Telefonaktiebolaget Lm Ericsson (publ)

Gm Global Technolgoy Operations, Inc.

Swarup Kumar Mohalik

Chakri Padala

Ramamurthy Badrinath

Anshu Shukla

Sandhya Baskaran

Yu Wang

Ramesh Sethu

Sambit Nayak

Efthymios Stathakis

Senthamiz Selvi Arumugam

Martha Vlachou-Konchylaki

Mahesh Babu Jayaraman

Arthur Gusmao

Joerg Niemoeller

Nanjangud Chandrasekhara Swamy Narendra

Perepu Satheesh Kumar

Manoj G Dixit

Jörg Niemöller

Saravanan M

Rana Pratap Sircar

Ramani Venkatakrishnan

Rajeev Ariyattu Chandrasekharan

Devesh B Chokshi

Prasanna Vignesh V Ganesan

Salem Abid Ahmed

Venkata Simhadri Nagender Yasalapu

Sai Chandrasekar

Smita Behera

A method implemented by a network device to predict smart contract violations, where the network device is communicatively coupled to one or more computing nodes that execute one or more smart contracts that are configured to perform transactions on a blockchain. The method includes generating a control flow graph for each of the one or more smart contracts, retrieving, from the blockchain, a current state of a variable utilized by the one or more smart contracts following execution of a transaction that updates the variable, generating or updating a state space tree based on the current state of the variable and the control flow graphs generated for the one or more smart contracts, determining whether the state space tree meets a predefined violation requirement, and sending an alert to the one or more computing nodes in response to determining that the state space tree meets the violation requirement.

Method and system for prediction of smart contract violation using dynamic state space creation

In a method performed by a first node, the node determines a plan based on: i) a first goal to be achieved according to capabilities of the first node, and a first set of first actions to be performed by the first node to achieve the first goal, and ii) for each second node of one or more second nodes: a) a respective goal to be achieved according to respective capabilities of each second node, and b) a respective set of first actions to be individually performed by each second node to achieve the respective goal. The plan is to collaboratively achieve each of the respective goals and the first goal, by determining a respective set of second actions to be respectively performed by each second node and the first node. The first node sends a respective indication indicating the determined second actions.

First node, second node, wireless communications network and methods performed thereby to achieve a set of goals in a geographical space

A method of configuration in a communications network includes: obtaining operational data relating to operation of at least one network node, the operational data including, for each of a plurality of time periods, data relating to at least one configuration parameter of the network node and data relating to at least one performance indicator of the network node; defining a plurality of actions, wherein each action corresponds to a specific set of values of the at least one configuration parameter in two successive time periods; and defining a plurality of states, wherein each state corresponds to one specific set of values of the operational data. A probabilistic graph is constructed from the operational data, the probabilistic graph indicating, for a plurality of the defined states, a probability that an action will lead to a transition to a respective other state.

Configuration of a communications network

A method and a system are presented for enabling coordinated executions of actions in a distributed computing system with untrusted local computing agents. A smart contract including plans is recorded in a blockchain database. Each plan includes actions to be executed by a respective one of the local computing agents. Execution of a first action of a first set of actions to be executed by a first local computing agent is requested. Execution of a second action of a second set of actions to be executed by a second local computing agent is requested. Responsive to determining, based on the smart contract, that the first action can be executed, the first local computing agent is caused to execute the first action and responsive to determining, based on the smart contract, that the second action cannot be executed, the second local computing agent is caused to not execute the second action.

In a distributed computing system with untrusted entities method and apparatus for enabling coordinated executions of actions

In a M2M device management system, a Task Orchestration Module, TOM () external to the M2M device () manages the execution of tasks wholly or partly on the M2M device (). This alleviates the M2M device () of the need to store code, execute tasks, monitor task execution, and the like. The tasks are specified using Finite State Machine, FSM, syntax. A task URL, tURL () resource on the M2M device () provides a tURL () to a resource hosting () a service () mapping task-IDs to FSM specifications. Communications between the TOM () and M2M device () is compactly and efficiently achieved using a device management protocol server/client system (), such as LightWeightM2M (LWM2M). A predetermined mapping () at the M2M device () maps action labels to library functions () of the M2M device (), obviating the need for code in the M2M device () to interpret and execute actions. An event trigger decision module () at the TOM () interprets events and conditions reported by the device management protocol client (), and determines when state transition events have occurred.

System and method for efficient execution and monitoring of machine-to-machine device management tasks

An artificial intelligence, AI, planning controller control the timing of when a plan () to accomplish a task () is synthesized. The AI planning controller in this regard determines a quiescent phase () during which values of at least some predicates describing a state of the system () will remain stable. The AI planning controller then controls artificial intelligence planning to synthesize the plan () during at least some of the quiescent phase ().

Automated plan synthesis and action dispatch

The properties of a plurality of operational units are estimated by generating a central system state graph model representing the properties of the plurality of operational units as probabilities of transitions between states for the plurality of operational units, where the states represent operational data. Then a respective updated system state graph model is generated for each of the plurality of operational units, based on the central system state graph model and based on new operational data for the respective operational unit. A distance measure is determined between the respective updated system state graph models. If the distance measure fulfils a divergence criterion, a plurality of new central system state graph models are generated, each representing the properties of a respective subset of the plurality of operational units as the probabilities of transitions between states for the respective subset of the plurality of operational units.

