Abstract: Approaches for determining scheduling assignments for the movement of people along a multi-segment path from a starting location to a destination location, are used to manage crowds, predict crowd behavior, and mitigate crowd turbulence. For example, to mitigate crowd congestion, routing solutions specifying an amount of time to spend at a destination and a departure time can be provided. Itinerary assignments, crowd data, and data associated with an event can be analyzed and weighted to determine scheduling assignments. Scheduling assignments can be validated against current crowd data and event data. Current crowd data and event data and crowd simulation can be used to predict future crowd behavior or crowd problems. Scheduling assignments can be rescheduled to mitigate crowd problems or emergencies.

Abstract: Approaches describe detecting and predicting crowd congestion in real-time for large gatherings, for example large religious mass gatherings. Approaches may utilize image and/or video data, and/or pedestrian trajectory data. The various pieces of information may be identified, extracted, and/or determined from a variety of different disaggregated sources and may be aggregated, and/or determined to generate a congestion detection score and/or score map that is indicative of the degree of crowd congestion in a geographic region and can forecast future crowd congestion in the geographic region. Approaches may be used to monitor a crowd to prevent or mitigate crowd disasters. Moreover, approaches may be used by crowd management entities to timely detect congested regions and manage the crowd efficiently.

Abstract: Approaches for determining scheduling assignments for the movement of people along a multi-segment path from a starting location to a destination location, are used to manage crowds, predict crowd behavior, and mitigate crowd turbulence. For example, to mitigate crowd congestion, routing solutions specifying an amount of time to spend at a destination and a departure time can be provided. Itinerary assignments, crowd data, and data associated with an event can be analyzed and weighted to determine scheduling assignments. Scheduling assignments can be validated against current crowd data and event data. Current crowd data and event data and crowd simulation can be used to predict future crowd behavior or crowd problems. Scheduling assignments can be rescheduled to mitigate crowd problems or emergencies.

Abstract: Approaches for determining scheduling assignments for the movement of people along a multi-segment path from a starting location to a destination location, are used to manage crowds, predict crowd behavior, and mitigate crowd turbulence. For example, to mitigate crowd congestion, routing solutions specifying an amount of time to spend at a destination and a departure time can be provided. Itinerary assignments, crowd data, and data associated with an event can be analyzed and weighted to determine scheduling assignments. Scheduling assignments can be validated against current crowd data and event data. Current crowd data and event data and crowd simulation can be used to predict future crowd behavior or crowd problems. Scheduling assignments can be rescheduled to mitigate crowd problems or emergencies.

Abstract: A vehicle localization system and method uses a hybrid communication layer. The system and method detect a vehicle location and transmit the vehicle location to an application layer using a plurality of communication methods. In addition, the method and system can determine a preferable communication method. The preferable communication method can be dependent on past vehicle locations.

Abstract: An apparatus and method for monitoring mixed pedestrian and motor vehicle traffic. The apparatus include an optical camera, an infrared camera, and a wireless-device counter, each obtaining complementary information regarding the crowd density and flow of the traffic. Performing heat signature analysis and blob detection of the infrared image data to discriminate pedestrians from motor vehicles. Performing optical flow computations with Gaussian pyramids, Histogram of Oriented Gradients calculations, support vector machine (SVM) classifications, and correlation-based clustering using the optical images to determine the crowd density and flow of pedestrians. Incorporating the (SVM) classifications with the blob detection to differentiate pedestrians from motor vehicles. Obtaining an estimate of the number of wireless devices using the wireless-device counter as an additional indicator of the number of pedestrians and motor-vehicles.

Abstract: A sensor network system for seas, rivers and other large water bodies to measure at least one of water current, temperature, salinity, turbidity, viscosity, depth, light-intensity at various depths in the water body. The system includes water-bed sensor nodes, anchored sensor nodes and buoy sensor nodes, and at least one central station configured to collect sea water information from at least one of buoy sensor node, anchored sensor node, and sea-bed sensor nodes. The system analyzes the collected information to identify changes of surrounding water body and updates a water map based on the collected information.

