshold of 0.01 for Milne Simpson and Adams-Bashforth-Moulton algorithms, respectively.
Article A prediction-error-based method for data transmission and damage detection in wireless sensor networks for structural health monitoring Umut Yildirim1, Onur Oguz2 and Nikola Bogdanovic3
prediction period, characteristic of time series, and size of time series [2]. In this research, we are interest in time series analysis with the most popular method, that is, the Box and Jenkins method [3]. The result model of this method is quite accurate compared to other methods and can be applied to all types of data movement. There were two forecasting techniques that were used in this
and the design of low power consumption communication protocols [3] One solution is clustering-based localized prediction[10], where a cluster head also a sensor node maintains a set of history data of each sensor node within a cluster. They expect the use of localized prediction techniques is highly energy efficient due to the reduced length of routing path for transmitting sensor data. On
Energy Efficient Residual Energy Monitoring in Wireless Sensor Networks EDWARD CHAN1 and SONG HAN2 1Department of Computer Science, City University of Hong Kong, Kowloon,
Specific Energy Consumption Prediction Method Based on Machine Tool Power Measurement * Guoyong Zhao, Shunt sensor, Power measurement, Specific energy consumption, Machining, BP neural network 1. Introduction The global energy crisis and climate warming situation become more and more serious [1]. Recently, the low carbon revolution aimed to high energy efficiency and low …
In this paper, we present AEON (Accurate Prediction of Power Consumption), a novel evaluation tool to quantitatively v predict energy consumption of sensor nodes and whole sensor networks. Our
FPGA Based Kalman Filter for Wireless Sensor Networks . Vikrant Vij* , Rajesh Mehra** *ME Student, Department of Electronics & Communication Engineering
Round robin cycle for predictions in wireless sensor networks. In Intelligent Sensors, Sensor Networks and Information Processing Conference (2005), 253–258. In Intelligent Sensors, Sensor Networks and Information Processing Conference (2005), 253–258.
Accurate Prediction of Power Consumption in Sensor Networks. University of Tubingen, Germany In EmNetS 2005 Presented by Han. Outline. Goal Approach to build AEON Power evaluation of TinyOS Comparison with PowerTossim.
Directional communication with movement prediction in mobile wireless sensor networks Article (PDF Available) in Personal and Ubiquitous Computing 18(8) · December 2014 with 137 Reads
A Wireless Sensor Network for Smart Roadbeds and Intelligent Transportation Systems by Ara N. Knaian S.B., Electrical Science and Engineering (1999) Massachusetts Institute of Technology Submitted to the Department of Electrical Engineering and Com puter Science in Partial Fulfillment of the Requirements for the Degree of Master of Engineering in Electrical Engineering and Computer …

Data Collection in Wireless Sensor Networks for Noise
A Study of Implanted and Wearable Body Sensor Networks

Wireless sensor networks (WSNs) demand low power and energy efficient hardware and software. Dynamic Power Management (DPM) technique reduces the maximum possible active states of a wireless sensor node by controlling the switching of the low power manageable components in power down or off states.
In this paper we performed a trade-off analysis of energy consumption vs. QoS gain in reliability, timeliness, and security for wireless sensor networks utilizing optimal sleep schedule with directional target prediction method to answer user queries. Finally, we applied our analysis results to the design of a OSS With DTP algorithm to identify and apply the best design parameter settings in
A Study of Implanted and Wearable Body Sensor Networks Sana Ullah1, Henry Higgin2, M. Arif Siddiqui1, A Study of Implanted and Wearable Body Sensor Networks 465 induced episodes of myocardial ischemia and their time cannot be predicted [6]. The accurate prediction of these episodes improves the quality of life. Body Sensor Network (BSN) is a key technology to prevent the …
Power consumption is a critical consideration in a wireless sensor network. The limited amount of energy stored The limited amount of energy stored at each node must support multiple functions, including sensor operations, on-board signal processing, and
In this paper, we present AEON (Accurate Prediction of Power Consumption), a novel evaluation tool to quantitatively v predict energy consumption of sensor nodes and whole sensor networks. Our
A Wireless Sensor Network for Smart Roadbeds and Intelligent Transportation Systems by Ara N. Knaian S.B., Electrical Science and Engineering (1999) Massachusetts Institute of Technology Submitted to the Department of Electrical Engineering and Com puter Science in Partial Fulfillment of the Requirements for the Degree of Master of Engineering in Electrical Engineering and Computer …
communication protocols for sensors and sensor networks must minimize power consumption. The existing research on energy consumption of sensors is usually based on either theoretical models or computer simulations.
consumption in wireless sensor networks, most of the proposed approaches focus on reducing communication among nodes while maintaining some form of cooperation.
Wireless sensor networks (WSNs) present a new way of data-stream sources in which data is received periodically from different sensors; resulting in a large amount of data accumulated over a short period. WSNs have limited resources in which fine-detailed data streams lead to an exhaustive energy consumption of the sensor nodes. In this paper

