Body attenuation and path loss exponent estimation for RSS-based positioning in WSN

The influence of the human body in antenna systems has significant impact in the received signal strength (RSS) of wireless transmissions. Accounting for body effect is generally considered as being able to improve position estimation based on RSS measurements. In this work we perform several experiments with a wireless sensor network, using a sensor node equipped with an inertial measurement unit (IMU), in order to obtain the relative orientation between the sensor node and multiple anchor nodes. A model of the RSS attenuation induced by the body was created using experimental measurements in a controlled environment and applied to a real-time positioning system. A path loss exponent (PLE) estimation method using RSS information from neighbor anchors was also implemented and evaluated. Weighted centroid localization (WCL) algorithm was the positioning method used in this work. When the sensor node was placed on the user’s body, accounting for body effect produced negligible improvements (6%) in the best-case scenario and consistently degraded accuracy under real conditions, whether the node was placed on the user’s body (in the order of 3%), 10 cm away (from 14% to 35%) or 20 cm away from the body (from 42% to 105%) for results in the 70th percentile. The PLE estimation method showed improvements (in the order of 11%) when the sensor node is further away from the body. Results demonstrate that the distance between sensor node and the body has an extremely important influence on the accuracy of the position estimate.This work has been supported by FCT (Fundação para a Ciência e Tecnologia) in the scope of the project UID/EEA/04436/2013. Helder D. Silva is supported by FCT under the grant SFRH/BD/78018/2011info:eu-repo/semantics/publishedVersio

Experimental study on RSS based indoor positioning algorithms

This work compares the performance of indoor positioning systems suitable for low power wireless sensor networks. The research goal is to study positioning techniques that are compatible with real-time positioning in wireless sensor networks, having low-power and low complexity as requirements. Map matching, approximate positioning (weighted centroid) and exact positioning algorithms (least squares) were tested and compared in a small predefined indoor environment. We found that, for our test scenario, weighted centroid algorithms provide better results than map matching. Least squares proved to be completely unreliable when using distances obtained by the one-slope propagation model. Major improvements in the positioning error were found when body influence was removed from the test scenario. The results show that the positioning error can be improved if the body effect in received signal strength is accounted for in the algorithms.Helder D. Silva is supported by the Portuguese Foundation for Science and Technology under the grant SFRBD/78018/2011.info:eu-repo/semantics/publishedVersio

Localization Process for WSNs with Various Grid-Based Topology Using Artificial Neural Network

Wireless Sensor Network (WSN) is a technology that can aid human life by providing ubiquitous communication, sensing, and computing capabilities. It allows people to be more able to interact with the environment. The environment contains many nodes to monitor and collect data. Localizing nodes distributed in different locations covering different regions is a challenge in WSN. Localization of accurate and low-cost sensors is an urgent need to deploy WSN in various applications. In this paper, we propose an artificial automatic neural network method for sensor node localization. The proposed method in WSN is implemented with network-based topology in different regions. To demonstrate the accuracy of the proposed method, we compared the estimated locations of the proposed feedforward neural network (FFNN) with the estimated locations of the deep feedforward neural network (DFF) and the weighted centroid localization (WCL) algorithm based on the strength of the received signal index. The proposed FFNN model outperformed alternative methods in terms of its lower average localization error which is 0.056m. Furthermore, it demonstrated its capability to predict sensor locations in wireless sensor networks (WSNs) across various grid-based topologies

Localization Process for WSNs with Various Grid-Based Topology Using Artificial Neural Network

Wireless Sensor Network (WSN) is a technology that can aid human life by providing ubiquitous communication, sensing, and computing capabilities. It allows people to be more able to interact with the environment. The environment contains many nodes to monitor and collect data. Localizing nodes distributed in different locations covering different regions is a challenge in WSN. Localization of accurate and low-cost sensors is an urgent need to deploy WSN in various applications. In this paper, we propose an artificial automatic neural network method for sensor node localization. The proposed method in WSN is implemented with network-based topology in different regions. To demonstrate the accuracy of the proposed method, we compared the estimated locations of the proposed feedforward neural network (FFNN) with the estimated locations of the deep feedforward neural network (DFF) and the weighted centroid localization (WCL) algorithm based on the strength of the received signal index. The proposed FFNN model outperformed alternative methods in terms of its lower average localization error which is 0.056m. Furthermore, it demonstrated its capability to predict sensor locations in wireless sensor networks (WSNs) across various grid-based topologies