Estimating properties of units using system state graph models

A method of monitoring objects capable of wireless communications is provided. The method includes detecting an activity performed by a user of a wireless communications device, acquiring information identifying an expected set of objects which are associated with the detected activity, determining whether at least one of the objects in the expected set is not in proximity of the wireless communications device, and if so, notifying the user that at least one of the objects in the expected set is not in proximity of the wireless communications device.

Monitoring objects capable of wireless communications

A method in a network node is provided for mitigating disruption during maintenance of an edge gateway node of a communication network. The edge gateway node connects devices to services(s) of the communication network. First device(s) are capable of connecting to a cloud environment in the absence of the edge gateway node, and second device(s) are incapable of connecting to the cloud environment in the absence of the edge gateway node. The method comprises: establishing respective virtual devices for the second device(s), the virtual devices comprising predictive models trained to replicate data output by the respective second device(s); and, during a time interval in which the maintenance of the edge gateway node is performed: configuring the virtual device(s) to connect to a virtual edge gateway node established in the cloud environment; and configuring at least one of the first device(s) to connect to the virtual edge gateway node.

Method for mitigating disruption during maintenance of an edge gateway node

Provided is a method for managing a network slice instance (NSI) in a communications network. A first performance of the NSI is monitored by a first network slice subnet instance (NSSI) associated with the NSI. A first node obtains, from a second node, a request to monitor a second performance of the NSI. The second performance is of one or more microservices to provide data on a sector of the network. The first node determines, based on one or more requirements of the request, to perform one of: i) initiating () a creation of a second NSSI adapted to monitor the second performance based on the requirements, and ii) modifying, based on the requirements, an existing second NSSI, to monitor the second performance. The created or modified second NSSI being: a) independent from, and b) sharable with, the first subnet slice instance.

First node, second node, and methods performed thereby for managing a network slice instance

A method of monitoring objects capable of wireless communications is provided. The method comprises detecting an activity performed by a user of a wireless communications device, acquiring information identifying an expected set of objects which are associated with the detected activity, determining whether at least one of the objects in the expected set is not in proximity of the wireless communications device, and if so notifying the user that at least one of the objects in the expected set is not in proximity of the wireless communications device.

A method and a location service component for providing an estimate of a location related to a device are disclosed. The location service component receives a request from a client for the estimate of the location and estimates the location based on a predictive model to obtain an initial set of data items. Each data item includes a respective estimate of the location and a set of identities representing one or more further devices in a neighborhood of the device. The location service component obtains a respective location for each of the further devices based on the set of identities. The location service component removes data items that are inconsistent with the respective location for the further devices to obtain a pruned set of data items. The location service component sends, estimates of the pruned set of data items to the client. A corresponding computer program and a computer program carrier are also disclosed.

Method and location service component for providing location of device

The invention relates to method and devices of enabling determination of overall Quality of Service (QoS) of at least one Internet of Things (IoT) device. In an aspect of the invention, a method of a network node () is provided of enabling determining of an overall QoS of at least one IoT device. The method comprises transmitting (S), to a device connectivity middleware (DCM) node (), an identifier of said at least one IoT device () for which a QoS measure is to be acquired, receiving (S), from the DCM node (), a network profile of the IoT device () associated with said identifier, transmitting (S), to a Service Capability Exposure Function (SCEF,), a request for the QoS measure of the IoT device () for the received network profile, the SCEF () acquiring the QoS measure from a Policy and Charging Control (PCC) function (), receiving (S), from the SCEF, the requested QoS measure of the IoT device (), transmitting (S), to a Lightweight Machine-to-Machine (LWM2M) device (), a request for availability information for said at least one IoT device (), and receiving (S), from the LWM2M device (), the requested availability information, wherein the received QoS measure and the availability information is taken into account for determining the overall QoS of said at least one IoT device ().

Devices and methods for QoS determination of IoT-based applications

It is provided a method for providing device data from a source device to a data requester. The method is performed in a data provider and comprises the steps of: determining operating mode being either a pass-through mode or a cache mode, based on estimated resource usage for each of the pass-through mode and the cache mode; receiving a read request from the data requester; when the data provider is in the pass-through mode, transmitting a corresponding read request to the source device and forwarding a read response from the source device to the data requester; when the data provider is in the cache mode, retrieving device data from storage of the data provider and transmitting a read response to the data requester, the read response comprising the device data from storage; and populating the storage of the data provider with device data received from the source device.

Data from a source device to a data requester

A method and tools for providing precise timing analysis scalable to industrial case studies with large numbers of tasks and messages are provided, including the capability to model and analyze task and message response times; ECU usage; bus usage; end-to-end latency of task/message chains; and timing synchronization problems in task/message graphs. System tasks and messages are modeled in a formalism known as calendar automaton. Models are written in a modeling language such as Promela and instrumented with code specific to the analysis specification. Models and instrumentation are automatically generated from the system description and analysis specification. The system model is subjected to exhaustive state space exploration by a compatible model checker, such as SPIN. During exploration, the instrumented code produces results for different timing analyses. Optimization techniques are provided to generate models which require less memory and time for analysis and make the method scalable to large, industrial case studies.

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Bengaluru, India

Swarup Kumar Mohalik