An Environment for Runtime Power Monitoring Of Wireless
Detection Classification and Tracking in Distributed

Accurate Modeling and Prediction of Energy Availability in Energy Harvesting Real-Time Embedded Systems Jun Lu, Shaobo Liu, Qing Wu and Qinru Qiu Department of Electrical and Computer Engineering Binghamton University, State University of New York Binghamton, New York, USA {jlu5, sliu5, qwu, qqiu}@binghamton.edu Abstract — Energy availability is the primary subject that drives …
To maximize the power saving in wireless sensor network, our adopted method achieves the accuracy of 60.28 and 59.2238 for prediction threshold of 0.01 for Milne Simpson and Adams-Bashforth-Moulton algorithms, respectively.
Estimation of energy demand is used as an important tool for decision makers determining company strategies and policies. Apart from this, the fact that the actual consumption differs from the forecast is harmful for the economy of the company and even for the economy of the big scale.
However, the aforementioned apparatus is limited for reducing power consumption of sensor where the signal to be sampled is periodic in nature and so is the prediction. Some signal are non-periodic but predictable all the same (a trivial example is a linear function). For these non-periodic signals, the aforementioned apparatus fail.
In this paper we performed a trade-off analysis of energy consumption vs. QoS gain in reliability, timeliness, and security for wireless sensor networks utilizing optimal sleep schedule with directional target prediction method to answer user queries. Finally, we applied our analysis results to the design of a OSS With DTP algorithm to identify and apply the best design parameter settings in
Round robin cycle for predictions in wireless sensor networks. In Intelligent Sensors, Sensor Networks and Information Processing Conference (2005), 253–258. In Intelligent Sensors, Sensor Networks and Information Processing Conference (2005), 253–258.
Aeon: Accurate Prediction of Power Consumption in Sensor Networks Olaf Landsiedel Klaus Wehrle Protocol Engineering and Distributed Systems Group
Tracking of Moving Object in Wireless Sensor Network 8 consumption, an assumption is made that all the sensor nodes have same energy level.
Landsiedel, O., Wehrle, K., Götz, S.: Accurate prediction of power consumption in sensor networks. In: Proceedings of the Second IEEE Workshop on Embedded Networked Sensors (EmNetS-II), Sidney, Australia (May 2005) Google Scholar
Energy model of sensor nodes in WSN factors are the size, cost and energy. Computational power and communication in general are considered to be of acceptable
Fig. 8 shows the aggregate energy consumption. To examine closely the trade-off of using an additional sensor for adaptive sampling, we separate the total energy consumption into three components: radio, sensor, and processing power consumptions.
In this paper, we present AEON (Accurate Prediction of Power Consumption), a novel evaluation tool to quantitatively v predict energy consumption of sensor nodes and whole sensor networks. Our

Modeling of Node Energy Consumption for Wireless Sensor
(HL)18. Localized Prediction for Power Efficient Object

Data fusion techniques reduce total network traffic in a wireless sensor network, since data fusion can integrate multiple raw data sets into one fused data set. However, the security or assurance of the data requires more processing power and is an important issue. Increasing the security of the fusion data increases factors such as power consumption, and packet overhead. Therefore any data
ables highly accurate, ultra-low power DNN accelerators (in the range of tens of milliwatts), making it feasible to deploy DNNs in power-constrained IoT and mobile devices.
Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions. This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings.
Prediction-Based Object Tracking in Visual Sensor Networks power consumption, and accuracy have to be considered in the tracking of mobile objects [3, 7, 11]. When monitoring mobile objects, visual sensors cannot remain in an active mode until the object is out of the sensing range, in order to conserve energy [16, 19, 20]. To track objects, a set of visual sensors must be active before
Fig. 8 shows the aggregate energy consumption. To examine closely the trade-off of using an additional sensor for adaptive sampling, we separate the total energy consumption into three components: radio, sensor, and processing power consumptions.

