Tao Ku

RFID network planning using a multi-swarm optimizer

In this paper, we develop an optimization model for planning the positions of readers in the RFID network based on a novel Multi-swarm Particle Swarm Optimizer called PS2O. The main idea of PS2O is to extend the single population PSO to the interacting multi-swarms model by constructing hierarchical interaction topology and enhanced dynamical update equations. This algorithm, which is conceptually simple and easy to implement, has considerable potential for solving complex optimization problems. Simulation results show that the proposed PS2O algorithm proves to be superior for planning RFID networks than the standard PSO and other two evolutionary algorithms, namely Genetic Algorithm (GA) and Evolution Strategy (ES), in terms of optimization accuracy and computation robustness.

Mining frequent trajectory pattern based on vague space partition

Frequent trajectory pattern mining is an important spatiotemporal data mining problem with broad applications. However, it is also a difficult problem due to the approximate nature of spatial trajectory locations. Most of the previously developed frequent trajectory pattern mining methods explore a crisp space partition approach 8,10] to alleviate the spatial approximation concern. However, this approach may cause the sharp boundary problem that spatially close trajectory locations may fall into different partitioned regions, and eventually result in failure of finding meaningful trajectory patterns. In this paper, we propose a flexible vague space partition approach to solve the sharp boundary problem. In this approach, the spatial plane is divided into a set of vague grid cells, and trajectory locations are transformed into neighboring vague grid cells by a distance-based membership function. Based on two classical sequential mining algorithms, the PrefixSpan and GSP algorithms, we propose two efficient trajectory pattern mining algorithms, called VTPM-PrefixSpan and VTPM-GSP, to mine the transformed trajectory sequences with time interval constraints. A comprehensive performance study on both synthetic and real datasets shows that the VTPM-PrefixSpan algorithm outperforms the VTPM-GSP algorithm in both effectiveness and scalability.

A Novel Distributed Complex Event Processing for RFID Application

Radio-frequency identification (RFID) technology has brought tremendous benefits for business processing, especially in incorporating RFID data into supply chain management (SCM). With the explosion of RFID data, there has raised important problem how to mine valuable information from tremendous and potentially infinite volumes of RFID data. Complex event processing (CEP) technology used to process RFID data has attracted increasing attention. However, the current researches usually focus on centralized CEP architecture, which requires greater bandwidth and computational capability, and lacks of robustness and scalability because of single point failure or network break. This paper first proposes a novel distributed CEP architecture, which spreads centralized CEP tasks load over multiple communicating stations by a space-based communication paradigm over a distributed message broker architecture based on Jini network. A distributed complex event detection algorithm based on master-workers pattern is proposed. The results of experiment show that the performance of our approach is remarkable in the large-scale RFID applications.

A Novel Complex Event Mining Network for Monitoring RFID-Enable Application

This paper presents a novel complex event mining network (CEMN) and defines the fundamentals of radio-frequency identification (RFID)-enabled supply chain event management and discusses how an complex event processing (CEP) can be used to resolve the underlying architecture challenges and complexities of integrating real-time decision support into the supply chain. The proposed CEP architecture is a distributed event processing networks (EPNs) capable middleware infrastructure which enables automatic and real-time routing, caching, filtering, aggregation and processing of RFID events. It provides a global platform for distributed execution and management of RFID-enabled supply chain data. It enables a federated control over supply chain nodes deployed in many different organizations, respecting diverse security requirements while supporting centralized deployment and management of processes and rules. Finally, a distributed complex event detection algorithm based on master-workers pattern is proposed to detect complex events and trigger correlation actions. The results showed that our proposed approach has more robust and scaleable in large-scale RFID applications.

A service recommender system based on the co-evolutionary contract net for migrating workflows

RFID (Radio Frequency Identification) technologies have dramatically lowered the cost of data acquisition and have enabled applications to constantly analyze and execute complex operations in real-time. However the technology poses many new challenges on current data management systems for turning this data into RFID applications and initiates the logic built into the system of real-time identifying, locating, tracking and monitoring physical world. In this paper we present the semantics-based CEP (Complex Event Processing) infrastructure on hierarchical data model for the capture, filtering and automatic transformation RFID data, and provide complex event detection patterns and algorithms for the generation of business logic semantic information.

