Wenwei Xue

Contour map matching for event detection in sensor networks

Many sensor network applications, such as object tracking and disaster monitoring, require effective techniques for event detection. In this paper, we propose a novel event detection mechanism based on matching the contour maps of in-network sensory data distribution. Our key observation is that events in sensor networks can be abstracted into spatio-temporal patterns of sensory data and that pattern matching can be done efficiently through contour map matching. Therefore, we propose simple SQL extensions to allow users to specify common types of events as patterns in contour maps and study energy-efficient techniques of contour map construction and maintenance for our pattern-based event detection. Our experiments with synthetic workloads derived from a real-world coal mine surveillance application validate the effectiveness and efficiency of our approach.

Object relevance weight pattern mining for activity recognition and segmentation

Monitoring daily activities of a person has many potential benefits in pervasive computing. These include providing proactive support for the elderly and monitoring anomalous behaviors. A typical approach in existing research on activity detection is to construct sequence-based models of low-level activity features based on the order of object usage. However, these models have poor accuracy, require many parameters to estimate, and demand excessive computational effort. Many other supervised learning approaches have been proposed but they all suffer from poor scalability due to the manual labeling involved in the training process. In this paper, we simplify the activity modeling process by relying on the relevance weights of objects as the basis of activity discrimination rather than on sequence information. For each activity, we mine the web to extract the most relevant objects according to their normalized usage frequency. We develop a KeyExtract algorithm for activity recognition and two algorithms, MaxGap and MaxGain, for activity segmentation with linear time complexities. Simulation results indicate that our proposed algorithms achieve high accuracy in the presence of different noise levels indicating their good potential in real-world deployment.

Distributed cross-layer scheduling for in-network sensor query processing

In-network sensor query processing is a cross-layer design paradigm in which networked sensor nodes process data acquisitional queries in collaboration with one another. As power efficiency is still one of the most severe constraints in this paradigm, we propose a distributed, cross-layer scheduling scheme for it. In this scheme, each node employs its MAC, routing, and query layers to negotiate with its parent its timing for transmission and constructs a schedule for its query processing. It then follows the schedule to compute, communicate, and sleep in each query processing cycle. This scheduling reduces wasted listening and receiving as well as the switching between active and sleeping modes. Consequently, it results in 50-60% of power saving on real sensor nodes in our experiments. Additionally, it outperforms two existing scheduling schemes both on schedule construction efficiency and on schedule quality

A survey on energy-efficient data management

Energy management has now become a critical and urgent issue in green computing. A lot of efforts have been made on energy-efficiency computing at various levels from individual hardware components, system software, to applications. In this paper, we describe the energyefficiency computing problem, as well as possible strategies to tackle the problem. We survey some recently developed energy-saving data management techniques. Benchmarks and power models are described in the end for the evaluation of energy-efficiency solutions.

Pattern-based event detection in sensor networks

Many applications of wireless sensor networks monitor the physical world and report events of interest. To facilitate event detection in these applications, in this paper we propose a pattern-based event detection approach and integrate the approach into an in-network sensor query processing framework. Different from existing threshold-based event detection, we abstract events into patterns in sensory data and convert the problem of event detection into a pattern matching problem. We focus on applying single-node temporal patterns, and define the general patterns as well as five types of basic patterns for event specification. Considering the limited storage on sensor nodes, we design an on-node cache manager to maintain the historical data required for pattern matching and develop event-driven processing techniques for queries in our framework. We have conducted experiments using patterns for events that are extracted from real-world datasets. The results demonstrate the effectiveness and efficiency of our approach.

MEADOWS: modeling, emulation, and analysis of data of wireless sensor networks

In this position paper, we present MEADOWS, a software framework that we are building at HKUST for modeling, emulation, and analysis of data of wireless sensor networks. This project is motivated by the unique need of intertwining modeling, emulation, and data analysis in studying sensor databases. We describe our design of basic data analysis tools along with an initial case study on HKUST campus. We also report our progress on modeling power consumption for sensor databases and on wireless sensor network emulation for query processing. Additionally, we outline our future directions on MEADOWS for discussion and feedback at the workshop.

Modeling and detecting events for sensor networks

Event detection is an essential element for various sensor network applications, such as disaster alarm and object tracking. In this paper, we propose a novel approach to model and detect events of interest in sensor networks. Our approach models an event using the kind of spatio-temporal sensor data distribution it generates, and specifies such distribution as a number of regression models over spatial regions within the network coverage at discrete points in time. The event is detected by matching the modeled distribution with the real-time sensor data collected at a gateway. Because the construction of a regression model is computation-intensive, we utilize the temporal data correlation in a region as well as the spatial relationships of multiple regions to maintain the models over these regions incrementally. Our evaluation results based on both real-world and synthetic data sets demonstrate the effectiveness and efficiency of our approach.

A context management framework for context‐aware applications in mobile spaces

Purpose – The rapid proliferation of mobile context aware applications has resulted in an increased research interest towards developing specialized context data management strategies for mobile entities. The purpose of this paper is to aim to develop a new way to model mobile entities and manage their contexts accordingly.Design/methodology/approach – This paper proposes the concept of “Mobile Space” to model mobile entities and presents strategies to manage the various contexts associated therein. To handle availability related issues, two system services are designed: the “Availability Updating Service” which is an identifier based mechanism and is designed to keep track of mobile objects and handle availability related issues, and the “Application Callback Service” which is a publish/subscribe based mechanism to handle application disruptions and interruptions arising due to mobility.Findings – The paper presents a detailed study of the proposed framework and a description of the underlying services a...

Systems Support for Pervasive Query Processing

Database queries, in particular, event-driven continuous queries, are useful for many pervasive computing applications, such as video surveillance. In order to enable these applications, we have developed a pervasive query processing framework called Aorta. Unlike traditional database systems, a pervasive query processor requires systems support for managing a large number of networked, heterogeneous devices. In this paper, the authors presented the communication, synchronization, and scheduling mechanisms in Aorta. Even though these techniques have their roots in distributed and parallel systems, the authors showed how these techniques are customized and applied for pervasive query processing. In essence, communication between heterogeneous devices enables network data independence, synchronization on devices protects action atomicity, and scheduling works for adaptive, cost-based multi-query optimization. Empirical studies on the prototype as well as simulation studies to evaluate the system performance were conducted

Managing context data for diverse operating spaces

Context-aware computing is an exciting paradigm in which applications perceive and react to changing environments in an unattended manner. To enable behavioral adaptation, a context-aware application must dynamically acquire context data from different operating spaces in the real world, such as homes, shops and persons. Motivated by the sheer number and diversity of operating spaces, we propose a scalable context data management system in this paper to facilitate data acquisition from these spaces. In our system, we design a gateway framework for all operating spaces and develop matching algorithms to integrate the local context schemas of operating spaces into a global set of domain schemas upon which SQL-based context queries can be issued from applications. The system organizes the operating space gateways as peers in semantic overlay networks and employs distributed query processing techniques over these overlays. Evaluation results on a prototype implementation demonstrate the effectiveness of our system design.