Social groups often exhibit a high degree of dynamism. Some groups thrive, while many others die over time. Modeling group stability dynamics and understanding whether/when a group will remain stable or shrink over time can be important in a number of social domains. In this paper, we study two different types of social networks as exemplar platforms for modeling and predicting group stability dynamics. We build models to predict if a group is going to remain stable or is likely to shrink over a period of time. We observe that both the level of member diversity and social activities are critical in maintaining the stability of groups. We also find that certain 'prolific' members play a more important role in maintaining the group stability. Our study shows that group stability can be predicted with high accuracy, and feature diversity is critical to prediction performance.

Creating floor plans for large areas via manual surveying is labor-intensive and error-prone. In this paper, we present a system, Walk&Sketch, that creates floor plans of an indoor environment by a person walking through the environment at a normal strolling pace and taking videos using a consumer RGB-D camera. The method computes floor maps represented by polylines from a 3D point cloud based on precise frame-to-frame alignment. It aligns a reference frame with the floor and computes the frame-to-frame offsets from the continuous RGB-D input. Line segments at a certain height are extracted from the 3D point cloud, and are merged to form a polyline map, which can be further modified and annotated by users. The explored area is visualized as a sequence of polygons, providing users with the information on coverage. Experiments have done in various areas of an office building and have shown encouraging results.

When a robotic team is deployed to provide sensing and communication support for human activities, it is crucial that the robotic team be able to not only react to the current perceived location of the human of interest, but also predict his/her future locations. In this work we present a novel approach to predicting human motion in indoor environments. Our approach consists of an offline pre-processing phase, in which we analyze a map of the indoor environment and identify distinct areas of interest, such as rooms, and a run-time phase, in which we maintain hypotheses of destinations and probabilistically update these hypotheses based on observations of the human's motion. Our approach creates a probability distribution over future locations for the human user at each time step in the future. Such predictions can be used for the robotic team to effectively track and relay information from the human user to the base station.

Mapping is important for mobile robots in unknown environments. However, existing work largely relies on long-range sensors such as expensive laser range finders and cameras. Little has been done for robots with only short-range sensors such as bumpers or wall sensors. In this paper, we fill in the gap and propose a method for real-time indoor mapping for blind robots, i.e. robots that have only short-range sensors. Several key problems for mapping are addressed in this paper: (1) generating map from a single robot's odometry and short-range sensor data, (2) combining and collating maps from multiple robots to generate a coherent bigger map, and (3) detecting loops in the map and performing loop closure. The method has been implemented on iRobot Create and tested in hallways of office buildings. Experimental results have shown that the system is robust to noisy odometry and obstacles along the walls.

We describe a computational architecture of a collision early-warning system for vehicles and other principals. Early-warnings allow drivers to make good judgments and to avoid emergency stopping or dangerous maneuvering. With many principals (vehicles, pedestrians, bicyclists, etc.) coexisting in a dense intersection, it is difficult to predict, even a few seconds in advance, since there are many possible scenarios. It is a major challenge to manage computational resources and human attention resources so that only the more plausible collisions are tracked, and of those, only the most critical collisions prompt warnings to drivers. In this paper, we propose a two-stage collision risk assessment process, including the following: 1) a preliminary assessment via simple efficient geometric computations, which thoroughly considers surrounding principals and identifies likely potential accidents, and 2) a specialized assessment that computes more accurate collision probabilities via sophisticated statistical inference. The whole process delivers an expected utility assessment to available user interfaces (UIs), allowing the UIs to make discriminating choices of when to warn drivers or other principals.

Mapping and localization for indoor robotic navigation is a well-studied field. However, existing work largely relies on long range perceptive sensors in addition to the robot's odometry, and little has been done with short range sensors. In this paper, we propose a method for real-time indoor mapping using only short range sensors such as bumpers and/or wall sensors that enable wall-following. The method uses odometry data from the robot's wall-following trajectory, together with readings from bumpers and wall sensors. The method first performs trace segmentation by fitting line segments to the noisy trajectory. Given the assumption of approximately rectilinear structure in the floor plans, typical for most indoor environments, a probabilistic rectification process is then applied to the segmented traces to obtain the orthogonal wall outlines. Both segmentation and rectification are performed on-line onboard the robot during its navigation through the environment. The resulting map is a set of line segments that represents the wall outline. The method has been tested in office buildings. Experimental results have shown that the method is robust to noisy odometry and non-rectilinear obstacles along the walls.

In this paper, we describe the architecture of the vision system for the Responsive Mirror, a novel system for retail fitting rooms that enables online social fashion comparisons in physical stores based on multi-camera perception. This vision system provides implicitly controlled real-time interaction for “self ” and “social ” clothing comparisons by automatically tracking user’s motion as she tries on clothes. We describe the key components of the motion-tracking and clothes-recognition systems and evaluate their effectiveness against images collected during a previous user study and a dataset of images representing content from a social fashion network. Author Keywords Ubiquitous computing, computer vision, multi-camera,

There has been a lot of work on localization for sensor networks. Most localization schemes assume an already deployed network. Very little research has been done for active sensor deployment with mobile robots. In this paper, we present a distributed sequential localization process which is tightly integrated with robotic sensor deployment. Being location-aware, sensors can be placed in advantageous locations to avoid big localization errors. In turn, the accurate localization result helps further sensor deployment. We compare our distributed sequential localization scheme with five existing localization algorithms, both through simulations and through repeated runs on a mote testbed. Results show that the distributed sequential localization algorithm produces comparable results to the state-of-the-art centralized localization algorithms. Experiments also show that pre-post processing plays important roles at getting good localization results for all localization algorithms

