Alice Leung

On Discovery of Traveling Companions from Streaming Trajectories

The advance of object tracking technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data stream. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions) from trajectory stream. Such technique has broad applications in the areas of scientific study, transportation management and military surveillance. To discover traveling companions, the monitoring system should cluster the objects of each snapshot and intersect the clustering results to retrieve moving-together objects. Since both clustering and intersection steps involve high computational overhead, the key issue of companion discovery is to improve the algorithms efficiency. We propose the models of closed companion candidates and smart intersection to accelerate data processing. A new data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery on trajectory stream. The traveling buddies are micro-groups of objects that are tightly bound together. By only storing the object relationships rather than their spatial coordinates, the buddies can be dynamically maintained along trajectory stream with low cost. Based on traveling buddies, the system can discover companions without accessing the object details. The proposed methods are evaluated with extensive experiments on both real and synthetic datasets. The buddy-based method is an order of magnitude faster than existing methods. It also outperforms other competitors with higher precision and recall in companion discovery.

A framework of traveling companion discovery on trajectory data streams

The advance of mobile technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data streams. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions) from trajectory data streams. Such technique has broad applications in the areas of scientific study, transportation management, and military surveillance. To discover traveling companions, the monitoring system should cluster the objects of each snapshot and intersect the clustering results to retrieve moving-together objects. Since both clustering and intersection steps involve high computational overhead, the key issue of companion discovery is to improve the efficiency of algorithms. We propose the models of closed companion candidates and smart intersection to accelerate data processing. A data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery from trajectory streams. The traveling buddies are microgroups of objects that are tightly bound together. By only storing the object relationships rather than their spatial coordinates, the buddies can be dynamically maintained along the trajectory stream with low cost. Based on traveling buddies, the system can discover companions without accessing the object details. In addition, we extend the proposed framework to discover companions on more complicated scenarios with spatial and temporal constraints, such as on the road network and battlefield. The proposed methods are evaluated with extensive experiments on both real and synthetic datasets. Experimental results show that our proposed buddy-based approach is an order of magnitude faster than the baselines and achieves higher accuracy in companion discovery.

Trustworthiness analysis of sensor data in cyber-physical systems

A Cyber-Physical System (CPS) is an integration of sensor networks with informational devices. CPS can be used for many promising applications, such as traffic observation, battlefield surveillance, and sensor-network-based monitoring. One key issue in CPS research is trustworthiness analysis of sensor data. Due to technology limitations and environmental influences, the sensor data collected by CPS are inherently noisy and may trigger many false alarms. It is highly desirable to sift meaningful information from a large volume of noisy data. In this study, we propose a method called Tru-Alarm, which increases the capability of a CPS to recognize trustworthy alarms. Tru-Alarm estimates the locations of objects causing alarms, constructs an object-alarm graph and carries out trustworthiness inference based on the graph links. The study also reveals that the alarm trustworthiness and sensor reliability could be mutually enhanced. The property is used to help prune the large search space of object-alarm graph, filter out the alarms generated by unreliable sensors and improve the algorithm@?s efficiency. Extensive experiments are conducted on both real and synthetic datasets, and the results show that Tru-Alarm filters out noise and false information efficiently and effectively, while ensuring that no meaningful alarms are missed.

Multidimensional Sensor Data Analysis in Cyber-Physical System: An Atypical Cube Approach

Cyber-Physical System (CPS) is an integration of distributed sensor networks with computational devices. CPS claims many promising applications, such as traffic observation, battlefield surveillance, and sensor-network-based monitoring. One important topic in CPS research is about the atypical event analysis, that is, retrieving the events from massive sensor data and analyzing them with spatial, temporal, and other multidimensional information. Many traditional methods are not feasible for such analysis since they cannot describe the complex atypical events. In this paper, we propose a novel model of atypical cluster to effectively represent such events and efficiently retrieve them from massive data. The basic cluster is designed to summarize an individual event, and the macrocluster is used to integrate the information from multiple events. To facilitate scalable, flexible, and online analysis, the atypical cube is constructed, and a guided clustering algorithm is proposed to retrieve significant clusters in an efficient manner. We conduct experiments on real sensor datasets with the size of more than 50 GB; the results show that the proposed method can provide more accurate information with only 15% to 20% time cost of the baselines.

The Information Funnel: Exploiting Named Data for Information-Maximizing Data Collection

This paper describes the exploitation of hierarchical data names to achieve information-utility maximizing data collection in social sensing applications. We describe a novel transport abstraction, called the information funnel. It encapsulates a data collection protocol for social sensing that maximizes a measure of delivered information utility, that is the minimized data redundancy, by diversifying the data objects to be collected. The abstraction leverages named-data networking, a communication paradigm where data objects are named instead of hosts. We argue that this paradigm is especially suited for utility-maximizing transport in resource constrained environments, because hierarchical data names give rise to a notion of distance between named objects that is a function of only the topology of the name tree. This distance, in turn, can expose similarities between named objects that can be leveraged for minimizing redundancy among objects transmitted over bottlenecks, thereby maximizing their aggregate utility. With a proper hierarchical name space design, our protocol prioritizes transmission of data objects over bottlenecks to maximize information utility, with very weak assumptions on the utility function. This prioritization is achieved merely by comparing data name prefixes, without knowing application-level name semantics, which makes it generalizable across a wide range of applications. Evaluation results show that the information funnel improves the utility of the collected data objects compared to other lossy protocols.

