WO2012140152A1 - Réseau comprenant des nœuds associés à des dispositifs d'éclairage d'extérieur - Google Patents

Réseau comprenant des nœuds associés à des dispositifs d'éclairage d'extérieur Download PDF

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Publication number
WO2012140152A1
WO2012140152A1 PCT/EP2012/056704 EP2012056704W WO2012140152A1 WO 2012140152 A1 WO2012140152 A1 WO 2012140152A1 EP 2012056704 W EP2012056704 W EP 2012056704W WO 2012140152 A1 WO2012140152 A1 WO 2012140152A1
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network
server
servers
distribution
nodes
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Aleksander Gerbec
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    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
    • H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
    • H05B47/10—Controlling the light source
    • H05B47/105—Controlling the light source in response to determined parameters
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
    • H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
    • H05B47/10—Controlling the light source
    • H05B47/155—Coordinated control of two or more light sources
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
    • H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
    • H05B47/10—Controlling the light source
    • H05B47/175—Controlling the light source by remote control
    • H05B47/19—Controlling the light source by remote control via wireless transmission

Definitions

  • the present invention is in the field of computer networks.
  • the present invention relates to a network comprising a plurality of nodes and a server or distribution of servers for providing web services.
  • the first problem is how to obtain the data that is meaningful for characterizing the "behavior” or “status” of the city as a highly complex system.
  • the second problem is how to handle the vast amount of data that will usually be produced such as to obtain meaningful information therefrom with feasible computation at effort and computation time.
  • the present invention has been devised in consideration of the above two problems.
  • the present invention provides a network comprising a plurality of nodes and a server or distribution of servers for providing web services, wherein at least a subset of the nodes are associated with outdoor lighting devices, in particular street lights, and comprise:
  • the server or distribution of servers is configured to
  • street lights are in many respect ideal objects for providing network nodes, for several reasons.
  • street lights are approximately evenly distributed throughout a city, so that the network nodes can be evenly distributed throughout the city as well.
  • a further advantage of streetlight is that it is per se connected to a power supply so that it can easily provide the electrical power needed to operate the network node and the one or more sensors associated with it.
  • streetlights usually stand clear from other objects, they are an ideal place for placing different types of sensors, e.g. optical sensors for monitoring the nearby street.
  • one aspect of a "smart city” would be to provide for an intelligent power management for the street lighting itself, so for this season too it is advantageous to include the street lights as nodes in the network themselves.
  • the server or distribution of servers is configured to build a model based on the received data, including the measurement data obtained by the sensors, to compare the received data with the current model and to update the model according to the received data.
  • this construction and update of the model can be carried out by various machine learning methods, preferably including Support Vector Machines, and can be used to generate the computation learning model of the entire network. That is to say, the learned model may represent the current status of the complete network of nodes, or part of the network of nodes, including a single node only.
  • An important advantage of a machine learned model for the network behavior is that it allows for a fast, typically realtime classification of any new data. Further, any information about the network can be obtained by querying the model, instead of analyzing a huge amount of data gathered from the network, therefore allowing for a real time-analysis of the network.
  • the server or distribution of servers further provides an interface for assessing and querying the model.
  • querying the model means any way of interacting with or analyzing the model such as to retrieve any information of interest. Again, querying the model is much more efficient than for example trying to analyze the vast amount of data provided by the multitude of sensors.
  • At least a subset of the nodes is provided with sensors for sensing one or more of the following conditions:
  • At least a subset of the nodes comprises one or more of the following devices:
  • a color detector an optical detector, a GPS device, a sound detector, an accelerometer, a gyroscope, a compass, a gas sensor, a Hall effect sensor, a vibration sensor, a motion sensor, an electronic range finder, and a capacitive or inductive touch sensor.
  • the nodes may comprise sensor's or detector's information which at first sight are not related to the street lighting at all, such as optical detectors or motion sensors which will e.g. allow for example estimating the traffic.
  • one of the advantages of model building by machine learning is that correlations between parameters that at first sight may appear unrelated will be reflected in the model, such as an increased traffic under certain conditions and an increased demand for street lighting.
  • At least a subset of the nodes comprises actuator means, in particular actuators for adjusting the color of street lighting or the position of the illumination spot provided by the streetlight.
  • actuator means in particular actuators for adjusting the color of street lighting or the position of the illumination spot provided by the streetlight.
  • At least a subset of the nodes comprises computation capabilities and preferably employs a local model derived from the model developed by the server or distribution of servers.
  • the network nodes can make direct use of the local information, i.e. information from the node's own sensors or information from sensors of nearby nodes.
  • the individual nodes acquire cognitive abilities of their own.
  • At least a subset of the nodes associated with the outdoor lighting devices has a modular sensor design, the modular sensor design being characterized by comprising plural receiving sections for selectively receiving one out of a plurality of sensor modules and/or communication modules.
  • the rationale behind this modular sensor design is that a large variety of sensors may in general be desirable for characterizing the status of the network, but that only a limited choice of sensors is actually required at any particular node. Based on a modular design, it is possible to provide exactly the selection of sensors at any given node that is needed, by simply providing the appropriate selection of sensors at the node's receiving sections.
