Wi-Fi fingerprinting based on collaborative confidence level training

Wi-Fi fingerprinting has been a popular indoor positioning technique with the advantage that infrastructures are readily available in most urban areas. However wireless signals are prone to fluctuation and noise, introducing errors in the final positioning result. This paper proposes a new fingerprint training method where a number of users train collaboratively and a confidence factor is generated for each fingerprint. Fingerprinting is carried out where potential fingerprints are extracted based on the confidence factor. Positioning accuracy improves by 40% when the new fingerprinting method is implemented and maximum error is reduced by 35%

Collaborative Wi-Fi fingerprint training for indoor positioning

As the scope of location-based applications and services further reach into our everyday lives, the demand for more robust and reliable positioning becomes ever more important. However indoor positioning has never been a fully resolved issue due to its complexity and necessity to adapt to different situations and environment. Inertial sensor and Wi-Fi signal integrated indoor positioning have become good solutions to overcome many of the problems. Yet there are still problems such as inertial heading drift, wireless signal fluctuation and the time required for training a Wi-Fi fingerprint database. The collaborative Wi-Fi fingerprint training (cWiDB) method proposed in this paper enables the system to perform inertial measurement based collaborative positioning or Wi-Fi fingerprinting alternatively according to the current situation. It also reduces the time required for training the fingerprint database. Different database training methods and different training data size are compared to demonstrate the time and data required for generating a reasonable database. Finally the fingerprint positioning result is compared which indicates that the cWiDB is able to achieve the same positioning accuracy as conventional training methods but with less training time and a data adjustment option enabled

Navigating in large hospitals

Navigating around large hospitals can be a stressful and time-consuming experience for all users of the hospital infrastructure. Navigation difficulties encountered by patients and visitors can result in missed appointments or simply create a poor impression of the hospital organisation. When staff encounter navigation difficulties this can lead to cost and efficiency issues and potentially put patient safety at risk. Despite the provision of an array of in-hospital navigational aids, ‘getting lost’ continues to be an everyday problem in these large complex environments. This study aims to to identify factors which affect navigation in hospitals. We do not seek to evaluate the effectiveness of a single navigation aid, instead the objective of this study was to understand the environment in which a new system must operate and the gaps in provision left by existing navigation aids. This study is intended to be used to inform the development of new in hospital navigational aids, be they technological or otherwise. Eleven participants, all users of a large hospital site, were asked to describe specific first hand experiences of navigating in a hospital. The ‘Critical Incidence Technique’ was applied in a series of semi-structured interviews to elicit information about a participants navigation experience. This work presents the results of these interviews, with concepts identified and organised into five themes: The ‘Impact’ of poor navigation, ‘Barriers’ to effective navigation, ‘Enhancers’ for effective navigation, ‘Types of Navigation Aids’ and user groups with ‘Specific Navigational Needs’. The number of navigation aids available to participants was identified as an issue in itself, we found examples of thirty seven distinct sources of information available to a hospital user. We begin by introducing previous work on in-hospital navigation before describing the study design employed in this research. The themes and categories identified from the interview data are enumerated and described, with examples given from the interview transcripts. Finally we go on to give a discussion of some potential navigation solutions in light of the identified factors. This study highlights that a candidate navigation aid must be carefully designed and implemented if it is to compliment the thirty seven other sources of navigation information available to the hospital user