Information-maximizing caching in Ad Hoc networks with named data networking

Named data networking has emerged as a new architectural paradigm that replaces host-based addressing in todays networks with content-centric addressing. Rather than assigning IP addresses to hosts, it assigns names to data objects and supports hierarchical name spaces. This paper explores the benefits of naming data for the design of information-maximizing caching policies in ad hoc networks. It is shown that the approach allows development of caching policies for ad hoc networks that simultaneously offer (i) better quality of information (in terms of coverage), (ii) higher throughput (in terms of responses per query), and (iii) lower latency.

A Framework of Mining Trajectories from Untrustworthy Data in Cyber-Physical System

A cyber-physical system (CPS) integrates physical (i.e., sensor) devices with cyber (i.e., informational) components to form a context-sensitive system that responds intelligently to dynamic changes in real-world situations. The CPS has wide applications in scenarios such as environment monitoring, battlefield surveillance, and traffic control. One key research problem of CPS is called mining lines in the sand. With a large number of sensors (sand) deployed in a designated area, the CPS is required to discover all trajectories (lines) of passing intruders in real time. There are two crucial challenges that need to be addressed: (1) the collected sensor data are not trustworthy, and (2) the intruders do not send out any identification information. The system needs to distinguish multiple intruders and track their movements. This study proposes a method called LiSM (Line-in-the-Sand Miner) to discover trajectories from untrustworthy sensor data. LiSM constructs a watching network from sensor data and computes the locations of intruder appearances based on the link information of the network. The system retrieves a cone model from the historical trajectories to track multiple intruders. Finally, the system validates the mining results and updates sensors’ reliability scores in a feedback process. In addition, LoRM (Line-on-the-Road Miner) is proposed for trajectory discovery on road networks—mining lines on the roads. LoRM employs a filtering-and-refinement framework to reduce the distance computational overhead on road networks and uses a shortest-path-measure to track intruders. The proposed methods are evaluated with extensive experiments on big datasets. The experimental results show that the proposed methods achieve higher accuracy and efficiency in trajectory mining tasks.

Exploiting Frequency Groups for Broadcasting in Multi-channel Multi-radio Networks

Network-wide broadcasting is a key requirement in military mobile ad hoc and sensor networks for supporting dissemination of routing control, situation reports, and other global traffic. Current methods for network-wide broadcasting are ill-suited for Multi-Channel Multi-Radio (MC-MR) networks because they do not exploit the presence of natural frequency groups for reducing the number of transmissions. We present Complex Activation Broadcasting (CAB) -- a decentralized algorithm for broadcasting in real-world MC-MR networks that captures naturally occurring groups and builds a tree that exploits them. Our approach is based on representing a network as a simplicial complex, which unlike a graph, allows higher-order aggregations necessary for natively capturing groups. Using a special kind of simplicial complex called a neighborhood sub complex, we present an algorithm for computing a broadcast tree. We have implemented our algorithm within the code base of a real-world military MANET, namely the DARPA WNaN network [1]. We compare the performance of our algorithm with the existing baseline. Results show that our algorithm uses 80 % less transmissions and 50% less receptions.

Social patterns: Community detection using behavior-generated network datasets

A set of behavior rules, personal characteristics, group affiliations and roles was used to generate a dataset of mixed communication actions modeling those at a large organization. Several different approaches to community detection and modeling were applied to this generated dataset, in order to compare the strengths and range of applicability of different algorithms. Graph partitioning methods performed well at assigning membership to formal, exclusive groups such as organizational departments, if there is a priori knowledge of the target number of groups. SSDE-cluster, a fast and scalable algorithm, performed well in detecting normal departments and can be used when the number of groups is not known. It also was able to detect small overlapping groups, but with only moderate accuracy. Clique enumeration performed well in detecting small overlapping groups, when a priori knowledge of average group size was used. Different methods of constructing social network graphs from the mixed communication actions were investigated, as well as different link weighing methods. We conclude that behavior-generated datasets with complex and complete ground truths are useful for collaborative validation of different community and role detection and modeling methods.

Poster abstract: information-maximizing data collection in social sensing using named-data

This poster describes the information funnel, a data collection protocol for social sensing that maximizes a measure of delivered information utility. We argue that information-centric networking (ICN), where data objects are named instead of hosts, is especially suited for utility-maximizing transport in resource-constrained environments, because data names can expose similarities between named objects that can be leveraged for minimizing redundancy, hence maximizing utility. We implement the funnel on the recently proposed named-data networking (NDN) stack, an instance of ICN. With proper name space design, a protocol prioritizes transmission of data items over bottlenecks to maximize information utility, with very weak assumptions on the utility function. This prioritization is achieved merely by comparing data names, without knowing application-level name semantics, which makes it generalizable across a wide range of applications. Evaluation results show the information funnel improves the utility of the collected data objects compared with state-of-the-art solutions.