Indoor localisation by using wireless sensor nodes

This study is devoted to investigating and developing WSN based localisation approaches with high position accuracies indoors. The study initially summarises the design and implementation of localisation systems and WSN architecture together with the characteristics of LQI and RSSI values. A fingerprint localisation approach is utilised for indoor positioning applications. A k-nearest neighbourhood algorithm (k-NN) is deployed, using Euclidean distances between the fingerprint database and the object fingerprints, to estimate unknown object positions. Weighted LQI and RSSI values are calculated and the k-NN algorithm with different weights is utilised to improve the position detection accuracy. Different weight functions are investigated with the fingerprint localisation technique. A novel weight function which produced the maximum position accuracy is determined and employed in calculations. The study covered designing and developing the centroid localisation (CL) and weighted centroid localisation (WCL) approaches by using LQI values. A reference node localisation approach is proposed. A star topology of reference nodes are to be utilized and a 3-NN algorithm is employed to determine the nearest reference nodes to the object location. The closest reference nodes are employed to each nearest reference nodes and the object locations are calculated by using the differences between the closest and nearest reference nodes. A neighbourhood weighted localisation approach is proposed between the nearest reference nodes in star topology. Weights between nearest reference nodes are calculated by using Euclidean and physical distances. The physical distances between the object and the nearest reference nodes are calculated and the trigonometric techniques are employed to derive the object coordinates. An environmentally adaptive centroid localisation approach is proposed.Weighted standard deviation (STD) techniques are employed adaptively to estimate the unknown object positions. WSNs with minimum RSSI mean values are considered as reference nodes across the sensing area. The object localisation is carried out in two phases with respect to these reference nodes. Calculated object coordinates are later translated into the universal coordinate system to determine the actual object coordinates. Virtual fingerprint localisation technique is introduced to determine the object locations by using virtual fingerprint database. A physical fingerprint database is organised in the form of virtual database by using LQI distribution functions. Virtual database elements are generated among the physical database elements with linear and exponential distribution functions between the fingerprint points. Localisation procedures are repeated with virtual database and localisation accuracies are improved compared to the basic fingerprint approach. In order to reduce the computation time and effort, segmentation of the sensing area is introduced. Static and dynamic segmentation techniques are deployed. Segments are defined by RSS ranges and the unknown object is localised in one of these segments. Fingerprint techniques are applied only in the relevant segment to find the object location. Finally, graphical user interfaces (GUI) are utilised with application program interfaces (API), in all calculations to visualise unknown object locations indoors

Localisation in wireless sensor networks for disaster recovery and rescuing in built environments

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyProgress in micro-electromechanical systems (MEMS) and radio frequency (RF) technology has fostered the development of wireless sensor networks (WSNs). Different from traditional networks, WSNs are data-centric, self-configuring and self-healing. Although WSNs have been successfully applied in built environments (e.g. security and services in smart homes), their applications and benefits have not been fully explored in areas such as disaster recovery and rescuing. There are issues related to self-localisation as well as practical constraints to be taken into account. The current state-of-the art communication technologies used in disaster scenarios are challenged by various limitations (e.g. the uncertainty of RSS). Localisation in WSNs (location sensing) is a challenging problem, especially in disaster environments and there is a need for technological developments in order to cater to disaster conditions. This research seeks to design and develop novel localisation algorithms using WSNs to overcome the limitations in existing techniques. A novel probabilistic fuzzy logic based range-free localisation algorithm (PFRL) is devised to solve localisation problems for WSNs. Simulation results show that the proposed algorithm performs better than other range free localisation algorithms (namely DVhop localisation, Centroid localisation and Amorphous localisation) in terms of localisation accuracy by 15-30% with various numbers of anchors and degrees of radio propagation irregularity. In disaster scenarios, for example, if WSNs are applied to sense fire hazards in building, wireless sensor nodes will be equipped on different floors. To this end, PFRL has been extended to solve sensor localisation problems in 3D space. Computational results show that the 3D localisation algorithm provides better localisation accuracy when varying the system parameters with different communication/deployment models. PFRL is further developed by applying dynamic distance measurement updates among the moving sensors in a disaster environment. Simulation results indicate that the new method scales very well

