Wan Mohd Yaakob Wan Bejuri

Ubiquitous Positioning: A Taxonomy for Location Determination on Mobile Navigation System

The location determination in obstructed area can be very challenging especially if Global Positioning System are blocked. Users will find it difficult to navigate directly on-site in such condition, especially indoor car park lot or obstructed environment. Sometimes, it needs to combine with other sensors and positioning methods in order to determine the location with more intelligent, reliable and ubiquity. By using ubiquitous positioning in mobile navigation system, it is a promising ubiquitous location technique in a mobile phone since as it is a familiar personal electronic device for many people. However, there is an increasing need for better development of proposed ubiquitous positioning systems. System developers are also lacking of good frameworks for understanding different options during building ubiquitous positioning systems. This paper proposes taxonomy to address both of these problems. The proposed taxonomy has been constructed from a literature study of papers and articles on positioning estimation that can be used to determine location everywhere on mobile navigation system. For researchers the taxonomy can also be used as an aid for scoping out future research in the area of ubiquitous positioning.

Ubiquitous positioning: integrated GPS/Wireless LAN positioning for wheelchair navigation system

The location determination in obstructed area can be very challenging especially when the Global Positioning System is blocked. Disable users will find it difficult to navigate directly on-site in such condition, particularly in obstructed environment. Sometimes, it needs to integrate with other sensors and positioning methods in order to determine the location with more intelligent, reliable and ubiquity. By using ubiquitous positioning, it provides the location technique inside the wheelchair navigation system that needed for disable people. In this paper, we utilizes the integration of wireless local area network and the Global Positioning System which receive signal strength from access point and at the same time, it retrieve Global Navigation System Satellite signal. This positioning information will be switched based on type of environment in order to ensure the ubiquity of wheelchair navigation system. Finally, we present our results by illustrating the performance of the system for an indoor/ outdoor environment set-up.

Offline Beacon Selection-Based RSSI Fingerprinting for Location-Aware Shopping Assistance: A Preliminary Result

The location determination in an obstructed area can be extremely challenging particularly when the Global Positioning System (GPS) is blocked. When this happens, users will encounter difficulty in navigating directly on-site, especially within an indoor environment. Occasionally, there is a need to integrate with other sensors in order to establish the location with greater intelligence, reliability, and ubiquity. The use of positioning integration may be useful since it involves as many beacons as necessary to determine positioning. However, the implementation of the integration in the mobile devices platform may lead high computation which in turn could increase power consumption. In this paper, an offline beacon selection-based RSSI fingerprinting is proposed in order to lessen the computation task during the location determination process, as it may cause huge power consumption in mobile devices. By reducing the number of beacons that will be processed, the number of RSSI fingerprinting searches of the location in the spatial database also reduced. Lastly, the preliminary results are presented to illustrate the performance of an indoor environment set-up.

Adaptive memory-based single distribution resampling for particle filter

AbstractThe restrictions that are related to using single distribution resampling for some specific computing devices’ memory gives developers several difficulties as a result of the increased effort and time needed for the development of a particle filter. Thus, one needs a new sequential resampling algorithm that is flexible enough to allow it to be used with various computing devices. Therefore, this paper formulated a new single distribution resampling called the adaptive memory size-based single distribution resampling (AMSSDR). This resampling method integrates traditional variation resampling and traditional resampling in one architecture. The algorithm changes the resampling algorithm using the memory in a computing device. This helps the developer formulate a particle filter without over considering the computing devices’ memory utilisation during the development of different particle filters. At the start of the operational process, it uses the AMSSDR selector to choose an appropriate resampling algorithm (for example, rounding copy resampling or systematic resampling), based on the current computing devices’ physical memory. If one chooses systematic resampling, the resampling will sample every particle for every cycle. On the other hand, if it chooses the rounding copy resampling, the resampling will sample more than one of each cycle’s particle. This illustrates that the method (AMSSDR) being proposed is capable of switching resampling algorithms based on various physical memory requirements. The aim of the authors is to extend this research in the future by applying their proposed method in various emerging applications such as real-time locator systems or medical applications.

Adaptive Memory Size Based Fuzzy Control for Mobile Pedestrian Navigation

The range of memory specifications of mobile pedestrian navigation systems poses difficulties for the developer (in terms of increased time and effort) when it comes to developing a resampling algorithm for mobile pedestrian navigation devices. Thus, a new resampling algorithm is required with a flexible capacity that would cater for a range of computing device memory specifications. This paper develops a new single distribution resampling algorithm, the Adaptive Memory Size-based Fuzzy Control (AMSFC), that integrates traditional resampling and traditional variation resampling in one architecture. The algorithm switches the resampling algorithm on the basis of the memory of the particular mobile pedestrian navigation, thus making it easier for the developer to develop a particle filter without having to consider the memory utilisation of mobile pedestrian navigation devices during different particle filter development processes. At the beginning of the operational process, the AMSFC selector is used to select a suitable resampling algorithm (for example, systematic resampling or rounding copy resampling) based on the physical memory of current computing devices. If systematic resampling is selected, the resampling algorithm samples each particle for each j cycle, while if the rounding copy resampling algorithm is selected, the resampling samples more than one particle of each j cycle. This demonstrates that the proposed method (AMSFC) can switch resampling algorithms to meet the differing physical memory requirements. The authors aim to extend this work in future by implementing their proposed method in a number of different emerging applications (in example, medical applications and real time locator systems).

A Proposal of Location Aware Shopping Assistance Using Memory-Based Resampling

The range of memory specifications of location aware shopping assistance poses difficulties for the developer (in terms of increased time and effort) when it comes to developing a resampling algorithm for mobile devices. Thus, a new resampling algorithm is required with a flexible capacity that would cater for a range of computing device memory devices specifications. This paper develops a memory based resampling in standard particle filter. The memory resampling is capable to read memory specifications of mobile devices before determines the most suitable resampling functions. The authors aim to extend this work in future by implementing their proposed method in a number of different emerging applications (in example, medical applications and real time locator systems).

Adaptive Resampling for Emergency Rescue Location: An Initial Concept

The different of memory specification mobile devices or smart phone make it hard for developer to establish a resampling in emergency rescue location for specific smart phone. It took much time for developer to develop it. In this paper, we will introduce a good solution for developer to develop a resampling algorithm for different mobile devices or smart phone. The proposed resampling can adapt memory specification of mobile device in order to determine which suitable resampling operation or function for specific mobile device. As overall, the paper just present a concept that can be used as a guideline to develop a flexible resampling.