Context detection and abstraction in smart environments

Context-aware computing is currently considered the most promising approach to overcome information overload and to speed up access to relevant information and services. Context-awareness may be derived from many sources, including user profile and preferences, network information, sensor analysis; usually context-awareness relies on the ability of computing devices to interact with the physical world, i.e. with the natural and artificial objects hosted within the "environment”. Ideally, context-aware applications should not be intrusive and should be able to react according to user’s context, with minimum user effort. Context is an application dependent multidimensional space and the location is an important part of it since the very beginning. Location can be used to guide applications, in providing information or functions that are most appropriate for a specific position. Hence location systems play a crucial role. There are several technologies and systems for computing location to a vary degree of accuracy and tailored for specific space model, i.e. indoors or outdoors, structured spaces or unstructured spaces. The research challenge faced by this thesis is related to pedestrian positioning in heterogeneous environments. Particularly, the focus will be on pedestrian identification, localization, orientation and activity recognition. This research was mainly carried out within the “mobile and ambient systems” workgroup of EPOCH, a 6FP NoE on the application of ICT to Cultural Heritage. Therefore applications in Cultural Heritage sites were the main target of the context-aware services discussed. Cultural Heritage sites are considered significant test-beds in Context-aware computing for many reasons. For example building a smart environment in museums or in protected sites is a challenging task, because localization and tracking are usually based on technologies that are difficult to hide or harmonize within the environment. Therefore it is expected that the experience made with this research may be useful also in domains other than Cultural Heritage. This work presents three different approaches to the pedestrian identification, positioning and tracking: Pedestrian navigation by means of a wearable inertial sensing platform assisted by the vision based tracking system for initial settings an real-time calibration; Pedestrian navigation by means of a wearable inertial sensing platform augmented with GPS measurements; Pedestrian identification and tracking, combining the vision based tracking system with WiFi localization. The proposed localization systems have been mainly used to enhance Cultural Heritage applications in providing information and services depending on the user’s actual context, in particular depending on the user’s location.

Middleware for quality-based context distribution in mobile systems

The continuous advancements and enhancements of wireless systems are enabling new compelling scenarios where mobile services can adapt according to the current execution context, represented by the computational resources available at the local device, current physical location, people in physical proximity, and so forth. Such services called context-aware require the timely delivery of all relevant information describing the current context, and that introduces several unsolved complexities, spanning from low-level context data transmission up to context data storage and replication into the mobile system. In addition, to ensure correct and scalable context provisioning, it is crucial to integrate and interoperate with different wireless technologies (WiFi, Bluetooth, etc.) and modes (infrastructure-based and ad-hoc), and to use decentralized solutions to store and replicate context data on mobile devices. These challenges call for novel middleware solutions, here called Context Data Distribution Infrastructures (CDDIs), capable of delivering relevant context data to mobile devices, while hiding all the issues introduced by data distribution in heterogeneous and large-scale mobile settings. This dissertation thoroughly analyzes CDDIs for mobile systems, with the main goal of achieving a holistic approach to the design of such type of middleware solutions. We discuss the main functions needed by context data distribution in large mobile systems, and we claim the precise definition and clean respect of quality-based contracts between context consumers and CDDI to reconfigure main middleware components at runtime. We present the design and the implementation of our proposals, both in simulation-based and in real-world scenarios, along with an extensive evaluation that confirms the technical soundness of proposed CDDI solutions. Finally, we consider three highly heterogeneous scenarios, namely disaster areas, smart campuses, and smart cities, to better remark the wide technical validity of our analysis and solutions under different network deployments and quality constraints.

Low carbon systems for climate change mitigation and adaptation

Global warming and climate change have been among the most controversial topics after the industrial revolution. The main contributor to global warming is carbon dioxide (CO2), which increases the temperature by trapping heat in the atmosphere. Atmospheric CO2 concentration before the industrial era was around 280 ppm for a long period, while it has increased dramatically since the industrial revolution up to approximately 420 ppm. According to the Paris agreement it is needed to keep the temperature increase up to 2°C, preferably 1.5° C, to prevent reaching the tipping point of climate change. To keep the temperature increase below the range, it is required to find solutions to reduce CO2 emissions. The solutions can be low-carbon systems and transition from fossil fuels to renewable energy sources (RES). This thesis is allocated to the assessment of low-carbon systems and the reduction of CO2 by using RES instead of fossil fuels. One of the most important aspects to define the location and capacity of low-carbon systems is CO2 mass estimation. As mentioned, high-emission systems can be substituted by low-carbon systems. An example of high-emission systems is dredging. The global CO2 emission from dredging is relatively high which is associated with the growth of marine transport in addition to its high emission. Thus, ejectors system as alternative for dredging is investigated in chapter 2. For the transition from fossil fuels to RES, it is required to provide solutions for the RES storage problem. A solution could be zero-emission fuels such as hydrogen. However, the production of hydrogen requires electricity, and electricity production emits a large amount of CO2. Therefore, the last three chapters are allocated to hydrogen generation via electrolysis, at the current condition and scenarios of RES and variation of cell characteristics and stack materials, and its delivery.