Abstract: A mechanism is described for facilitating portable, reusable, and shareable Internet of Things-based services and resources according to one embodiment. A method of embodiments, as described herein, includes wherein receiving a recipe request for selecting a recipe relating to Internet of Things (IoT) services, where selecting includes at least one of searching the recipe and modifying the recipe. The method may further include selecting the recipe, where the recipe includes a set of resource requirements and business logic. The method may further include modifying the set of resource requirements, where the modified set of resource requirements is associated with the business logic to modify the recipe, and deploying the modified recipe at one or more computing devices, where the modified recipe to facilitate management for one or more IoT devices at one or more locations.

Abstract: Systems and methods for autonomously generating temporally limited targeted offers and/or directed advertisements based on detected interaction between customers and products are provided. A regional monitoring system may detect the movement of a customer in the establishment. A customer that moves within a limited area of the establishment may be identified by targeted offer distribution circuitry as interested in one or more products in the area. A customer interest monitoring device detects interaction between the customer and the product. Using information obtained from the customer interest monitoring device, the targeted offer distribution circuitry autonomously generates a temporally limited targeted offer or directed advertisement. The targeted offer distribution circuitry delivers the targeted offer or directed advertisement to an output device proximate the customer or to a processor-based device or media carried or possessed by the customer.

Abstract: Classification techniques are disclosed that take into account the “cost” of each type of classification error for minimizing total cost of errors. In one example embodiment, a pre-trained cost-sensitive auto-encoder can be used in combination with a training (fine-tuning) stage for cost-sensitive deep learning. Thus, cost information is effectively combined with deep learning by modifying the objective function in the pre-training phase. By minimizing the modified objective function, the auto-encoder not only tries to capture underlying pattern, it further “learns” the cost information and “stores” it in the structure. By later fine-tuning at the training stage, the classification system yields improved performance (lower cost) than a typical classification system that does not take cost information into account during pre-training.

Abstract: A particular device is provided with a communications module to receive signals of a plurality of devices within range of the particular device and further provisioned with grouping logic. The grouping logic is executable by one or more processors to determine from each of the signals a respective identifier for each of the plurality of devices, determine, based at least in part on the identifiers, that a particular subset of the plurality of devices are also included with the particular device in a particular one of a plurality of defined groups, and converge data received from the particular subset of devices based on the particular group.

Abstract: This disclosure presents embodiments for performing privacy-preserving distributed computing. For example, a workload that includes a plurality of tasks may be identified, and a workload graph may be generated to represent the workload. In addition, a device connectivity graph with connectivity information for a plurality of processing devices may be identified. A privacy policy associated with the workload, along with privacy level information associated with the plurality of processing devices, may also be identified. A privacy constraint may then be identified based on the privacy policy and the privacy level information. A workload schedule that includes a mapping of the workload onto the plurality of processing devices may be determined based on the privacy constraint, the workload graph, and the device connectivity graph. The workload schedule may be sent to the plurality of processing devices, which may execute the workload based on the workload schedule.

Abstract: An embodiment of a semiconductor package apparatus may include technology to associate an asset to a fixture with a device positioned proximate to the fixture, and determine a location of the fixture based on a location of the device. Other embodiments are disclosed and claimed. Non-limiting example applications may include shipping, logistics, warehouse asset tracking, retail, etc.

Abstract: A simulation is defined to include a particular test environment and one or more device models, where each device model simulates a respective device, the test environment simulates a physical environment, and the one or more device models are populated in the IoT testing environment to model a deployment of devices corresponding to the device models in the physical environment. Modeled device data is generated, based on the one or more device models, to simulate data generated by corresponding devices. The modeled device data is transmitted to an application, and application data is received in response from the application and used in the simulation.

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: 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: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data captured by one or more sensors associated with a first device. Further, the processor comprises circuitry to: access the sensor data captured by the one or more sensors associated with the first device; determine that an incident occurred within a vicinity of the first device; identify a first collection of sensor data associated with the incident, wherein the first collection of sensor data is identified from the sensor data captured by the one or more sensors; preserve, on the memory, the first collection of sensor data associated with the incident; and notify one or more second devices of the incident, wherein the one or more second devices are located within the vicinity of the first device.

Abstract: Localization data is accessed, which is based on information collected by one or more sensor devices deployed within an environment. The localization data identifies presence of a person within a particular location within the environment. A feature vector is access, which includes values to describe a plurality of different physical characteristics of the person as measured by a set of one or more sensor devices deployed within the environment when the person was present within the particular location. An emotion of the person is determined from the feature vector data, and the emotion is associated with the particular location within the environment. An emotion heat map is generated of the environment to map emotional responses determined within the environment to specific locations within the environment.

