Alvis Cheuk M. Fong

On the scalability of fixed broadband wireless access network deployment

Fixed broadband wireless access systems, such as the local multipoint distribution service, use an open system architecture that supports a scalable solution for the Internet services over IEEE 802.16 wireless networks. This article presents an overview of various features of BWA systems toward realizing a high level of scalability to support a potentially fast expanding network. This is achieved by optimizing various network resources, which include utilizing the available bandwidth efficiency, making a minor enhancement to an existing system that minimizes the disruption to network services during the network expansion process, and combining the benefits of different features to increase network capacity.

Factors causing uncertainties in outdoor wireless wearable communications

Wearable computers are becoming increasingly popular as new features emerge and device portability improves-making them suitable for accessing multimedia services on the move. Wireless LANs are compatible with Ethernet and asynchronous-transfer-mode-based fiber-optic networks, providing high mobility to user nodes while integrated and connected to high-speed networks wired with broadband. With a range covering several kilometers, WLANs are advantageous for providing multimedia services to wearable computers. Various factors influence the operation of wirelessly connected wearable computers, but we emphasize the rainfall influence.

Efficient sequential access pattern mining for web recommendations

Sequential access pattern mining discovers interesting and frequent user access patterns from web logs. Most of the previous studies have adopted Apriori-like sequential pattern mining techniques, which faced the problem on requiring expensive multiple scans of databases. More recent algorithms that are based on the Web Access Pattern tree (or WAP-tree) can achieve an order of magnitude faster than traditional Apriori-like sequential pattern mining techniques. However, the use of conditional search strategies in WAP-tree based mining algorithms requires re-construction of large numbers of intermediate conditional WAP-trees during mining process, which is also very costly. In this paper, we propose an efficient sequential access pattern mining algorithm, known as CSB-mine (Conditional Sequence Base mining algorithm). The proposed CSB-mine algorithm is based directly on the conditional sequence bases of each frequent event which eliminates the need for constructing WAP-trees. This can improve the efficiency of the mining process significantly compared with WAP-tree based mining algorithms, especially when the support threshold becomes smaller and the size of database gets larger. In this paper, the proposed CSB-mine algorithm and its performance will be discussed. In addition, we will also discuss a sequential access-based web recommender system that has incorporated the CSB-mine algorithm for web recommendations.

Generation of Personalized Ontology Based on Consumer Emotion and Behavior Analysis

The relationships between consumer emotions and their buying behaviors have been well documented. Technology-savvy consumers often use the web to find information on products and services before they commit to buying. We propose a semantic web usage mining approach for discovering periodic web access patterns from annotated web usage logs which incorporates information on consumer emotions and behaviors through self-reporting and behavioral tracking. We use fuzzy logic to represent real-life temporal concepts (e.g., morning) and requested resource attributes (ontological domain concepts for the requested URLs) of periodic pattern-based web access activities. These fuzzy temporal and resource representations, which contain both behavioral and emotional cues, are incorporated into a Personal Web Usage Lattice that models the users web access activities. From this, we generate a Personal Web Usage Ontology written in OWL, which enables semantic web applications such as personalized web resources recommendation. Finally, we demonstrate the effectiveness of our approach by presenting experimental results in the context of personalized web resources recommendation with varying degrees of emotional influence. Emotional influence has been found to contribute positively to adaptation in personalized recommendation.

A comparative analysis of video codecs for multihop wireless video sensor networks

Wireless video sensor networks (WVSNs) have drawn significant attention in recent years due to the advent of low-cost miniaturized cameras, which makes it feasible to realize large-scale WVSNs for a variety of applications including security surveillance, environmental tracking, and health monitoring. However, the conventional video coding paradigms are not suitable for WVSNs due to resource constraints such as limited computation power, battery energy, and network bandwidth. In this paper, we evaluated and analyzed the performance of video codecs based on emerging video coding paradigms such as distributed video coding and distributed compressive video sensing for multihop WVSNs. The main objective of this work was to provide an insight into the computational (encoding/decoding) complexity, energy consumption, node and network lifetime, processing and memory requirements, and the quality of reconstruction of these video codecs. Based on the findings, this paper also provides some guidelines for the selection of appropriate video codecs for a given WVSN application.