Indoor positioning system for wireless sensor networks

Tese de Doutoramento - Programa Doutoral em Engenharia Electrónica e ComputadoresPositioning technologies are ubiquitous nowadays. From the implementation of the global positioning system (GPS) until now, its evolution, acceptance and spread has been unanimous, due to the underlying advantages the system brings. Currently, these systems are present in many different scenarios, from the home to the movie theatre, at work, during a walk in the park. Many applications provide useful information, based on the current position of the user, in order to provide results of interest. Positioning systems can be implemented in a wide range of contexts: in hospitals to locate equipment and guide patients to the necessary resources, or in public spaces like museums, to guide tourists during visits. They can also be used in a gymnasium to point the user to his next workout machine and, simultaneously, gather information regarding his fitness plan. In a congress or conference, the positioning system can be used to provide information to its participants about the on-going presentations. Devices can also be monitored to prevent thefts. Privacy and security issues are also important in positioning systems. A user might not want to be localized or its location to be known, permanently or during a time interval, in different locations. This information is therefore sensitive to the user and influences directly the acceptance of the system itself. Concerning outdoor systems, GPS is in fact the system of reference. However, this system cannot be used in indoor environment, due to the high attenuation of the satellite signals from non-line-of-sight conditions. Another issue related to GPS is the power consumption. The integration of these devices with wireless sensor networks becomes prohibitive, due to the low power consumption profile associated with devices in this type of networks. As such, this work proposes an indoor positioning system for wireless sensor networks, having in consideration the low energy consumption and low computational capacity profile. The proposed indoor positioning system is composed of two modules: the received signal strength positioning module and the stride and heading positioning module. For the first module, an experimental performance comparison between several received signal strength based algorithms was conducted in order to assess its performance in a predefined indoor environment. Modifications to the algorithm with higher performance were implemented and evaluated, by introducing a model of the effect of the human body in the received signal strength. In the case of the second module, a stride and heading system was proposed, which comprises two subsystems: the stride detection and stride length estimation system to detect strides and infer the travelled distance, and an attitude and heading reference system to provide the full three-dimensional orientation stride-by-stride. The stride detection enabled the identification of the gait cycle and detected strides with an error percentage between 0% and 0.9%. For the stride length estimation two methods were proposed, a simplified method, and an improved method with higher computational requirements than the former. The simplified method estimated the total distance with an error between 6.7% and 7.7% of total travelled distance. The improved method achieved an error between 1.2% and 3.7%. Both the stride detection and the improved stride length estimation methods were compared to other methods in the literature with favourable results. For the second subsystem, this work proposed a quaternion-based complementary filter. A generic formulation allows a simple parameterization of the filter, according to the amount of external influences (accelerations and magnetic interferences) that are expected, depending on the location that the device is to be attached on the human body. The generic formulation enables the inclusion/exclusion of components, thus allowing design choices according to the needs of applications in wireless sensor networks. The proposed method was compared to two other existing solutions in terms of robustness to interferences and execution time, also presenting a favourable outcome.Os sistemas de posicionamento fazem parte do quotidiano. Desde a implementação do sistema GPS (Global Positioning System) até aos dias que correm, a evolução, aceitação e disseminação destes sistemas foi unânime, derivada das vantagens subjacentes da sua utilização. Hoje em dia, eles estão presentes nos mais variados cenários, desde o lar até́ à sala de cinema, no trabalho, num passeio ao ar livre. São várias as aplicações que nos fornecem informação útil, usando como base a descrição da posição atual, de modo a produzir resultados de maior interesse para os utilizadores. Os sistemas de posicionamento podem ser implementados nos mais variados contextos, como por exemplo: nos hospitais, para localizar equipamento e guiar os pacientes aos recursos necessários, ou nas grandes superfícies públicas, como por exemplo museus, para guiar os turistas durante as visitas. Podem ser igualmente utilizados num ginásio para indicar ao utilizador qual a máquina para onde se deve dirigir durante o seu treino e, simultaneamente, obter informação acerca desta mesma máquina. Num congresso ou conferência, o sistema de localização pode ser utilizado para fornecer informação aos seus participantes sobre as apresentações que estão a decorrer no momento. Os dispositivos também podem ser monitorizados para prevenir roubos. Existem também questões de privacidade e segurança associados aos sistemas de posicionamento. Um utilizador poderá não desejar ser localizado ou que a sua localização seja conhecida, permanentemente ou num determinado intervalo de tempo, num ou em vários locais. Esta informação é por isso sensível ao utilizador e influencia diretamente a aceitação do próprio sistema. No que diz respeito aos sistemas utilizados no exterior, o GPS (ou posicionamento por satélite) é de facto o sistema mais utilizado. No entanto, em ambiente interior este sistema não pode ser usado, por causa da grande atenuação dos sinais provenientes dos satélites devido à falta de linha de vista. Um outro problema associado ao recetor GPS está relacionado com as suas características elétricas, nomeadamente os consumos energéticos. A integração destes dispositivos nas redes de sensores sem fios torna-se proibitiva, devido ao perfil de baixo consumo associado a estas redes. Este trabalho propõe um sistema de posicionamento para redes de sensores sem fio em ambiente interior, tendo em conta o perfil de baixo consumo de potência e baixa capacidade de processamento. O sistema proposto é constituído por dois módulos: o modulo de posicionamento por potência de sinal recebido e o módulo de navegação inercial pedestre. Para o primeiro módulo foi feita uma comparação experimental entre vários algoritmos que utilizam a potência do sinal recebido, de modo a avaliar a sua utilização num ambiente interior pré-definido. Ao algoritmo com melhor prestação foram implementadas e testadas modificações, utilizando um modelo do efeito do corpo na potência do sinal recebido. Para o segundo módulo foi proposto um sistema de navegação inercial pedestre. Este sistema é composto por dois subsistemas: o subsistema de deteção de passos e estimação de distância percorrida; e o subsistema de orientação que fornece a direção do movimento do utilizador, passo a passo. O sistema de deteção de passos proposto permite a identificação das fases da marcha, detetando passos com um erro entre 0% e 0.9%. Para o sistema de estimação da distância foram propostos dois métodos: um método simplificado de baixa complexidade e um método melhorado, mas com maiores requisitos computacionais quando comparado com o primeiro. O método simplificado estima a distância total com erros entre 6.7% e 7.7% da distância percorrida. O método melhorado por sua vez alcança erros entre 1.2% e 3.7%. Ambos os sistemas foram comparados com outros sistemas da literatura apresentando resultados favoráveis. Para o sistema de orientação, este trabalho propõe um filtro complementar baseado em quaterniões. É utilizada uma formulação genérica que permite uma parametrização simples do filtro, de acordo com as influências externas (acelerações e interferências magnéticas) que são expectáveis, dependendo da localização onde se pretende colocar o dispositivo no corpo humano. O algoritmo desenvolvido permite a inclusão/exclusão de componentes, permitindo por isso liberdade de escolha para melhor satisfazer as necessidades das aplicações em redes de sensores sem fios. O método proposto foi comparado com outras soluções em termos de robustez a interferências e tempo de execução, apresentando também resultados positivos

