Optimized Packet Scheduling in Multiview Video Navigation Systems

In multiview video systems, multiple cameras generally acquire the same scene from different perspectives, such that users have the possibility to select their preferred viewpoint. This results in large amounts of highly redundant data, which needs to be properly handled during encoding and transmission over resource-constrained channels. In this work, we study coding and transmission strategies in multicamera systems, where correlated sources send data through a bottleneck channel to a central server, which eventually transmits views to different interactive users. We propose a dynamic correlation-aware packet scheduling optimization under delay, bandwidth, and interactivity constraints. The optimization relies both on a novel rate-distortion model, which captures the importance of each view in the 3D scene reconstruction, and on an objective function that optimizes resources based on a client navigation model. The latter takes into account the distortion experienced by interactive clients as well as the distortion variations that might be observed by clients during multiview navigation. We solve the scheduling problem with a novel trellis-based solution, which permits to formally decompose the multivariate optimization problem thereby significantly reducing the computation complexity. Simulation results show the gain of the proposed algorithm compared to baseline scheduling policies. More in details, we show the gain offered by our dynamic scheduling policy compared to static camera allocation strategies and to schemes with constant coding strategies. Finally, we show that the best scheduling policy consistently adapts to the most likely user navigation path and that it minimizes distortion variations that can be very disturbing for users in traditional navigation systems

Enhancing the efficiency of electricity utilization through home energy management systems within the smart grid framework

The concept behind smart grids is the aggregation of “intelligence” into the grid, whether through communication systems technologies that allow broadcast/data reception in real-time, or through monitoring and systems control in an autonomous way. With respect to the technological advancements, in recent years there has been a significant increment in devices and new strategies for the implementation of smart buildings/homes, due to the growing awareness of society in relation to environmental concerns and higher energy costs, so that energy efficiency improvements can provide real gains within modern society. In this perspective, the end-users are seen as active players with the ability to manage their energy resources, for example, microproduction units, domestic loads, electric vehicles and their participation in demand response events. This thesis is focused on identifying application areas where such technologies could bring benefits for their applicability, such as the case of wireless networks, considering the positive and negative points of each protocol available in the market. Moreover, this thesis provides an evaluation of dynamic prices of electricity and peak power, using as an example a system with electric vehicles and energy storage, supported by mixed-integer linear programming, within residential energy management. This thesis will also develop a power measuring prototype designed to process and determine the main electrical measurements and quantify the electrical load connected to a low voltage alternating current system. Finally, two cases studies are proposed regarding the application of model predictive control and thermal regulation for domestic applications with cooling requirements, allowing to minimize energy consumption, considering the restrictions of demand, load and acclimatization in the system

Distributed estimation over a low-cost sensor network: a review of state-of-the-art

Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted

Modelling and optimisation of resource usage in an IoT enabled smart campus

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilized efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organizations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory. The building blocks of this thesis consist of three pillars of execution, namely, IoT deployment, predictive modelling, and optimization. Together, these components create an end-to-end framework that provides informed decisions to estate manager in regards to the optimal allocation of campus resources. The main contributions of this thesis are three application domains, which lies on top of the execution pillars, defining campus resources as classrooms, car parks, and transit buses. Specifically, our contributions are: i) We evaluate several IoT occupancy sensing technologies and instrument 9 lecture halls of varying capacities with the most appropriate sensing solution. The collected data provides us with insights into attendance patterns, such as cancelled lectures and class tests, of over 250 courses. We then develop predictive models using machine learning algorithms and quantile regression technique to predict future attendance patterns. Finally, we propose an intelligent optimisation model that allows allocations of classes to rooms based on the dynamics of predicted attendance as opposed to static enrolment number. We show that the data-driven assignment of classroom resources can achieve a potential saving in room cost of over 10\% over the course of a semester, while incurring a very low risk of disrupting student experience due to classroom overflow; ii) We instrument a car park with IoT sensors for real-time monitoring of parking demand and comprehensively analyse the usage data spanning over 15 months. We then develop machine learning models to forecast future parking demand at multiple forecast horizons ranging from 1 day to 10 weeks, our models achieve a mean absolute error (MAE) of 4.58 cars per hour. Finally, we propose a novel optimal allocation framework that allows campus manager to re-dimension the car park to accommodate new paradigms of car use while minimizing the risk of rejecting users and maintaining a certain level of revenue from the parking infrastructure; iii) We develop sensing technology for measuring an outdoor orderly queue using ultrasonic sensor and LoRaWAN, and deploy the solution at an on campus bus stop. Our solution yields a reasonable accuracy with MAE of 10.7 people for detecting a queue length of up to 100 people. We then develop an optimisation model to reschedule bus dispatch times based on the actual dynamics of passenger demand. The result suggests that a potential wait time reduction of 42.93% can be achieved with demand-driven bus scheduling. Taken together, our contributions demonstrates that there are significant resource efficiency gains to be realised in a smart-campus that employs IoT sensing coupled with predictive modelling and dynamic optimisation algorithms

