Abstract: Techniques are provided for adaptive distribution of video analysis workload over a network of video processor nodes. The nodes may include, for example, internet protocol (IP) cameras, video recorders and/or data centers. The network may also include a management system configured to assign video analysis tasks to the nodes based on the node resources and predictive modelling of the node workload. The management system may re-distribute the tasks based on performance monitoring. Some assigned tasks may be bound to the node while other tasks may be transferrable, by the node, to other nodes. The nodes may be configured to determine which of the transferrable tasks will be locally executed or transferred based on a check of resource usage against a usage policy that specifies thresholds for the determinations. The nodes may be configured to transmit video analysis packets, including image data, analysis completion status and analysis results, to other nodes.

Abstract: In one embodiment, an apparatus comprises a processor to: identify a workload comprising a plurality of tasks; generate a workload graph based on the workload, wherein the workload graph comprises information associated with the plurality of tasks; identify a device connectivity graph, wherein the device connectivity graph comprises device connectivity information associated with a plurality of processing devices; identify a privacy policy associated with the workload; identify privacy level information associated with the plurality of processing devices; identify a privacy constraint based on the privacy policy and the privacy level information; and determine a workload schedule, wherein the workload schedule comprises a mapping of the workload onto the plurality of processing devices, and wherein the workload schedule is determined based on the privacy constraint, the workload graph, and the device connectivity graph.

Abstract: Multiple devices are detected in an environment and a user input is received to define a relationship between two or more devices in the plurality of devices. A system can determine that a first of the two or more devices includes a sensor resource and a second of the two or more devices includes an actuator resource. Data is identified describing outputs of the first device corresponding to the sensor resource and inputs of the second device corresponding to the actuator resource. A model is generated modeling interoperation of the sensor resource and actuator resource based at least in part on the data.

Abstract: Sensors provisioned on a first device detect a movement of the first device corresponding to the first device changing position within an environment. Location information for the first device is updated based on the position change. A plurality of signals are detected, at the first device, from a second device in the environment, determine, and a distance between the first and second devices is determined based on each of the signals. From the signals, another change in position of the first device within the environment is determined and the location information updated for the first device. The movement is detected at the first device at an instance between two of the plurality of signals, and location information for the first device based on the first position change is updated prior to detection of the later of the two signals in the plurality of signals.

Abstract: Data is received describing a local model of a first device generated by the first device based on sensor readings at the first device and a global model is updated that is hosted remote from the first device based on the local model and modeling devices in a plurality of different asset taxonomies. A particular operating state affecting one or more of a set of devices deployed in a particular machine-to-machine network is detected and the particular machine-to-machine network is automatically reconfigured based on the global model.

Abstract: A set of samples are returned by radio frequency identifier (RFID) reader corresponding to the readings of signals emitted from a particular RFID tag, each sample including a respective set of features identifying values of the attributes of the signals as detected. At least some of the features are provided as inputs to a random forest of decision trees, each providing a prediction that the particular RFID tag is located in one of a plurality of defined zones in a particular environment. From outputs of the plurality of decision trees based on the set of samples, it can be determined that the particular RFID tag is located in a particular one of the plurality of zones at a first instance in time.

Abstract: A plurality of sensor data instances from a sensor device are identified and one or more tensors for a data set based on the plurality of sensor data instances is determined. A predicted value for each instance in the data set based on the tensors, as well as a predicted variance for each instance in the data set. A sampling rate to be applied at the sensor device is determined based on the predicted variances.

Abstract: A plurality of devices are detected within range of a particular device, and capabilities of each of the plurality of devices are determined, as well as a respective taxonomy to be associated with each device based on the device's capabilities. A set of asset abstractions are identified, referenced by a particular software application configured to manage a particular machine-to-machine network. Each asset abstraction can correspond to a respective one of the set of taxonomies, and the particular machine-to-machine network can be built from an instance of each one of the set of asset abstractions. A subset of the plurality of devices can be selected for deployment in the particular machine-to-machine network based on the associated taxonomies, the subset of devices representing an instance of each one of the set of asset abstractions. The subset of devices are then automatically deployed to implement the instance of the particular machine-to-machine network.

Abstract: An adaptive video end-to-end network is described that uses local abstraction. One example includes an image sensor to generate a sequence of images, a processor coupled to the image sensor to analyze the sequence of images to detect an event, to select images related to the event and to generate metadata regarding the event, and a communications interface coupled to the processor to send the metadata information through a network connection to a central node.

Abstract: Various systems and methods for generating navigation route plans based on user intents are described herein. A navigation system is disclosed that generates route plans based on user intents. The system includes a search engine for retrieving location data. The location data includes geolocations and location constraints associated with the geolocations. The system also includes an input device to receive selections of a plurality of geolocations and user intents corresponding to the plurality of geolocations. The system further includes a route generator to generate a route plan including a sequence of the plurality of geolocations. The sequence is based at least in part on comparing the user intents to location constraints associated with the plurality of geolocations. The system additionally includes a display device to present the route plan.

