On the performance of emerging wireless mesh networks

Wireless networks are increasingly used within pervasive computing. The recent development of low-cost sensors coupled with the decline in prices of embedded hardware and improvements in low-power low-rate wireless networks has made them ubiquitous. The sensors are becoming smaller and smarter enabling them to be embedded inside tiny hardware. They are already being used in various areas such as health care, industrial automation and environment monitoring. Thus, the data to be communicated can include room temperature, heart beat, user’s activities or seismic events. Such networks have been deployed in wide range areas and various levels of scale. The deployment can include only a couple of sensors inside human body or hundreds of sensors monitoring the environment. The sensors are capable of generating a huge amount of information when data is sensed regularly. The information has to be communicated to a central node in the sensor network or to the Internet. The sensor may be connected directly to the central node but it may also be connected via other sensor nodes acting as intermediate routers/forwarders. The bandwidth of a typical wireless sensor network is already small and the use of forwarders to pass the data to the central node decreases the network capacity even further. Wireless networks consist of high packet loss ratio along with the low network bandwidth. The data transfer time from the sensor nodes to the central node increases with network size. Thus it becomes challenging to regularly communicate the sensed data especially when the network grows in size. Due to this problem, it is very difficult to create a scalable sensor network which can regularly communicate sensor data. The problem can be tackled either by improving the available network bandwidth or by reducing the amount of data communicated in the network. It is not possible to improve the network bandwidth as power limitation on the devices restricts the use of faster network standards. Also it is not acceptable to reduce the quality of the sensed data leading to loss of information before communication. However the data can be modified without losing any information using compression techniques and the processing power of embedded devices are improving to make it possible. In this research, the challenges and impacts of data compression on embedded devices is studied with an aim to improve the network performance and the scalability of sensor networks. In order to evaluate this, firstly messaging protocols which are suitable for embedded devices are studied and a messaging model to communicate sensor data is determined. Then data compression techniques which can be implemented on devices with limited resources and are suitable to compress typical sensor data are studied. Although compression can reduce the amount of data to be communicated over a wireless network, the time and energy costs of the process must be considered to justify the benefits. In other words, the combined compression and data transfer time must also be smaller than the uncompressed data transfer time. Also the compression and data transfer process must consume less energy than the uncompressed data transfer process. The network communication is known to be more expensive than the on-device computation in terms of energy consumption. A data sharing system is created to study the time and energy consumption trade-off of compression techniques. A mathematical model is also used to study the impact of compression on the overall network performance of various scale of sensor networks

Device-free indoor localisation with non-wireless sensing techniques : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Electronics and Computer Engineering, Massey University, Albany, New Zealand

Global Navigation Satellite Systems provide accurate and reliable outdoor positioning to support a large number of applications across many sectors. Unfortunately, such systems do not operate reliably inside buildings due to the signal degradation caused by the absence of a clear line of sight with the satellites. The past two decades have therefore seen intensive research into the development of Indoor Positioning System (IPS). While considerable progress has been made in the indoor localisation discipline, there is still no widely adopted solution. The proliferation of Internet of Things (IoT) devices within the modern built environment provides an opportunity to localise human subjects by utilising such ubiquitous networked devices. This thesis presents the development, implementation and evaluation of several passive indoor positioning systems using ambient Visible Light Positioning (VLP), capacitive-flooring, and thermopile sensors (low-resolution thermal cameras). These systems position the human subject in a device-free manner (i.e., the subject is not required to be instrumented). The developed systems improve upon the state-of-the-art solutions by offering superior position accuracy whilst also using more robust and generalised test setups. The developed passive VLP system is one of the first reported solutions making use of ambient light to position a moving human subject. The capacitive-floor based system improves upon the accuracy of existing flooring solutions as well as demonstrates the potential for automated fall detection. The system also requires very little calibration, i.e., variations of the environment or subject have very little impact upon it. The thermopile positioning system is also shown to be robust to changes in the environment and subjects. Improvements are made over the current literature by testing across multiple environments and subjects whilst using a robust ground truth system. Finally, advanced machine learning methods were implemented and benchmarked against a thermopile dataset which has been made available for other researchers to use

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

Outdoor-Indoor tracking systems through geomatic techniques: data analysis for marketing and safety management