LOBIN: e-textile and wirelles-sensor-network-based platform for healthcare monitoring in future hospital environments

This paper describes a novel healthcare IT platform developed under the LOBIN project, which allows monitoring several physiological parameters, such as ECG, heart rate, body temperature, etc., and tracking the location of a group of patients within hospital environments. The combination of e-textile and wireless sensor networks provides an efficient way to support noninvasive and pervasive services demanded by future healthcare environments. This paper presents the architecture, system deployment as well as validation results from both laboratory tests and a pilot scheme developed with real users in collaboration with the Cardiology Unit at La Paz Hospital, Madrid, SpainThis work was supported in part by the Spanish Ministry of Industry, Tourism, and Trade under the LOBIN project (TSI-020302–2008-57

Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks

The paper introduces a method which improves localization accuracy of the signal strength fingerprinting approach. According to the proposed method, entire localization area is divided into regions by clustering the fingerprint database. For each region a prototype of the received signal strength is determined and a dedicated artificial neural network (ANN) is trained by using only those fingerprints that belong to this region (cluster). Final estimation of the location is obtained by fusion of the coordinates delivered by selected ANNs. Sensor nodes have to store only the signal strength prototypes and synaptic weights of the ANNs in order to estimate their locations. This approach significantly reduces the amount of memory required to store a received signal strength map. Various ANN topologies were considered in this study. Improvement of the localization accuracy as well as speed-up of learning process was achieved by employing fully connected neural networks. The proposed method was verified and compared against state-of-the-art localization approaches in realworld indoor environment by using both stationary andmobile sensor nodes