Abstract:
In one embodiment, a time at which a first device in a frequency-hopping communication network is expected to transmit a data message is determined. A first schedule is then generated based on the determined time, and the first schedule is overlaid on a frequency-hopping schedule for a second device in the network. The first schedule defines a first timeslot during which the second device listens for the data message, while the frequency-hopping schedule defines second timeslots during which the second device listens for data messages from other devices in the network. Notably, a duration of the first timeslot is greater than respective durations of the second timeslots.
Abstract:
In one embodiment, an aggregating node receives feedback messages from one or more destination nodes in the network. The destination nodes are designated to receive data as packets from a source node using rateless coding. Further, the feedback messages indicate whether packets are needed at a corresponding destination node to complete the data. Then, the feedback messages are aggregated into a single aggregated message, and the aggregated message is transmitted toward the source node.
Abstract:
In one embodiment, network parameters are dynamically adjusted using weather forecasts. The embodiments include determining a weather forecast that predicts a weather condition proximate to a network. Network parameters are then selected for adjustment based on the predicted weather condition. The selected network parameters are adjusted to improve performance of the network in response to the predicted weather condition.
Abstract:
In one embodiment, a first node in a shared-media communication network may receive a message indicated a scheduled downtime of a second node located between the first node and a destination. The first node may determine whether to perform a search for an alternate route toward the destination. In response to determining to perform the search, the first node may perform the search for an alternate route toward the destination for use at least during the scheduled downtime.
Abstract:
In one embodiment, techniques are shown and described relating to a Hidden Markov Model based architecture to monitor network node activities and predict relevant periods. In particular, in one embodiment, a device determines a statistical model for each of one or more singular-node traffic profiles (e.g., based on one or more Hidden Markov Models (HMMs) each corresponding to a respective one of the one or more traffic profiles). By analyzing respective traffic from individual nodes in a computer network, and matching the respective traffic against the statistical model for the one or more traffic profiles, the device may detecting a matching traffic profile for the individual nodes in a computer network. In addition, the device may predict relevant periods of traffic for the individual nodes by extrapolating a most-likely future sequence based on prior respective traffic of the individual nodes and the corresponding matching traffic profile.
Abstract:
In one embodiment, techniques are shown and described relating to traffic-based inference of influence domains in a network by using learning machines. In particular, in one embodiment, a management device computes a time-based traffic matrix indicating traffic between pairs of transmitter and receiver nodes in a computer network, and also determines a time-based quality parameter for a particular node in the computer network. By correlating the time-based traffic matrix and time-based quality parameter for the particular node, the device may then determine an influence of particular traffic of the traffic matrix on the particular node.
Abstract:
In one embodiment, a state tracking engine (STE) defines one or more classes of elements that can be tracked in a network. A set of elements to track is determined from the one or more classes, and the set of elements is tracked in the network. Access to the tracked set of elements then provided via one or more corresponding application programming interfaces (APIs). In another embodiment, a metric computation engine (MCE) defines one or more network metrics to be tracked in the network. One or more tracked elements are received from the STE. The one or more network metrics are tracked in the network based on the received one or more tracked elements. Access to the tracked network metrics is then provided via one or more corresponding APIs.
Abstract:
In one embodiment, nodes are polled in a network for Quality of Service (QoS) measurements, and a QoS anomaly that affects a plurality of potentially faulty nodes is detected based on the QoS measurements. A path, which traverses the plurality of potentially faulty nodes, is then computed from a first endpoint to a second endpoint. Also, a median node that is located at a point along the path between the first endpoint and the second endpoint is computed. Time-stamped packets are received from the median node, and the first endpoint and the second endpoint of the path are updated based on the received time-stamped packets, such that an amount of potentially faulty nodes is reduced. Then, the faulty node is identified from a reduced amount of potentially faulty nodes.
Abstract:
In one embodiment, a root of a directed acyclic graph (DAG) may determine transmission of critical traffic from a first device to a second device in a computer network using the DAG, and may also determine a maximum tolerable delay of the critical traffic. As such, the root may compute, based on a known topology of the computer network, a constrained shortest path first (CSPF) point-to-point (P2P) path from the first device to the second device to meet the maximum tolerable delay. The root may then inform the first device of the P2P path to the second device to cause the first device to use the P2P path for the critical traffic.
Abstract:
In one embodiment, a client device determines when it is coupled to an IoT/LLN device to establish and enable an IP link between a headset interface on the client device and a signal interface on the IoT/LLN device. Once the IP link is established, a duplex data signal is transmitted between the client device and the IoT/LLN device, via the IP link.