  • the communication means of the node comprise one or more of the following: ZigBee, 6L0WPAN, IEEE 802.15.4, Blue Tooth, WLAN, GSM/GPRS, Ethernet or proprietary communication means.
  • the network may include further nodes provided in vehicles.
  • vehicles such as cars are particular useful network nodes, because drivers are typical recipients of information provided by the network, for example navigation instructions, traffic information, parking space directions and the like, and because cars are at the same time particularly suitable for monitoring the traffic flow and providing corresponding information to the network.
  • a further example of network nodes not associated with streetlights are nodes which are provided in operative connection with municipal supply systems, in particular one or more of a water supply system, a waste water system, a high power cable, a low power cable, a transformer, a data cable, a data switch, a gas supply system and a district heating system.
  • the communication means of a node associated with a streetlight may serve as a gateway to the network for a node provided in a vehicle or a node provided in operable connection within municipal supply systems. That is to say, the streetlight-nodes may provide the "backbone" of the network infrastructure to which other nodes can be coupled.
  • the server or distribution of servers comprises at least one machine learning algorithm, where the machine algorithm includes one or more of the following: support vector machines (SVM), Bayesian network, reinforcement learning, principal component analysis, clustering, association rule learning, decision tree learning.
  • SVM support vector machines
  • Bayesian network Bayesian network
  • reinforcement learning principal component analysis
  • clustering clustering
  • association rule learning decision tree learning.
  • the model can be built and updated efficiently and automatically.
  • the server or distribution of servers is adapted to clean data received from the nodes such as to exclude errors in sensor reading and/or communication errors.
  • This cleaning may for example be performed using semi-supervised learning methods with background common-sense knowledge. This way, errors in the model due to erroneous sensor values can be efficiently avoided.
  • the server or distribution of servers is adapted to extract information from the data received.
  • the server or distribution of servers may be adapted to extract information about the energy efficiency from received data related to energy consumption, lighting and external factors. Based on this extraction, a more efficient use of energy can be achieved.
  • the model is presented as a multi-dimensional matrix.
  • the model may be developed in the form of a hyperplane in a multi-dimensional space.
  • the model allows for classification of received data with regard to the location of corresponding data points with regard to said hyperplane in multi-dimensional space.
  • hyperplanes are hyperplanes constructed by Support Vector Machines in a high or infinite dimensional space which can be used for classification or regression in a way per se known in the art.
  • the server or distribution of servers is adapted to develop a plurality of models and storing the same.
  • the server or distribution of servers is preferably adapted to store data that triggered deviances in the model. While it is generally advantageous to deal with the model rather than the vast amount of data underlying the model, for analysis purposes it may be advantageous to take notice of the data that triggered significant changes to the model.
  • server or distribution of servers is preferably adapted to analyze data received from the network nodes with reference to the available model or models in real time.
  • the server or distribution of servers is configured to receive, in addition to data from the network, information from external information sources, in particular weather conditions and/or traffic information.
  • the interface for assessing and querying the model is preferably adapted to allow one or more of the following: status monitoring, keyword-based searching and browsing, complex events browsing, alerting and searching, visualization of short-term trends, visualization of long-term trends, visualization of predictions.
  • the server or distribution of servers is configured to optimize energy consumption of the outdoor lighting based on a model reflecting information from the past and current information received from network nodes. It has been confirmed by the inventors that this way the energy consumption of the outdoor lighting can be dramatically decreased without compromising the lighting quality at any particular point in time.
  • the server or distribution of servers is configured to compare a recent model with at least one previous model and to detect an anomaly if the difference between the two models exceeds a previously defined or an expected threshold.
  • the server or distribution of servers may be configured to report an error, a warning or an information message in response of detecting said anomaly.
  • the network is not only suitable for detecting anomalies, but also suitable for predicting the behavior of the network or subset of nodes of the network.
  • the prediction may be based on historic models from the past and current data received from the nodes and/or from external information sources.
  • the prediction may be based on one or more of the following:
  • the server or distribution of servers may be configured to predict the consumption of energy of individual nodes or the entire network of nodes for a period of time ahead, based on past models.
  • the server or distribution of servers is configured to predict traffic jams or to predict air pollution.
  • the server or distribution of servers is configured to predict failure of single devices associated with a node, failure of a single node or a group of nodes.
  • the server or distribution of servers may be configured to report a prediction of failures as a rank list of failures that are most likely to occur in a given period of time. The failure prediction is preferably based on an analysis of historic models of the past, technical data of the related devices and detected trends.
  • the server or distribution of servers is configured to monitor the evolution of the updates of the model to determine trends in the behavior of the network or parts of the network.
  • the server may be configured to determine micro-trends related to the behavior of individual nodes or a subset of nodes and/or macro-trends related to the behavior of the entire network.
  • the server or distribution of servers may be configured to monitor the time evolution of trends and to compare this time evolution with an expected trend, wherein the server or distribution of servers may be configured to issue a warning if a divergence between the monitored trend and the expected trend is detected, that exceeds a predetermined threshold.
  • the server or distribution of servers is configured to extract patterns of information that frequently emerge.
