Abstract: Embodiments described herein relate to methods and apparatus for positioning a device to communicate with a transmitter. In one embodiment the method comprises receiving a communications signal (115, 390) from the transmitter (405), the communications signal comprising electromagnetic radiation having a wavelength corresponding to line-of-sight propagation only; determining a first spatial parameter of the device and a signal parameter of the communications signal using the received communications signal (410, 415, 420); determining second spatial parameters using the first spatial parameter and the signal parameter (425), the second spatial parameters for positioning the device to improve communication with the transmitter.

Abstract: A method, performed by communications system (100), for a prediction of an event. The first node (111) determines (603) a first set of nodes (121) and sends (604), a first indication of it. The second node (112) determines (606), a vote for one of the nodes in the first set of nodes (121) to be leader, and sends (607) a second indication indicating the determined vote. The second node (112) exchanges (610) a third indication indicating that the third node (113) is the leader. The third node (113) receives (612) from the other nodes in the first set of nodes (121), a respective fourth indication of a respective prediction on the event. The third node (113) updates (613) a machine-learning model of the event based on the received fourth indications and an own prediction, and sends (614), an indication of the updated machine-learning model to another node (114).

Abstract: A method for managing computation load of a fog node is disclosed, wherein a computation capacity of the fog node is predicted to become unavailable to a fog network. The method comprises identifying a candidate set of nodes for computational load transfer from the fog node. The method further comprises obtaining a computation graph representing computation in the fog network, and using a learning model to identify a morphism from the obtained computation graph to a new computation graph, in which the fog node is not included. The identified morphism comprises a sequence of one or more morphing operations that replaces the fog node in the obtained computation graph with a topology of one or more nodes selected from the candidate set. The method further comprises causing computation performed at the fog node to be transferred to one or more nodes of the candidate set.

Abstract: In one aspect, a method performed by a network node for predicting a probability of state change of a node (e.g., a fog node) in a network is provided. The network node determines a set of weights based on attributes of the node. The network node estimates the probability of state change of the node using the determined set of weights and a set of one or more attribute values related to the node where determining the set of weights includes maximizing an evaluation value associated to the node.

Abstract: A method for managing resources includes applying an ensemble model having a plurality of sub-models such that an output of the ensemble model is a weighted average of predictions from the sub-models, and is a prediction of multiple parameters. The method includes determining that an accuracy of the ensemble model is below a first threshold; and as a result, optimizing weights for the predictions from the sub-models. Optimizing weights for the predictions from the sub-models includes: updating the weights selected by the reinforcement learning by looking ahead over a prediction horizon and optimizing the reward function at the given time instance. The method further includes using the prediction of the multiple parameters to manage resources.

Abstract: The present disclosure relates to methods, as well as corresponding systems, computer programs, computer program products, and computer-readable media. The method comprises training a model to predict, based on data about a collection of uses of a communication service, whether the collection of uses of the communication service is likely to lead to a billing dispute. The training is performed using historical data. The historical data includes data about multiple collections of uses of the communication service and information regarding whether bills generated for the respective collections of uses of the communications service have been disputed by customers. The method comprises obtaining data about a new collection of uses of the communication service by a customer. The method comprises predicting, using the trained model, whether the new collection of uses of the communication service is likely to lead to a billing dispute.

Abstract: The embodiments herein relate to a method performed by a cloud node. The cloud node obtains measurements from at least some of the sensor nodes. The cloud node mathematically determines a minimum number of sensor nodes and their optimal locations. Based on the obtained measurements and the mathematically determined optimal locations, the cloud node graphically determines an optimal location for each of the minimum number of sensor nodes. The cloud node compares the mathematically and the graphically determined optimal locations. When the comparison indicates that the mathematically and graphically determined optimal locations are the same, the cloud node determines a minimum number of fog nodes. Based on the optimal location of sensor nodes, the cloud node determines an optimal location for each of the minimum number of fog nodes.

Abstract: A method for managing computation load of a fog node is disclosed, wherein a computation capacity of the fog node is predicted to become unavailable to a fog network. The method comprises identifying a candidate set of nodes for computational load transfer from the fog node. The method further comprises obtaining a computation graph representing computation in the fog network, and using a learning model to identify a morphism from the obtained computation graph to a new computation graph, in which the fog node is not included. The identified morphism comprises a sequence of one or more morphing operations that replaces the fog node in the obtained computation graph with a topology of one or more nodes selected from the candidate set. The method further comprises causing computation performed at the fog node to be transferred to one or more nodes of the candidate set.

Abstract: In one aspect, a method performed by a network node for predicting a probability of state change of a node (e.g., a fog node) in a network is provided. The network node determines a set of weights based on attributes of the node. The network node estimates the probability of state change of the node using the determined set of weights and a set of one or more attribute values related to the node where determining the set of weights includes maximizing an evaluation value associated to the node.

