Kawuu W. Lin

Energy efficient strategies for object tracking in sensor networks: A data mining approach

In recent years, a number of studies have been done on object tracking sensor networks (OTSNs) due to the wide applications. One important research issue in OTSNs is the energy saving strategy in considering the limited power of sensor nodes. The past studies on energy saving in OTSNs considered the objects movement behavior as randomness. In some real applications, however, the object movement behavior is often based on certain underlying events instead of randomness completely. In this paper, we propose a novel data mining algorithm named TMP-Mine with a special data structure named TMP-Tree for efficiently discovering the temporal movement patterns of objects in sensor networks. To our best knowledge, this is the first work on mining the movement patterns associated with time intervals in OTSNs. Moreover, we propose novel location prediction strategies that utilize the discovered temporal movement patterns so as to reduce the prediction errors for energy savings. Through empirical evaluation on various simulation conditions and real dataset, TMP-Mine and the proposed prediction strategies are shown to deliver excellent performance in terms of scalability, accuracy and energy efficiency.

Mining sequential mobile access patterns efficiently in mobile Web systems

The rapid advance of wireless and Web technologies enable the mobile Web applications to provide plenty kinds of services for mobile users. Under a mobile Web system, analyzing mobile users movement sequences and requested services is important for wide applications in wireless communication like data allocation, data replication, location-based and personalization services. The main challenge in this research issue is to effectively deal with the users diverse behavior and the huge amount of data. However, to our best knowledge, no studies have been done on the problem of mining sequential mobile access patterns with both movement and service requests considered simultaneously. In this paper, we propose a novel data mining method, namely SMAP-Mine, that can discover patterns of sequential movement associated with requested services for mobile users in mobile Web systems. Through empirical evaluation on various simulation conditions, the proposed method is shown to deliver excellent performance in terms of accuracy, execution efficiency, and scalability.

A novel prediction-based strategy for object tracking in sensor networks by mining seamless temporal movement patterns

Energy saving in sensor networks has received a great deal of research attention in recent years due to its wide applications. One important research issue is energy efficient object tracking in sensor networks (OTSNs). Past studies on energy saving in OTSNs can be divided into two main directions: (1) improvement in hardware design; and (2) improvement in software approaches. Many research papers save energy in hardware design, but few discuss software approaches. The intuitive way to conserve the energy of sensor nodes is to reduce the operation time of high-powered components. Utilizing the movement patterns of objects to save energy is one software approach. However, it did not take temporal information into consideration nor did it define a suitable segmenting time unit of time interval in advance. Due to the time interval between movements is a real number, an improper segmenting time unit may not discover the useful patterns, directly resulting in the inefficient object tracking. In this paper, we propose a seamless data mining algorithm named STMP-Mine to efficiently discover the temporal movement patterns of objects in sensor networks without predefining the segmenting time unit. Moreover, we propose novel location prediction strategies that employ the discovered temporal movement patterns to reduce prediction errors to save energy. With empirical evaluation on simulated data, STMP-Mine and the proposed prediction strategies are shown to deliver excellent performance in terms of scalability and energy efficiency.

A smart exponential-threshold-linear backoff mechanism for IEEE 802.11 WLANs

Based on the standardized IEEE 802.11 Distributed Coordination Function (DCF) protocol, this paper proposes a new backoff mechanism, called Smart Exponential-Threshold-Linear (SETL) Backoff Mechanism, to enhance the system performance of contention-based wireless networks. In the IEEE 802.11 DCF scheme, the smaller contention window (CW) will increase the collision probability, but the larger CW will delay the transmission. Hence, in the proposed SETL scheme, a threshold is set to determine the behavior of CW after each transmission. When the CW is smaller than the threshold, the CW of a competing station is exponentially adjusted to lower collision probability. Conversely, if the CW is larger than the threshold, the CW size is tuned linearly to prevent large transmission delay. Through extensive simulations, the results show that the proposed SETL scheme provides a better system throughput and lower collision rate in both light and heavy network loads than the related backoff algorithm schemes, including Binary Exponential Backoff (BEB), Exponential Increase Exponential Decrease (EIED) and Linear Increase Linear Decrease (LILD). Copyright

A novel parallel algorithm for frequent pattern mining with privacy preserved in cloud computing environments

Parallel and distributed computing techniques have attracted extensive attentions on the ability to manage and compute the significant amount of data in the past decades. The difficulty of mining large database launched the research of designing parallel and distributed algorithms to solve the problem. In this paper, we propose a novel data mining algorithm, named Cloud-based Association Rule Mining (CARM), abbreviated as CARM, which is able to efficiently utilise the nodes to discover frequent patterns in cloud computing environments with data privacy preserved. Through empirical evaluations on various simulation conditions, the proposed CARM delivers excellent performance in terms of scalability and execution time.

