Abstract: A computer-implemented method is provided for discovering heterogenous neighbors in coexisting IoT networks including at least one Wi-Fi device and at least one of Zigbee coordinators, ZigBee routers and ZigBee end devices. The method includes generating a broadcast packet such that the broadcast packet emulates a ZigBee broadcast frame, transmitting the emulated broadcast packet using a transceiver of the at least one Wi-Fi device according to cross-technology communication (CTC) method, wherein the emulated broadcast packet is configured to trigger the at least one of the Zigbee coordinators and ZigBee routers having received the emulated broadcast packet to rebroadcast the received packet.

Abstract: A computer-implemented method is provided for performing energy disaggregation of a distribution system-level net-load measurements using continuous-point-on-wave (CPOW) measurement units.

Abstract: A computer-implemented method is provided for training a global machine learning model using a learning server and a set of vehicle agents connected to roadside units (RSUs). The method includes steps of selecting vehicle agents from a pool of the vehicle agents connected to the RSUs, associating the selected vehicle agents and the RSUs respectively based on distances from the selected vehicle agents to the RSUs configured to provide measurements of the distances to the learning server, and transmitting a global model, a selected agent set and deadline thresholds in each global training round to the RSUs configured to transmit the global model and training deadlines to the selected vehicle agents. The associated RSUs compute the training deadlines of the corresponding selected vehicle agents and the selected vehicle agents locally train the global model independently using the local datasets collected by the on-board sensors of the selected vehicle agents to generate locally trained models.

Abstract: A method for semi-persistent scheduling (SPS) based resource allocation in a private 5G network comprises receiving location data of a plurality of user equipment (UEs) in the private 5G network in a last subframe of respective current communication periods of the UEs and determining distance distribution of the UEs in a subsequent communication period of a candidate UE of the plurality of UEs, based on the respective location data of the UEs. The method further comprises determining signal to noise ratio expectation of the UEs for the subsequent communication period, solving an optimization problem optimizing an expectation of data to be carried by each of the one or more SPS channels in the subsequent communication period, to obtain scheduling result for each of the one or more SPS channels and generating resource allocation data for the candidate UE based on the scheduling result.

Abstract: A method for communication between a communication device and a base station in a private 5G network where the communication device and the base station have a base period of transmission defined there between as a minimal scheduling period, comprises transmitting location data of the communication device in a last subframe of a current communication period of the communication device. The method further comprises receiving resource allocation data including a transmission period widening factor for one or more subsequent communication periods of the communication device and computing a semi-persistent scheduling (SPS) period as a subsequent communication period of the one or more subsequent communication periods, based on the transmission period widening factor and the base period of transmission. Uplink communication is performed in the one or more subsequent periods, based on the resource allocation data and the SPS period.

Abstract: A wireless smart utility network (Wi-SUN) device participating in a Wi-SUN network for coexistence with a Wi-Fi HaLow network sharing frequency spectra between the networks is provided.

Abstract: A computer-implemented pre-processed time-delay autoencoder based anomaly detection method are provided for detecting anomalous states of machines arranged in a factory automation (FA) system or a manufacturing production line.

Abstract: A computer-executed method is provided for IEEE 802.15.4 devices based on an active carrier sense based carrier-sense multiple access with collision avoidance (ACS-CSMA/CA) control program and standard CSMA/CA control program for coexistence of an IEEE 802.15.4 network composing of IEEE 802.15.4 devices and IEEE 802.11 network composing of IEEE 802.11 devices sharing frequency spectra between the networks. The computer-executed method is provided on an IEEE 802.15.4 device, and causes a processor of the IEEE 802.15.4 device to perform steps that include estimating a severity of IEEE 802.11 interference based on a severity estimation metric, selecting the ACS-CSMA/CA control program based on the estimated severity.

Abstract: A node device for forming a multi-hop network is provided. The node device is configured to avoid interference from coexisting interfering networks and includes a transceiver configured to receive and transmit data with respect to a Destination Oriented Directed Acyclic Graph (DODAG) Information Object message (DIO message), a memory configured to store computer executable programs including an interfered-node count (IC), single-rate link count (SLC), multi-rate link count (MLC), hop count (HP), path communication latency (PCL) and an interference efficient and multi-rate supported routing program CoM-RPL, and a processor configured to perform steps of the computer executable programs. The steps include determining if the received DIO message indicates a new DODAG or an existing DODAG.

Abstract: A distributed machine learning based traffic prediction method is provided for predicting traffic of roads. In this case, the distributed machine learning based traffic prediction method includes distributing global multi-task traffic models by a learning server to learning agents to locally train the traffic models, uploading locally trained traffic models by learning agents to the learning server, updating global multi-task traffic models by the learning server using locally trained traffic model parameters acquired from learning agents, generating a time-dependent global traffic map by the learning server using the well trained global multi-task traffic models, distributing the time-dependent global traffic map to vehicles traveling on the roads, and computing an optimal travel route with the least travel time by a vehicle using the time-dependent global traffic map based on a driving plan.

Abstract: A node device for forming a multi-hop network is provided. The node device is configured to avoid interference from coexisting interfering networks and includes a transceiver configured to receive and transmit data with respect to a Destination Oriented Directed Acyclic Graph (DODAG) Information Object message (DIO message), a memory configured to store computer executable programs including an interfered-node count (IC), single-rate link count (SLC), multi-rate link count (MLC), hop count (HP), path communication latency (PCL) and an interference efficient and multi-rate supported routing program CoM-RPL, and a processor configured to perform steps of the computer executable programs. The steps include determining if the received DIO message indicates a new DODAG or an existing DODAG.

