Abstract: Techniques for distributed sub-controller permission for control of data-traffic flow within software-defined networking (SDN) mesh networks to limit control plane traffic of the network are described herein. A technique described herein includes a network node of a data-traffic path of an SDN mesh network obtaining SDN sub-controller permission from a border controller of the SDN mesh network. Further, the technique includes suppression of data traffic from sibling and children nodes of data-traffic path allied nodes to the data-traffic path allied nodes. The data-traffic path allied nodes include network nodes that are part of the data-traffic path of the SDN mesh network. Further still, the technique includes the transmission of data across the data-traffic path.

Abstract: In one embodiment, a method comprises: identifying, by a low power and lossy network (LLN) device in a low power and lossy network, a minimum distance value and a distance limit value for limiting multicast propagation, initiated at the LLN device, of a multicast data message in the LLN; and multicast transmitting, by the LLN device, the multicast data message with a current distance field specifying the minimum distance value and a distance limit field specifying the distance limit value, the multicast transmitting causing a receiving LLN device having a corresponding rank in the LLN to respond to the multicast data message by: (1) determining an updated distance based on adding to the current distance field a rank difference between the receiving LLN device and the LLN device, and (2) selectively retransmitting the multicast data message if the updated distance is less than the distance limit value.

Abstract: A cleaning robot, includes a body provided with a dust box and a water tank, a water outlet for discharging liquid in the water tank and a dust suction port connected with the dust box are provided with on a bottom of the body, and the water outlet is disposed rearward of the dust suction port and adjacent thereto.

Abstract: Techniques for utilizing Software-Defined Field-Area Network (SD-FAN) controllers to receive a geographic location and transmission power of individual nodes and generate a geographic location topology of a Field-Area Network (FAN) to provide nodes with location-aware route paths for data transmission. One or more SD-FAN controller(s) may maintain a geographic location database to store the geographic location and transmission power of the individual nodes. Each node may utilize a Destination Address Object to advertise its geographic location and transmission power to the SD-FAN controller. The SD-FAN controller(s) may utilize the geographic location table to generate the geographic location topology of the FAN and determine a location-aware route path for optimized data transmission between nodes in the FAN.

Abstract: A cleaning robot, includes a body provided with a dust box, a water tank, a cleaning unit disposed at a bottom of the body, a water outlet mechanism outputting liquid in the water tank through the water discharge pipeline and a water discharge pipeline independently disposed outside the water tank and at least partially located directly below the dust box, so that the capacity of the water tank is increased, without requiring adding water frequently, thereby prolonging the time of operation, the layout of various parts in the body is more compact and feasible, a longer water discharge pipeline enables the corresponding cleaning unit to be made larger, by which a working area of the robot is expanded and the working efficiency is enhanced, the probability for damage is reduced, and the service life of the machine is prolonged.

Abstract: A method includes identifying a potentially malicious node using a rating assigned to nodes within the network and decrementing the rating based on detected dropped messages to identify a potentially malicious node. The malicious node is identified based on location information obtained from the nodes within the network and comparable distances from the potentially malicious node. The method further includes ending communications with the malicious node and selecting a new parent node based on a presumption that any of the plurality of nodes other than the malicious node are non-malicious.

Abstract: An intelligent transfer vehicle for shared bicycles includes: a cockpit, a vehicle body platform, shared bicycle parking racks, a wheeled device, a power source unit, and a vehicle-mounted unit. The intelligent transfer vehicle combines the function of a parking platform and a transfer platform into one, and can have three transfer modes to reduce reliance on a dispatch center, save costs and improve transfer efficiency.

Abstract: Techniques for distributed sub-controller permission for control of data-traffic flow within software-defined networking (SDN) mesh networks to limit control plane traffic of the network are described herein. A technique described herein includes a network node of a data-traffic path of an SDN mesh network obtaining SDN sub-controller permission from a border controller of the SDN mesh network. Further, the technique includes suppression of data traffic from sibling and children nodes of data-traffic path allied nodes to the data-traffic path allied nodes. The data-traffic path allied nodes include network nodes that are part of the data-traffic path of the SDN mesh network. Further still, the technique includes the transmission of data across the data-traffic path.