Energies Free Full-Text An Application of Non-Linear
Minerva Enabling Low-Power Highly-Accurate Deep Neural

A novel target important research issues in wireless sensor networks tracking protocol using sensor networks was proposed by since it determines the lifetime of the sensor network TSAI et al [16] for mobile users. It was assumed that a deployed for the intended applications, such as mobile target may move in any way, so in all the ways environmental monitoring, area surveillance and target the
prediction period, characteristic of time series, and size of time series [2]. In this research, we are interest in time series analysis with the most popular method, that is, the Box and Jenkins method [3]. The result model of this method is quite accurate compared to other methods and can be applied to all types of data movement. There were two forecasting techniques that were used in this
Accurate Prediction of Power Consumption in Sensor Networks. University of Tubingen, Germany In EmNetS 2005 Presented by Han. Outline. Goal Approach to build AEON Power evaluation of TinyOS Comparison with PowerTossim.
UNIVERSITY OF CALIFORNIA Santa Barbara Modeling, Predicting and Reducing Energy Consumption in Resource Restricted Computers A Dissertation submitted in partial satisfaction
Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions. This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings.
localized prediction for power efficient object tracking in sensor networks by harsh-484388 localized prediction for power efficient object tracking in sensor networks Search Search

A Survey About Prediction-Based Data Reduction in Wireless
Detection Classification and Tracking in Distributed

Wireless sensor networks (WSNs) present a new way of data-stream sources in which data is received periodically from different sensors; resulting in a large amount of data accumulated over a short period. WSNs have limited resources in which fine-detailed data streams lead to an exhaustive energy consumption of the sensor nodes. In this paper
Energy Efficient Residual Energy Monitoring in Wireless Sensor Networks EDWARD CHAN1 and SONG HAN2 1Department of Computer Science, City University of Hong Kong, Kowloon,
Abstract: Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management.
this basic approach for solar power prediction in Section V that adapt to seasonal variations in sunlight [9], [10], [20] or sudden changes in cloud cover [15].
Accurate Modeling and Prediction of Energy Availability in Energy Harvesting Real-Time Embedded Systems Jun Lu, Shaobo Liu, Qing Wu and Qinru Qiu Department of Electrical and Computer Engineering Binghamton University, State University of New York Binghamton, New York, USA {jlu5, sliu5, qwu, qqiu}@binghamton.edu Abstract — Energy availability is the primary subject that drives …
Accurate Prediction of Power Consumption in Sensor Networks. University of Tubingen, Germany In EmNetS 2005 Presented by Han. Outline. Goal Approach to build AEON Power evaluation of TinyOS Comparison with PowerTossim.
and the design of low power consumption communication protocols [3] One solution is clustering-based localized prediction[10], where a cluster head also a sensor node maintains a set of history data of each sensor node within a cluster. They expect the use of localized prediction techniques is highly energy efficient due to the reduced length of routing path for transmitting sensor data. On

Analyzing Mobile Application Software Power Consumption
US20100025483A1 Sensor-Based Occupancy and Behavior

Wireless sensor network is the first choice to complete these types of tasks. Basically, information prediction scheme is an important feature in any sensor nodes. The efficiency of the sensor network can be improved to large extent with a suitable information prediction scheme. Previously, there were several efforts to resolve this problem, but their accuracy is decreased as the prediction
Power consumption is a critical consideration in a wireless sensor network. The limited amount of energy stored The limited amount of energy stored at each node must support multiple functions, including sensor operations, on-board signal processing, and
Abstract: Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management.
A novel target important research issues in wireless sensor networks tracking protocol using sensor networks was proposed by since it determines the lifetime of the sensor network TSAI et al [16] for mobile users. It was assumed that a deployed for the intended applications, such as mobile target may move in any way, so in all the ways environmental monitoring, area surveillance and target the
UNIVERSITY OF CALIFORNIA Santa Barbara Modeling, Predicting and Reducing Energy Consumption in Resource Restricted Computers A Dissertation submitted in partial satisfaction
International Journal of Computer Science & Engineering Survey (IJCSES) Vol.1, No.1, August 2010 NEURAL NETWORK BASED ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS: A SURVEY Neda Enami1, Reza Askari Moghadam1, Kourosh Dadashtabar2 & Mojtaba Hoseini3 1
Wireless sensor networks (WSNs) present a new way of data-stream sources in which data is received periodically from different sensors; resulting in a large amount of data accumulated over a short period. WSNs have limited resources in which fine-detailed data streams lead to an exhaustive energy consumption of the sensor nodes. In this paper
consumption in wireless sensor networks, most of the proposed approaches focus on reducing communication among nodes while maintaining some form of cooperation.
Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol
Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a …
Object Tracking Sensor Networks impacts on the energy consumption in OTSN: Object tracking sensor networks (OTSNs) have • Network workload is related to the number of mo- widespread use in applications such as security surveil- bile objects inside the network, which has an im- lance and wildlife habitat monitoring [18]. OTSNs pact on the overall energy dissipation of OTSNs face severe energy

