Bayesian dynamic multihop wireless best path prediction

An apparatus, comprising:

• one or more network interfaces to communicate with a network;
• a processor coupled to the network interfaces and configured to execute one or more processes;
• and
• a memory configured to store a process executable by the processor, the process when executed configured to:
• receive observed node characteristics of a node in a network, wherein the node characteristics include a link cost metric for a network link associated with the node;
• use a Bayesian learning model to estimate a virtual link cost metric based on the observed node characteristics, wherein the model uses statistics regarding the observed link cost metric as background belief measures;
• and
• form a routing path in the network that includes the network link in part based on an objective function that uses the virtual link cost metric as a parameter.

A tangible, non-transitory, computer-readable medium storing program instructions that cause a processor of a device to execute a process comprising:

• receiving, at the processor, observed node characteristics of a node in a network, wherein the node characteristics include a link cost metric for a network link associated with the node;
• using, by the processor, a Bayesian learning model to estimate a virtual link cost metric based on the observed node characteristics, wherein the model uses statistics regarding the observed link cost metric as background belief measures;
• and
• forming, by the processor, a routing path in the network that includes the network link in part based on an objective function that uses the virtual link cost metric as a parameter.