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: User inputs received through a graphical user interface of a programming tool are used to define a set of capability abstractions for a particular application and further define, based on the one or more user inputs, relationships between the set of capability abstractions. The particular application is to utilize a machine-to-machine network, and the set of capability abstractions include: a sensor capability abstraction, an actuator capability abstraction, a computation logic capability abstraction, an input user interface (UI) capability abstraction, and an output UI capability abstraction for the particular application. The relationships include a relationship between the input UI capability abstraction and the computation logic capability abstraction, where the input UI capability is to provide an input to the computation logic capability abstraction.

Abstract: In one embodiment, an apparatus may comprise a sensor to detect a plurality of radio signals from one or more transmitters. The apparatus may further comprise a processor to: identify the plurality of radio signals detected by the sensor; detect a proximity of one or more assets based on the plurality of radio signals, wherein the one or more assets are associated with the one or more transmitters; identify the one or more assets based on an identity of the one or more transmitters, wherein each transmitter is associated with a particular asset; identify a plurality of signal characteristics associated with the plurality of radio signals; detect a proximity of a human based on the plurality of signal characteristics; and detect one or more human-asset interactions based on the plurality of signal characteristics.

Abstract: In one embodiment, an apparatus comprises processing circuitry to: receive, via a network interface, a video stream comprising a plurality of video frames; identify a plurality of dependencies among the plurality of video frames; identify, based on the plurality of dependencies, a first subset of video frames to be transmitted using a first transmission method and a second subset of video frames to be transmitted using a second transmission method, wherein the first subset of video frames and the second subset of video frames are identified from the plurality of video frames, and wherein the first transmission method provides a higher quality of service than the second transmission method; transmit, via the network interface, the first subset of video frames to a corresponding destination using the first transmission method; and transmit, via the network interface, the second subset of video frames to the corresponding destination using the second transmission method.

Abstract: In one embodiment, an apparatus comprises processing circuitry to: receive, via a communication interface, a compressed video stream captured by a camera, wherein the compressed video stream comprises: a first compressed frame; and a second compressed frame, wherein the second compressed frame is compressed based at least in part on the first compressed frame, and wherein the second compressed frame comprises a plurality of motion vectors; decompress the first compressed frame into a first decompressed frame; perform pixel-domain object detection to detect an object at a first position in the first decompressed frame; and perform compressed-domain object detection to detect the object at a second position in the second compressed frame, wherein the object is detected at the second position in the second compressed frame based on: the first position of the object in the first decompressed frame; and the plurality of motion vectors from the second compressed frame.

Abstract: In one embodiment, an apparatus comprises a display interface to communicate with a display device and a processor. The processor is to: identify a mobile transaction associated with a user, wherein the mobile transaction comprises a potential transaction to be completed using a mobile device of the user; access transaction data associated with the mobile transaction; encrypt the transaction data based on an encryption key associated with the user; generate a visual code associated with the encrypted transaction data, wherein the visual code comprises visually encoded data, and wherein the visual code is generated within a protected execution environment of the processor; establish a protected datapath between the processor and the display device via the display interface; and transmit the visual code to the display device via the protected datapath, wherein the visual code is to be displayed on the display device.

Abstract: Techniques related to applying computer vision to decompressed video are discussed. Such techniques may include generating a region of interest in an individual video frame by translating spatial indicators of a first detected computer vision result from a reference video frame to the individual video frame and applying a greater threshold within the region of interest than outside of the region of interest for computer vision evaluation in the individual frame.

Abstract: A gateway is provided with configuration management logic to identify a set of configurations corresponding to a deployment of a particular application, and automatically send corresponding configuration data to a set of devices in range of the gateway. Service management logic of the gateway determines that assets on the set of devices correspond to one or more asset abstractions defined for the particular application, where the configuration data is sent to the set of devices based on the assets corresponding to the asset abstractions. Sensor data is received during the deployment as generated by a sensor asset of one of the devices, the sensor data is processed according to service logic of the particular application to generate a result, and actuating data is generated and sent during the deployment to an actuator asset on the set of devices based on the result.

Abstract: A registrar device is delegated enrollment authority for a group by a group authority. The registrar device identifies a particular asset device in association with a reconfiguration of a machine-to-machine (M2M) system that includes the group. The registrar device identifies an enrollment request for enrollment of the particular asset device within the group of devices in association with the reconfiguration of the M2M system and communicates with the particular device to initiate enrollment of the particular device within the group of devices, where enrollment of the particular device includes provisioning the particular device with a group access credential.