Prediction-based Strategies for Energy Saving in Object
Minerva Enabling Low-Power Highly-Accurate Deep Neural

with each other, such as, Data Acquisition, Power Consumption, Wireless Sensor Networks, Radio and Mobile Communications, Data Analytic and Processing , Internet Technology. IoT takes its name from its wide spread applications from wearable fitness trackers to connected cars, spanning the industries of utilities, transportation, healthcare, con sumer electronics, and many others. The
To maximize the power saving in wireless sensor network, our adopted method achieves the accuracy of 60.28 and 59.2238 for prediction threshold of 0.01 for Milne Simpson and Adams-Bashforth-Moulton algorithms, respectively.
communication protocols for sensors and sensor networks must minimize power consumption. The existing research on energy consumption of sensors is usually based on either theoretical models or computer simulations.
Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a …
Power consumption is a critical consideration in a wireless sensor network. The limited amount of energy stored The limited amount of energy stored at each node must support multiple functions, including sensor operations, on-board signal processing, and
prediction period, characteristic of time series, and size of time series [2]. In this research, we are interest in time series analysis with the most popular method, that is, the Box and Jenkins method [3]. The result model of this method is quite accurate compared to other methods and can be applied to all types of data movement. There were two forecasting techniques that were used in this
Energy Efficient Residual Energy Monitoring in Wireless Sensor Networks EDWARD CHAN1 and SONG HAN2 1Department of Computer Science, City University of Hong Kong, Kowloon,
A novel target important research issues in wireless sensor networks tracking protocol using sensor networks was proposed by since it determines the lifetime of the sensor network TSAI et al [16] for mobile users. It was assumed that a deployed for the intended applications, such as mobile target may move in any way, so in all the ways environmental monitoring, area surveillance and target the
International Journal of Computer Science & Engineering Survey (IJCSES) Vol.1, No.1, August 2010 NEURAL NETWORK BASED ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS: A SURVEY Neda Enami1, Reza Askari Moghadam1, Kourosh Dadashtabar2 & Mojtaba Hoseini3 1

Minerva Enabling Low-Power Highly-Accurate Deep Neural
Journal of Vibration and Control A prediction-error-based

Specific Energy Consumption Prediction Method Based on Machine Tool Power Measurement * Guoyong Zhao, Shunt sensor, Power measurement, Specific energy consumption, Machining, BP neural network 1. Introduction The global energy crisis and climate warming situation become more and more serious [1]. Recently, the low carbon revolution aimed to high energy efficiency and low …
Wireless sensor networks (WSNs) present a new way of data-stream sources in which data is received periodically from different sensors; resulting in a large amount of data accumulated over a short period. WSNs have limited resources in which fine-detailed data streams lead to an exhaustive energy consumption of the sensor nodes. In this paper
A key problem in sensor networks is to decide which sensors to query when, in order to obtain the most useful information (e.g., for performing accurate prediction), subject to con-
Prior art keywords energy consumption building sensing device future method Prior art date 2008-07-31 Legal status (The legal status is an assumption and is not a legal conclusion.

Modeling of Node Energy Consumption for Wireless Sensor
A Study of Efficient Power Consumption Wireless

wireless sensor networks typically rely on indirect measurement methods, such as counting the number of transmitted packets or CPU duty cycles, which pro- vide limited accuracy as pointed out in [11].
Wireless sensor networks (WSNs) present a new way of data-stream sources in which data is received periodically from different sensors; resulting in a large amount of data accumulated over a short period. WSNs have limited resources in which fine-detailed data streams lead to an exhaustive energy consumption of the sensor nodes. In this paper
To maximize the power saving in wireless sensor network, our adopted method achieves the accuracy of 60.28 and 59.2238 for prediction threshold of 0.01 for Milne Simpson and Adams-Bashforth-Moulton algorithms, respectively.
this basic approach for solar power prediction in Section V that adapt to seasonal variations in sunlight [9], [10], [20] or sudden changes in cloud cover [15].