Location knowledge of nodes in a network is essential for many tasks such as routing, cooperative sensing, or service delivery in ad hoc, mobile, or sensor networks. This paper introduces a novel iterative method ILS for node localization starting with a relatively small number of anchor nodes in a large network. At each iteration, nodes are localized using a least-squares based algorithm. The computation is lightweight, fast, and any-time. To prevent error from propagating and accumulating during the iteration, the error control mechanism of the algorithm uses an error registry to select nodes that participate in the localization, based on their relative contribution to the localization accuracy. Simulation results have shown that the active selection strategy significantly mitigates the effect of error propagation. The algorithm has been tested on a network of Berkeley Mica2 motes with ultrasound TOA ranging devices. We have compared the algorithm with more global methods such as MDS-MAP and SDP-based algorithm both in simulation and on real hardware. The iterative localization achieves comparable location accuracy in both cases, compared to the more global methods, and has the advantage of being fully decentralized.

We consider the problem of information dissemination in wireless ad-hoc networks. Capacity constraints and the varying needs of applications lead to the need for delivering data at varying resolutions as a function of distance. We introduce a primitive, which we call visibility, to quantify the variable-resolution requirement. We design and analyze new variable resolution multicast algorithms based on simple probabilistic schemes to achieve a required visibility. We also examine how multi-path effects influence visibility and propagation algorithms. Finally, we consider the problem of designing the visibility function to maximize an overall utility function (specified by applications) over the network. We derive a condition relating optimal utility and visibility, which can be used to derive the optimal visibility for a class of utility functions. 1

In a sensor network, data routing is tightly coupled to the needs of a sensing task, and hence the application semantics. This paper introduces the novel idea of information-directed routing, in which routing is formulated as a joint optimization of data transport and information aggregation. The routing objective is to minimize communication cost, while maximizing information gain, differing from routing considerations for more general ad hoc networks. The paper uses the concrete problem of locating and tracking possibly moving signal sources as an example of information generation process, and considers two common information extraction patterns in a sensor network: routing a user query from an arbitrary entry node to the vicinity of signal sources and back, or to a prespecified exit node, maximizing information accumulated along the path. We derive information constraints from realistic signal models, and present several routing algorithms that find near-optimal solutions for the joint optimization problem. Simulation results have demonstrated that information-directed routing is a significant improvement over a previously reported greedy algorithm, as measured by sensing quality such as localization and tracking accuracy and communication quality such as success rate in routing around sensor holes.

This paper investigates the problem of designing decentralized representations to support monitoring and inferences in sensor networks. State-space models of physical phenomena such as those arising from tracking multiple interacting targets, while commonly used in signal processing and control, suffer from the curse of dimensionality as the number of phenomena of interest increases. Furthermore, mapping an inference algorithm onto a distributed sensor network must appropriately allocate scarce sensing and communication resources. We address the state-space explosion problem by developing a distributed state-space model that switches between factored and joint state spaces as appropriate. We develop a collaborative group abstraction as a mechanism to effectively support the information ow within and across subspaces of the state-space model, which can be efficiently supported in a communication-constrained network. The approach has been implemented and demonstrated in a simulation of tracking multiple interacting targets.

The ability to characterize sensing quality is central to the design and deployment of practical distributed sensor networks. This paper introduces the concept of a sensing field defining, for each point in the physical space of a phenomenon of interest, a measure of how well a sensor network can sense the phenomenon at that point. Using target localization and tracking as examples, the paper derives an upper bound for this measure of goodness measure, using the Cramer-Rao bound and models of sensor observation and network layout. It then evaluates the validity of statistical observation models used by a family of estimators. Simulation results of applying the analytical analysis to a randomly spaced network are presented.

Sensor tasking is essential to many sensing applications in resource-constrained wireless ad hoc sensor networks. In this paper, we present a multi-step lookahead algorithm for sensor selection and information routing. The algorithm is based on the information-driven sensor querying (IDSQ) that uses mutual information as a utility measure for potential information contribution of individual sensors, and extend it to prediction of information gain over a finite horizon while balancing cost such as the number of communication hops. Simulation results on target tracking problems have shown that the multi-step lookahead algorithm significantly improves the tracking performance compared to the original greedy algorithm, when "sensor holes" are present in a sensor network.

This paper describes information-based approaches to processing and organizing spatially distributed, multimodal sensor data in a sensor network. Energy-constrained networked sensing systems must rely on collaborative signal and information processing (CSIP) to dynamically allocate resources, maintain multiple sensing foci, and attend to new stimuli of interest, all based on task requirements and resource constraints. Target tracking is an essential capability for sensor networks and is used as a canonical problem for studying information organization problems in CSIP. After formulating a CSIP tracking problem in a distributed constrained optimization framework, the paper describes information-driven sensor query and other techniques for tracking individual targets as well as combinatorial tracking problems such as counting targets. Results from simulations and experimental implementations have demonstrated that these information-based approaches are scalable and make efficient use of scarce sensing and communication resources.