The potential of electromyography to aid personal navigation

This paper reports on research to explore the potential for using electromyography (EMG) measurements in pedestrian navigation. The aim is to investigate whether the relationship between human motion and the activity of skeletal muscles in the leg might be used to aid other positioning sensors, or even to determine independently the path taken by a pedestrian. The paper describes an exercise to collect sample EMG data alongside leg motion data, and the subsequent analysis of this data set using machine learning techniques to infer motion from a set of EMG sensors. The sample data set included measurements from multiple EMG sensors, a camera-based motion tracking system and a foot mounted inertial sensor. The camera based motion tracking system at MMU allowed many targets on the subjects lower body to be tracked in a small (3m x 3m x 3m) volume to millimetre accuracy. Processing the data revealed a strong, but not trivial, relation-ship between leg muscle activity and motion. Each type of motion involves many different muscles, and it is not possible to conclude merely from the triggering of any single muscle that a particular type of motion has occurred. For instance, a similar set of leg muscles is involved in both forward and backward steps. It is the precise sequencing, duration and magnitude of multiple muscle activity that allows us to determine what type of motion has occurred. Preliminary analyses of the data suggest that subsets of the EMG sensors can be used to distinguish, for instance, forward motion from backward motion, and it is expected that further analysis will reveal additional correlations that will be useful in inferring the subjects motion in more detail. This paper will introduce the EMG personal navigation con-cept, describe the data collected, explore the machine learning techniques applied to the dataset, and present the results of the analysis

A smart phone based multi-floor indoor positioning system for occupancy detection

At present there is a lot of research being done simulating building environment with artificial agents and predicting energy usage and other building performance related factors that helps to promote understanding of more sustainable buildings. To understand these energy demands it is important to understand how the building spaces are being used by individuals i.e. the occupancy pattern of individuals. There are lots of other sensors and methodology being used to understand building occupancy such as PIR sensors, logging information of Wi-Fi APs or ambient sensors such as light or CO2 composition. Indoor positioning can also play an important role in understanding building occupancy pattern. Due to the growing interest and progress being made in this field it is only a matter of time before we start to see extensive application of indoor positioning in our daily lives. This research proposes an indoor positioning system that makes use of the smart phone and its built-in integrated sensors; Wi-Fi, Bluetooth, accelerometer and gyroscope. Since smart phones are easy to carry helps participants carry on with their usual daily work without any distraction but at the same time provide a reliable pedestrian positioning solution for detecting occupancy. The positioning system uses the traditional Wi-Fi and Bluetooth fingerprinting together with pedestrian dead reckoning to develop a cheap but effective multi floor positioning solution. The paper discusses the novel application of indoor positioning technology to solve a real world problem of understanding building occupancy. It discusses the positioning methodology adopted when trying to use existing positioning algorithm and fusing multiple sensor data. It also describes the novel approach taken to identify step like motion in absence of a foot mounted inertial system. Finally the paper discusses results from limited scale trials showing trajectory of motion throughout the Nottingham Geospatial Building covering multiple floors

Learning capacity: predicting user decisions for vehicle-to-grid services

The electric vehicles (EV) market is projected to continue its rapid growth, which will profoundly impact the demand on the electricity network requiring costly network reinforcements unless EV charging is properly managed. However, as well as importing electricity from the grid, EVs also have the potential to export electricity through vehicle-to-grid (V2G) technology, which can help balance supply and demand and stabilise the grid through participation in flexibility markets. Such a scenario requires a population of EVs to be pooled to provide a larger storage resource. Key to doing so effectively however is knowledge of the users, as they ultimately determine the availability of a vehicle. In this paper we introduce a machine learning model that aims to learn both a) the criteria influencing users when they decided whether to make their vehicle available and b) their reliability in following through on those decisions, with a view to more accurately predicting total available capacity from the pool of vehicles at a given time. Using a series of simplified simulations, we demonstrate that the learning model is able to adapt to both these factors, which allows the required capacity of a market event to be satisfied more reliably and using a smaller number of vehicles than would otherwise be the case. This in turn has the potential to support participation in larger and more numerous market events for the same user base and use of the technology for smaller groups of users such as individual communities

