Chao Chun Chen

Continuous K-Nearest Neighbor Query for Moving Objects with Uncertain Velocity

One of the most important queries in spatio-temporal databases that aim at managing moving objects efficiently is the continuous K-nearest neighbor (CKNN) query. A CKNN query is to retrieve the K-nearest neighbors (KNNs) of a moving user at each time instant within a user-given time interval [ts, te]. In this paper, we investigate how to process a CKNN query efficiently. Different from the previous related works, our work relieves the past assumption, that an object moves with a fixed velocity, by allowing that the velocity of the object can vary within a known range. Due to the introduction of this uncertainty on the velocity of each object, processing a CKNN query becomes much more complicated. We will discuss the complications incurred by this uncertainty and propose a cost-effective P2KNN algorithm to find the objects that could be the KNNs at each time instant within the given query time interval. Besides, a probability-based model is designed to quantify the possibility of each object being one of the KNNs. Comprehensive experiments demonstrate the efficiency and the effectiveness of the proposed approach.

Improving location management for mobile users with frequency visited locations

Abstract In wireless computing environment, users’ mobility brings new challenges to traditional computing systems. The explosive growth of demand in communication bandwidth requires new schemes to effectively and efficiently locate users. The Basic HLR/VLR locating scheme does not exploit the fact that many mobile users’ (such as commuters’) moving patterns are likely to be known. In this paper, we propose one locating scheme, the frequently visited locations first (FVLF) scheme, to efficiently locate a mobile user. The FVLF scheme is proposed to prestore the RAs that a mobile user frequently visits according to the probabilities of the RAs that the mobile user may appear. When the system receives a call, these prestored RAs are examined first to locate the callee. This scheme can avoid the high cost of querying the callee’s HLR. The cost model of the proposed scheme is derived and the optimal number of prestored RAs is also found. Our evaluation shows that benefit does exist in various conditions by using this scheme.

On optimal scheduling for time-constrained services in multi-channel data dissemination systems

We study the problem of disseminating data of time-constrained services through multiple broadcast channels. By time-constrained services, we mean those services whose data must reach clients before a certain constrained time. Otherwise, the data would become useless or substantially less valuable to the clients. We first explore the difficulties of solving the problem and derive the theoretical minimum number of channels required for the task. Then, we propose a transformation-based data allocation (TDA) algorithm that guarantees to fulfill the task (i.e., all requested data reach the clients within the constrained time) by using the minimum number of channels. Finally, we analyze the computation complexity and prove the validity and optimality of the TDA algorithm.

Time-Constrained Service on Air

Data broadcasting is an efficient and highly scalable technique for delivering data to mobile clients in wireless environments. In this paper, we study the problem of scheduling broadcast data that are with an expected time within which the client is expecting to receive the data item. We analyze the problem and derive the minimum number of broadcast channels required for such a task. Also, we discuss the problems when the number of available channels is not enough. We propose novel solutions for both of the cases and the performance study indicates that our method is much better than the previous ones and performs very close to optimal

Benefit-oriented data retrieval in data broadcast environments

Broadcast disk technology has become a popular method for data dissemination in wireless information systems. However, factors such as intentional periodic disconnection by the mobile hosts (MH) result in further needs for managing access modes so as to let the mobile hosts procure highest benefit through the disconnections. We address this issue in this paper and propose solutions for the MH to retrieve the broadcast data. Our performance results show that the proposed algorithms indeed achieve almost the optimal performance, but require only 5–30% of the cost of an algorithm that is otherwise designed.

POOT: An efficient object tracking strategy based on short-term optimistic predictions for face-structured sensor networks

The advance of wireless sensor networks has enabled the development of a great number of applications in various areas, such as biology, military and environmental surveillance. Among these applications, object tracking systems have particularly useful functions, and have been studied by many researchers in recent years. In the design of a sensor network system, energy consumption is a critical consideration. In this paper, we propose a short-term Prediction-based Optimistic Object Tracking strategy (POOT) to reduce energy consumption and prolong the lifetime of sensor nodes while sacrificing only minimal tracking precision. Furthermore, we present two schemes, a Time-efficient Object Recovery Scheme (TORS) and a Communication-efficient Object Recovery Scheme (CORS), to improve object recovery. We also derive cost models for POOT. Through a set of experiments, our proposed prediction-based optimistic object tracking scheme can save up to 23% energy consumption compared to the related scheme, DOT. Meanwhile, the accuracy of POOT is still higher than 97.5% which reveals the optimistic design does not affect the tracking accuracy. Hence, POOT is shown to effectively conserve energy and achieve the objective of tracking of moving objects.

