Fast and robust anchor calibration in range-based wireless localization

In this paper we investigate the anchor calibration problem where we want to find the anchor positions when the anchors are not able to range between each other. This is a problem of practical interest because in many systems, the anchors are not connected in a network but are just simple responders to range requests. The proposed calibration method is designed to be fast and simple using only a single range-capable device. For the estimation of the inter-anchor distances, we propose a Total Least Squares estimator as well as a L1 norm estimator. Real life experiments using publicly available hardware validate the proposed calibration technique and show the robustness of the algorithm to non-line-of-sight measurements

Inferring Person-to-person Proximity Using WiFi Signals

Today's societies are enveloped in an ever-growing telecommunication infrastructure. This infrastructure offers important opportunities for sensing and recording a multitude of human behaviors. Human mobility patterns are a prominent example of such a behavior which has been studied based on cell phone towers, Bluetooth beacons, and WiFi networks as proxies for location. However, while mobility is an important aspect of human behavior, understanding complex social systems requires studying not only the movement of individuals, but also their interactions. Sensing social interactions on a large scale is a technical challenge and many commonly used approaches---including RFID badges or Bluetooth scanning---offer only limited scalability. Here we show that it is possible, in a scalable and robust way, to accurately infer person-to-person physical proximity from the lists of WiFi access points measured by smartphones carried by the two individuals. Based on a longitudinal dataset of approximately 800 participants with ground-truth interactions collected over a year, we show that our model performs better than the current state-of-the-art. Our results demonstrate the value of WiFi signals in social sensing as well as potential threats to privacy that they imply

SINR-based Network Selection for Optimization in Heterogeneous Wireless Networks (HWNs)

To guarantee the phenomenon of "Always Best Connection" in heterogeneous wireless networks, a vertical handover optimization is necessary to realize seamless mobility. Received signal strength (RSS) from the user equipment (UE) contains interference from surrounding base stations, which happens to be a function of the network load of the nearby cells. An expression is derived for the received SINR (signal to interference and noise ratio) as a function of traffic load in interfering cells of data networks. A better estimate of the UE SINR is achieved by taking into account the contribution of inter-cell interference. The proposed scheme affords UE to receive high throughput with less data rate, and hence benefits users who are located far from the base station. The proposed scheme demonstrates an improved throughput between the serving base station and the cell boundary

SILS: a Smart Indoors Localization Scheme based on on-the-go cooperative Smartphones networks using onboard Bluetooth, WiFi and GNSS

Seamless outdoors-indoors localization based on Smartphones sensors is essential to realize the full potential of Location Based Services. This paper proposes a Smart Indoors Localization Scheme (SILS) whereby participating Smartphones (SPs) in the same outdoors and indoors vicinity, form a Bluetooth network to locate the indoors SPs. To achieve this, SILS will perform 3 functions: (1) synchronize & locate all reachable WiFi Access Points (WAPs) with live GNSS time available on the outdoors SPs; 2) exchange a database of all SPs location and time-offsets; 3) calculate approximate location of indoor-SPs based on hybridization of GNSS, Bluetooth and WiFi measurements. These measurements includes a) Bluetooth to Bluetooth relative pseudo ranges of all participating SPs based on hop-synchronization and Master-Slave role switching to minimize the pseudo-ranges error, b) GNSS measured location of outdoors-SPs with good geometric reference points, and c) WAPs-SPs Trilateration estimates for deep indoors localization. Results, obtained from OPNET simulation and live trials of SILS built for various SPs network size and indoors/outdoors combinations scenarios, show that we can locate under 1 meter in near-indoors while accuracy of around 2-meters can be achieved when locating SPs at deep indoors situations. Better accuracy can be achieved when large numbers of SPs (up to 7) are available in the network/vicinity at any one time and when at least 4 of them have a good sky view outdoors

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

The Optimum Location for Access Point Deployment based on RSS for Indoor Communication

YesIn indoor wireless communication networks, the optimal locations had been known to deploy the access points (AP's) which has a significant impact on improving various aspects of network operation, management, and coverage. In addition, develop the behavioral characteristics of the wireless network. The most used approach for localization purposes was based on Received Signal Strength (RSS) measurements, which is widely used in the wireless network. As well as, it can be easily accessed from different operating systems. In this paper, we proposed an optimal AP localization algorithm based on RSS measurement obtained from different received points. This localization algorithm works as a complementary to the 3D Ray tracing model based REMCOM wireless InSite software and considered two-step localization approach, data collection phase, and localization phase. Obtained result give relatively high accuracy to select the optimum location for AP compare with other selected locations. It is worth to mention that effect of different building materials on signal propagation has been considered with specifying the optimum location of deployment. Furthermore, channel characterizations that based on path losses have been considered as a confirmation for the optimum location being selected

Long-Range Indoor Emitter Localization from 433MHz and 2.4GHz WLAN Received Signal Strengths

