Fingerprint indoor positioning based on user orientations and minimum computation time

Indoor Positioning System (IPS) has an important role in the field of Internet of Thing. IPS works based on many existing radio frequency technologies. One of the most popular methods is WLAN Fingerprint because this technology has been installed widely inside buildings and it provides a high level of accuracy. The performance is affected by people who hold mobile devices (user) and also people around the users. This research aimed to minimize the computation time of kNN searching process. The results showed that when the value of k in kNN was greater, the computation time increased, especially when using Cityblock and Minkowski distance function. The smallest average computation time was 2.14 ms, when using Cityblock. Then the computational time for Euclidean and Chebychev were relatively stable, i.e. 2.2 ms and 2.23 ms, respectively

11th International Conference on Practical Applications of Agents and Multi-Agent Systems

Research on Agents and Multi-agent Systems has matured during the last decade and many effective applications of this technology are now deployed. PAAMS provides an international forum to presents and discuss the latest scientific developments and their effective applications, to assess the impact of the approach, and to facilitate technology transfer. PAAMS started as a local initiative, but since grown to become the international yearly platform to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to Exchange their experience in the development and deployment of Agents and Multiagents systems. PAAMS intends to bring together researchers and developers from industry and the academic world to report on the latest scientific and technical advances on the application of multi-agent systems, to discuss and debate the major issues, and to showcase the latest systems using agent based technology. It will promote a forum for discussion on how agent based techniques, methods and tools help system designers to accomplish the mapping between available agent technology and application needs. Other stakeholders should be rewarded with a better understanding of the potential and challenges of the agent-oriented approach. This edition of PAAMS special sessions is organized by the Bioinformatics, Intelligent System and Educational Technology Research Group (http://bisite.usal.es) of the University of Salamanca. The present edition was held in Salamanca, Spain, from 22nd to 24th May 2013

Simultaneous localization and mapping in wireless sensor networks

AbstractMobile device localization in wireless sensor networks is a challenging task. It has already been addressed when the WiFi propagation maps of the access points are modeled deterministically or estimated using an offline human training calibration. However, these techniques do not take into account the environmental dynamics. In this paper, the maps are assumed to be made of an average indoor propagation model combined with a perturbation field which represents the influence of the environment. This perturbation field is embedded with a distribution describing the prior knowledge about the environmental influence. The device is localized with Sequential Monte Carlo methods and relies on the estimation of the propagation maps. This inference task is performed online, using the observations sequentially, with a new online Expectation Maximization based algorithm. The performance of the algorithm is illustrated with Monte Carlo experiments using both simulated data and a true data set

Trends in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection

PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2014 special sessions: Agents Behaviours and Artificial Markets (ABAM), Agents and Mobile Devices (AM), Bio-Inspired and Multi-Agents Systems: Applications to Languages (BioMAS), Multi-Agent Systems and Ambient Intelligence (MASMAI), Self-Explaining Agents (SEA), Web Mining and Recommender systems (WebMiRes) and Intelligent Educational Systems (SSIES)

Sensor-Assisted Wi-Fi Indoor Location System for Adapting to Environmental Dynamics

利用環境中既有的無線網路設備來推算使用者位置座標的室內定位系統，經過這幾年的研究，已充份顯示其低成本、高準確率的優勢。然而，仍然有二個主要的技術問題未被解決。第一個是，該系統的定位準確率容易受到環境變因的變化而影響；第二個則是佈建及調校該系統需要花費大量的時間。為了解決這二個問題，本論文找出了三項在室內定位系統中容易干擾定位準確度的環境因素(包括了門的開關、溼度、以及人群的聚集)並提出了一個以感測器輔助之環境調適方法。該方法利用環境感測器及接近感測器使室內定位系統能夠自動適應環境變因的改變。除此之外，我們提出了一個協同演算法，利用鄰近的使用者彼此的資訊來增加在人群聚集處的定位準確率。實驗結果顯示，當環境變因不斷改變時，比起不具環境調適性及協同演算法的一般方式，經由我們演算法改進後，系統可增加43.7% ~ 236.6%定位準確率。Wi-Fi based indoor location systems have been shown to be both cost-effective and accurate, since they can attain meter-level positional accuracy by using existing Wi-Fi infrastructure in the environment. However, two major technical challenges persist for current Wi-Fi based location systems: instability in positional accuracy due to changing environment dynamics, and the need for manual online calibration during site survey. To address these two challenges, three environment factors (doors, humidity, and human cluster) that can interfere with radio signals and cause positional inaccuracy in the Wi-Fi location systems are identified. Then, we propose a sensor-assisted adaptation method that employs environment and proximity sensors to adapt the location systems automatically to the changing environment dynamics. In addition, a collaborative method is applied to leverage more accurate location information from nearby neighbor nodes to enhance the positional accuracy of a human cluster. Experiments were performed on the sensor-assisted adaptation and collaboration methods. The experimental results show that our enhancement can avoid adverse reduction (43.7% ~ 236.6%) in positional accuracy that can often occur in conventional non-adaptive & non-collaborative methods under changing environment dynamics.Acknowledgments ........................................ i Abstract ............................................... iii List of Figures ........................................ vii List of Tables ......................................... ix Chapter 1 Introduction ................................. 1 1.1 Environment Dynamics ............................... 3 1.2 Further Analysis on Human Clustering Problem ....... 7 1.3 Sensor-assisted Adaptation & Collaboration ......... 10 Chapter 2 Related Work ................................. 15 Chapter 3 Sensor-Assisted Adaptive Localization ........ 20 3.1 Sensor-assisted Sample Collection Phase ............ 20 3.2 Online Calibration Phase ........................... 24 3.3 Adaptive Localization Phase ........................ 25 Chapter 4 Sensor-Assisted Collaborative Localization ... 26 4.1 Neighborhood Detection ............................. 28 4.2 Con‾dence Estimation ............................... 28 4.3 Collaborative Error Correction ..................... 29 Chapter 5 Experiments .................................. 32 5.1 Performance Evaluation on RFID-assisted Online Calibration ........................................................ 33 5.2 Performance Evaluation on Adaptive Localization .... 35 5.2.1 Impact of Closed/Open Doors ...................... 36 5.2.2 Impact of Relative Humidity ...................... 39 5.3 Performance Evaluation on Collaborative Localization 41 Chapter 6 Conclusions and Future Work .................. 45 Appendix A Yi-Chao Chen's Publications ................. 47 Bibliography ........................................... 4