  • the server or distribution of servers may be configured to model the behavior of individual nodes.
  • the present invention further relates to a method of processing data according to claim 44 and a network node according to claim 46.
  • Fig. 1 is a schematic illustration of elements of the network of the invention.
  • Fig. 2 is a schematic diagram illustrating the mechanics for an SVM machine learning algorithm that is employed in one embodiment of the present invention.
  • Fig. 3 is a schematic drawing illustrating a divergence between an expected trend and observed measurements.
  • Fig. 4 is a block diagram illustrating components of the network according to an embodiment of the present invention.
  • Fig. 5 is a diagram similar to Fig. 5, wherein the structure of an individual node is further illustrated.
  • Fig. 6 is a schematic figure showing the integration of nodes that are in operative connection with municipal supply systems with nodes associated with street lights.
  • a node in a network is composed of a set of sensors, communication means and computational abilities.
  • sensors provide to every node information about energy consumption, lighting and environment conditions (see Fig. 1).
  • the data being measured in a basic sensor set is energy consumption, lighting on the street, environment temperature, humidity, air pressure, C0 2 rate and NHx rate.
  • Other sensors that can be included are Colour light, Optical Detector, GPS, Sound, Accelerometer, Gyroscope, Compass, Gas sensor, Hall effect, Vibration, Motion, Ultrasonic range finder, Capacitive/inductive touch sensors.
  • the sensor information is transmitted to the server or distribution of servers and between neighbouring network nodes by using various communication means like ZigBee, 6LoWPAN,IEEE 802.15.4, Bluetooth, WLAN, GSM/GPRS, ETHERNET, or Proprietary communication means.
  • various communication means like ZigBee, 6LoWPAN,IEEE 802.15.4, Bluetooth, WLAN, GSM/GPRS, ETHERNET, or Proprietary communication means.
  • the information received from plural nodes is processed with a series of machine learning algorithms including Support vector machines, Bayesian networks, Reinforcement learning, Principal components analysis, Clustering, Association rule learning, and/or Decision tree learning. While reference is made to "a" server in the following, it is understood that a distribution of servers, such as a server cloud may be employed.
  • the data is automatically cleaned by using semi-supervised learning methods supported with background common-sense knowledge to exclude obvious errors in sensor reading and/or communication errors.
  • the data gathered can be in a form of different modalities, such as: structured numerical data, unstructured textual data, picture data, streamed video data and other data streams.
  • the data After the data has been cleaned, the data is fused into meaningful information.
  • the measured data about energy consumption, lighting and external factors generates coherent information for the energy efficiency in respect to the effect (i.e. lighting value) and environmental information.
  • Such information is used for further processing and model construction.
  • Various machine learning methods preferably including support vector machines can be used to generate the computation learned model of the network (see Fig. 2).
  • a model that represents the whole network of nodes, specifics of a subset of nodes or specifics of a particular node is presented as a multidimensional matrix with many multidimensional vectors.
  • the learned model shows the current status of the complete network e.g. outdoor city lighting network, the subset of nodes, e.g. one particular street in a city or a model of a particular node, respectively.
  • the first learned model in a setup process is the default model representing the normal situation.
  • the next instance of the learned model in time represents the deviation of the previous model, the next the deviation of the previous and so on. Based on the deviances that are based on comparing a previous model with the current information from the network, the previous model is updated with a new one.
  • Fig. 2 schematically illustrates the mechanics of Support Vector Machines (SVM) machine learning algorithms.
  • the algorithm develops a model in the form of a hyperplane based on the learning data.
  • a developed model allows for fast classification of new data.
  • Fig. 2 illustrates a simplified case of classification of two classes based on the two values (categories).
  • all models are stored to be used for further analytics, in particular time dependent analytics like trend detection and prediction.
  • the server stores two types of information, namely built models that are used for the real time processing and analytics, and the data that triggered the deviances in the models for long term off-line analytics.
  • Built models cannot be represented in formalised and structured way as "if- then" rules, but are rather represented as a multidimensional matrix.
  • the machine learning methods applied herein allow for modelling the vast complex space of multisensory nodes network.
  • the server stores models that are than being used for further analysis and querying.
  • the server also provides interfaces for status monitoring, simple and keyword based searching and browsing, complex events browsing, alerting and searching, short and long term trends visualisation and prediction visualisation.
  • Different interactive visualisation methods are implemented to allow various views on the developed models, the methods also include geographically and temporarily information and information received from external and public sources.
  • network nodes have cognitive abilities of their own. Accordingly, every network node preferably has abilities to locally behave as an independent entity that is able to respond accurately on the information received from the environment. Data gathered from several sensors, data received from the neighbouring nodes and from the server is processed by the node's own processing capabilities to form a local control loop. The network node may pre-process the information that is gathered locally and adopt a local developed model that has been sent to the node by the server. The local model can be "localised" from the global model by the server. Using integrated causal and temporal reasoning methods, a node can decide how to react to information gathered from local sensors that significantly modify the current local model.
  • the light can decide to increase its lighting power in order to give the neighbouring light enough time to communicate its problem to the server and to be repaired.
