Chih-Chieh Hung

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.

Tru-Alarm: Trustworthiness Analysis of Sensor Networks in Cyber-Physical Systems

A Cyber-Physical System (CPS) integrates physical devices (e.g., sensors, cameras) with cyber (or informational)components to form a situation-integrated analytical system that responds intelligently to dynamic changes of the real-world scenarios. One key issue in CPS research is trustworthiness analysis of the observed data: Due to technology limitations and environmental influences, the CPS data are inherently noisy that may trigger many false alarms. It is highly desirable to sift meaningful information from a large volume of noisy data. In this paper, we propose a method called Tru-Alarm which finds out trustworthy alarms and increases the feasibility of CPS. Tru-Alarm estimates the locations of objects causing alarms, constructs an object-alarm graph and carries out trustworthiness inferences based on linked information in the graph. Extensive experiments show that Tru-Alarm filters out noises and false information efficiently and guarantees not missing any meaningful alarms.

Clustering and aggregating clues of trajectories for mining trajectory patterns and routes

In this paper, we propose a new trajectory pattern mining framework, namely Clustering and Aggregating Clues of Trajectories (CACT), for discovering trajectory routes that represent the frequent movement behaviors of a user. In addition to spatial and temporal biases, we observe that trajectories contain silent durations, i.e., the time durations when no data points are available to describe the movements of users, which bring many challenging issues to trajectory pattern mining. We claim that a movement behavior would leave some clues in its various sampled/observed trajectories. These clues may be extracted from spatially and temporally co-located data points from the observed trajectories. Based on this observation, we propose clue-aware trajectory similarity to measure the clues between two trajectories. Accordingly, we further propose the clue-aware trajectory clustering algorithm to cluster similar trajectories into groups to capture the movement behaviors of the user. Finally, we devise the clue-aware trajectory aggregation algorithm to aggregate trajectories in the same group to derive the corresponding trajectory pattern and route. We validate our ideas and evaluate the proposed CACT framework by experiments using both synthetic and real datasets. The experimental results show that CACT is more effective in discovering trajectory patterns than the state-of-the-art techniques for mining trajectory patterns.

Using sensorranks for in-network detection of faulty readings in wireless sensor networks

In this paper, the problem of determining faulty readings in a wireless sensor network without compromising detection of important events is studied. By exploring correlations between readings of sensors, a correlation network is built based on similarity between readings of two sensors. By exploring Markov Chain in the network, a mechanism for rating sensors in terms of the correlation, called SensorRank, is developed. In light of SensorRank, an efficient in-network voting algorithm, called TrustVoting, is proposed to determine faulty sensor readings. Performance studies are conducted via simulation. Experimental results show that the proposed algorithm outperforms majority voting and distance weighted voting, two state-of-the-art approaches for in-network faulty reading detection.

Mining trajectory profiles for discovering user communities

With the rapid development of positioning techniques (e.g., GPS), users can easily collect their trajectories. Furthermore, with the growing of Web 2.0, some web sites allow users to share their own trajectories. In such web sites, users are able to search trajectories that are interested by users. To provide more insights into these trajectories, in this paper, we target at the problem of discovering communities among users, where users in the same community have similar moving behaviors. Note that moving behaviors are usually represented as trajectory patterns where a user frequently travels. In this paper, we propose a framework to discover communities of users. Explicitly, we adopt a probabilistic suffix tree (abbreviated as PST) as a trajectory profile which truly reflects user moving behavior of a user. In light of trajectory profiles, we further formulate a similarity measurement among trajectory profiles of users. Based on the similarity measurement, we develop algorithm CI (standing for Community Identification) to discover user communities. Furthermore, for the same community, one representative PST is selected. When a new user is added, one could simply derive the similarity measurement by comparing representative PSTs, which is able to efficiently determine which community this new user should join. To evaluate our proposed methods, we conduct experiments on the synthetic dataset generated from one real dataset. Experimental results show that the trajectory profile proposed can effectively reflect user moving behavior, and our proposed methods can accurately identify communities among users.

Energy-Aware Set-Covering Approaches for Approximate Data Collection in Wireless Sensor Networks

To conserve energy, sensor nodes with similar readings can be grouped such that readings from only the representative nodes within the groups need to be reported. However, efficiently identifying sensor groups and their representative nodes is a very challenging task. In this paper, we propose a centralized algorithm to determine a set of representative nodes with high energy levels and wide data coverage ranges. Here, the data coverage range of a sensor node is considered to be the set of sensor nodes that have reading behaviors very close to the particular sensor node. To further reduce the extra cost incurred in messages for selection of representative nodes, a distributed algorithm is developed. Furthermore, maintenance mechanisms are proposed to dynamically select alternative representative nodes when the original representative nodes run low on energy, or cannot capture spatial correlation within their respective data coverage ranges. Using experimental studies on both synthesis and real data sets, our proposed algorithms are shown to effectively and efficiently provide approximate data collection while prolonging the network lifetime.