Negli ultimi decenni, l'utilizzo di sistemi di gestione delle informazioni nel trattamento dei dati edilizi ha portato a cambiamenti radicali nei metodi di produzione, documentazione e archiviazione dei dati. Dato il crescente interesse per i dati e la loro gestione, l'obiettivo di questa tesi è quello di creare un flusso di lavoro efficace e chiaro a partire dai rilievi geomatici in un'ottica di miglioramento dei dati raccolti sul territorio, sugli edifici circostanti e su quelli relativi al comportamento umano, in modo che possano essere meglio sfruttati e integrati in modelli di gestione intelligenti. Come primo passo, questa tesi mira a comprendere i limiti dell'interoperabilità e dell'integrazione dei dati nei GIS. Per promuovere l'interoperabilità dei dati GIS, è necessario analizzare i metodi di conversione nei diversi modelli di archiviazione dei dati, come CityGML e IndoorGML, definendo un dominio ontologico. Questo ha portato alla creazione di un nuovo modello arricchito, basato sulle connessioni tra i diversi elementi del modello urbano in GIS. Il secondo passo consiste nel raccogliere tutti i dati tradotti in un database a grafo sfruttando il web semantico. Il risultato offrirà vantaggi sostanziali durante l'intero ciclo di vita del progetto. Questa metodologia può essere applicata anche al patrimonio culturale, dove la gestione delle informazioni gioca un ruolo fondamentale. Un altro lavoro di ricerca è stato quello di sviluppare un sistema di gestione SMART per le attività di conservazione dei borghi storici attraverso la gestione di tipologie eterogenee di dati, dal rilievo alla documentazione tecnica. Il flusso di lavoro è stato strutturato come segue: (i) acquisizione dei dati; (ii) modellazione 3D; (iii) modellazione della conoscenza; (iv) modellazione della gestione SMART. Questa ricerca apre la strada allo sviluppo di una piattaforma web in cui importare i dati GIS per un approccio di digital twin. Tutte le ricerche svolte fino a questo punto sono state finalizzate a comprendere la capacità di creare modelli e sistemi informativi intelligenti per capire la fattibilità di ospitare dati eterogenei che potrebbero essere inclusi in futuro. Il passo successivo consiste nel comprendere il comportamento umano in uno spazio. Finora sono pochi i lavori di ricerca che si occupano di sistemi di mappatura e posizionamento che tengono conto sia degli spazi esterni che di quelli interni. Questo argomento, anche se ha pochi articoli di ricerca, rappresenta un aspetto cruciale per molte ragioni, soprattutto quando si tratta di gestire la sicurezza degli edifici danneggiati. Angelats e il suo gruppo di ricerca al CTTC hanno lavorato su questo aspetto, fornendo un sistema in grado di seguire in tempo reale le persone dall'esterno all'interno di spazi chiusi e viceversa. L'uso di sensori GNSS combinato con l'odometria inerziale visiva fornisce una traiettoria continua senza perdere il percorso seguito dall'utente monitorato. Una parte di questa tesi si è concentrata sul miglioramento della traiettoria finale ottenuta con il sistema appena descritto, effettuando test sulla traiettoria esterna del GNSS per capire il comportamento della traiettoria quando si avvicina agli edifici o quando l'utente si sposta in indoor. L'ultimo aspetto su cui si concentrerà la tesi è il tracciamento delle persone in ambienti chiusi. Il comportamento umano è al centro di numerosi studi in diversi campi, come quello scientifico, sociale ed economico. A differenza del precedente caso di studio sul tracciamento delle persone in aree esterne/interne, l'obiettivo è stato quello di raccogliere informazioni sul posizionamento dinamico delle persone in ambienti indoor, sulla base del segnale WiFi. Verrà effettuata una breve analisi dei dati per dimostrare il corretto funzionamento del sistema, per sottolineare l'importanza della conoscenza dei dati e l'uso che se ne può fare.In the last decades, the use of information management systems in the building data processing led to radical changes to the methods of data production, documentation and archiving. Given the ever-increasing interest in data and their management, the aim of this thesis is to create an effective and clear workflow starting from geomatic surveys in a perspective of improving the collected data on the territory, surrounding buildings and those related to human behaviour so they can be better exploited and integrated into smart management models As first step this thesis aims to understand the limits of data interoperability and integration in GIS filed. Before that, the data must be collected as raw data, then processed and interpret in order to obtain information. At the end of this first stage, when the information is well organized and can be well understanded and used it becomes knowledge. To promote the interoperability of GIS data, it is necessary at first to analyse methods of conversion in different data storage models such as CityGML and IndoorGML, defining an ontological domain. This has led to the creation of a new enriched model, based on connections among the different elements of the urban model in GIS environment, and to the possibility to formulate queries based on these relations. The second step consists in collecting all data translated into a specific format that fill a graph database in a semantic web environment, while maintaining those relationships. The outcome will offer substantial benefits during the entire project life cycle. This methodology can also be applied to cultural heritage where the information management plays a key role. Another research work, was to develop a SMART management system for preservation activities of historical villages through the management of heterogeneous types of data, from the survey to the technical documentation. The workflow was structured as follows: (i) Data acquisition; (ii) 3D modelling; (iii) Knowledge modelling; (iv) SMART management modelling. This research paves the way to develop a web platform where GIS data would be imported for a digital twin approach. All the research done up to this point was to understand the capability of creating smart information models and systems in order to understand the feasibility to host heterogeneous data that may be included in the future. The next step consist of understanding human behaviour in a space. So far only a few research papers are addressed towards mapping and positioning systems taking into account both outdoor and indoor spaces. This topic, even though it has few research articles, represents a crucial aspect for many reasons, especially when it comes to safety management of damaged building. Angelats and his research team at CTTC have been working on this aspect providing a system able to track in real time people from outdoor to indoor areas and vice-versa. The use of GNSS sensors combined with Visual Inertial Odometry provide a continuous trajectory without losing the path followed by the monitored user. A part of this thesis focused on enhancing the final trajectory obtained with the described system above, carrying out tests on the outdoor trajectory of GNSS in order to understand behaviour of the trajectory when it gets close to buildings or when the user moves indoor. The last aspect this thesis will focus on is the tracking of people indoor. Human behaviour is at the centre of several studies in different fields such as scientific subjects, social and economics. Differently from the previous case study of tracking people in outdoor/indoor areas, the scope was to collect information about the dynamic indoor positioning of people, based on the WiFi signal. A brief analysis of the data will be made to demonstrate the correct functioning of the system, to emphasise the importance of data knowledge and the use that can be made of it