  • developed models at the server side current data from the network and external information sources like weather conditions, traffic information etc. form input information for the analytics, optimisation, prediction and simulation features.
  • the server may use the information from the past in the form of models together with the current information to develop an optimisation model for energy consumption in the outdoor lighting. Based on the measured lighting environment and the past occurrences, the server may propose an optimized energy consumption scenario. For example, consider that the model at the server indicates that during the period from November to February, traffic in a particular zone is usually increased due to the skiing season. The model may further indicate that based on the previous similar weather condition as the current one, the light has to be switched on for additional 30 min as compared to normal to decrease the risk for traffic accidents. Furthermore, local light's cognitive abilities enables to react on the information at the respective location. For example, if there is no traffic, the lights may be dimmed by 50%.
  • the difference between the current model and the previous model is spotted as the difference in the network behaviour. If the difference exceeds an expected or pre-defined difference, this is regarded as an anomaly that server has to report. For example, the failure of a lamp, decreased traffic due to the changed traffic regime in the city, increased value of C0 2 or NHx is defined as anomaly. Based on the detected anomalies that exceed certain threshold, the server reports an error, warning or information.
  • Another example is finding anomalies in the road segments that are then used for optimal route planning. For each segment one can determine the tracks that are anomalous. Removing the anomalous tracks and computing a set of features for each path can enable a global path based analysis. The analysis can, however, be applied only to a set of short road segments that are well represented with a large number of tracks. It has been found that low numbers of tracks that match a particular segment can lead to unreliable analysis.
  • the server Given the information of the global network behaviour, the behaviour of local network subsets and individual nodes from the past, and given the technical data related to specific devices, the server is able to predict the forthcoming failure of the nodes, devices and the network. Predictions are reported as a ranked list of failures that might occur in the next period (hours, days, weeks). The severity measure is calculated on the basis of the historical models, technical data and detected trends. On a more advance level, using stored models and the current information from the network can be used by the server to perform simple and deep analytics by combining machine learning methods with semantics and reasoning methods.
  • the server may calculate micro and macro trends in the complete network, in the subset of nodes (sub-network) and on a particular node or device.
  • Fig. 3 schematically illustrates the method for detecting and calculating unexpected trends.
  • Micro trends are trends that cannot be spotted by using only traditional analytic techniques. Micro trends are usually local and are not detected in the complete network. Examples of micro trends include energy consumption increase in a subnetwork, decrease of the lighting intensity in a particular light over the time, increase of the air pollution (e.g. a combined value of different variables like C0 2 , NHx, Ozone, etc.).
  • Macro trends are the ones that are usually detected on the complete network or on large subnetworks. Examples include detecting changed environmental data due to pollution, drivers behaviour in a street, changed energy consumption due to economic or environmental influences, change in the topology of a network due to a different lighting policies, etc.
  • Detected trends are being further monitored by the server.
  • the server reports a critical warning to the monitoring service.
  • the server ranges all detected trends in respect to their severity and lists them accordingly.
  • the server may calculate the predicted behaviour of a network, subset of nodes (sub-network) or a particular device.
  • the server may in particular also employ an interaction between different models.
  • the server may calculate macro predictions and micro predictions. Exemplary methods used in prediction are: parametric modelling (e.g. autoregressive models, moving average models), nonparametric modelling (e.g. wavelet based), and regression approaches.
  • the server mostly uses a data mining oriented regression approach.
  • An example of a micro prediction is a prediction of the energy consumption for the next period (hour) that is calculated from the past models.
  • Another example is the prediction of light (or other device) failure that is calculated based on the stored models from the past, detected trends on the light, current data and light technical specifications.
  • An example of the macro prediction is the prediction of the energy consumption of the complete network over larger time scales (several weeks, months).
  • a further example is the prediction of network failures that are caused by failure of dependent devices.
  • a model developed by the server may also include information about device dependencies that cause cascade failure effects in the network. Even more complex scenarios can be handled, like predicting traffic jams, community infrastructure network failures, environment pollution prediction etc.
  • the server is able to calculate the optimal outcomes by using optimisation methods.
  • optimisation methods may be chosen from simple mathematical optimisation methods to complex constraint logic programming methods and forward chain reasoning.
  • the server may calculate different optimisation plans, evaluate them and propose the ranking list of potential scenarios. For example, based on the predictive energy consumption, the server may calculate the optimal scenario given the expected external conditions. This optimisation may include energy optimisation, road traffic optimisation, lighting optimisation, network devices optimisation etc.
  • the server can describe a decision that has been taken in the modelling phase.
  • "A common sense knowledge base” is available as a network of concepts that are connected through many different types of relations.
  • the server may use predefined knowledge to reason about the developed model, gathered information and decisions taken. Nodes that are able to perform some of the cognitive abilities make own plans in a limited context that is provided by the server.
  • the server thus models the network and node's behaviour. For example, the server knows from the developed model how a particular node in the network behaves under particular sensed conditions. For example, when there is a need for higher luminance on the street, particular lights coordinate their behaviour. The server also knows how the sub-network or network as a whole behaves in particular conditions.