Indoor positioning model based on people effect and ray tracing propagation

WLAN-fingerprinting has been highlighted as the preferred technology in an Indoor Positioning System (IPS) due to its accurate positioning results and minimal infrastructure cost. However, the accuracy of IPS fingerprinting is highly influenced by the fluctuation in signal strength as a result of encountering obstacles. Many researchers have modelled static obstacles such as walls and ceilings, but hardly any have modelled the effect of people presence as an obstacle although the human body significantly impacts signal strength. Hence, the people presence effect must be considered to obtain highly accurate positioning results. Previous research proposed a model that only considered the direct path between the transmitter and the receiver. However, for indoor propagation, multipath effects such as reflection can also have a significant influence, but were not considered in past work. Therefore, this research proposes an accurate indoor positioning model that considers people presence using a ray tracing (AIRY) model in a dynamic environment which relies on existing infrastructure. Three solutions were proposed to construct AIRY: an automatic radio map using ray tracing (ARM-RT), a new human model in ray tracing (HUMORY), and a people effect constant for received signal strength indicator (RSSI) adaptation. At the offline stage, 30 RSSIs were recorded at each point using a smartphone to create a radio map database (523 points). The real-time RSSI was then compared to the radio map database at the online stage using MATLAB software to determine the user position (65 test points). The proposed model was tested at Level 3 of Razak Tower, UTM Kuala Lumpur (80 × 16 m). To test the influence of people presence, the number, position, and distance of the people around the mobile device (MD) were varied. The results showed that the closer the people were to the MD in both the Line of Sight (LOS) and Non-LOS position, the greater the decrease in RSSI, in which the increment number of people will increase the amount of reflection signals to be blocked. The signal strength reduction started from 0.5 dBm with two people and reached 0.9 dBm with seven people. In addition, the ray tracing model produced smaller errors on RSSI prediction than the multi-wall model when considering the effect of people presence. The k-nearest neighbour (KNN) algorithm was used to define the position. The initial accuracy was improved from 2.04 m to 0.57 m after people presence and multipath effects were considered. In conclusion, the proposed model successfully increased indoor positioning accuracy in a dynamic environment by overcoming the people presence effect

Sensor-Assisted Wi-Fi Indoor Location System for Adapting to Environmental Dynamics

Wi-Fi based indoor location systems have been shown to be both cost-effective and accurate, since they can attain meter-level positioning accuracy by using existing Wi-Fi infrastructure in the environment. However, two major technical challenges persist for current Wi-Fi based location systems, instability in positioning accuracy due to changing environmental dynamics, and the need for manual offline calibration during site survey. To address these two challenges, three environmental factors (people, doors, and humidity) that can interfere with radio signals and cause positioning inaccuracy are identified. Then, we have proposed a sensor-assisted adaptation method that employs RFID sensors and environment sensors to adapt the location systems automatically to the changing environmental dynamics. The proposed adaptation method performs online calibration to build multiple contextaware radio maps under various environmental conditions. Experiments were performed on the sensor-assisted adaptation method. The experimental results show that the proposed adaptive method can avoid adverse reduction in positioning accuracy under changing environmental dynamics

Wireless Strain Sensing as NDT Method for Plastic Composites

The rapid increase in the use of plastics composites mainly in primary structures has brought with it the challenge of ensuring the damage inspection in a fast and precise way. Traditional NDT methods are not adequate for the complexity and anisotropy of composite materials. New and improved NDT methods are a necessity. A strain sensing technique to be applied on the non-destructive evaluation of fiber reinforced plastic composites have been designed and developed. The system is based on a combination involving WSN technology and the conventional resistance strain gauges sensors, which detect surface strains on the components. The experimental results demonstrated that this technique is capable of collecting strain information of the composites under service and conditions where the components are subjected to bending strains and under harsh environments, such as during contact with seawater. The setup is an inexpensive technology that is easy and practical to install even in the most difficult production sites since it is a wireless based system. The long duration tests showed that this battery powered based system has the ability to collect strain data for a long period of time allowing saving energy. The strain sensing system developed here has several practical applications in strain and stress measurements in the structural monitoring and quality assurance of virtually every sector of industry in which plastic composites are related. Due to the positive results obtained in this study, further research on this field should be encouraged. Additional research on the reliability of this wireless strain sensing system in different environments (e.g temperature, humidity) and loading conditions are crucial for the deployment of this technology into different applications and composite industries