Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Wi-Fi Finger-Printing Based Indoor Localization Using Nano-Scale Unmanned Aerial Vehicles

Explosive growth in the number of mobile devices like smartphones, tablets, and smartwatches has escalated the demand for localization-based services, spurring development of numerous indoor localization techniques. Especially, widespread deployment of wireless LANs prompted ever increasing interests in WiFi-based indoor localization mechanisms. However, a critical shortcoming of such localization schemes is the intensive time and labor requirements for collecting and building the WiFi fingerprinting database, especially when the system needs to cover a large space. In this thesis, we propose to automate the WiFi fingerprint survey process using a group of nano-scale unmanned aerial vehicles (NAVs). The proposed system significantly reduces the efforts for collecting WiFi fingerprints. Furthermore, since these NAVs explore a 3D space, the WiFi fingerprints of a 3D space can be obtained increasing the localization accuracy. The proposed system is implemented on a commercially available miniature open-source quadcopter platform by integrating a contemporary WiFi - fingerprint - based localization system. Experimental results demonstrate that the localization error is about 2m, which exhibits only about 20cm of accuracy degradation compared with the manual WiFi fingerprint survey methods

Situation Goodness Method for Weighted Centroid-Based Wi-Fi APs Localization

Knowing the location of Wi-Fi antennas may be critical for indoor localization. However, in a real environment, their positions may be unknown since they can be managed by external entities. This paper introduces a new method for evaluating the suitability of using the weighted centroid method for the 2D localization of a Wi-Fi AP. The method is based on the idea that the weighted centroid method provides its best results when there are fingerprints taken around the AP. In order to find the probability of being in the presence of such situations, a natural neighbor interpolation method is used to find the regions with the highest signal strengths. A geometrical method is then used to characterize that probability based on the distribution of those regions in relation to the AP position estimation given by the weighted centroid method. The paper describes the testing location and the used Wi-Fi fingerprints database. That database is used to create new databases that recreate different sampling possibilities through a samples deletion strategy. The original database and the newly created ones are then used to evaluate the localization results of several AP localization methods and the new method proposed in this paper. The evaluation results have shown that the proposed method is able to provide a proper probability for the suitability of using the weighted centroid method for localizing a Wi-Fi AP

An Enhanced Indoor Positioning Method Based on Wi-Fi RSS Fingerprinting

In WiFi-based indoor positioning, the received signal strength (RSS) measurements are commonly used to estimate the mobile user location. However, these measurements significantly fluctuate over time and are susceptible to human movement, multipath and Non-Line-of-Sight (NLOS) propagation, which reduce the location accuracy. In this paper, an enhancement positioning method based on the nearest neighbor algorithm is proposed. The distribution of the RSS samples recorded from several Access Points (APs) are used rather than their average, for reducing the location errors introduced by the RSS variations and the multipath problem. The proposed algorithm, named the Nearest Kth Nearest Neighbor (NK-NN) is experimentally evaluated and compared to other powerful methods. The results show that the proposed method outperforms these methods

Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned From the IPIN 2018 Competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75 th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Low-Cost Indoor Localisation Based on Inertial Sensors, Wi-Fi and Sound