  • the server may use knowledge extraction methods to extract meaningful patterns from the data gathered.
  • a pattern is a set of similar information that emerges very often.
  • the server may transform the patterns to the formalised descriptions that are inserted in the common- sense knowledge base. Formalised descriptions are in the form of "if-thenrules" or semantic networks presented in RDFs.
  • the server will typically use data mining and text mining algorithms for knowledge extraction and association rules for knowledge formalisation.
  • Reference sign 10 denotes a geographic area, where a plurality of nodes are provided at street lights, and further nodes may be provided, such as on-board devices of vehicles or municipal supply systems.
  • the sensor data provided by the nodes is communicated to the server or distribution of servers which is adapted to carry out the steps described above, i.e. the cleaning and fusion of data, model building, anomaly detection, trend detection, prediction, knowledge discovery, reasoning and context generation.
  • an interface 14 is provided for assessing and querying the model developed by the server or distribution of servers 12. Via this interface 14, numerous GUI applications can query the model in any desired way, such as for decision making support, visualisation and the like.
  • Fig. 5 is a diagram that is partly identical with the diagram of Fig. 4. However, instead of the geographic region, in Fig. 5 a node 16 is symbolically represented. As explained above, according to the present invention at last some of the nodes 16 are associated with street lights, symbolically represented at reference sign 18, and comprise a modular sensor 20. Further, the local node 16 has local computation capability. Together with a local model, that has been localized from the global model by the server, the node 16 acquires cognitive abilities of its own. To illustrate this, in Fig. 5 the difference between the "global intelligence" held by the server 12 and the "local intelligence" acquired by the individual node 16 is highlighted.
  • Nodes in the network can also perform actions and can provide wireless access points for further devices.
  • Several additional scenarios are to be implemented in the network.
  • ⁇ By installing nodes on other infrastructures like waste water management, water pipes, gas distribution network, electricity network, telecommunication network and the like, existing network allows for integrated city infrastructure management. This is illustrated in Fig. 6.
  • the network By installing wireless access points of either 3G or 4G, the network can also be used to automatically connect mobile devices. Mobile devices can be either personal or public. Similarly, if nodes are installed in cars and other vehicles, they can be automatically connected to the network.
  • actuators can be installed and managed by the network.
  • Pulse Width Modulation can be employed that manages the colour and frequency of the RGB LED lights or the actuation of a motor. Possible scenarios include light shows, or the modification of the colour of the light in extreme weather conditions on the roads, such as to enhance visibility.
  • Servo motors may be employed to move the lighting device. Further scenarios include controlling the light dispersion, moving lighting to a needed spot, wave lighting function, etc.

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Abstract

La présente invention se rapporte à un réseau qui comprend une pluralité de nœuds et un serveur ou une distribution de serveurs permettant d'offrir des services Web. Au moins un sous-ensemble des nœuds est associé avec des dispositifs d'éclairage d'extérieur, en particulier des réverbères, et comprend un ou plusieurs capteurs et un moyen de communication qui permet une communication avec les autres nœuds et avec ledit serveur ou ladite distribution de serveurs. Ledit serveur ou ladite distribution de serveurs est configuré, ou configurée, pour construire un modèle sur la base de données reçues, comparer les données reçues au modèle actuel et mettre à jour le modèle selon les données reçues.
PCT/EP2012/056704 2011-04-12 2012-04-12 Réseau comprenant des nœuds associés à des dispositifs d'éclairage d'extérieur Ceased WO2012140152A1 (fr)

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Cited By (39)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8732031B2 (en) 2012-06-12 2014-05-20 Sensity Systems, Inc. Lighting infrastructure and revenue model
CN104093239A (zh) * 2014-06-26 2014-10-08 中国科学技术大学苏州研究院 一种基于物联网的led路灯控制管理系统和方法