Modelling and optimisation of resource usage in an IoT enabled smart campus

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilized efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organizations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory. The building blocks of this thesis consist of three pillars of execution, namely, IoT deployment, predictive modelling, and optimization. Together, these components create an end-to-end framework that provides informed decisions to estate manager in regards to the optimal allocation of campus resources. The main contributions of this thesis are three application domains, which lies on top of the execution pillars, defining campus resources as classrooms, car parks, and transit buses. Specifically, our contributions are: i) We evaluate several IoT occupancy sensing technologies and instrument 9 lecture halls of varying capacities with the most appropriate sensing solution. The collected data provides us with insights into attendance patterns, such as cancelled lectures and class tests, of over 250 courses. We then develop predictive models using machine learning algorithms and quantile regression technique to predict future attendance patterns. Finally, we propose an intelligent optimisation model that allows allocations of classes to rooms based on the dynamics of predicted attendance as opposed to static enrolment number. We show that the data-driven assignment of classroom resources can achieve a potential saving in room cost of over 10\% over the course of a semester, while incurring a very low risk of disrupting student experience due to classroom overflow; ii) We instrument a car park with IoT sensors for real-time monitoring of parking demand and comprehensively analyse the usage data spanning over 15 months. We then develop machine learning models to forecast future parking demand at multiple forecast horizons ranging from 1 day to 10 weeks, our models achieve a mean absolute error (MAE) of 4.58 cars per hour. Finally, we propose a novel optimal allocation framework that allows campus manager to re-dimension the car park to accommodate new paradigms of car use while minimizing the risk of rejecting users and maintaining a certain level of revenue from the parking infrastructure; iii) We develop sensing technology for measuring an outdoor orderly queue using ultrasonic sensor and LoRaWAN, and deploy the solution at an on campus bus stop. Our solution yields a reasonable accuracy with MAE of 10.7 people for detecting a queue length of up to 100 people. We then develop an optimisation model to reschedule bus dispatch times based on the actual dynamics of passenger demand. The result suggests that a potential wait time reduction of 42.93% can be achieved with demand-driven bus scheduling. Taken together, our contributions demonstrates that there are significant resource efficiency gains to be realised in a smart-campus that employs IoT sensing coupled with predictive modelling and dynamic optimisation algorithms

Data Processing and Fusion For Multi-Source Wireless Systems

The constant evolution of the telecommunication technologies is one fundamental aspect that characterizes the modern era. In the context of healthcare and security, different scenarios are characterized by the presence of multiple sources of information that can support a large number of innovative services. For example, in emergency scenarios, reliable transmission of heterogeneous information (health conditions, ambient and diagnostic videos) can be a valid support for managing the first-aid operations. The presence of multiple sources of information requires a careful communication management, especially in case of limited transmission resource availability. The objective of my Ph.D. activity is to develop new optimization techniques for multimedia communications, considering emergency scenarios characterized by wireless connectivity. Different criteria are defined in order to prioritize the available heterogeneous information before transmission. The proposed solutions are based on the modern concept of content/context awareness: the transmission parameters are optimized taking into account the informative content of the data and the general context in which the information sources are located. To this purpose, novel cross-layer adaptation strategies are proposed for multiple SVC videos delivered over wireless channel. The objective is to optimize the resource allocation dynamically adjusting the overall transmitted throughput to meet the actual available bandwidth. After introducing a realistic camera network, some numerical results obtained with the proposed techniques are showed. In addition, through numerical simulations the benefits are showed, in terms of QoE, introduced by the proposed adaptive aggregation and transmission strategies applied in the context of emergency scenarios. The proposed solution is fully integrated in European research activities, including the FP7 ICT project CONCERTO. To implement, validate and demonstrate the functionalities of the proposed solutions, extensive transmission simulation campaigns are performed. Hence, the presented solutions are integrated on a common system simulator which is been developed within the CONCERTO project

Design of a protocol for event-based network reconfiguration of active vision systems