The average life expectancy has been increasing in the last decades, creating the need for new technologies to improve the quality of life of the elderly. In the Ambient Assisted Living scope, indoor location systems emerged as a promising technology capable of sup porting the elderly, providing them a safer environment to live in, and promoting their autonomy. Current indoor location technologies are divided into two categories, depend ing on their need for additional infrastructure. Infrastructure-based solutions require expensive deployment and maintenance. On the other hand, most infrastructure-free systems rely on a single source of information, being highly dependent on its availability. Such systems will hardly be deployed in real-life scenarios, as they cannot handle the absence of their source of information. An efficient solution must, thus, guarantee the continuous indoor positioning of the elderly. This work proposes a new room-level low-cost indoor location algorithm. It relies on three information sources: inertial sensors, to reconstruct users’ trajectories; environ mental sound, to exploit the unique characteristics of each home division; and Wi-Fi, to estimate the distance to the Access Point in the neighbourhood. Two data collection protocols were designed to resemble a real living scenario, and a data processing stage was applied to the collected data. Then, each source was used to train individual Ma chine Learning (including Deep Learning) algorithms to identify room-level positions. As each source provides different information to the classification, the data were merged to produce a more robust localization. Three data fusion approaches (input-level, early, and late fusion) were implemented for this goal, providing a final output containing complementary contributions from all data sources. Experimental results show that the performance improved when more than one source was used, attaining a weighted F1-score of 81.8% in the localization between seven home divisions. In conclusion, the evaluation of the developed algorithm shows that it can achieve accurate room-level indoor localization, being, thus, suitable to be applied in Ambient Assisted Living scenarios.O aumento da esperança média de vida nas últimas décadas, criou a necessidade de desenvolvimento de tecnologias que permitam melhorar a qualidade de vida dos idosos. No âmbito da Assistência à Autonomia no Domicílio, sistemas de localização indoor têm emergido como uma tecnologia promissora capaz de acompanhar os idosos e as suas atividades, proporcionando-lhes um ambiente seguro e promovendo a sua autonomia. As tecnologias de localização indoor atuais podem ser divididas em duas categorias, aquelas que necessitam de infrastruturas adicionais e aquelas que não. Sistemas dependentes de infrastrutura necessitam de implementação e manutenção que são muitas vezes dispendiosas. Por outro lado, a maioria das soluções que não requerem infrastrutura, dependem de apenas uma fonte de informação, sendo crucial a sua disponibilidade. Um sistema que não consegue lidar com a falta de informação de um sensor dificilmente será implementado em cenários reais. Uma solução eficiente deverá assim garantir o acompanhamento contínuo dos idosos. A solução proposta consiste no desenvolvimento de um algoritmo de localização indoor de baixo custo, baseando-se nas seguintes fontes de informação: sensores inerciais, capazes de reconstruir a trajetória do utilizador; som, explorando as características dis tintas de cada divisão da casa; e Wi-Fi, responsável pela estimativa da distância entre o ponto de acesso e o smartphone. Cada fonte sensorial, extraída dos sensores incorpora dos no dispositivo, foi, numa primeira abordagem, individualmente otimizada através de algoritmos de Machine Learning (incluindo Deep Learning). Como os dados das diversas fontes contêm informação diferente acerca das mesmas características do sistema, a sua fusão torna a classificação mais informada e robusta. Com este objetivo, foram implementadas três abordagens de fusão de dados (input data, early and late fusion), fornecendo um resultado final derivado de contribuições complementares de todas as fontes de dados. Os resultados experimentais mostram que o desempenho do algoritmo desenvolvido melhorou com a inclusão de informação multi-sensor, alcançando um valor para F1- score de 81.8% na distinção entre sete divisões domésticas. Concluindo, o algoritmo de localização indoor, combinando informações de três fontes diferentes através de métodos de fusão de dados, alcançou uma localização room-level e está apto para ser aplicado num cenário de Assistência à Autonomia no Domicílio

A noncontact ultrasonic platform for structural inspection

Miniature robotic vehicles are receiving increasing attention for use in nondestructive testing (NDE) due to their attractiveness in terms of cost, safety, and their accessibility to areas where manual inspection is not practical. Conventional ultrasonic inspection requires the provision of a suitable coupling liquid between the probe and the structure under test. This necessitates either an on board reservoir or umbilical providing a constant flow of coupling fluid, neither of which are practical for a fleet of miniature robotic inspection vehicles. Air-coupled ultrasound offers the possibility of couplant-free ultrasonic inspection. This paper describes the sensing methodology, hardware platform and algorithms used to integrate an air-coupled ultrasonic inspection payload into a miniature robotic vehicle platform. The work takes account of the robot's inherent positional uncertainty when constructing an image of the test specimen from aggregated sensor measurements. This paper concludes with the results of an automatic inspection of a aluminium sample

Evaluating Sensor Data in the Context of Mobile Crowdsensing

With the recent rise of the Internet of Things the prevalence of mobile sensors in our daily life experienced a huge surge. Mobile crowdsensing (MCS) is a new emerging paradigm that realizes the utility and ubiquity of smartphones and more precisely their incorporated smart sensors. By using the mobile phones and data of ordinary citizens, many problems have to be solved when designing an MCS-application. What data is needed in order to obtain the wanted results? Should the calculations be executed locally or on a server? How can the quality of data be improved? How can the data best be evaluated? These problems are addressed by the design of a streamlined approach of how to create an MCS-application while having all these problems in mind. In order to design this approach, an exhaustive literature research on existing MCS-applications was done and to validate this approach a new application was designed with its help. The procedure of designing and implementing this application went smoothly and thus shows the applicability of the approach