ITRM20130274A1 (it) * 2013-05-08 2014-11-09 Smart I S R L Sistema di sensori ottici distribuiti ed intelligenti per il controllo adattivo, predittivo ed on-demand dell'illuminazione pubblica
WO2014205547A1 (fr) * 2013-06-28 2014-12-31 Regulus Solutions Inc. Procédés et dispositifs se rapportant à un éclairage à semi-conducteurs
CN104486875A (zh) * 2014-12-01 2015-04-01 苏州市欧博锐自动化科技有限公司 一种基于ZigBee的路灯控制方法
WO2014207592A3 (fr) * 2013-06-26 2015-05-14 Koninklijke Philips N.V. Appareil et procédé employant des luminaires à base de capteurs pour détecter des zones de visibilité réduite et leur sens de déplacement
WO2015077626A1 (fr) * 2013-11-21 2015-05-28 General Electric Company Procédé et système de rapport, prédiction et planification de circulation
US9084308B2 (en) 2012-05-07 2015-07-14 Starfield Controls, Inc. Self calibrating, adaptive setpoint daylighting
WO2015132717A1 (fr) * 2014-03-06 2015-09-11 Koninklijke Philips N.V. Système d'éclairage intelligent à programmation de maintenance prédictive et son procédé de fonctionnement
US9374870B2 (en) 2012-09-12 2016-06-21 Sensity Systems Inc. Networked lighting infrastructure for sensing applications
US9420674B2 (en) 2013-11-21 2016-08-16 General Electric Company System and method for monitoring street lighting luminaires
US9439269B2 (en) 2013-11-21 2016-09-06 General Electric Company Powerline luminaire communications
US9456293B2 (en) 2013-03-26 2016-09-27 Sensity Systems Inc. Sensor nodes with multicast transmissions in lighting sensory network
US9582671B2 (en) 2014-03-06 2017-02-28 Sensity Systems Inc. Security and data privacy for lighting sensory networks
US9621265B2 (en) 2013-11-21 2017-04-11 General Electric Company Street lighting control, monitoring, and data transportation system and method
CN106658821A (zh) * 2016-10-26 2017-05-10 华南理工大学 基于非视觉光生物效应的智能led照明系统及其控制方法
CN106717032A (zh) * 2014-09-25 2017-05-24 飞利浦灯具控股公司 通过使用移动设备和短程无线通信扩展室外照明系统中的覆盖范围
US9674929B2 (en) 2014-03-10 2017-06-06 Massachusetts Institute Of Technology Methods and apparatus for illumination control
US9715661B2 (en) 2014-10-31 2017-07-25 Waylay Bvba Tool for modelling, instantiating and/or executing a bayesian agent in an application
US9746370B2 (en) 2014-02-26 2017-08-29 Sensity Systems Inc. Method and apparatus for measuring illumination characteristics of a luminaire
US9933297B2 (en) 2013-03-26 2018-04-03 Sensity Systems Inc. System and method for planning and monitoring a light sensory network
US9998332B2 (en) 2013-11-15 2018-06-12 Massachusetts Institute Of Technology Signal-flow architecture for cooperative control and resource allocation
WO2018122784A1 (fr) * 2016-12-30 2018-07-05 Tata Consultancy Services Limited Procédé et système de gestion de programme d'éclairage de lampes
CN109661081A (zh) * 2018-12-18 2019-04-19 温州大学 基于6LoWPAN技术实现路灯远程控制的控制系统
CN109691234A (zh) * 2016-07-26 2019-04-26 飞利浦照明控股有限公司 照明传感器分析
WO2019115290A1 (fr) * 2017-12-15 2019-06-20 Signify Holding B.V. Optique sans fil sur des luminaires immobiles sujets à un déplacement
WO2019136474A1 (fr) 2018-01-08 2019-07-11 Ubicquia Llc Dernier état connu sur une pluralité de capteurs géographiques dispersés synchronisés sur une horloge commune
US10362112B2 (en) 2014-03-06 2019-07-23 Verizon Patent And Licensing Inc. Application environment for lighting sensory networks
US10417570B2 (en) 2014-03-06 2019-09-17 Verizon Patent And Licensing Inc. Systems and methods for probabilistic semantic sensing in a sensory network
US10477641B2 (en) 2017-09-24 2019-11-12 Massachusetts Institute Of Technology Methods and apparatus for image analysis for lighting control
US10509101B2 (en) 2013-11-21 2019-12-17 General Electric Company Street lighting communications, control, and special services
WO2021005479A1 (fr) * 2019-07-10 2021-01-14 Rebernig Supervisioni Srl Procédé de commande d'éclairage prédictif et adaptatif et système d'éclairage prédictif et adaptatif
EP4098085A1 (fr) * 2020-03-11 2022-12-07 Tridonic GmbH & Co. KG Grille d'une pluralité de modules de capteur de technologie de construction et système comprenant une telle grille
CN117494484A (zh) * 2024-01-02 2024-02-02 北京泰豪智能工程有限公司 基于大模型训练的建筑物故障诊断预测与健康管理方法
CN117580229A (zh) * 2023-11-30 2024-02-20 南京江行联加智能科技有限公司 路灯智能调节方法、装置、设备及存储介质
CN119001030A (zh) * 2024-10-21 2024-11-22 普利莱（天津）燃气设备有限公司 一种基于多传感器融合的天然气加臭剂浓度检测系统
CN119729967A (zh) * 2025-03-04 2025-03-28 深圳市垅运照明电器有限公司 基于环境参数的轨道照明控制方法、系统、终端及介质
CN119893796A (zh) * 2025-03-27 2025-04-25 漳州电子信息集团有限公司 一种城市照明控制融合网关系统
US12579336B2 (en) 2024-05-10 2026-03-17 Evarilabs Llc. Geographic setpoint matching system for outdoor street lighting