Projecte final de carrera fet en col.laboració amb Leibniz Universtät HannoverCatalà: Avui en dia la vigilancia de grans àreas, com ara bancs, aeroports o ciutats es basa principalment en sistemas de video. Les Active Cameras (ACs) juguen un paper important per als sistemes de seguretat, ja que combinen video detecció, processament de video i comunicació en un sol dispositiu. Un punt feble és que les ACs son generalment fixes i poden apareixer oclusions que poden crear punts cecs al sistema. Aquests punts cecs poden ser superats mitjançant l?ús de ACs mòbils crean una xarxa mòbil, anomenada Active Camera Network (ACN), presentades en aquesta tesi. No obstant això, la mobilitat de les ACs ve juntament amb desafiaments en termes de coordinació i configuración. A més d?això, el cost de les ACs es més gran en comparació a les xarxes de cameres estatiques, però el nombre de guàrdies necessaris per inspeccionar una gran àrea com una ciutat per exemple o per controlar una gran quantitat de monitors es pot reduir considerablement amb les ACNs. El nostre objectiu és implementar l?arquitectura de un sistema auto-reconfigurable per una xarxa de ACs que poden anar muntades en robots mòbils pel terra o en microvehicles aeris (MAV). Així, les ACs decidirán per si mateixes on actualizar la seva posición per tal d?aconseguir un rendiment òptim del sistema. Per assolir aquest objectiu, les ACs aumentaran o disminuiran les regions espacials redundants amb el seus veïns fent focus en les regions mes sobrecarregades. El protocol presentat en aquesta tesi adapta la posición de les ACs per detectar les diferents trajectories que travessan la zona de vigilancia i que poden evolucionar amb el temps. Les simulacions han demostrat que el protocol presentat augmenta el rendiment general del sistema fins un 190% més gràcies a la reconfiguració i cooperación entre les ACs veïnes.Castellano: Hoy en día la vigilancia de grandes áreas, tales como bancos, aeropuertos o ciudades se basa principalmente en sistemas de video vigilancia. Las Active Cameras (ACs) juegan un papel importante para los sistemas de seguridad, ya que combinan video detección, procesamiento de video y comunicación en un solo dispositivo. Un punto débil es que las ACs son generalmente fijas y pueden aparecer oclusiones que creen puntos ciegos en el sistema. Estos puntos ciegos pueden ser superados mediante el uso de ACs móviles creando una red móvil, llamada Active Camera Network (ACN), presentadas en esta tesis. Sin embargo, la movilidad de las ACs viene junto con desafíos en términos de coordinación y configuración. Además de esto, el coste de las ACs es mayor en comparación a las redes de cámaras estáticas, pero el número de guardias necesarios para inspeccionar una gran área como una ciudad por ejemplo o para controlar una gran cantidad de monitores se puede reducir considerablemente con las ACNs. Nuestro objetivo es implementar la arquitectura de un sistema auto-reconfigurable para una red de ACs que pueden ir montadas en robots móviles por el suelo o en micro vehículos aéreos (MAV). Así, las ACs decidirán por sí mismas donde actualizar sus posiciones con el fin de conseguir un rendimiento óptimo del sistema. Para alcanzar este objetivo, las ACs aumentarán o disminuirán las regiones espaciales redundantes con sus vecinos haciendo foco en las regiones más sobrecargadas. El protocolo presentado en esta tesis adapta la posición de las ACs para detectar las diferentes trayectorias que atraviesan la zona de vigilancia y que pueden evolucionar con el tiempo. Las simulaciones han demostrado que el protocolo presentado aumenta el rendimiento general del sistema hasta un 190% más gracias a la reconfiguración y cooperación entre las ACs vecinas.English: Nowadays surveillance of large areas, such as banks, airports or cities is mostly based on vision systems. Smart Cameras (SCs) play an important role for security systems as they combine video sensing, video processing and communication within a single device. One weak point is that SCs are usually stationary and so occlusions may create blind spots in the system. These blind spots may be overcome by using mobile SCs, so called Active Camera Networks (ACNs), as introduced in this thesis. Nevertheless, mobility of SCs come along with challenges in terms of coordination and configuration. In addition to this, the cost of ACs is higher in comparison to static camera networks but the number of guards needed to survey a large area like a city or to control a lot of monitors can be reduced considerably with AC networks. Our goal is to implement a self-reconfiguration system architecture for networked smart cameras that could be mounted either on mobile robots on the ground or Micro Air Vehicles (MAVs). Thus, the ACs will decide by themselves where to update their position in order to achieve the optimal system's performance. To reach that goal, ACs will increase or decrease spatial redundancy regions with their neighbours to overcome overloaded regions. The protocol presented in this thesis adapts the position of the ACN to the different trajectories that traverse a surveillance area over time. The simulations have shown that the presented protocol increase the overall performance due to the node reconfiguration and cooperation between neighbouring ACs

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things