Predictive weather-aware communication network management

A system comprising:

• a communication module to obtain measurement data from wireless nodes, forming at least part of a wireless communication network, during a first interval of time, the measurement data including measurements indicating whether there are potential disturbances to operation of the wireless communication network;
• and
• a processor-based resource management controller, coupled to the communication module, configured to:
• determine, based on the obtained measurement data, predictive data representative of future performance of the wireless communication network at one or more future time instances subsequent to an end of the first interval of time;
• and
• manage resources of the wireless communication network based, at least in part, on the determined predictive data representative of the future performance of the wireless communication network at the one or more future time instances subsequent to the end of the first interval, wherein the processor-based resource management controller configured to manage resources of the wireless communication network is configured to:
• derive throughput values between pairs of wireless nodes of the wireless communication network, to optimize an overall throughput at a future time instance of data traffic to and from one or more sink nodes of the wireless communication network, subject to predicted capacities of wireless links of the communication network computed according to the predictive data of the future performance of the wireless communication network at the one or more future time instances following the end of the first interval;
• and
• configure one or more of the wireless nodes based on the derived throughput values.

A non-transitory computer readable media comprising computer instructions executable on a processor-based device to:

• obtain measurement data from at least part of a wireless communication network during a first interval of time, wherein the measurement data includes measurements indicating whether there are potential disturbances to operation of the wireless communication network;
• determine, based on the obtained measurement data, predictive data representative of future performance of the wireless communication network at one or more future time instances subsequent to an end of the first interval of time;
• and
• manage resources of the wireless communication network based, at least in part, on the determined predictive data representative of the future performance of the wireless communication network at the one or more future time instances subsequent to the end of the first interval, wherein the computer instructions executable on the processor-based device to manage resources of the wireless communication network include one or more instructions to:
• derive throughput values between pairs of wireless nodes of the wireless communication network, to optimize an overall throughput at the future time instance of data traffic to and from one or more sink nodes of the wireless communication network, subject to predicted capacities of wireless links of the communication network computed according to the predictive data of the future performance of the wireless communication network at the one or more future time instances following the end of the first interval;
• and
• configure one or more of the wireless nodes based on the derived throughput values.

A method comprising:

• obtaining measurement data from at least part of a wireless communication network during a first interval of time, wherein the measurement data includes measurements indicating whether there are potential disturbances to operation of the wireless communication network;
• determining, based on the obtained measurement data, predictive data representative of future performance of the wireless communication network at one or more future time instances subsequent to an end of the first interval of time, wherein determining the predictive data comprises determining the predictive data using a learning engine, trained using attenuation time-series data collected from multiple wireless links defined between one or more pairs of wireless nodes of the wireless communication network during dry and wet periods, applied to the measurement data obtained for the wireless communication network, including determining the predictive data representative of future attenuation behavior for wireless links defined between wireless nodes of the wireless communication network for H subsequent time instances following the end of the first interval, wherein determining the predictive data using the learning engine comprises:
• deriving, using a Long Short-Term Memory (LSTM) encoder network, a fixed-length hidden state vector for a time instance in the first interval based on the measurement data obtained for the wireless communication network, and on a previous state vector computed for a previous set of measurement data, the LSTM encoder implementing a non-linear activation function to compute the hidden state vector;
• and
• applying an LSTM decoder to the hidden state vector to determine the predictive data representative of future attenuation behavior for the wireless links of the wireless communication network for the H subsequent time instances;
• and
• managing resources of the wireless communication network based, at least in part, on the determined predictive data representative of the future performance of the wireless communication network at the one or more future time instances subsequent to the end of the first interval.

