Improved Indoor Location Systems in a Controlled Environments

The precise localization by using Wi-Fi Access Point (AP) has become a very important issue for indoor location based services such as marketing, patient follow up and so on. Present AP localization systems are working on specially designed Wi-Fi units, and their algorithms using radio signal strength (RSS) exhibit (relatively) high errors, so industry looks more precise and fast adaptable methods. A new model considering/eliminating strong RSS levels in addition to close distance error elimination algorithm (CDEEA) combined with median filters has been proposed in order to increase the performance of conventional RSS based location systems. Collecting local signal strengths by means of an ordinary WiFi units present on any laptop as a receiver is followed by the application of CDEEA to eliminate strong RSS levels. Median filter is then applied to those eliminated values, and AP based path loss model is generated, adaptivelly. Finally, the proposed algorithm predicts locations within a maximum mean error of 2.96m for 90% precision level. This achievement with an ordinary wifi units present on any commercial laptop is comparably at very good level in literature

A survey of deep learning approaches for WiFi-based indoor positioning

One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments

iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs

Innovative Wireless Localization Techniques and Applications

Innovative methodologies for the wireless localization of users and related applications are addressed in this thesis. In last years, the widespread diffusion of pervasive wireless communication (e.g., Wi-Fi) and global localization services (e.g., GPS) has boosted the interest and the research on location information and services. Location-aware applications are becoming fundamental to a growing number of consumers (e.g., navigation, advertising, seamless user interaction with smart places), private and public institutions in the fields of energy efficiency, security, safety, fleet management, emergency response. In this context, the position of the user - where is often more valuable for deploying services of interest than the identity of the user itself - who. In detail, opportunistic approaches based on the analysis of electromagnetic field indicators (i.e., received signal strength and channel state information) for the presence detection, the localization, the tracking and the posture recognition of cooperative and non-cooperative (device-free) users in indoor environments are proposed and validated in real world test sites. The methodologies are designed to exploit existing wireless infrastructures and commodity devices without any hardware modification. In outdoor environments, global positioning technologies are already available in commodity devices and vehicles, the research and knowledge transfer activities are actually focused on the design and validation of algorithms and systems devoted to support decision makers and operators for increasing efficiency, operations security, and management of large fleets as well as localized sensed information in order to gain situation awareness. In this field, a decision support system for emergency response and Civil Defense assets management (i.e., personnel and vehicles equipped with TETRA mobile radio) is described in terms of architecture and results of two-years of experimental validation

Indoor Localization for Personalized Ambient Assisted Living of Multiple Users in Multi-Floor Smart Environments

This paper presents a multifunctional interdisciplinary framework that makes four scientific contributions towards the development of personalized ambient assisted living, with a specific focus to address the different and dynamic needs of the diverse aging population in the future of smart living environments. First, it presents a probabilistic reasoning-based mathematical approach to model all possible forms of user interactions for any activity arising from the user diversity of multiple users in such environments. Second, it presents a system that uses this approach with a machine learning method to model individual user profiles and user-specific user interactions for detecting the dynamic indoor location of each specific user. Third, to address the need to develop highly accurate indoor localization systems for increased trust, reliance, and seamless user acceptance, the framework introduces a novel methodology where two boosting approaches Gradient Boosting and the AdaBoost algorithm are integrated and used on a decision tree-based learning model to perform indoor localization. Fourth, the framework introduces two novel functionalities to provide semantic context to indoor localization in terms of detecting each user's floor-specific location as well as tracking whether a specific user was located inside or outside a given spatial region in a multi-floor-based indoor setting. These novel functionalities of the proposed framework were tested on a dataset of localization-related Big Data collected from 18 different users who navigated in 3 buildings consisting of 5 floors and 254 indoor spatial regions. The results show that this approach of indoor localization for personalized AAL that models each specific user always achieves higher accuracy as compared to the traditional approach of modeling an average user

Napredna (edge computing) softverska arhitektura za upravljanje resursima i unutrašnje pozicioniranje