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070222581A1 (en) * 2005-10-05 2007-09-27 Guardian Networks, Inc. Method and System for Remotely Monitoring and Controlling Field Devices with a Street Lamp Elevated Mesh Network
US20070273307A1 (en) * 2006-05-26 2007-11-29 Westrick Rich L Distributed Intelligence Automated Lighting Systems and Methods
US20090066258A1 (en) * 2007-09-07 2009-03-12 Streetlight Intelligence, Inc. Streelight monitoring and control
US20100264846A1 (en) * 2008-04-14 2010-10-21 Digital Lumens, Inc. Power Management Unit with Adaptive Dimming

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070222581A1 (en) * 2005-10-05 2007-09-27 Guardian Networks, Inc. Method and System for Remotely Monitoring and Controlling Field Devices with a Street Lamp Elevated Mesh Network
US20070273307A1 (en) * 2006-05-26 2007-11-29 Westrick Rich L Distributed Intelligence Automated Lighting Systems and Methods
US20090066258A1 (en) * 2007-09-07 2009-03-12 Streetlight Intelligence, Inc. Streelight monitoring and control
US20100264846A1 (en) * 2008-04-14 2010-10-21 Digital Lumens, Inc. Power Management Unit with Adaptive Dimming

Cited By (67)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9084308B2 (en) 2012-05-07 2015-07-14 Starfield Controls, Inc. Self calibrating, adaptive setpoint daylighting
US9965813B2 (en) 2012-06-12 2018-05-08 Sensity Systems Inc. Lighting infrastructure and revenue model
US8732031B2 (en) 2012-06-12 2014-05-20 Sensity Systems, Inc. Lighting infrastructure and revenue model
US10290065B2 (en) 2012-06-12 2019-05-14 Verizon Patent And Licensing Inc. Lighting infrastructure and revenue model
US9374870B2 (en) 2012-09-12 2016-06-21 Sensity Systems Inc. Networked lighting infrastructure for sensing applications
US9959413B2 (en) 2012-09-12 2018-05-01 Sensity Systems Inc. Security and data privacy for lighting sensory networks
US9699873B2 (en) 2012-09-12 2017-07-04 Sensity Systems Inc. Networked lighting infrastructure for sensing applications
US9933297B2 (en) 2013-03-26 2018-04-03 Sensity Systems Inc. System and method for planning and monitoring a light sensory network
US9456293B2 (en) 2013-03-26 2016-09-27 Sensity Systems Inc. Sensor nodes with multicast transmissions in lighting sensory network
US10158718B2 (en) 2013-03-26 2018-12-18 Verizon Patent And Licensing Inc. Sensor nodes with multicast transmissions in lighting sensory network
US9402061B2 (en) 2013-05-08 2016-07-26 Smart-I, S.R.L. Smart optical sensor for adaptive, predictive, and on-demand control of public lighting
WO2014181369A1 (fr) * 2013-05-08 2014-11-13 Smart-L S.R.L. Capteur optique intelligent de commande adaptative, prédictive et sur demande de l'éclairage public
ITRM20130274A1 (it) * 2013-05-08 2014-11-09 Smart I S R L Sistema di sensori ottici distribuiti ed intelligenti per il controllo adattivo, predittivo ed on-demand dell'illuminazione pubblica
CN105324661A (zh) * 2013-06-26 2016-02-10 皇家飞利浦有限公司 采用基于传感器的照明器探测能见度降低的区域及其移动方向的装置和方法
WO2014207592A3 (fr) * 2013-06-26 2015-05-14 Koninklijke Philips N.V. Appareil et procédé employant des luminaires à base de capteurs pour détecter des zones de visibilité réduite et leur sens de déplacement
CN105324661B (zh) * 2013-06-26 2019-07-05 飞利浦灯具控股公司 采用基于传感器的照明器探测能见度降低的区域及其移动方向的装置和方法
US9542841B2 (en) 2013-06-26 2017-01-10 Philips Lighting Holding B.V. Apparatus and method employing sensor-based luminaires to detect areas of reduced visibility and their direction of movement
EP3014248B1 (fr) 2013-06-26 2020-08-05 Signify Holding B.V. Appareil et procédé employant des luminaires à base de capteurs pour détecter des zones de visibilité réduite et leur sens de déplacement
WO2014205547A1 (fr) * 2013-06-28 2014-12-31 Regulus Solutions Inc. Procédés et dispositifs se rapportant à un éclairage à semi-conducteurs
US9998332B2 (en) 2013-11-15 2018-06-12 Massachusetts Institute Of Technology Signal-flow architecture for cooperative control and resource allocation
US10764137B2 (en) 2013-11-15 2020-09-01 Massachusetts Institute Of Technology Signal-flow architecture for cooperative control and resource allocation
US9420674B2 (en) 2013-11-21 2016-08-16 General Electric Company System and method for monitoring street lighting luminaires
US9646495B2 (en) 2013-11-21 2017-05-09 General Electric Company Method and system for traffic flow reporting, forecasting, and planning
US9622324B2 (en) 2013-11-21 2017-04-11 General Electric Company Geolocation aid and system
WO2015077626A1 (fr) * 2013-11-21 2015-05-28 General Electric Company Procédé et système de rapport, prédiction et planification de circulation