Accurate Modeling and Prediction of Energy Availability in
A review on stochastic approach for dynamic power

localized prediction for power efficient object tracking in sensor networks by harsh-484388 localized prediction for power efficient object tracking in sensor networks Search Search
Transmission power control using state estimation-based received signal strength prediction for energy efficiency in wireless sensor networks. Turkish Journal of Electrical Engineering and Computer Sciences , 25 (1), 591–604.
Accurate Prediction of Power Consumption in Sensor Networks. University of Tubingen, Germany In EmNetS 2005 Presented by Han. Outline. Goal Approach to build AEON Power evaluation of TinyOS Comparison with PowerTossim.
A Wireless Sensor Network for Smart Roadbeds and Intelligent Transportation Systems by Ara N. Knaian S.B., Electrical Science and Engineering (1999) Massachusetts Institute of Technology Submitted to the Department of Electrical Engineering and Com puter Science in Partial Fulfillment of the Requirements for the Degree of Master of Engineering in Electrical Engineering and Computer …
To maximize the power saving in wireless sensor network, our adopted method achieves the accuracy of 60.28 and 59.2238 for prediction threshold of 0.01 for Milne Simpson and Adams-Bashforth-Moulton algorithms, respectively.

Minerva Enabling Low-Power Highly-Accurate Deep Neural
Optimal Sleep Schedule And Directional Target Prediction

power consumption at the decoder outweighs the transmitted power savings due to using ECC, then ECC would not be energy-efficient compared with an uncoded system. Previous research using ECC in wireless sensor networks focused primarily on longtime industry-standard
Article A prediction-error-based method for data transmission and damage detection in wireless sensor networks for structural health monitoring Umut Yildirim1, Onur Oguz2 and Nikola Bogdanovic3
The performance evaluation is based on the prediction accuracy, the achieved sensor node throughput, and the execution time. The latter is a good measure of the implementation complexity of the algorithm whereas the achieved throughput is a good measure of the effectiveness of the prediction policy when integrated in an actual sensor network. We also develop a new energy management …
sions, such as sensor sample rate, on overall application power consumption, and (5) describing how data produced by SPOT can be used to refine and o ptimize application power consumption.
2) Prediction Model Update: Once the sensor is in the re- sampling phase, the system will not only get precise readings but can also refresh the empirical model parameters.
and the design of low power consumption communication protocols [3] One solution is clustering-based localized prediction[10], where a cluster head also a sensor node maintains a set of history data of each sensor node within a cluster. They expect the use of localized prediction techniques is highly energy efficient due to the reduced length of routing path for transmitting sensor data. On
Wireless sensor network is the first choice to complete these types of tasks. Basically, information prediction scheme is an important feature in any sensor nodes. The efficiency of the sensor network can be improved to large extent with a suitable information prediction scheme. Previously, there were several efforts to resolve this problem, but their accuracy is decreased as the prediction
for Wireless Sensor Networks Qingquan Zhang, Yu Gu, Tian He and Gerald E. Sobelman Abstract—Dynamic scheduling management in wireless sensor networks is one of the most challenging problems in long lifetime monitoring applications. In this paper, we propose and evaluate a novel data correlation-based stochastic scheduling algorithm, called Cscan. Our system architecture integrates …
consumption in wireless sensor networks, most of the proposed approaches focus on reducing communication among nodes while maintaining some form of cooperation.
A Wireless Sensor Network for Smart Roadbeds and Intelligent Transportation Systems by Ara N. Knaian S.B., Electrical Science and Engineering (1999) Massachusetts Institute of Technology Submitted to the Department of Electrical Engineering and Com puter Science in Partial Fulfillment of the Requirements for the Degree of Master of Engineering in Electrical Engineering and Computer …
Round robin cycle for predictions in wireless sensor networks. In Intelligent Sensors, Sensor Networks and Information Processing Conference (2005), 253–258. In Intelligent Sensors, Sensor Networks and Information Processing Conference (2005), 253–258.
A novel target important research issues in wireless sensor networks tracking protocol using sensor networks was proposed by since it determines the lifetime of the sensor network TSAI et al [16] for mobile users. It was assumed that a deployed for the intended applications, such as mobile target may move in any way, so in all the ways environmental monitoring, area surveillance and target the
Accurate Prediction of Power Consumption in Sensor Networks Olaf Landsiedel, Klaus Wehrle, Stefan Gotz¨ Protocol Engineering and Distributed Systems Group