An adaptive weighting based on modified DOP for collaborative indoor positioning

Indoor localisation has always been a challenging problem due to poor Global Navigation Satellite System (GNSS) availability in such environments. While inertial measurement sensors have become popular solutions for indoor positioning, they suffer large drifts after initialisation. Collaborative positioning enhances positioning robustness by integrating multiple localisation information, especially relative ranging measurements between local users and transmitters. However, not all ranging measurements are useful throughout the whole positioning process and integrating too much data will increase the computation cost. To enable a more reliable positioning system, an adaptive collaborative positioning algorithm is proposed which selects units for the collaborative network and integrates ranging measurement to constrain inertial measurement errors. The algorithm selects the network adaptively from three perspectives: the network geometry, the network size and the accuracy level of the ranging measurements between the units. The collaborative relative constraint is then defined according to the selected network geometry and anticipated measurement quality. In the case of trials with real data, the positioning accuracy is improved by 60% by adjusting the range constraint adaptively according to the selected network situation, while also improving the system robustness

On the impact of intra-system interference for ranging and positioning with Bluetooth low energy

This paper focuses on the study of intra-system interference for ranging and positioning applications using Bluetooth Low Energy (BLE). While BLE tries to avoid interference with other protocols in the same frequency band, such as Wi-Fi, the intra-system interference is unavoidable, either due to multipath or simultaneous transmissions in the same channel. This study shows that intra-system interference contributes with a deviation of approximately 5 dBm in the Received Signal Strength (RSS) and by taking this into account the ranging and positioning accuracy can be significantly improved. The study uses data collected from two different environments

Decision-making within missing person search

This paper reports the findings of a series of interviews with search and rescue volunteers. Participants were asked to recall accounts of particular incidents which involved searching for a missing adult who could be considered ‘vulnerable’. The purpose of this study was to discover what types of decisions are made during missing incidents; including a consideration of the factors which affect these decisions and the main focuses of attention throughout the incident. Such an understanding may help to shed light on best practices which could inform decision-making support tools for families of the missing and identify the user-requirements of a future technology designed to help find missing people. Interviews were conducted using the critical decision method (CDM) to elicit specific information about the decisions and challenges faced by search and rescue teams during missing person searches. Critical decision points were identified and sequenced for each incident. Emergent thematic analysis (EMA) was applied to the transcripts to identify themes across various incidents; these themes were explored in detail using a mixed-method approach. This study builds upon the methodological approach of CDM using a two-tiered approach to analysis which seeks to discover the focus of practitioners’ attention as they progress through missing person searches. A decision-sequence diagram was created to clearly show the sequence of each decision and trends across all incidents; a table was produced to show the relative importance of each aspect across decisions. Finally, strengths and weaknesses of this approach to incident analysis are discussed

A Model-based Tightly Coupled Architecture for Low-Cost Unmanned Aerial Vehicles for Real-Time Applications

This paper investigates the navigation performance of a vehicle dynamic model-based (VDM-based) tightly coupled architecture for a fixed-wing Unmanned Aerial Vehicle (UAV) during a global navigation satellite system (GNSS) outage for real-time applications. Unlike an Inertial Navigation System (INS) which uses inertial sensor measurements to propagate the navigation solution, the VDM uses control inputs from either the autopilot system or direct pilot commands to propagate the navigation states. The proposed architecture is tested using both raw GNSS observables (Pseudorange and Doppler frequency) and Micro-Electro-Mechanical Systems-grade (MEMS) Inertial Measurement Unit (IMU) measurements fused using an extended Kalman filter (EKF) to aid the navigation solution. Other than the navigation states, the state vector also includes IMU errors, wind velocity, VDM parameters, and receiver clock bias and drift. Simulation results revealed significant performance improvements with a decreasing number of satellites in view during 140 seconds of a GNSS outage. With two satellites visible during the GNSS outage, the position error improved by one order of magnitude as opposed to a tightly coupled INS/GNSS scheme. Real flight tests on a small fixed-wing UAV show the benefits of the approach with position error being an order of magnitude better as opposed to a tightly coupled INS/GNSS scheme with two satellites in view during 100 seconds of a GNSS outage