A Data Fusion Approach to Mobile Location Estimation based on Ellipse Propagation Model within a Cellular Radio Network
Mobile location estimation is drawing considerable attention in the field of wireless communications. In this paper, we present a new estimator which considers all the information to reduce the effect of signal fluctuation and fading-the statistical estimation. The Statistical Estimation is derived from the information of the received signal strengths (RSSs) and the locations of their corresponding base stations (BSs) and then estimates the location of the mobile station (MS). The statistical estimation uses all the information to provide the estimation of the location of the MS, which can provide an accurate estimation and reduce the effect of signal fluctuation and fading. It is a data fusion method to handle the signal fluctuation and fading problem. We test our approach with real data collected from Hong Kong. Experimental results show that our approach outperforms other existing location estimation algorithms among different kinds of terrains. The improvements based on the geometric algorithm with EPM and the iterative algorithm with EPM are 18.87% and 4.46%, respectively.

title: An improved ellipse propagation model for location estimation in facilitating ubiquitous computing
abstract: Positioning is a crucial technology for ubiquitous computing. A directional propagation model - the ellipse propagation model (EPM) is proposed by our research group for locating a mobile station (MS) within a radio cellular network with an accuracy that can enable a number of location based services to realize ubiquitous computing. By using a geometric algorithm, the location of the mobile station can be estimated. However, since one parameter in our geometric algorithm is fixed, errors may be induced as the surrounding environment changes. In view of this, we would like to propose a new algorithm - the iterative algorithm to provide the positioning based on EPM. With the technical support of two local mobile phone operators, we have conducted a series of experiments using real data and experiment results showed that the proposed iterative algorithm outperforms the geometric algorithm by a good margin of 18% in terms of average error.

title: A selector method for providing mobile location estimation services within a radio cellular network
abstract: Mobile location estimation or mobile positioning is becoming an important service for a mobile phone network. It is well-known that GPS can provide accurate location estimation, but it is also a known fact that GPS does not perform well in urban areas like downtown New York and cities like Hong Kong. Then many mobile location estimation approaches based on radio cellular networks have been proposed to compensate the problem of the lost of GPS signals in providing location services to mobile users in metropolitan areas. In this paper, we present a selector method with the linear discriminant analysis (LDA) among different kinds of mobile location estimation technologies we had proposed in previous work in order to combine their merits, then provide a more accurate estimation for location services. We build up a three-level binary tree to classify these four algorithms. These three levels are named as Stat-Geo level, CG-nonCG level and CT-EPM level. And these success ratios of these three levels are 85.22%, 88.45% and 88.89% respectively. We have tested our selector method with real data taken in Hong Kong and it is proven that it outperforms other existing location estimation algorithms among different kinds of terrains.

title: Algorithm Selectors for Providing Location Estimation Services within a Cellular Radio Network
abstract: Mobile location estimation is becoming an important value-added service for mobile phone operators. Many mobile location estimation algorithms based on the cellular radio networks have been proposed but there exists no general solution since each algorithm has its own advantage depending on specific terrain and environmental factors. In this paper, we propose and investigate three algorithm selectors, one with a LDA classifier and the other two with Bayes classifiers using either a Naive Bayes probabilistic model or a Bayes probabilistic model, to select the best mobile location estimation algorithms from our previous work in order to combine their merits, and provide a more accurate estimation for location services. We have tested these three algorithm selectors with real data taken in Hong Kong. Experiment results have shown that they are all useful in particular, and the one with a Bayes probabilistic model outperforms all other existing location algorithms among different kinds of terrains in terms of average errors

title: Exploiting medium access diversity in rate adaptive wireless LANs
abstract: Recent years have seen the growing popularity of multi-rate wireless network devices (e.g., 802.11a cards) that can exploit variations in channel conditions and improve overall network throughput. Concurrently, rate adaptation schemes have been developed that selectively increase data transmissions on a link when it offers good channel quality. In this paper, we propose a Medium Access Diversity (MAD) scheme that leverages the benefits of rate adaptation schemes by aggressively exploiting multiuser diversity. The basic mechanism of MAD is to obtain instantaneous channel condition information from multiple receivers and selectively transmit data to a receiver that improves the overall throughput of the network, while maintaining temporal fairness among multiple data flows. We identify and address the challenges in the design and implementation of MAD's three phases: channel probing, data transmission, and receiver scheduling. We also use analytical models to examine the tradeoff between network performance improvement and overhead of channel probing, and derive an asymptotic performance bound for the receiver scheduling algorithms used by MAD. Results from the analysis and the extensive simulations demonstrate that, on average, MAD can improve the overall throughput of IEEE 802.11 wireless LANs by 50% as compared with the best existing rate adaptation scheme.

title: Unconstrained numeral pair recognition using enhanced error correcting output coding: a holistic approach
abstract: This paper describes a new approach to recognize touching numeral strings. Currently most methods for numeral string recognition require segmenting the string image into separate numerals. As a result, the recognition system heavily depends on the reliability of the segmentation module. This study explores the holistic strategy directly on the string images without segmentation. It builds the novel classifier by combining binary classifiers based on data-driven error correcting output coding (DECOC). The dimensions of input images are reduced using principal components analysis. Support vector machines are used as base learners. Experiments on NIST SD19 touching numeral pairs confirm that DECOC can achieve favorable performance compared with other multi-class holistic classifiers. The method provides the flexibility of controlling the computational complexity versus accuracy. We also discuss an implementation suitable for distributing computing by decomposing the ensemble into subtasks.

title: A Robust Double Auction Protocol Based on a Hybrid Trust Model
abstract: Recently, Wang and Leung proposed a set of double auction protocols with full privacy protection based on distributed ElGamal encryption. Unfortunately, their protocols are expensive in computation and are not robust in dealing with system malfunction or user misbehavior. In this paper, we propose a secure and practical double auction protocol based on a hybrid trust model, where computation load is distributed to buyers and sellers while a semi-trusted manager handles the registration phase. A prominent feature of the proposed protocol is its high robustness, achieved by using a publicly verifiable secret sharing scheme with threshold access structure.

title: Diverging Keys in Wireless Sensor Networks
abstract: Currently, the most popular ways of dealing with the key distribution problem in sensor networks are random predistribution schemes. For relaxed, realistic assumptions about the attacker, the key infection protocol  is also available. In this paper, by accepting the relaxed assumptions from [1], we propose a scheme which makes pairwise keys “drift” or diverge, which enhances security and can be used as a key distribution method. The most notable feature of this scheme is that, under some assumptions about the sensor nodes, it incurs no communication overhead at all.

title: Optimizing parallel execution of detailed wireless network simulation
abstract: With parallel and discrete event simulation (PDES) techniques, the runtime performance of detailed wireless network simulation can be improved significantly without compromising fidelity of the simulation results. However, modelling characteristics of wireless communications such as signal propagation and interference may severely hinder the potential speedup yielded by PDES. This paper proposes various optimization techniques to address three major concerns in achieving efficient parallel execution of wireless network simulation: i.e., (1) reducing communication and computation overhead of simulating signal propagation across multiple logical processes; (2) reducing synchronization overhead among logical processes; (3) minimizing event scheduling overhead within individual logical processes. These techniques have been implemented in a parallel version of GloMoSim and QualNet. The experimental results with mobile ad hoc networking scenarios demonstrate that the proposed optimization techniques can improve the performance of parallel wireless network simulation by up to an order of magnitude.