Abstract: In one embodiment, a technique for load balancing of throughput for multi-PHY networks using decision trees is provided. A first device of a mesh communication network may collect at least one transmission metric indicative of a primary link and a secondary link between the first device and a second device of the mesh communication network. The first device may provide the at least one transmission metric as input to one or more decision trees comprising one or more attributes that are each indicative of a threshold for a corresponding transmission metric. The first device may obtain an output from the decision tree comprising a selection of either the primary link or the secondary link. The first device may send, based on the output from the decision tree, one or more packets to the second device using the selected link.

Abstract: In one embodiment, a method comprises: determining, by a network device in a wireless data network, a past throughput of broadcast data packets transmitted at broadcast transmission intervals of a prescribed broadcast schedule over a selected measurement interval, the broadcast transmission intervals each adjacent to unicast transmission intervals allocated in the wireless data network, each of the broadcast transmission intervals in the prescribed broadcast schedule initially set at a prescribed duration; predicting, by the network device, a predicted throughput of a future broadcast transmission interval of the prescribed broadcast schedule, for transmission of at least a future broadcast data packet, based on executing a trendline prediction of the predicted throughput using the past throughput over the selected measurement interval; and adjusting the corresponding prescribed duration of the future broadcast transmission interval, relative to the corresponding adjacent unicast transmission interval followi

Abstract: Techniques for utilizing Software-Defined Field-Area Network (SD-FAN) controllers to receive a geographic location and transmission power of individual nodes and generate a geographic location topology of a Field-Area Network (FAN) to provide nodes with location-aware route paths for data transmission. One or more SD-FAN controller(s) may maintain a geographic location database to store the geographic location and transmission power of the individual nodes. Each node may utilize a Destination Address Object to advertise its geographic location and transmission power to the SD-FAN controller. The SD-FAN controller(s) may utilize the geographic location table to generate the geographic location topology of the FAN and determine a location-aware route path for optimized data transmission between nodes in the FAN.

Abstract: In one embodiment, a method comprises: identifying, by a low power and lossy network (LLN) device in a low power and lossy network, a minimum distance value and a distance limit value for limiting multicast propagation, initiated at the LLN device, of a multicast data message in the LLN; and multicast transmitting, by the LLN device, the multicast data message with a current distance field specifying the minimum distance value and a distance limit field specifying the distance limit value, the multicast transmitting causing a receiving LLN device having a corresponding rank in the LLN to respond to the multicast data message by: (1) determining an updated distance based on adding to the current distance field a rank difference between the receiving LLN device and the LLN device, and (2) selectively retransmitting the multicast data message if the updated distance is less than the distance limit value.

Abstract: In one embodiment, a method comprises: detecting, by a root network device in a low power and lossy network (LLN) operating in a downward-routing mode, an outage among at least a substantial number of LLN devices in the LLN; initiating, by the root network device, a dynamic suspension of network operations in the LLN during the outage, including causing existing Internet Protocol (IP) addresses of all the LLN devices to be maintained during the outage, and causing all the LLN devices to limit transmissions to Power Outage Notification (PON) messages, Power Restoration Notification (PRN) messages, or minimal-bandwidth data packets, based on the root network device switching the LLN from the downward-routing mode to a collection-only mode; and selectively restoring, by the root network device, the LLN to the downward-routing mode in response to detecting PRN messages from at least substantially all the substantial number of LLN devices.

Abstract: In one embodiment, a method comprises: generating and storing, by a parent device in a low power lossy network (LLN), first state variables associated with a first execution state of an attached child during an active state of the child; determining a request, received by the parent device, is destined for the child and specifying a second execution state to be executed by the child; selectively updating, by the parent device, the first state variables with second state variables associated with the second execution state identifying an updated execution state for the child; detecting, by the parent device, that the child has transitioned from a sleep state to a second active state, and in response generating an instruction based on the second state variables; and transmitting the instruction to the child during the second active state, causing the child to execute the second execution state.