Abstract: Systems, apparatuses and methods may receive, at a local Internet of Things (IOT) device, a request to deploy an IOT application. Additionally, the IOT application may be partitioned into a plurality of atomic nodes, wherein configuration information for the plurality of atomic nodes may be sent, at runtime, to a plurality of remote IOT devices having abstracted resources that support operation of the first plurality of atomic nodes. In one example, the configuration information is sent via a device independent message protocol having a universal namespace.

Abstract: Systems, apparatuses and methods may identify a capability abstraction in a request to configure a first Internet of Things (IOT) application in a physical environment including a plurality of IOT devices and select a resource abstraction from a plurality of resource abstractions based on the capability abstraction. The selected resource abstraction may correspond to a first IOT device in the plurality of IOT devices. Additionally, the first IOT application may be bound with the first IOT device. In one example, first data originating from the first IOT device is received, a first runtime abstraction is selected from a plurality of runtime abstractions, wherein the first runtime abstraction corresponds to the first IOT application, and the first data is sent to the first IOT application via the first runtime abstraction.

Abstract: A set of data is identified that includes a plurality of observed values generated by a plurality of sensor devices located in a plurality of different locations. For each of the plurality of observed values, a modality of the value, a spatial location of the value, and a timestamp of the value is determined. Values for one or more missing values in the set of data are determined from the modalities, spatial locations, and timestamps of the plurality of observed values.

Abstract: An embodiment includes at least one computer readable storage medium comprising instructions that when executed enable a system to: receive (a)(i) first radio signal location data for a first object from a radio sensor; and (a)(ii) first visual signal location data for the first object from a camera sensor; perform feature extraction on (b)(i) the first radio signal location data to determine first extracted radio signal features; and (b)(ii) the first visual signal location data to determine first extracted visual signal features; solve a first association problem between the first extracted radio signal features and the first extracted visual signal features to determine first fused location data; and store the first fused location data in the at least one computer readable storage medium. Other embodiments are described herein.

Abstract: A weighting value is determined for each of a plurality of decision trees in a random forest model hosted on a particular device, where the weighting is based on entropy of the respective decision tree. A new decision tree is received at the particular device and a weighting value is determined for the new decision tree based on entropy of the new decision tree. Based on the determined weighting value, it is determined whether to add the new the decision tree to the random forest model. A classification for data generated at the particular device is predicted using the random forest model.

Abstract: Example task assignment methods disclosed herein for video analytics processing in a cloud computing environment include determining a graph, such as a directed acyclic graph, including nodes and edges to represent a plurality of video sources, a cloud computing platform, and a plurality of intermediate network devices in the cloud computing environment. Disclosed example task assignment methods also include specifying task orderings for respective sequences of video analytics processing tasks to be executed in the cloud computing environment on respective video source data generated by respective ones of the video sources.

Abstract: An anomaly detection model generator accesses sensor data generated by a plurality of sensors, determines a plurality of feature vectors from the sensor data, and executes a plurality of unsupervised anomaly detection machine learning algorithms in an ensemble using the plurality of feature vectors to generate a set of predictions. Respective entropy-based weightings are determined for each of the plurality of unsupervised anomaly detection machine learning algorithms from the set of predictions. A set of pseudo labels is generated based on the predictions and weightings, and a supervised machine learning algorithm uses the set of pseudo labels as training data to generate an anomaly detection model corresponding to the plurality of sensors.

Abstract: An adaptive video end-to-end network is described that uses local abstraction. One example includes an image sensor to generate a sequence of images, a processor coupled to the image sensor to analyze the sequence of images to detect an event, to select images related to the event and to generate metadata regarding the event, and a communications interface coupled to the processor to send the metadata information through a network connection to a central node.

Abstract: A system for locating a mobile device includes an input that accesses a plurality of scans of wireless network access signaling, where the scans indicate received signal measurement results. A similarity measure module executes comparisons between the data of different scans in order to assess the similarity between those scans. The comparisons produce multi-dimensional comparison results. A dimension reduction module reduces dimensionality of the multi-dimensional comparison results to produce a dimension-reduced set of comparison results. A clustering module identifies groupings of similar scans based on the dimension-reduced set of comparison results.

Abstract: A system for locating a mobile device includes an input that accesses a plurality of scans of wireless network access signaling, where the scans indicate received signal measurement results. A similarity measure module executes comparisons between the data of different scans in order to assess the similarity between those scans. The comparisons produce multi-dimensional comparison results. A dimension reduction module reduces dimensionality of the multi-dimensional comparison results to produce a dimension-reduced set of comparison results. A clustering module identifies groupings of similar scans based on the dimension-reduced set of comparison results.