An improved search method for localizing a radio emitter in a building from its signal strength is proposed and implemented. It starts from floor level determination, which samples the signal strength on each floor and determines the floor level of the emitter. Then the search is conducted iteratively on a specific floor. For each round of search, one-dimensional (1-D) or two-dimensional (2-D) signal strength is collected according to the actual structure of the floor. The signal strength data are processed to fit a 1-D curve or a 2-D surface with regression models to establish an indicator or trend, which can either locate the emitter or provide direction for the next round of search. The main contribution of this thesis is that the data processing results for 2- D signal strength data can locate the emitter or show the direction of the emitter through gradient, which is helpful to future search. Our approach has been implemented with two wireless protocols: 433MHz protocol and 2.4GHz wireless local area network (WLAN) protocol. A 433MHz module with LoRa modulation is chosen to provide long propagation distance. A 2.4GHz WLAN tester is used for close range search where 433MHz signal does not show enough attenuation spread to be effective. 433MHz implementation consists of an emitter, a radio tester and an Android APP on a smartphone. The emitter is a board with an Arduino Uno and a 433MHz transceiver. The radio tester is a board with an Arduino Uno, a 433MHz transceiver and a Bluetooth-to-serial module to communicate with a smartphone. The radio tester and the APP work together to localize the emitter. 2.4GHz WLAN implementation is composed of an emitter, which is emulated with a smartphone, a radio tester which consists of a smartphone, and a router and two Android APPs. Both phones are connected through the router and socket communication is initiated with the radio tester working as a server and the emitter working as a client. The APP on the emitter implements the client functions. The radio tester controls data acquisition process. The APP on the tester establishes the server functions and deals with received data. It compares signal strengths in different locations and finds the position that has the strongest signal strength to locate the emitter. The innovative idea of this thesis is to use 1-D and 2-D signal strength with regression models as it is convenient to provide location or unique search direction of the emitter. 1-D data is processed with linear and polynomial regressions to fit curves in order to find possible location of the emitter in either a narrow strip or a half a plane. 2-D data is processed with multiple regressions to fit contour-line surfaces in order to find either location of the emitter on the top of a surface or a unique search direction of the location of the emitter as indicated by the highest surface gradient. Our approach is compared with the centroid algorithm with an example. The centroid algorithm assumes the emitter is located in the search area and it is also easily influenced by sampling location biases. Our approach has two advantages over the centroid algorithm. The first advantage is that our approach can work even when the emitter is out of the initial search area since it searches iteratively. The second advantage is that when the emitter is in the initial search area, our approach is not influenced by sampling location biases

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

Localização indoor em ambientes inteligentes

Dissertação de mestrado em Engenharia InformáticaO envelhecimento de um ser humano acarreta condições de vida e de saúde mais sensíveis a ocorrências do nosso dia-a-dia. Nestes termos podemos assumir que a condição de saúde de uma pessoa idosa é bastante frágil, muito limitada a acontecimentos e fatores externos, como por exemplo quedas. Este tipo de problemas incidem diretamente sobre a saúde da pessoa, logo sobre a sua qualidade de vida. Numa queda, uma pessoa idosa, para além das complicações de saúde, tem complicações que criam impacto na sua vida, como por exemplo a limitação de mobilidade. As soluções tecnológicas podem ser usadas para ajudar pessoas com limitações neste tipo de situações. O recurso à utilização de câmaras de segurança ou de assistentes auxiliares a tempo inteiro podem ser uma resposta. No entanto, violam a privacidade das pessoas e são dispendiosos. Outro recurso passa pelo conceito de Ambient Assisted Living (AAL) que permite auxiliar essas pessoas nas suas habitações através da utilização de tecnologia que têm ao se dispor. A crescente evolução tecnologicas permitem sistemas de localização cada vez mais precisos, mas a maior parte desses sistemas estão focados para o exterior através do conhecido serviço GPS (Global Positioning System). Já existem alguns sistemas de localização indoor mas ainda são muito caros e com baixa precisão. Neste trabalho é apresentado um sistema de triangulação móvel dentro de edifícios, com recurso às tecnologias sem fios Wi-Fi que integrado num ALL permite melhorar as condições de vida do utilizador. Recorrendo este sistema podemos limitar a habitação em várias áreas sensíveis e proibidas, por acarretarem muitos perigos. Ao localizar pessoas nessas zonas podemos inferir se necessitam de alguma ajuda, tendo em conta diversos fatores resultantes da sua localização.The aging of the human being entails life and health conditions more sensitive to even the more ordinary occurrences of life. In these terms, we can assume that the health condition of an elder person is rather frail, very prone to events and external factors, i.e. falls. These type of problems focus, directly, on a person’s health and, consequently, over its quality of life. In a fall, an elder person, besides health complications, has complications that impact their own life as, for example, mobility limitations. The technological solutions may be used to help people with the aforementioned limitations. Recurring to the use of security cameras or full-time help assistants may be an answer to these problems. However, this answer violates the privacy of people and are very expensive. Another solution indices over intelligent environments, and the concept of Ambient Assisted Living (ALL), which provides help to these people in their own homes through the use of technology that they have at their own service. The increasing technological evolution allows location systems to be ever more precise, notwithstanding the fact that most of these systems are only focused on their external application, mainly through the, already know, GPS (Global Positioning System) service. There are already some location systems that operate indoor but they are still very costly and with a low accuracy. In this dissertation, it will be presented a mobile triangulation system, to be used inside buildings, by resourcing to the wireless technology Wi-FI integrated within AAL, which allows the improvement of its users’ life conditions. Through the use of this system, we can constrain a building, in regards to its sensitive and forbidden areas due to the dangers yielded. By locating people in these areas, we can infer if they need any help, taking into consideration several factors resulting from their location