RSSI Based Indoor Localization for Smartphone Using Fixed and Mobile Wireless Node

Nowadays with the dispersion of wireless networks, smartphones and diverse related services, different localization techniques have been developed. Global Positioning System (GPS) has a high rate of accuracy for outdoor localization but the signal is not available inside of buildings. Also other existing methods for indoor localization have low accuracy. In addition, they use fixed infrastructure support. In this paper, we present a novel system for indoor localization, which also works well outside. We have developed a mathematical model for estimating location (distance and direction) of a mobile device using wireless technology. Our experimental results on Smartphones (Android and iOS) show good accuracy (an error less than 2.5 meters). We have also used our developed system in asset tracking and complex activity recognition

Étude et positionnement utilisant le réseau de capteur sans fil dans un environnement minier souterrain

La sécurité et la communication posent des problèmes majeurs auxquels il faut remédier dans les environnements hostiles comme les mines souterraines. Pour une communication fiable ainsi que pour tracer la position exacte d’un objet dans les mines souterraines, différentes technologies ont été déployé. Parmi ces dernières, le réseau de capteurs sans fil est considéré comme un outil prometteur pour les applications basées sur la localisation, à savoir, la surveillance des lieux, le repérage des mobiles et la navigation. En fait, les réseaux de capteur sans-fil fournissent une couverture d’une vaste gamme d’équipements fiables, efficaces, tolérants aux défaillances et évolutives. Cependant, les travaux de recherches précédents ont divisé la localisation en deux parties: les méthodes basées sur la portée et celles non-basées sur la portée. Où la première est précise et coûteuse tandis que la deuxième est présentée pour réduire la quantité d’énergie consommée du côté capteur dont les ressources sont limitées. Notre recherche se focalise sur la localisation basée sur la portée utilisant le réseau de capteurs sans fil dans les milieux internes et mines souterrains. Plusieurs techniques ont été proposées pour la localisation comme la réception de l'indicateur de force de signal (RSSI), le temps d'arrivée (TOA), la différence de temps d'arrivée (TDOA), l'angle d'arrivée (AOA). Bien que plusieurs travaux de recherches utilisant ces techniques aient été exécutés, l'approche de localisation à base de temps pour les environnements complexe comme la mine souterraine demeure limitée. Cette thèse offre de nouvelles solutions pour combler l’écart entre la localisation à base de temps et le réseau de capteurs sans fil à haute précision, pour l’environnement minier souterrain. De plus, nous avons utilisé une technologie émergente, à savoir les communications ultra-large bande, pour booster la performance et l'exactitude. Notre travail de recherche est subdivisé en deux principales parties : une partie simulation et une partie pratique. Dans la première, nous avons utilisé MATLAB pour faire les différentes simulations. La deuxième partie consiste en plusieurs mesures pratiques réalisées dans un environnement intérieur ainsi que dans une mine souterraine. Les résultats montrent une amélioration remarquable et une meilleure précision de la technique UWB à base de temps

A Wireless Sensor Network Based Personnel Positioning Scheme in Coal Mines with Blind Areas

This paper proposes a novel personnel positioning scheme for a tunnel network with blind areas, which compared with most existing schemes offers both low-cost and high-precision. Based on the data models of tunnel networks, measurement networks and mobile miners, the global positioning method is divided into four steps: (1) calculate the real time personnel location in local areas using a location engine, and send it to the upper computer through the gateway; (2) correct any localization errors resulting from the underground tunnel environmental interference; (3) determine the global three-dimensional position by coordinate transformation; (4) estimate the personnel locations in the blind areas. A prototype system constructed to verify the positioning performance shows that the proposed positioning system has good reliability, scalability, and positioning performance. In particular, the static localization error of the positioning system is less than 2.4 m in the underground tunnel environment and the moving estimation error is below 4.5 m in the corridor environment. The system was operated continuously over three months without any failures

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Application of rasch model on resilience in higher education: an examination of validity and reliability of Malaysian academician happiness index (MAHI)

This preliminary study was conducted to examine and verify the validity and reliability of the instrument on the Malaysian Academician Happiness Index (MAHI) on resilience. MAHI could be seen as a tool to measure the level of happiness and stress of academicians before determining how resilient the academicians were. Resilience can be defined as a mental ability of a person to recover quickly from illness or depression. MAHI instrument consisted of 66 items. The instrument was distributed to 40 academicians from three groups of universities which were the Focus University, Comprehensive University and Research University is using a survey technique. The instrument was developed to measure three main constructs which were the organization, individual and social that would affect the happiness and stress levels of academicians. This preliminary study employed the Rasch Measurement Model uses Winsteps software version 3.69.1.11. to examine the validity and reliability of the items. The results of the analysis of the MAHI instrument showed that the item reliability was 0.87, person reliability was 0.83 and value of Alpha Cronbach was 0.84. Meanwhile, misfit analysis showed that only there was one item with 1.46 logit that could be considered for dropping or needed improvement. Therefore, it highlighted that most of the items met the constructs’ need and can be used as a measurement indicator of MAHI. The implication of this instrument can help Malaysian academicians to be more resilient in facing challenges in the future

A Performance Evaluation of Modified Weighted Pathloss Scenario Based on the Cluster Based-PLE for an Indoor Positioning of Wireless Sensor Network