A system comprising:

• a communication module to obtain measurement data from wireless nodes, forming at least part of a wireless communication network, during a first interval of time, the measurement data including measurements indicating whether there are potential disturbances to operation of the wireless communication network;
• and
• a processor-based resource management controller, coupled to the communication module, configured to:
• determine, based on the obtained measurement data, predictive data representative of future performance of the wireless communication network at one or more future time instances subsequent to an end of the first interval of time, wherein the processor-based resource management controller configured to determine the predictive data is configured to:
• determine the predictive data using a learning engine, trained using attenuation time-series data collected from multiple wireless links defined between one or more pairs of wireless nodes of the wireless communication network during dry and wet periods, applied to the measurement data, wherein the predictive data is representative of future attenuation behavior for wireless links defined between wireless nodes of the wireless communication network for H subsequent time instances following the end of the first interval;
• and
• manage resources of the wireless communication network based, at least in part, on the determined predictive data representative of the future performance of the wireless communication network at the one or more future time instances subsequent to the end of the first interval.

A method comprising:

• obtaining measurement data from at least part of a wireless communication network during a first interval of time, wherein the measurement data includes measurements indicating whether there are potential disturbances to operation of the wireless communication network;
• determining, based on the obtained measurement data, predictive data representative of future performance of the wireless communication network at one or more future time instances subsequent to an end of the first interval of time;
• and
• managing resources of the wireless communication network based, at least in part, on the determined predictive data representative of the future performance of the wireless communication network at the one or more future time instances subsequent to the end of the first interval, wherein managing the resources of the communication network based, at least in part, on the determined predictive data comprises dynamically deriving, based on the determined predictive data, a sequence of network routing steps, during a future time window, to connect wireless nodes of the wireless communication network to achieve an optimized level of network traffic volume allowed through the communication network during the future time window, and wherein dynamically deriving the sequence of network routing steps comprises one or more of:
• a) evaluating traffic volume performance for candidate sequences of link configurations between the wireless nodes during the future time window that maximizes a cumulative sum of network traffic volume flowing through the communication network subject to a constraint of a minimum traffic volume requirement allocated for data sources generating the traffic volume in the communication network;
• or
• b) determining the sequence of network routing steps based on a backward induction optimization approach, including:
• dividing the future time window into two or more temporal sequential sections,
• determining a network routing optimization solution for a later section, from the divided two or more sequential sections, prior to determining a network routing optimization solution for an earlier section from the divided two or more sequential sections, and
• determining the network routing optimization solution for the earlier section based on the network routing optimization solution determined for the later section from the divided two or more sequential sections for the future time window.

A system comprising:

• a communication module to obtain measurement data from wireless nodes, forming at least part of a wireless communication network, during a first interval of time, the measurement data including measurements indicating whether there are potential disturbances to operation of the wireless communication network;
• and
• a processor-based resource management controller, coupled to the communication module, configured to:
• determine, based on the obtained measurement data, predictive data representative of future performance of the wireless communication network at one or more future time instances subsequent to an end of the first interval of time;
• and
• manage resources of the wireless communication network based, at least in part, on the determined predictive data representative of the future performance of the wireless communication network at the one or more future time instances subsequent to the end of the first interval, wherein the controller configured to manage the resources of the communication network based, at least in part, on the determined predictive data is configured to dynamically derive, based on the determined predictive data, a sequence of network routing steps, during a future time window, to connect wireless nodes of the wireless communication network to achieve an optimized level of network traffic volume allowed through the communication network during the future time window, and wherein the processor-based resource management controller configured to dynamically derive the sequence of network routing steps is configured to perform one or more of:
• evaluate traffic volume performance for candidate sequences of link configurations between the wireless nodes during the future time window that maximizes a cumulative sum of network traffic volume flowing through the wireless communication network subject to a constraint of a minimum traffic volume requirement allocated for data sources generating the traffic volume in the wireless communication network;
• or
• determine the sequence of network routing steps based on a backward induction optimization approach, including to:
• divide the future time window into two or more temporal sequential sections,
• determine a network routing optimization solution for a later section, from the divided two or more sequential sections, prior to determining a network routing optimization solution for an earlier section from the divided two or more sequential sections, and
• determine the network routing optimization solution for the earlier section based on the network routing optimization solution determined for the later section from the divided two or more sequential sections for the future time window.