Discovering diverse association rules from multidimensional schema

The integration of data mining techniques with data warehousing is gaining popularity due to the fact that both disciplines complement each other in extracting knowledge from large datasets. However, the majority of approaches focus on applying data mining as a front end technology to mine data warehouses. Surprisingly, little progress has been made in incorporating mining techniques in the design of data warehouses. While methods such as data clustering applied on multidimensional data have been shown to enhance the knowledge discovery process, a number of fundamental issues remain unresolved with respect to the design of multidimensional schema. These relate to automated support for the selection of informative dimension and fact variables in high dimensional and data intensive environments, an activity which may challenge the capabilities of human designers on account of the sheer scale of data volume and variables involved. In this research, we propose a methodology that selects a subset of informative dimension and fact variables from an initial set of candidates. Our experimental results conducted on three real world datasets taken from the UCI machine learning repository show that the knowledge discovered from the schema that we generated was more diverse and informative than the standard approach of mining the original data without the use of our multidimensional structure imposed on it.

Hybrid RF Mapping and Kalman Filtered Spring Relaxation for Sensor Network Localization

An accurate and low-cost hybrid solution to the problem of autonomous self-localization in wireless sensor networks (WSN) is presented. The solution is designed to perform robustly under challenging radio propagation conditions in mind, while requiring low deployment efforts, and utilizing only low-cost hardware and light-weight distributed algorithms for location computation. Our solution harnesses the strengths of two approaches for environments with complex propagation characteristics: RF mapping to provide an initial estimate of each sensors position based on a coarse-grain RF map acquired with minimal efforts; and a cooperative light-weight spring relaxation technique for each sensor to refine its estimate using Kalman filtered inter-node distance measurements. Using Kalman filtering to pre-process noisy distance measurements inherent in complex propagation environments, is found to have significant positive impacts on the subsequent accuracy and the convergence of our spring relaxation algorithm. Through extensive simulations using realistic settings and real data set, we show that our approach is a practical localization solution which can achieve sub-meter accuracy and fast convergence under harsh propagation conditions, with no specialized hardware or significant efforts required to deploy.

Prognostics and health management for wireless telemedicine networks

Prognostics and health management has been widely used in predicting the time at which a system will no longer perform its intended function. This article aims at providing a detailed discussion of reliability optimization for wireless telemedicine network by using a prognostics approach. The science of prognostics, which is based on the analysis of failure modes, detection of early signs of wear and aging, and fault conditions, has been applied to electronic components and systems as well as structural monitoring. Using data-driven prognostics techniques, the condition of a network can also be monitored using operational data related to data packets as they are delivered across the network. Prognostics are particularly important for wireless telemedicine networks since these networks must operate reliably irrespective of abruptly changing operating conditions in order to support life-saving missions.

Location Estimation in Wireless Sensor Networks Using Spring-Relaxation Technique

Accurate and low-cost autonomous self-localization is a critical requirement of various applications of a large-scale distributed wireless sensor network (WSN). Due to its massive deployment of sensors, explicit measurements based on specialized localization hardware such as the Global Positioning System (GPS) is not practical. In this paper, we propose a low-cost WSN localization solution. Our design uses received signal strength indicators for ranging, light weight distributed algorithms based on the spring-relaxation technique for location computation, and the cooperative approach to achieve certain location estimation accuracy with a low number of nodes with known locations. We provide analysis to show the suitability of the spring-relaxation technique for WSN localization with cooperative approach, and perform simulation experiments to illustrate its accuracy in localization.

An efficient multi-objective optimization approach for Online Test Paper Generation

With the rapid growth of the Internet and mobile devices, Online Test Paper Generation (Online-TPG) is a promising approach for self-assessment especially in an educational environment. Online-TPG is challenging as it is a multi-objective optimization problem that is NP-hard, and it is also required to satisfy the online generation requirement. The current techniques such as dynamic programming, tabu search, swarm intelligence and biologically inspired algorithms generally require long runtime for generating good quality test papers. In this paper, we propose an efficient multi-objective optimization approach for Online-TPG. The proposed approach is based on the Constraint-based Divide-and-Conquer (DAC) technique for constraint decomposition and multi-objective optimization. In this paper, we present the proposed DAC approach for Online-TPG and its performance evaluation. The performance results have shown that the proposed approach has outperformed other TPG techniques in terms of runtime efficiency and paper quality.