Mining temporal moving patterns in object tracking sensor networks

Advances in wireless communication and microelectronic devices technologies have enabled the development of low-power micro-sensors and the deployment of large-scale sensor networks. With the capabilities of pervasive surveillance, sensor networks can be very useful in a lot of commercial and military applications for collecting and processing the environmental data. One of the very interesting research issues is the energy saving in object tracking sensor networks (OTSNs). However, most of the past studies focused only on the aspect of movement behavior analysis or location tracking and did not consider the temporal characteristics, which are very critical in OTSNs. In this paper, we propose a novel data mining method named TMP-Mine with a special data structure named TMP-Tree for discovering temporal moving patterns efficiently. To our best knowledge, this is the first study that explores the issue of discovering temporal moving patterns that contain both movement and time interval simultaneously. Through empirical evaluation on various simulation conditions, TMP-Mine is shown to deliver excellent performance in terms of accuracy, execution efficiency, and scalability.

Prediction of user navigation patterns by mining the temporal web usage evolution

Advances in the data mining technologies have enabled the intelligent Web abilities in various applications by utilizing the hidden user behavior patterns discovered from the Web logs. Intelligent methods for discovering and predicting user’s patterns is important in supporting intelligent Web applications like personalized services. Although numerous studies have been done on Web usage mining, few of them consider the temporal evolution characteristic in discovering web user’s patterns. In this paper, we propose a novel data mining algorithm named Temporal N-Gram (TN-Gram) for constructing prediction models of Web user navigation by considering the temporality property in Web usage evolution. Moreover, three kinds of new measures are proposed for evaluating the temporal evolution of navigation patterns under different time periods. Through experimental evaluation on both of real-life and simulated datasets, the proposed TN-Gram model is shown to outperform other approaches like N-gram modeling in terms of prediction precision, in particular when the web user’s navigating behavior changes significantly with temporal evolution.

Quantum Algorithms for Biomolecular Solutions of the Satisfiability Problem on a Quantum Machine

In this paper, we demonstrate that the logic computation performed by the DNA-based algorithm for solving general cases of the satisfiability problem can be implemented more efficiently by our proposed quantum algorithm on the quantum machine proposed by Deutsch. To test our theory, we carry out a three-quantum bit nuclear magnetic resonance experiment for solving the simplest satisfiability problem.

Efficient algorithms for frequent pattern mining in many-task computing environments

The goal of data mining is to discover hidden useful information in large databases. Mining frequent patterns from transaction databases is an important problem in data mining. As the database size increases, the computation time and required memory also increase. Because the number of items increases, the user behaviours also become more complex. To solve the problem of increasing complexity, many researchers have applied parallel and distributed computing techniques to the discovery of frequent patterns from large amounts of data. However, most studies have focused on improving the performance for a single task and have neglected the many-task computing issue, which is important in the current cloud-computing environments. In these environments, an application is often provided as a service, e.g., the Google search engine, implying that many users can use it simultaneously. In this paper, we propose a set of algorithms, containing the Equal Working Set (EWS) algorithm, the Request On Demand (ROD) algorithm, the Small Size Working Set (SSWS) algorithm and the Progressive Size Working Set (PSWS) algorithm, for frequent pattern mining that provides a fast and scalable mining service in many-task computing environments. Through empirical evaluations in various simulation conditions, the proposed algorithms are shown to deliver excellent performance with respect to scalability and execution time.

Semantic trajectory-based high utility item recommendation system

The topic on recommendation systems for mobile users has attracted a lot of attentions in recent years. However, most of the existing recommendation techniques were developed based only on geographic features of mobile users’ trajectories. In this paper, we propose a novel approach for recommending items for mobile users based on both the geographic and semantic features of users’ trajectories. The core idea of our recommendation system is based on a novel cluster-based location prediction strategy, namely TrajUtiRec, to improve items recommendation model. Our proposed cluster-based location prediction strategy evaluates the next location of a mobile user based on the frequent behaviors of similar users in the same cluster determined by analyzing users’ common behaviors in semantic trajectories. For each location, high utility itemset mining algorithm is performed for discovering high utility itemset. Accordingly, we can recommend the high utility itemset which is related to the location the user might visit. Through a comprehensive evaluation by experiments, our proposal is shown to deliver excellent performance.