Telefonaktiebolaget Lm Ericsson (publ)

László Toka

Péter Kersch

István Gódor

Péter Hága

Zsolt Kenesi

Gabor Nemeth

Gabor Feher

Andras Balazs Lajtha

Attila Korösi

Attila Vidacs

Geza Szabo

András Veres

Gergely Pongrácz

Peter Vaderna

Gábor Magyar

Péter Mátray

László Kovács

Attila Körösi

Péter Bognár

Tamas Zambo

Eszter Bencsne Bokanyi

Methods and apparatus for use in an Internet-of-Things (IoT) system are presented. A method performed by a network node in an Internet-of-Things (IoT) system is presenting in an example embodiment. According to this example method, a network node or a plurality of network nodes working collaboratively can obtain data captured by one or more IoT devices and associated with an interactive activity with which different participants interface at different sites. Furthermore, the network node(s) may determine a state of the activity by applying the data to a data model. Moreover, the network node(s) can, in an example method, provide one or more feedback signals to feedback devices associated with one or more of the different sites to actualize the determined state of the activity, where at least two of the different sites have disparate, meaning non-identical, feedback device sets.

Technique for online video-gaming with sports equipment

Methods and apparatus for resource optimization in Internet-of-Things (IoT) networks are presented. For instance, the disclosure presents an example method executed by a network node () in an IoT system (). In some embodiments, the example method includes predicting () a likelihood that a future event will be detected by one or more IoT devices () in the IoT system () under different potential resource allocation and IoT device settings. The predicting () is conducted subject to resource availability constraints in some examples. In addition, the example method includes, based on the predicting (), adapting () at least one of resource allocation and IoT device settings in the IoT system () for the future time. This adapting () is conducted such that the likelihood that the future event will be detected is maximized under the resource availability constraints according to a target optimization function.

System and method for resource-aware and time-critical IoT frameworks

Methods and apparatuses for generating real-time or near-real-time transport modality statistics by correlating mobile network and public transport location data are provided. In an example, a processing node may perform a method using one or more processing circuits, the method including identifying paths traveled by multiple vehicles and determining paths traveled by multiple subscriber telecommunications devices of one or more network or service providers. In addition, the method may include correlating the paths traveled by the multiple vehicles and the paths traveled by the multiple subscriber telecommunications devices to determine which one or more modes of transportation each subscriber telecommunications device used over the path traveled by the subscriber telecommunications device. Corresponding apparatus, computer programs, and signals are also described.

Technique for generating near real-time transport modality statistics

Handover bursts are identified for source and target cell pairs along a given public transport line, and each of the handover bursts are tagged with anonymized identifiers of participating users. By searching for handover bursts with a significant share of common user identifiers, it is possible to recursively connect bursts corresponding to a particular public transport vehicle trip. Further, by projecting the estimated location of all connected handover bursts to the path of the given public transport line, it is possible to interpolate stop times and locations for the given vehicle along the entire trip.

Timetables for public transport lines from mobile network handovers

The disclosure pertains to determining a performance index related to a performance of a communication service provided in a network, wherein the communication service is usable by a mobile user terminal. A method aspect is directed to determining the performance index by comparing performance information associated with a characteristic location of the user terminal and performance information associated with at least one other location visited by the user terminal, the performance index being indicative of a relative performance of the communication service at the characteristic location of the user terminal compared to the at least one other location visited by the user terminal.

Performance index determination for a communication service

A traffic analysis system monitors data traffic in a communication network. In the data traffic, flows are detected which are based on the Hypertext Transfer Protocol (HTTP). For each of the flows, a data record is created. The data record comprises at least a User Agent identifier from a message header of a HTTP message of the flow and a device identifier of a user equipment transmitting the flow. The data records are analyzed to determine a mapping of at least one User Agent identifier in the data records to a corresponding device category.

Traffic analysis for HTTP user agent based device category mapping

A technique for predicting network load in a communication network is described. For multiple subscriptions, a network load snapshot is provided. The network load snapshot comprises multiple network load records. Each network load record reflects network load caused by an associated subscription in the network at a particular point of time. Each network load record comprises at least one network load value reflecting a network load caused by the corresponding subscription and at least one network load influencing factor reflecting a parameter associated with the subscription which has the potential of influencing the network load value. The network load snapshot is transformed into a predicted network load snapshot comprising at least one predicted network load record, each predicted network load record reflecting estimated network load caused by the corresponding subscription at a particular point of time in the future. The transformation of a network load snapshot into a corresponding predicted network load snapshot is done based on both the network load values and the associated network load influencing factors.

Technique for projecting network load in a communication network

This disclosure relates to a method, and a network node for determining a charging of a battery of a user device. Network traffic of the user device is monitored, where the network traffic comprises network traffic of an application and/or a service of the user device. A network traffic pattern is identified for the monitored network traffic and a battery-charging event is detected based on the identified network traffic pattern. Charging of the battery is determined of the user device, based on the detected battery-charging event. These features and operations offer a number of advantages. For example, they allow a network operator to implement detection of battery-charging events using the operator's current network setup, because the teachings herein require no changes to existing network deployments, nor are additional protocols needed.

Method and arrangement for determining a charging of a battery of a user device

A technique for evaluating events in a telecommunication network based on an occurrence of a plurality of the events in the telecommunications network is disclosed. In a first method aspect, the method comprises the steps of selecting at least two parameters of the events; categorizing each of the parameters into a respective finite set of values; defining, for at least a part of conjunctions between the respective finite sets of values, a dedicated counter per conjunction; and altering, upon the occurrence of an individual event, the respective counter if the parameters of the occurred event match the respective conjunction between the finite sets of values.

Technique for evaluating events in a telecommunication network

One or more embodiments of a method and device taught herein provide for monitoring wireless terminal behavior in a wireless communication network, where each wireless terminal has a defined terminal type. Data describing the behavior of a plurality of wireless terminals in the wireless communication network is obtained. Based on the obtained data, an aggregated severity score for a given terminal type is determined that is indicative of the extent to which one or more of a plurality of performance criteria have not been met by wireless terminals of the given terminal type. If the aggregated severity score for the given terminal type exceeds a corresponding severity score threshold, one or more of a plurality of predefined actions are performed based on the extent to which the threshold is exceeded.

Method and device for monitoring wireless terminal behavior according to terminal type

A subscriber portfolio model is created by forming mutually exclusive and completely exhaustive groups of subscribers in a way that maximizes the intra-group similarity and inter-group dissimilarity of the value of a target attribute among subscribers in the groups. The subscriber groups thus generated are self-descriptive, as the groups are defined by the attributes used to form them. A segmenting algorithm groups subscribers along permutations of attributes. Relevant data about subscribers is collected from various sources, correlated, and subscriber identities removed to ensure privacy. After analyzing the aggregated data, a subscriber portfolio model is built. The subscriber portfolio model is created in an iterative process; in each step, a selected subscriber group is subdivided into smaller subscriber groups along different value ranges of a statistically decisive attribute that is selected and evaluated based on specific methods. The subdivision is performed by computing filtering conditions based on an in-depth statistical analysis.

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Budapest, Hungary

László Toka