Abstract: A system for a region including a processor to receive data including weather and component data. Generate a dataset for the region to identify weather events and a plurality of model forcings converted into tabular form. A machine learning (ML) model for each model forcing for each component uses dataset. Iteratively, for each weather event: identify for each component a corresponding model forcing with weather variables. Generate an output value for ML model corresponding to the identified components to the set of weather variables and update ML model. Receive observed data over time periods of an impending weather (IW) event. Iteratively, for each time period: identify, for each component a corresponding model forcing with weather variables and update ML model. Generate for the updated ML model, an output value predicting a component status as a failed or not failed for the time period.

Abstract: A system for a region including a processor to receive data including weather and component data. Generate a dataset for the region to identify weather events and a plurality of model forcings converted into tabular form. A machine learning (ML) model for each model forcing for each component uses dataset. Iteratively, for each weather event: identify for each component a corresponding model forcing with weather variables. Generate an output value for ML model corresponding to the identified components to the set of weather variables and update ML model. Receive observed data over time periods of an impending weather (IW) event. Iteratively, for each time period: identify, for each component a corresponding model forcing with weather variables and update ML model. Generate for the updated ML model, an output value predicting a component status as a failed or not failed for the time period.

Abstract: A traffic control system for controlling traffic at interconnected intersections is provided, where the system comprises a receiver that receives traffic data that indicates states of vehicles approaching an intersection of the interconnected intersections and directions of the vehicles exiting the intersection. Further, the system comprises a processor that determines intersection crossing times and velocities of vehicles approaching the intersection by minimizing at least one of a total travel time or a maximum travel time of the vehicles for crossing the intersection. The contribution of each vehicle of the vehicles approaching the intersection in the at least one of a total travel time or a maximum travel time is weighted based on directions of the vehicles and traffic at next intersection. Further, the system comprises a transmitter that transmits the intersection crossing times and velocities to the vehicles exiting the intersection for controlling the traffic at the interconnected intersections.

Abstract: A wireless smart utility network (Wi-SUN) device participating in a Wi-SUN network for coexistence with a Wi-Fi HaLow network sharing frequency spectra between the networks is provided.

Abstract: The communication system is provided, where the communication system comprises multiple cluster distributed antenna system (MC-DAS) network and a controller. Each cluster in the MC-DAS comprises a cluster master (CM) and remote radio units (RRU), which are in the coverage area of the controller. The controller and the DAS clusters are synchronized using a hierarchical precision time protocol (HPTP). Each DAS cluster is configured to transmit messages independently from other DAS clusters in the plurality of DAS clusters using a distributed cyclic delay diversity (CDD) scheme with a determined length of a cyclic prefix. The controller further comprises a controller configured to transmit a message from the controller to a receiver through one or more DAS clusters of the plurality of the DAS clusters.

Abstract: Systems and methods for scheduling data traffic in a wireless time sensitive network (TSN). A computer configured to synchronize clocks of all nodes with a common clock in the TSN. Obtain data traffic information for the TSN using a network scheduler to establish routing paths. Determine routing paths using the obtained data traffic information and stored routing information via a memory. Compute a link communication delay for each link of the one or more relay nodes connecting a source node to a destination node of the TSN for each TSN stream using a network scheduler. Determine interfering links for each wireless link using the network scheduler. Determining a scheduling period using the network scheduler. Determine optimal scheduling using an optimal scheduling module. Generate the gate control list for each egress port of the wired node and the wireless transmitter of the TSN, and begin transmission of the data.

Abstract: A wireless smart utility networks (Wi-SUN) device participating in a Wi-SUN network for coexistence with a Wi-Fi HaLow network sharing frequency spectra between the networks is disclosed. The Wi-SUN device includes a receiver to receive packets of neighbor Wi-SUN devices, a memory configured to store computer executable programs including a Hybrid carrier-sense multiple access (CSMA) control program and Wi-SUN Backoff control Program, a processor configured to execute the Hybrid CSMA control program, including instructions that cause the processor to perform steps of estimating a severity of Wi-Fi Halow interference based on a severity detection method, switching a backoff mode between predetermined backoff modes in response to the estimated severity, and performing a backoff procedure according to the selected backoff mode, and a transmitter to transmit packets.

Abstract: A system jointly controls vehicles according to traffic configuration of a zone of intersection of a first road and a second road. The intersection zone includes a sequencing zone and a control zone covering sections of the first and the second road in proximity to the intersection. The system groups vehicles traveling within the sequencing zone on the first and the second roads into a set of groups of the vehicles and prevents the vehicles of different groups to travel concurrently in the control zone such that the first vehicle of the following group cannot pass the last vehicle of the preceding group. The system determines motion trajectories for the vehicles of the same group traveling in the control zone on the first and the second roads to pass the intersection and transmits the motion trajectories to the corresponding vehicles.

Abstract: Systems and methods for scheduling data traffic in a wireless time sensitive network (TSN). A computer configured to synchronize clocks of all nodes with a common clock in the TSN. Obtain data traffic information for the TSN using a network scheduler to establish routing paths. Determine routing paths using the obtained data traffic information and stored routing information via a memory. Compute a link communication delay for each link of the one or more relay nodes connecting a source node to a destination node of the TSN for each TSN stream using a network scheduler. Determine interfering links for each wireless link using the network scheduler. Determining a scheduling period using the network scheduler. Determine optimal scheduling using an optimal scheduling module. Generate the gate control list for each egress port of the wired node and the wireless transmitter of the TSN, and begin transmission of the data.