Energy Efficient Geo-Localization for a Wearable Device

During the last decade there has been a surge of smart devices on markets around the world. The latest trend is devices that can be worn, so called wearable devices. As for other mobile devices, effective localization are of great interest for many different applications of these devices. However they are small and usually set a high demand on energy efficiency, which makes traditional localization techniques unfeasible for them to use. In this thesis we investigate and succeed in providing a localization solution for a wearable camera that is both accurate and energy efficient. Localization is done through a combination of Wi-Fi and GPS positioning with a mean accuracy of 27 m. Furthermore we utilize an activity recognition algorithm with data from an accelerometer to decide when a new position estimate should be obtained. Our evaluation of the algorithm shows that by applying this method, 83.2 % of the position estimates can be avoided with an insignificant loss in accuracy

Sounds of Silence: A Study of Stability and Diversity of Web Audio Fingerprints

Browser fingerprinting presents a grave threat to privacy as it allows user tracking even in private browsing modes. Prior measurement studies on HTML5-based fingerprinting have been limited to Canvas and WebGL but not Web Audio APIs. We aim to fill this gap by conducting the first large-scale systematic study of web audio fingerprints and studying their stability as well as diversity properties. Using MTurk and social media platforms, we collected 8 different audio fingerprints from 694 users. Firstly, we show that the audio fingerprints are unstable unlike other fingerprinting methods with some users having as many as 20 different fingerprints. Despite this, we show that audio fingerprinting can still be used as an effective fingerprinting vector as most fingerprints tend to repeat quite often. We devised a graph-based fingerprint matching mechanism to measure the diversity of audio fingerprints. Our results show that audio fingerprints are much less diverse with only 45 distinct fingerprints among 694 users

Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems

The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas

Mustang Daily, January 8, 2004

Student newspaper of California Polytechnic State University, San Luis Obispo, CA.https://digitalcommons.calpoly.edu/studentnewspaper/7099/thumbnail.jp

Federated Sensor Network architectural design for the Internet of Things (IoT)

An information technology that can combine the physical world and virtual world is desired. The Internet of Things (IoT) is a concept system that uses Radio Frequency Identification (RFID), WSN and barcode scanners to sense and to detect physical objects and events. This information is shared with people on the Internet. With the announcement of the Smarter Planet concept by IBM, the problem of how to share this data was raised. However, the original design of WSN aims to provide environment monitoring and control within a small scale local network. It cannot meet the demands of the IoT because there is a lack of multi-connection functionality with other WSNs and upper level applications. As various standards of WSNs provide information for different purposes, a hybrid system that gives a complete answer by combining all of them could be promising for future IoT applications. This thesis is on the subject of `Federated Sensor Network' design and architectural development for the Internet of Things. A Federated Sensor Network (FSN) is a system that integrates WSNs and the Internet. Currently, methods of integrating WSNs and the Internet can follow one of three main directions: a Front-End Proxy solution, a Gateway solution or a TCP/IP Overlay solution. Architectures based on the ideas from all three directions are presented in this thesis; this forms a comprehensive body of research on possible Federated Sensor Network architecture designs. In addition, a fully compatible technology for the sensor network application, namely the Sensor Model Language (SensorML), has been reviewed and embedded into our FSN systems. The IoT as a new concept is also comprehensively described and the major technical issues discussed. Finally, a case study of the IoT in logistic management for emergency response is given. Proposed FSN architectures based on the Gateway solution are demonstrated through hardware implementation and lab tests. A demonstration of the 6LoWPAN enabled federated sensor network based on the TCP/IP Overlay solution presents a good result for the iNET localization and tracking project. All the tests of the designs have verified feasibility and achieve the target of the IoT concept