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Journal ArticleDOI

Detecting Presence From a WiFi Router’s Electric Power Consumption by Machine Learning

25 Jan 2018-IEEE Access (Institute of Electrical and Electronics Engineers (IEEE))-Vol. 6, pp 9679-9689
TL;DR: It is concluded that a WiFi router’s power consumption can improve presence detection in home environments and occupancy estimation in office environments, and where possible, should be analysed separately from the aggregated power consumption.
Abstract: Presence and occupancy detection in residential and office environments is used to predict movement of people, detect intruders, and manage electric power consumption. Specifically, we are developing methods to improve demand side electrical power management by reducing electrical power waste in unoccupied spaces. In this paper, we conduct an extensive analysis on the applicability of using a WiFi router’s electrical power consumption in different types of environments to determinate the number or people present in a space. We show the importance of a moving average filter for electrical load time series data, confirm the correlation between control packets and increased minimal router power consumption, and present our results on the accuracy of our approach. We conclude that a WiFi router’s power consumption can improve presence detection in home environments and occupancy estimation in office environments, and where possible, should be analysed separately from the aggregated power consumption.
Citations
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01 Jan 2010
TL;DR: In this article, the authors present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices, which is low-cost, wireless, and incrementally deployable within existing buildings.
Abstract: Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.

489 citations

Journal ArticleDOI
TL;DR: The design of an RL Agent able to learn the behavior of a Timing Recovery Loop (TRL) through the Q-Learning algorithm is proposed and it is able to adapt its behavior to different modulation formats without the need of any tuning for the system parameters.
Abstract: Machine Learning (ML) based on supervised and unsupervised learning models has been recently applied in the telecommunication field. However, such techniques rely on application-specific large datasets and the performance deteriorates if the statistics of the inference data changes over time. Reinforcement Learning (RL) is a solution to these issues because it is able to adapt its behavior to the changing statistics of the input data. In this work, we propose the design of an RL Agent able to learn the behavior of a Timing Recovery Loop (TRL) through the Q-Learning algorithm. The Agent is compatible with popular PSK and QAM formats. We validated the RL synchronizer by comparing it to the Mueller and Muller TRL in terms of Modulation Error Ratio (MER) in a noisy channel scenario. The results show a good trade-off in terms of MER performance. The RL based synchronizer loses less than 1 dB of MER with respect to the conventional one but it is able to adapt its behavior to different modulation formats without the need of any tuning for the system parameters.

18 citations


Additional excerpts

  • ...ML is applied in several fields, such as medicine [2], financial trading [3], big data management [4], imaging and image processing [5], security [6], [7], mobile apps [8] and more....

    [...]

Book ChapterDOI
30 Oct 2019
TL;DR: This paper defines a cyber deception game between the Advanced Metering Infrastructure (AMI) network administrator (henceforth, defender) and attacker and model this interaction as a Bayesian game with complete but imperfect information.
Abstract: In this paper, we define a cyber deception game between the Advanced Metering Infrastructure (AMI) network administrator (henceforth, defender) and attacker. The defender decides to install between a low-interaction honeypot, high-interaction honeypot, and a real system with no honeypot. The attacker decides on whether or not to attack the system given her belief about the type of device she is facing. We model this interaction as a Bayesian game with complete but imperfect information. The choice of honeypot type is private information and characterizes the essence and objective of the defender i.e., the degree of deception and amount of threat intelligence. We study the players’ equilibrium strategies and provide numerical illustrations. The work presented in this paper has been motivated by the H2020 SPEAR project which investigates the implementation of honeypots in smart grid infrastructures to: (i) contribute towards creating attack data sets for training a SIEM (Security Information and Event Management) and (ii) to support post-incident forensics analysis by having recorded a collection of evidence regarding an attacker’s actions.

15 citations

Dissertation
01 Jan 2018
TL;DR: This research presents a probabilistic framework for estimating the level of uncertainty in the response of artificial intelligence systems to natural language processing tasks.
Abstract: Natural Sciences and Engineering Research Council, Vector Institute for Artificial Intelligence

3 citations

References
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Journal ArticleDOI
TL;DR: The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in theTop-20, respectively).
Abstract: We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest-neighbors, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R (with and without the caret package), C and Matlab, including all the relevant classifiers available today. We use 121 data sets, which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behavior, not dependent on the data set collection. The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package). The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively).

2,616 citations


"Detecting Presence From a WiFi Rout..." refers background in this paper

  • ...Our decision is based on scientific literature [18], previous experiences in electric load classification which showed decision trees to gives the best results [19], the implementation potential of decision trees in embedded hardware and, finally, the fact that the number of extracted features is small and they are relatively independent of each other....

    [...]

Journal ArticleDOI
TL;DR: In this paper, the authors present a review of recent developed models for predicting building energy consumption, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods, and further prospects are proposed for additional research reference.
Abstract: The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, the operation of sub-level components like lighting and HVAC systems, occupancy and their behavior. This complex situation makes it very difficult to accurately implement the prediction of building energy consumption. This paper reviews recently developed models for solving this problem, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods. Previous research work concerning these models and relevant applications are introduced. Based on the analysis of previous work, further prospects are proposed for additional research reference.

1,509 citations

Journal ArticleDOI
TL;DR: A novel survey of prominent international intelligent buildings research efforts with the theme of energy saving and user activity recognition is provided, determining the most valuable activities and behaviours and their impact on energy saving potential for each of the three main subsystems, i.e., HVAC, light, and plug loads.

658 citations


"Detecting Presence From a WiFi Rout..." refers background in this paper

  • ...We focus on electric waste in residential and office environments, since the residential and commercial sectors make up approximately 2/3 of national power consumption which translate to the fact that buildings take up between 40 to 60% of electric power consumption in developed countries [1]–[3]....

    [...]

Proceedings ArticleDOI
03 Nov 2010
TL;DR: How to use cheap and simple sensing technology to automatically sense occupancy and sleep patterns in a home, and how to use these patterns to save energy by automatically turning off the home's HVAC system, called the smart thermostat.
Abstract: Heating, ventilation and cooling (HVAC) is the largest source of residential energy consumption. In this paper, we demonstrate how to use cheap and simple sensing technology to automatically sense occupancy and sleep patterns in a home, and how to use these patterns to save energy by automatically turning off the home's HVAC system. We call this approach the smart thermostat. We evaluate this approach by deploying sensors in 8 homes and comparing the expected energy usage of our algorithm against existing approaches. We demonstrate that our approach will achieve a 28% energy saving on average, at a cost of approximately $25 in sensors. In comparison, a commercially-available baseline approach that uses similar sensors saves only 6.8% energy on average, and actually increases energy consumption in 4 of the 8 households.

632 citations


"Detecting Presence From a WiFi Rout..." refers methods in this paper

  • ...[7] use this approach to infer occupancy with 88% accuracy, pointing out that for only 25 dollars’ worth of sensors it is possible to reduce the electrical energy consumption by 28% in HVAC....

    [...]

01 Jan 2010
TL;DR: In this article, the authors present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices, which is low-cost, wireless, and incrementally deployable within existing buildings.
Abstract: Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.

489 citations