Abstract: In one embodiment, a method comprises determining, by a network device in a wireless data network, that a prescribed Clear Channel Assessment (CCA) threshold is unsuitable for use on an identified wireless network channel among available wireless network channels, based on a determined variance of a corresponding channel transmit attempt failure rate for the identified wireless network channel, relative to an expected variance in channel transmit attempt failure rates that is determined across the respective available wireless network channels; and selectively adjusting the corresponding CCA threshold for the identified wireless network channel, until a second determined variance of the corresponding channel transmit attempt failure rate, at the identified wireless network channel, falls below a prescribed channel failure rate variance factor relative to the expected variance.

Abstract: In one embodiment, a method comprises: generating and storing, by a parent device in a low power lossy network (LLN), first state variables associated with a first execution state of an attached child during an active state of the child; determining a request, received by the parent device, is destined for the child and specifying a second execution state to be executed by the child; selectively updating, by the parent device, the first state variables with second state variables associated with the second execution state identifying an updated execution state for the child; detecting, by the parent device, that the child has transitioned from a sleep state to a second active state, and in response generating an instruction based on the second state variables; and transmitting the instruction to the child during the second active state, causing the child to execute the second execution state.

Abstract: In one embodiment, a technique for dynamic transmission power in wireless mesh networks using supervised and semi-supervised learning is provided. A first device of a mesh communication network may receive a set of transmission power metrics indicative of network conditions between a second device of the mesh communication network and a plurality of child nodes associated with the second device. The first device may provide the set of transmission power metrics as input to a supervised machine learning process that probabilistically determines one or more minimum transmission power thresholds for nodes of the mesh communication network. The first device may obtain an output from the supervised machine learning process comprising an indication of a particular minimum transmission power threshold for the second device. The first device may control the second device to exchange packets with the plurality of child nodes using the particular minimum transmission power threshold.

Abstract: In one embodiment, a technique for load balancing of throughput for multi-PHY networks using decision trees is provided. A first device of a mesh communication network may collect at least one transmission metric indicative of a primary link and a secondary link between the first device and a second device of the mesh communication network. The first device may provide the at least one transmission metric as input to one or more decision trees comprising one or more attributes that are each indicative of a threshold for a corresponding transmission metric. The first device may obtain an output from the decision tree comprising a selection of either the primary link or the secondary link. The first device may send, based on the output from the decision tree, one or more packets to the second device using the selected link.

Abstract: Techniques and mechanisms for detecting and deducing of urgent messages in low-power and lossy networks (LLNs) using a correlation analysis of the nodes within a network and machine learning (ML) models. Utilizing these techniques, a field network director (FND) of the network can determine neighboring devices within the network. ML models may be utilized to determine that based upon receipt of a power outage notification (PON) message and/or a power restoration notification (PRN) message from nodes, neighboring nodes of the nodes may also have suffered a power outage and/or a subsequent power restoration, even if the FND did not receive a corresponding PON message and/or a corresponding PRN message from the neighboring nodes of the network. Thus, loss of power and subsequent power restoration may be handled for large numbers of neighboring nodes within the network, even when only a few PON messages and/or subsequent PRN messages are received.

Abstract: In one embodiment, a technique for dynamic transmission power in wireless mesh networks using supervised and semi-supervised learning is provided. A first device of a mesh communication network may receive a set of transmission power metrics indicative of network conditions between a second device of the mesh communication network and a plurality of child nodes associated with the second device. The first device may provide the set of transmission power metrics as input to a supervised machine learning process that probabilistically determines one or more minimum transmission power thresholds for nodes of the mesh communication network. The first device may obtain an output from the supervised machine learning process comprising an indication of a particular minimum transmission power threshold for the second device. The first device may control the second device to exchange packets with the plurality of child nodes using the particular minimum transmission power threshold.