In Part I, this thesis aims to shed light on IoT and edge com-puting systems and accompanying computing and architectural paradigms, their definition, areas of application, and common use-cases, as well as operational, business, economical, social challenges and benefits. It illustrates modern needs and requests in building IoT systems and current State-of-The-Art (SoTA) approaches to designing them. Additionally, it discusses the security and privacy topics of IoT and edge computing systems. It also encompasses research, design, and implementation of an MQTT-based Resource Management Framework for Edge Com-puting systems that handle: resource management, failover detection and handover administration, logical and physical workload balancing and protection, and monitoring of physical and logical system resources designed for a real-world IoT platform. The thesis offers insights into modern requests for such frameworks, current SoTA approaches, and offer a solution in the form of a software framework, with minimal implementation and communication overhead. In Part II, the thesis elaborates on IPS, their definition, deploy-ment types, commonly used positioning techniques, areas of application, and common use-cases, as well as operational, business, economic, social challenges, and benefits. It specifically discusses designing IPS for the typical IoT infrastructure. It offers insights to modern IPS requests, current SoTA in solving them, and under-line original approaches from this thesis. It elaborates on the research, design and authors’ implementation of an IPS for the IoT – Bluetooth LowEnergyMicrolocation Asset Tracking (BLEMAT), including its software engines (collections of software components) for: indoor positioning, occupancy detection, visualization, pattern discovery and prediction, geofencing, movement pattern detection, visualization, discovery and prediction, social dynamics analysis, and indoor floor plan layout detection.Deo I teze ima je za cilj da rasvetli IoT i edge computing računarske sisteme i prateće računarske paradigme softverskih arhitektura, njihovu definiciju, područja primene i slučajeve uobičajene upotrebe, kao i operativne, poslovne, ekonomske, i socijalne izazove i koristi. Teza ilustruje savremene potrebe i zahtevi u izgradnji IoT sistema i najsavremeniji pristupi u njihovom dizajniranju. Raspravlja se o temama bezbednosti i privatnosti u IoT i edge computing računarskim sistemima. Kao još jedan glavni zadatak, teza je obuhvata istraživanje, dizajn i implementaciju softverske arhitekture za upravljanje resursima zasnovanim na MQTT komunikacionom protokolu za edge computing računarske sisteme koja se bavi: upravljanjem resursima, detekcijom prestanka rada upravljačkih algoritama i administracijom primopredaje tj. transporta upravljačkih algoritama, i logičkim i fizičkim balansiranjem i zaštitom radnog opterećenja sistema. Diskutuju se savremeni zahtevi za takve softverske arhitekture, trenutni pristupi. Na kraju, prikazuje se rešenje sa minimalnim troškovima implementacije i  komunikacije. Deo II teze ima za cilj da objasni sisteme za unutrašnje pozicioniranje, njihovu definiciju, vrste primene, najčešće korišćene tehnike pozicioniranja, područja primene i uobičajene slučajeve upotrebe, kao i operativne, poslovne, ekonomske, i socijalne izazove i koristi. Posebno se diskutuje o dizajniranju ovakvih sistema za tipičnu IoT infrastrukturu. Nudi se uvid u savremene zahteve sisteme za unutrašnje pozicioniranje, trenutne pristupe u rešavanju istih, i naglašeni su originalni pristupe iz ove teze. Dalje je fokus na istraživanju, dizajniranju i implementaciji sistema za unutrašnje pozicioniranje (BLEMAT), uključujući njegove softverske podsisteme (kolekcije softverskih komponenti) za: pozicioniranje u zatvorenom prostoru, detekciju zauzeća prostorija, vizualizaciju, otkrivanje i predviđanje obrazaca kretanja, geofencing, vizualizaciju i analizu društvene dinamike i detekciju rasporeda prostorija unutrašnjeg prostora

Indoor navigation for the visually impaired : enhancements through utilisation of the Internet of Things and deep learning

Wayfinding and navigation are essential aspects of independent living that heavily rely on the sense of vision. Walking in a complex building requires knowing exact location to find a suitable path to the desired destination, avoiding obstacles and monitoring orientation and movement along the route. People who do not have access to sight-dependent information, such as that provided by signage, maps and environmental cues, can encounter challenges in achieving these tasks independently. They can rely on assistance from others or maintain their independence by using assistive technologies and the resources provided by smart environments. Several solutions have adapted technological innovations to combat navigation in an indoor environment over the last few years. However, there remains a significant lack of a complete solution to aid the navigation requirements of visually impaired (VI) people. The use of a single technology cannot provide a solution to fulfil all the navigation difficulties faced. A hybrid solution using Internet of Things (IoT) devices and deep learning techniques to discern the patterns of an indoor environment may help VI people gain confidence to travel independently. This thesis aims to improve the independence and enhance the journey of VI people in an indoor setting with the proposed framework, using a smartphone. The thesis proposes a novel framework, Indoor-Nav, to provide a VI-friendly path to avoid obstacles and predict the user s position. The components include Ortho-PATH, Blue Dot for VI People (BVIP), and a deep learning-based indoor positioning model. The work establishes a novel collision-free pathfinding algorithm, Orth-PATH, to generate a VI-friendly path via sensing a grid-based indoor space. Further, to ensure correct movement, with the use of beacons and a smartphone, BVIP monitors the movements and relative position of the moving user. In dark areas without external devices, the research tests the feasibility of using sensory information from a smartphone with a pre-trained regression-based deep learning model to predict the user s absolute position. The work accomplishes a diverse range of simulations and experiments to confirm the performance and effectiveness of the proposed framework and its components. The results show that Indoor-Nav is the first type of pathfinding algorithm to provide a novel path to reflect the needs of VI people. The approach designs a path alongside walls, avoiding obstacles, and this research benchmarks the approach with other popular pathfinding algorithms. Further, this research develops a smartphone-based application to test the trajectories of a moving user in an indoor environment