Abstract: A pipeline monitoring system and method include wireless sensor nodes positioned along a length of fluid transportation pipeline. Each of the wireless sensor nodes is configured to measure and classify sensor data collected from one or more associated sensors. The pipeline monitoring system also includes sink nodes interconnected to a respective base station. Each of the sink nodes is configured to analyze the classified sensor data and determine a size and location of a leakage within the fluid transportation pipeline. The pipeline monitoring system also includes a central-controlling infrastructure, interconnected to the base stations. The central-controlling infrastructure is configured to analyze leakage data from the base stations and implement a course of action in response to the analyzed leakage data.

Abstract: A pipeline system and method include a sensor node having one or more sensors configured to measure sensory information collected from fluid flowing through a fluid transportation infrastructure. The sensor node also includes a processor configured to remove noise and unnecessary data from the collected sensory information and to extract statistical attributes associated with a leakage within the fluid transportation infrastructure. The sensor node also includes a trained classifier configured to test real-time sensor data received from the one or more sensors and processed by the processor. The sensor node also includes a transmitter configured to forward classified data from the trained classifier to a sink node within the fluid transportation infrastructure.

Abstract: A single disaster could leave hundred thousands of people injured or even dead. In order for the rescue team to reach this knowledge they should send one of the team members to investigate the scene. Body Sensor Networks are emerging systems that can be easily used to measure patient's physiological data and communicate relevant data to other patient using their smart phones. We propose to use Bloom filters, a space efficient probabilistic data structure, for efficiently collecting the dynamic status of patients in a mass casualty scenario. The collected data is disseminated to all nodes in the network to make it available to the rescue team wherever they arrive. In particular, we show that the members of the most urgent cases found at each node are found to be very close to the set of the actual urgent cases.

Abstract: A vehicle localization system and method uses a hybrid communication layer. The system and method detect a vehicle location and transmit the vehicle location to an application layer using a plurality of communication methods. In addition, the method and system can determine a preferable communication method. The preferable communication method can be dependent on past vehicle locations.

Abstract: A comfort level monitoring system monitors the physical and surrounding environment of an individual includes a wearable item to house a plurality of sensors, at least one physical pressure measuring sensor to detect an amount of external physical pressure exerted on a person wearing the wearable item, a first set of sensors to detect environmental data associated with an environmental comfort level of the person wearing the wearable item, a second set of sensors to detect oxygen ratios associated with the environment surrounding the person, a third set of sensors to detect total energy expenditure of the person, a user interface to receive personal physical data associated with the person, a fuzzy logic circuit to calculate, in response to received inputs, an overall comfort level associated with the person, and an activity alarm triggered when the overall comfort level falls below a predetermined threshold.

Abstract: The environmental test chamber for wireless sensor networks includes a structural enclosure having peripheral walls. A dynamic topology builder is a hanging mechanism disposed in a ceiling portion of the structure and is used to suspend the wireless devices below the ceiling portion via a cable. A weather conditioning machine simulating environmental conditions is disposed in at least one of peripheral walls. A controller sends commands to control the suspension height of the wireless units and to control environmental parameters of the weather conditioning machine. This allows the test chamber to test the communication and networking aspects of the wireless sensor network devices in different environmental conditions, such as sand storms and different temperatures, and humidity levels, within a 3-D topology specified by the controller.

Abstract: The method of predicting tag detection probability for RFID framed slotted ALOHA anti-collision protocols is uses recursive calculations to accurately estimate the probability of discovering RFID tags in a multiple rounds discovery system. First, the method estimates the probability of detecting a given number of tags in a single round. Then, using a probability map, the method estimates the probability of detecting the given number of tags in multiple rounds. The probabilities are used to adjust the number of slots in a frame and the number of interrogation rounds used by the RFID tag reader to minimize collisions and optimize tag reading time.

Abstract: Provided is a method of scheduling timing for communication between sensor nodes in a wireless sensor network, the method including categorizing a plurality of sensor nodes in a cluster into several groups, respectively assigning time slots to the groups, and establishing data communication according to the assigned time slots.