Abstract: A method for transferring semantic knowledge between domains of a network is disclosed, the network comprising a first domain and a second domain. The method comprises establishing a semantic knowledge base for the first domain, the semantic knowledge base comprising concepts of the first domain, properties of the first domain concepts, relationships between the first domain concepts, and constraints governing the first domain concepts. The method further comprises establishing a semantic information base for the second domain, the semantic information base comprising concepts of the second domain. The method further comprises, for a concept of the second domain, determining measures of similarity between the second domain concept and concepts of the first domain and identifying, on the basis of the determined measures of similarity, a first domain concept which is equivalent to the second domain concept.

Abstract: A method (100, 400) for managing access to a database is disclosed. The method comprises receiving a database query, (110), executing the query on the database to obtain a result, (120), generating a noise value, (130), perturbing the result with the generated noise value, (140), and outputting the perturbed result, (150). The noise value is generated from a bimodal probability distribution having a minimum probability at zero noise. Also disclosed is an access management processing element, (200, 300, 600) for a database.

Abstract: A method of processing a data stream of a communication network in a distributed processing architecture comprising a plurality of processing units comprises the steps of extracting data from the data stream, wherein the data is extracted for a particular time window of a sliding time window. The extracted data is converted into a format suitable for graphical representation. A graphical representation of the converted extracted data is generated, and an estimated value of at least one structural property of the graphical representation of the data determined. The estimated value of the at least one structural property is compared with a threshold value, and a change condition reported based on the outcome of the comparison step. The change condition may be used, for example, to change a location based quality of service parameter.

Abstract: A behavior investigating device of a system investigating arrangement in a communication network continuously obtains statistical network usage data of users, analyzes the statistical network usage data with regard to the pattern of behavior of the users, prioritizes the users based on the analysis and informs a traffic prioritizing function about the priority of at least one of the users. The traffic prioritizing function, which may be provided in the traffic prioritizing node, determines that there is network congestion, identifies a communication session of a user in the communication network, obtains a priority of the user from a behavior investigating device and determine whether the user is to be allowed to use the communication network based on the priority and the congestion determination.

Abstract: A method for improving performance of a network includes identifying at least two communities of users within the network, analysing a structure of each community and clustering the communities according to community performance. The method further includes comparing a structure of a lower performing community with a structure of a higher performing community, and changing the structure of the lower performing community substantially to resemble the structure of the higher performing community. Related computer program product for carrying out a method of introducing connections among users of a network and an apparatus configured to introduce connections among users in a network are disclosed.

Abstract: A method (100, 400) for managing access to a database is disclosed. The method comprises receiving a database query, (110), executing the query on the database to obtain a result, (120), generating a noise value, (130), perturbing the result with the generated noise value, (140), and outputting the perturbed result, (150). The noise value is generated from a bimodal probability distribution having a minimum probability at zero noise. Also disclosed is an access management processing element, (200, 300, 600) for a database.

Abstract: A method of processing a data stream of a communication network in a distributed processing architecture comprising a plurality of processing units comprises the steps of extracting data from the data stream, wherein the data is extracted for a particular time window of a sliding time window. The extracted data is converted into a format suitable for graphical representation. A graphical representation of the converted extracted data is generated, and an estimated value of at least one structural property of the graphical representation of the data determined. The estimated value of the at least one structural property is compared with a threshold value, and a change condition reported based on the outcome of the comparison step. The change condition may be used, for example, to change a location based quality of service parameter.

Abstract: A behavior investigating device of a system investigating arrangement in a communication network continuously obtains statistical network usage data of users, analyzes the statistical network usage data with regard to the pattern of behavior of the users, prioritizes the users based on the analysis and informs a traffic prioritizing function about the priority of at least one of the users. The traffic prioritizing function, which may be provided in the traffic prioritizing node, determines that there is network congestion, identifies a communication session of a user in the communication network, obtains a priority of the user from a behavior investigating device and determine whether the user is to be allowed to use the communication network based on the priority and the congestion determination.

Abstract: A method of evaluating trust in a network may include defining a community of nodes of the network from a larger set of nodes of the network. A respective individual trust value may be generated for each node of the community of nodes, and a community trust value may be generated based on the individual trust values of nodes of the community. Related systems are also discussed.

Abstract: A billing arrangement continuously receives messages from a variety of service providing entities, the messages being associated with consumption of the user in a variety of service domains, compares, for at least some of the service domains, the consumption within the service domain with a corresponding consumption threshold, if the consumption threshold is crossed, gives the user at least one reward in relation to at least one further service domain, computes a bill based on the degree of consumption within all service domains using corresponding charging rates while considering rewards given to the user and bills the user using the computed bill with possible rewards.

Abstract: In one particular aspect a method for categorizing a user of a communication service as a churner or non-churner is provided. In some embodiments, the method includes obtaining call data for the user. Next, the call data is used to determine a value (v1) for a graph parameter for the user, the graph parameter being one of: (a) an out-degree parameter, (b) a Shapley Value parameter, (c) a proximity prestige parameter, and (d) closeness centrality parameter. Next, the determined value (v1) is used to determine a churn value (cv). The churn value (cv) is then used to categorize the user as a churner or a non-churner.