A regression-based approach for mining user movement patterns from random sample data

Mobile computing systems usually express a user movement trajectory as a sequence of areas that capture the user movement trace. Given a set of user movement trajectories, user movement patterns refer to the sequences of areas through which a user frequently travels. In an attempt to obtain user movement patterns for mobile applications, prior studies explore the problem of mining user movement patterns from the movement logs of mobile users. These movement logs generate a data record whenever a mobile user crosses base station coverage areas. However, this type of movement log does not exist in the system and thus generates extra overheads. By exploiting an existing log, namely, call detail records, this article proposes a Regression-based approach for mining User Movement Patterns (abbreviated as RUMP). This approach views call detail records as random sample trajectory data, and thus, user movement patterns are represented as movement functions in this article. We propose algorithm LS (standing for Large Sequence) to extract the call detail records that capture frequent user movement behaviors. By exploring the spatio-temporal locality of continuous movements (i.e., a mobile user is likely to be in nearby areas if the time interval between consecutive calls is small), we develop algorithm TC (standing for Time Clustering) to cluster call detail records. Then, by utilizing regression analysis, we develop algorithm MF (standing for Movement Function) to derive movement functions. Experimental studies involving both synthetic and real datasets show that RUMP is able to derive user movement functions close to the frequent movement behaviors of mobile users.

Clustering object moving patterns for prediction-based object tracking sensor networks

Prior works have shown that probabilistic suffix trees (PST) could predict accurately the moving behaviors of objects for prediction-based object tracking sensor networks. However, maintaining PSTs for objects incurs a considerable amount of storage spaces for resource-constrained sensor nodes. In this paper, we derive a distance function between two PSTs and propose an algorithm to determine the similarity between them. By the distance between PSTs, we propose a clustering algorithm to partition objects with similar moving behaviors into groups. Furthermore, for each group, one PST is selected to predict movements of objects within one group. Experimental results show that our proposed approaches not only effectively reduce the storage cost but also provide good prediction accuracy.

Optimizing in-network aggregate queries in wireless sensor networks for energy saving

This study proposes a method of in-network aggregate query processing to reduce the number of messages incurred in a wireless sensor network. When aggregate queries are issued to the resource-constrained wireless sensor network, it is important to efficiently perform these queries. Given a set of multiple aggregate queries, the proposed approach shares intermediate results among queries to reduce the number of messages. When the sink receives multiple queries, it should be propagated these queries to a wireless sensor network via existing routing protocols. The sink could obtain the corresponding topology of queries and views each query as a query tree. With a set of query trees collected at the sink, it is necessary to determine a set of backbones that share intermediate results with other query trees (called non-backbones). First, it is necessary to formulate the objective cost function for backbones and non-backbones. Using this objective cost function, it is possible to derive a reduction graph that reveals possible cases of sharing intermediate results among query trees. Using the reduction graph, this study first proposes a heuristic algorithm BM (standing for Backbone Mapping). This study also develops algorithm OOB (standing for Obtaining Optimal Backbones) that exploits a branch-and-bound strategy to obtain the optimal solution efficiently. This study tests the performance of these algorithms on both synthesis and real datasets. Experimental results show that by sharing the intermediate results, the BM and OOB algorithms significantly reduce the total number of messages incurred by multiple aggregate queries, thereby extending the lifetime of sensor networks.

Model-Driven Traffic Data Acquisition in Vehicular Sensor Networks

In recent years, the global position system (GPS) is widely used in technical products, such as navigation devices, GPS loggers, PDAs and mobile phones. Hence, traffic data collection platforms are proposed to collect GPS data points for traffic monitoring. In traffic data collection platforms, each vehicle equips with GPS modules and the wireless communication interfaces, such as 3G or WiFi networks, and the GPS data sensed (e.g., the speed and the position) are sent to the server. One challenge issue is that if a significant number of vehicles upload their GPS data points at the same time, it is possible that the wireless network cannot offer enough network resources for simultaneous network connections. This paper proposes a framework MDC (standing for Model-based Data Collection) to reduce the amount of data transmission and the number of vehicles reporting their GPS data points. The MDC framework is executed at the server and vehicle side collaboratively. In the vehicle side, given a series of GPS data points, model functions are derived to represent the raw GPS data points. Hence, each vehicle could report some coefficients that describe its movements instead of reporting all position information. Since vehicles move along with road segments that are usually a set of line segments, algorithm LR (standing for Liner Regression) is proposed to determine a set of line functions to represent movements of vehicles. By observing the spatial-temporal locality in traffic data, algorithm KR (standing for Kernel Regression) is developed to derive a set of kernel functions to model a series of speed readings sensed. Moreover, with the spatial-temporal locality feature in traffic data, an in-network aggregation mechanism are proposed to determine a set of groups and for each group, only one vehicle needs to report traffic data, thereby further reducing the number of simultaneous connections. Experimental results show that MDC can collect traffic data effectively and the efficiently.