Telefonaktiebolaget Lm Ericsson (publ)

Perepu Satheesh Kumar

Saravanan Mohan

Arindam Banerjee

Swarup Kumar Mohalik

Ramamurthy Badrinath

Anshu Shukla

Sandhya Baskaran

Mohan Saravanan

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

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.

Method and node for handling sensor nodes and fog nodes in a communications system

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.

Managing computation load in a fog network

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.

System and method for predicting the state changes of network nodes

A method and a determining device for determining a set of locations for a number of devices are disclosed. For each distance measure of multiple distance measures, the determining device generates a respective pair of clusters. The determining device determines information metrics. The determining device performs first actions. While a difference between a respective information metric and a further respective information metric is greater than a threshold, the determining device performs second actions, comprising replacing said replaceable pair with said weighted pair, replacing the respective information metric for said replaceable pair with said further respective information metric for said weighted pair, and performing said first actions. The determining device selects the set of locations based on the weighted pair of clusters. A corresponding computer program is also disclosed.

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Chennai, India

Perepu Satheesh Kumar