The indoor positioning system is one of the popular topics in the current study; it is mainly due to the inability of the Global Positioning System (GPS) applied inside a building. LANDMARC (Location Identification based on Dynamic Active RFID Calibration) and Enhanced LANDMARC Scenario use calibration RSSI values and weighted process to obtain accuracy of position estimation. Meanwhile, WPL (Weighted Pathloss) method improves the positioning accuracy of the two previous methods (LANDMARC and Enhanced LANDMARC) by observing the Path Loss Exponent (PLE) value in an indoor environment, followed by using the value to estimate the object position. We propose a Modified WPL uses Cluster Based Pathloss Exponent (PLE) method by combining the functions of the existing calibration in LANDMARC Scenario with the Cluster Based PLE value. The test bed was conducted in an indoor area on the 3rd floor of the PENS Postgraduate Building. The nodes were connected to each other using X-Bee Pro S2 module. RSSI (Received Signal Strength Indicator) value was used to estimate the distance between transmitter and receiver nodes. The result of the MSE estimation position using the proposed method was 3.80 meters, whereas WPL method was 5.78 meters. Overall, the proposed Modified WPL with Cluster Based PLE method showed that it had the capability to enhance the accuracy of localization; 34% better than the standard WPL method

Localization Context-Aware Models for Wireless Sensor Network

Wireless sensor networks (WSNs) are emerging as the key technology to support the Internet of Things (IoT) and smart objects. Small devices with low energy consumption and limited computing resources have wide use in many applications and different fields. Nodes are deployed randomly without a priori knowledge of their location. However, location context is a fundamental feature necessary to provide a context-aware framework to information gathered from sensors in many services such as intrusion detection, surveillance, geographic routing/forwarding, and coverage area management. Nevertheless, only a little number of nodes called anchors are equipped with localization components, such as Global Positioning System (GPS) chips. Worse still, when sensors are deployed in an indoor environment, GPS serves no purpose. This chapter surveys a variety of state-of-the-art existing localization techniques and compares their characteristics by detailing their applications, strengths, and challenges. The specificities and enhancements of the most popular and effective techniques are as well reported. Besides, current research directions in localization are discussed

Train Localisation using Wireless Sensor Networks

Safety and reliability have always been concerns for railway transportation. Knowing the exact location of a train enables the railway system to react to an unusual situation for the safety of human lives and properties. Generally, the accuracy of localisation systems is related with their deployment and maintenance costs, which can be on the order of millions of dollars a year. Despite a lot of research efforts, existing localisation systems based on different technologies are still limited because most of them either require expensive infrastructure (ultrasound and laser), have high database maintenance, computational costs or accumulate errors (vision), offer limited coverage (GPS-dark regions, Wi-Fi, RFID) or provide low accuracy (audible sound). On the other hand, wireless sensor networks (WSNs) offer the potential for a cheap, reliable and accurate solutions for the train localisation system. This thesis proposes a WSN-based train localisation system, in which train location is estimated based on the information gathered through the communication between the anchor sensors deployed along the track and the gateway sensor installed on the train, such as anchor sensors' geographic coordinates and the Received Signal Strength Indicator (RSSI). In the proposed system, timely anchor-gateway communication implies accurate localisation. How to guarantee effective communication between anchor sensors along the track and the gateway sensor on the train is a challenging problem for WSN-based train localisation. I propose a beacon driven sensors wake-up scheme (BWS) to address this problem. BWS allows each anchor sensor to run an asynchronous duty-cycling protocol to conserve energy and establishes an upper bound on the sleep time in one duty cycle to guarantee their timely wake-up once a train approaches. Simulation results show that the BWS scheme can timely wake up the anchor sensors at a very low energy consumption cost. To design an accurate scheme for train localisation, I conducted on-site experiments in an open field, a railway station and a tunnel, and the results show that RSSI can be used as an estimator for train localisation and its applicability increases with the incorporation of another type of data such as location information of anchor sensors. By combining the advantages of RSSI-based distance estimation and Particle Filtering techniques, I designed a Particle-Filter-based train localisation scheme and propose a novel Weighted RSSI Likelihood Function (WRLF) for particle update. The proposed localisation scheme is evaluated through extensive simulations using the data obtained from the on-site measurements. Simulation results demonstrate that the proposed scheme can achieve significant accuracy, where average localisation error stays under 30 cm at the train speed of 40 m=s, 40% anchor sensors failure rate and sparse deployment. In addition, the proposed train localisation scheme is robust to changes in train speed, the deployment density and reliability of anchor sensors. Anchor sensors are prone to hardware and software deterioration such as battery outage and dislocation. Therefore, in order to reduce the negative impacts of these problems, I designed a novel Consensus-based Anchor sensor Management Scheme (CAMS), in which each anchor sensor performs a self-diagnostics and reports the detected faults in the neighbourhood. CAMS can assist the gateway sensor to exclude the input from the faulty anchor sensors. In CAMS, anchor sensors update each other about their opinions on other neighbours and develops consensus to mark faulty sensors. In addition, CAMS also reports the system information such as signal path loss ratio and allows anchor sensors to re-calibrate and verify their geographic coordinates. CAMS is evaluated through extensive simulations based on real data collected from field experiments. This evaluation also incorporated the simulated node failure model in simulations. Though there are no existing WSN-based train localisation systems available to directly compare our results with, the proposed schemes are evaluated with real datasets, theoretical models and existing work wherever it was possible. Overall, the WSN-based train localisation system enables the use of RSSI, with combination of location coordinates of anchor sensors, as location estimator. Due to low cost of sensor devices, the cost of overall system remains low. Further, with duty-cycling operation, energy of the sensor nodes and system is conserved