US9439269B2 (en) 2013-11-21 2016-09-06 General Electric Company Powerline luminaire communications
US9622323B2 (en) 2013-11-21 2017-04-11 General Electric Company Luminaire associate
US10509101B2 (en) 2013-11-21 2019-12-17 General Electric Company Street lighting communications, control, and special services
US9621265B2 (en) 2013-11-21 2017-04-11 General Electric Company Street lighting control, monitoring, and data transportation system and method
US9560720B2 (en) 2013-11-21 2017-01-31 General Electric Company Emergency vehicle alert system
US9746370B2 (en) 2014-02-26 2017-08-29 Sensity Systems Inc. Method and apparatus for measuring illumination characteristics of a luminaire
US11544608B2 (en) 2014-03-06 2023-01-03 Verizon Patent And Licensing Inc. Systems and methods for probabilistic semantic sensing in a sensory network
US11616842B2 (en) 2014-03-06 2023-03-28 Verizon Patent And Licensing Inc. Application environment for sensory networks
US10791175B2 (en) 2014-03-06 2020-09-29 Verizon Patent And Licensing Inc. Application environment for sensory networks
WO2015132717A1 (fr) * 2014-03-06 2015-09-11 Koninklijke Philips N.V. Système d'éclairage intelligent à programmation de maintenance prédictive et son procédé de fonctionnement
US9582671B2 (en) 2014-03-06 2017-02-28 Sensity Systems Inc. Security and data privacy for lighting sensory networks
US10417570B2 (en) 2014-03-06 2019-09-17 Verizon Patent And Licensing Inc. Systems and methods for probabilistic semantic sensing in a sensory network
US10362112B2 (en) 2014-03-06 2019-07-23 Verizon Patent And Licensing Inc. Application environment for lighting sensory networks
US9674929B2 (en) 2014-03-10 2017-06-06 Massachusetts Institute Of Technology Methods and apparatus for illumination control
CN104093239A (zh) * 2014-06-26 2014-10-08 中国科学技术大学苏州研究院 一种基于物联网的led路灯控制管理系统和方法
CN106717032A (zh) * 2014-09-25 2017-05-24 飞利浦灯具控股公司 通过使用移动设备和短程无线通信扩展室外照明系统中的覆盖范围
EP3198985B1 (fr) 2014-09-25 2019-06-12 Signify Holding B.V. Extension de couverture dans un système d'éclairage extérieur à l'aide d'un dispositif mobile et de communications sans fil à courte portée
CN106717032B (zh) * 2014-09-25 2021-03-16 飞利浦灯具控股公司 灯管理的方法，移动设备和灯管理网络
US9715661B2 (en) 2014-10-31 2017-07-25 Waylay Bvba Tool for modelling, instantiating and/or executing a bayesian agent in an application
CN104486875A (zh) * 2014-12-01 2015-04-01 苏州市欧博锐自动化科技有限公司 一种基于ZigBee的路灯控制方法
US11026318B2 (en) 2016-07-26 2021-06-01 Signify Holding B.V. Lighting sensor analysis
CN109691234A (zh) * 2016-07-26 2019-04-26 飞利浦照明控股有限公司 照明传感器分析
CN109691234B (zh) * 2016-07-26 2022-12-06 昕诺飞控股有限公司 照明传感器分析
CN106658821A (zh) * 2016-10-26 2017-05-10 华南理工大学 基于非视觉光生物效应的智能led照明系统及其控制方法
CN106658821B (zh) * 2016-10-26 2019-04-09 华南理工大学 基于非视觉光生物效应的智能led照明系统及其控制方法
WO2018122784A1 (fr) * 2016-12-30 2018-07-05 Tata Consultancy Services Limited Procédé et système de gestion de programme d'éclairage de lampes
US10708992B2 (en) 2017-09-24 2020-07-07 Massachusetts Institute Of Technology Methods and apparatus for image analysis for lighting control
US10477641B2 (en) 2017-09-24 2019-11-12 Massachusetts Institute Of Technology Methods and apparatus for image analysis for lighting control
WO2019115290A1 (fr) * 2017-12-15 2019-06-20 Signify Holding B.V. Optique sans fil sur des luminaires immobiles sujets à un déplacement
WO2019136474A1 (fr) 2018-01-08 2019-07-11 Ubicquia Llc Dernier état connu sur une pluralité de capteurs géographiques dispersés synchronisés sur une horloge commune
US12212610B2 (en) 2018-01-08 2025-01-28 Ubicquia, Inc. Last known state across a plurality of dispersed geographic sensors synchronized to a common clock
CN109661081A (zh) * 2018-12-18 2019-04-19 温州大学 基于6LoWPAN技术实现路灯远程控制的控制系统
WO2021005479A1 (fr) * 2019-07-10 2021-01-14 Rebernig Supervisioni Srl Procédé de commande d'éclairage prédictif et adaptatif et système d'éclairage prédictif et adaptatif
US11895755B2 (en) 2019-07-10 2024-02-06 Reberuig Super Visioni Srl Predictive and adaptive lighting control method and predictive and adaptive lighting system
EP4098085A1 (fr) * 2020-03-11 2022-12-07 Tridonic GmbH & Co. KG Grille d'une pluralité de modules de capteur de technologie de construction et système comprenant une telle grille
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US12579336B2 (en) 2024-05-10 2026-03-17 Evarilabs Llc. Geographic setpoint matching system for outdoor street lighting
CN119001030A (zh) * 2024-10-21 2024-11-22 普利莱（天津）燃气设备有限公司 一种基于多传感器融合的天然气加臭剂浓度检测系统
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