Model-based object tracking in wireless sensor networks

Tracking moving objects is one of the most common requirements in wireless sensor network applications. Most tracking schemes predict a target’s location based on a single object movement model and periodically activate nearby sensors to monitor the target. However, in most real-world situations, a target exhibits multiple movement patterns. Thus, multiple movement models are required to accurately describe the target’s movement. This paper proposes a tracking framework, called model-based object tracking system (MOTS), that allows a sensor network to adaptively apply the most suitable tracking mechanism to monitor the target under various circumstances. To fairly and accurately evaluate all tracking modules, this study further develops a monitoring-cost evaluator to evaluate the monitoring cost of the inactive tracking modules, and then designs three tracking module selection strategies, the Greedy Strategy, Min-Max Strategy, and Weighted Moving Average Strategy, to select the most effective tracking module to monitor the target in each period. A set of experiments is conducted to evaluate MOTS and compare it against existing tracking systems. The obtained results reveal that the cost efficiency of MOTS is considerably better than that of existing tracking systems.

Mining group stock portfolio by using grouping genetic algorithms

In this paper, a grouping genetic algorithm based approach is proposed for dividing stocks into groups and mining a set of stock portfolios, namely group stock portfolio. Each chromosome consists of three parts. Grouping and stock parts are used to indicate how to divide stocks into groups. Stock portfolio part is used to represent the purchased stocks and their purchased units. The fitness of each chromosome is evaluated by the group balance and the portfolio satisfaction. The group balance is utilized to make the groups represented by the chromosome have as similar number of stocks as possible. The portfolio satisfaction is used to evaluate the goodness of profits and satisfaction of investors requests of all possible portfolio combinations that can generate from a chromosome. Experiments on a real data were also made to show the effectiveness of the proposed approach.

A dynamic traffic-aware duty cycle adjustment MAC protocol for energy conserving in wireless sensor networks

Wireless sensors are battery-limited sensing and computing devices. How to prolong the lifetime of wireless sensors becomes an important issue. In order to reduce the energy consumptions when nodes are in idle listening, duty-cycle-based MAC protocols are introduced to let node go into sleep mode periodically or aperiodically. The long duty cycle makes sensors increase the transmission throughput but consumes more energy. The short duty cycle makes sensors have low energy consumption rate but increases the transmission delay. In this paper, a dynamic traffic-aware MAC protocol for energy conserving in wireless sensor networks is proposed. The proposed MAC protocol can provide better data transmission rate when sensors are with high traffic loading. On the other hand, the proposed MAC protocol can save energy when sensors are with low traffic loading. Simulation results show that the proposed protocol has better data throughput than other duty-cycle-based MAC protocols, for example, S-MAC and U-MAC. We also developed a set of comprehensive experiments based on the well-known OMNET++ simulator and revealed that our proposed TA-MAC performs significantly outstanding than related schemes under various situations.

Development of Advanced Manufacturing Cloud of Things (AMCoT)—A Smart Manufacturing Platform

As semiconductor manufacturing processes are becoming more and more sophisticated, how to maintain their feasible production yield becomes an important issue. Also, how to build a smart manufacturing platform that can facilitate realizing smart factories is essential and desirable for current manufacturing industries. Aimed at addressing the above-mentioned two issues, in this letter, a five-stage approach for enhancing and assuring yield is proposed. Also, a smart manufacturing platform- Advanced Manufacturing Cloud of Things (AMCoT) based on Internet of Things, cloud computing, big data analytics, cyber-physical systems, and prediction technologies is designed and implemented to realize the proposed five-stage approach of yield enhancement and assurance. Finally, AMCoT is applied to a bumping process of a semiconductor company in Taiwan to conduct industrial case studies. Testing results demonstrate that AMCoT possesses capabilities of conducting total inspection in production, providing prognosis, and predictive maintenance on equipment, finding the root cause of yield loss, and storing and handling big production data, which as a whole is promising to achieve the goal of zero defects.