A Novel Pattern for Complex Event Processing in RFID Applications

This study investigates Complex Event Processing (CEP) Pattern on Radio-Frequency Identification (RFID) applications. RFID technolgy has brought tremendous benefits for the business by incorporating RFID data into supply chain planning and business processing, however, this progress has also raised important problems for RFID data processing. How to mine the signification information from RFID events is a challenge in RFID applications. In this paper, we take an CEP Pattern-oriented approach to process RFID data. A novel event pattern based on semantics operator is proposed. A formalization event hierarchy is used to model complex event with the event ontology, and provides abstract hierarchical views allowing us to view the system activities at different levels. Several complex event patterns are proposed based on semantic event operators. An algorithm is performed to test reorganization performance of patterns, a rule-based method is used to recognize efficiently on primitive event and composite event. The results showed that the advantage of the proposed complex event pattern approach is remarkable. It is concluded that the method of CEP pattern can simplify and improve RFID application.

A Novel Complex Event Mining Network for RFID-Enable Supply Chain Information Security

This paper presents a novel complex event mining network (CEMN) and defines the fundamentals of radio-frequency identification (RFID)-enabled supply chain event management and discusses how an complex event processing (CEP) can be used to resolve the underlying architecture challenges and complexities of integrating real-time decision support into the supply chain. The proposed CEP architecture is a distributed event processing networks (EPNs) capable middleware infrastructure which enables automatic and real-time routing, caching, filtering, aggregation and processing of RFID events. It provides a global platform for distributed execution and management of RFID-enabled supply chain data. It enables a federated control over supply chain nodes deployed in many different organizations, respecting diverse security requirements while supporting centralized deployment and management of processes and rules. Finally, a distributed complex event detection algorithm based on Master-workers pattern is proposed to detect complex events and trigger correlation actions. The results showed that our proposed approach has more robust and scaleable in large-scale RFID applications.

Global optimization based on hierarchical coevolution model

This paper presents a novel optimization algorithm that we call the particle swarms swarm optimizer (PS2O), which based on a hierarchical coevolution model (HCO model) of symbiotic species. HCO model introduced a number of M species each possesses a number of N individuals to represent the ldquobiological communityrdquo. Both the heterogeneous coevolution and the homogeneous coevolution aspects are simulated in this model to maintain the community biodiversity. This strategy enable the symbiotic species find the optima faster and discourage premature convergence effectively. The experiments compare the performance of PS2O with the canonical PSO, the fully informed particle swarm (FlPS), the unified particle swarm (UPSO) and the Fitness-Distance-Ratio based PSO (FDR-PSO) on a set of 6 benchmark functions. The simulation results show the PS2O algorithm markedly outperforms the four mentioned algorithms on all benchmark functions and has the potential to solve the complex problems with high dimensionality.

PS 2 O: A multi-swarm optimizer for discrete optimization

In this paper, we implement an entire social system which consists of both heterogeneous cooperation and homogeneous cooperation aspects to formulate our simulation models of coevolution. We introduced a number of N species each possesses a number of M individuals into this coevolution model to represents the ldquobiological communityrdquo. Each individual of the community evolves based on the knowledge integration of itself, its species member and its symbiotic partners from other species. Since the community is made up of a swarm of agents who are species while each species is made up of a swarm of species members (individuals), our swarms within swarm model is instantiated as a hierarchical coevolutionary optimization algorithm, namely Particle Swarms Swarm Optimizer (PS2O). The PS2O algorithm is evaluated on four discrete optimization problems for compared with the canonical discrete PSO algorithm. The comparisons show that on average, PS2O outperforms the PSO in terms of accuracy and convergence speed on all benchmark functions.

Urban Mobility Dynamics Based on Flexible Discrete Region Partition

Understanding the urban mobility patterns is essential for the planning and management of public infrastructure and transportation services. In this paper we focus on taxicab moving trajectory records and present a new approach to modeling and analyzing urban mobility dynamics. The proposed method comprises two phases. First, discrete space partition based on flexible grid is developed to divide urban environment into finite nonoverlapping subregions. By integrating mobility origin-destination points with covered region, the partitioned discrete subregions have better spatial semantics scalability. Then, we study mobility activity and its distribution randomness during given time periods among discrete subregions. Moreover, we also carry out the analysis of mobility linkage of mobility trips between different regions by O-D matrix. We present a case study with real dataset of taxicab mobility logs in Shenzhen, China, to demonstrate and evaluate the methodology. The experimental results show that the proposed method outperforms the clustering partition and regular partition methods.