Wireless Sensor Networks (WSNs) offer a new solution for distributed monitoring, processing and communication. First of all, the stringent energy constraints to which sensing nodes are typically subjected. WSNs are often battery powered and placed where it is not possible to recharge or replace batteries. Energy can be harvested from the external environment but it is a limited resource that must be used efficiently. Energy efficiency is a key requirement for a credible WSNs design. From the power source's perspective, aggressive energy management techniques remain the most effective way to prolong the lifetime of a WSN. A new adaptive algorithm will be presented, which minimizes the consumption of wireless sensor nodes in sleep mode, when the power source has to be regulated using DC-DC converters. Another important aspect addressed is the time synchronisation in WSNs. WSNs are used for real-world applications where physical time plays an important role. An innovative low-overhead synchronisation approach will be presented, based on a Temperature Compensation Algorithm (TCA). The last aspect addressed is related to self-powered WSNs with Energy Harvesting (EH) solutions. Wireless sensor nodes with EH require some form of energy storage, which enables systems to continue operating during periods of insufficient environmental energy. However, the size of the energy storage strongly restricts the use of WSNs with EH in real-world applications. A new approach will be presented, which enables computation to be sustained during intermittent power supply. The discussed approaches will be used for real-world WSN applications. The first presented scenario is related to the experience gathered during an European Project (3ENCULT Project), regarding the design and implementation of an innovative network for monitoring heritage buildings. The second scenario is related to the experience with Telecom Italia, regarding the design of smart energy meters for monitoring the usage of household's appliances.

Combinatorial Optimization is becoming ever more crucial, in these days. From natural sciences to economics, passing through urban centers administration and personnel management, methodologies and algorithms with a strong theoretical background and a consolidated real-word effectiveness is more and more requested, in order to find, quickly, good solutions to complex strategical problems. Resource optimization is, nowadays, a fundamental ground for building the basements of successful projects. From the theoretical point of view, Combinatorial Optimization rests on stable and strong foundations, that allow researchers to face ever more challenging problems. However, from the application point of view, it seems that the rate of theoretical developments cannot cope with that enjoyed by modern hardware technologies, especially with reference to the one of processors industry. In this work we propose new parallel algorithms, designed for exploiting the new parallel architectures available on the market. We found that, exposing the inherent parallelism of some resolution techniques (like Dynamic Programming), the computational benefits are remarkable, lowering the execution times by more than an order of magnitude, and allowing to address instances with dimensions not possible before. We approached four Combinatorial Optimization’s notable problems: Packing Problem, Vehicle Routing Problem, Single Source Shortest Path Problem and a Network Design problem. For each of these problems we propose a collection of effective parallel solution algorithms, either for solving the full problem (Guillotine Cuts and SSSPP) or for enhancing a fundamental part of the solution method (VRP and ND). We endorse our claim by presenting computational results for all problems, either on standard benchmarks from the literature or, when possible, on data from real-world applications, where speed-ups of one order of magnitude are usually attained, not uncommonly scaling up to 40 X factors.

The energy harvesting research field has grown considerably in the last decade due to increasing interests in energy autonomous sensing systems, which require smart and efficient interfaces for extracting power from energy source and power management (PM) circuits. This thesis investigates the design trade-offs for minimizing the intrinsic power of PM circuits, in order to allow operation with very weak energy sources. For validation purposes, three different integrated power converter and PM circuits for energy harvesting applications are presented. They have been designed for nano-power operations and single-source converters can operate with input power lower than 1 μW. The first IC is a buck-boost converter for piezoelectric transducers (PZ) implementing Synchronous Electrical Charge Extraction (SECE), a non-linear energy extraction technique. Moreover, Residual Charge Inversion technique is exploited for extracting energy from PZ with weak and irregular excitations (i.e. lower voltage), and the implemented PM policy, named Two-Way Energy Storage, considerably reduces the start-up time of the converter, improving the overall conversion efficiency. The second proposed IC is a general-purpose buck-boost converter for low-voltage DC energy sources, up to 2.5 V. An ultra-low-power MPPT circuit has been designed in order to track variations of source power. Furthermore, a capacitive boost circuit has been included, allowing the converter start-up from a source voltage VDC0 = 223 mV. A nano-power programmable linear regulator is also included in order to provide a stable voltage to the load. The third IC implements an heterogeneous multisource buck-boost converter. It provides up to 9 independent input channels, of which 5 are specific for PZ (with SECE) and 4 for DC energy sources with MPPT. The inductor is shared among channels and an arbiter, designed with asynchronous logic to reduce the energy consumption, avoids simultaneous access to the buck-boost core, with a dynamic schedule based on source priority.

This thesis presents a new approach for the design and fabrication of bond wire magnetics for power converter applications by using standard IC gold bonding wires and micro-machined magnetic cores. It shows a systematic design and characterization study for bond wire transformers with toroidal and race-track cores for both PCB and silicon substrates. Measurement results show that the use of ferrite cores increases the secondary self-inductance up to 315 µH with a Q-factor up to 24.5 at 100 kHz. Measurement results on LTCC core report an enhancement of the secondary self-inductance up to 23 µH with a Q-factor up to 10.5 at 1.4 MHz. A resonant DC-DC converter is designed in 0.32 µm BCD6s technology at STMicroelectronics with a depletion nmosfet and a bond wire micro-transformer for EH applications. Measures report that the circuit begins to oscillate from a TEG voltage of 280 mV while starts to convert from an input down to 330 mV to a rectified output of 0.8 V at an input of 400 mV. Bond wire magnetics is a cost-effective approach that enables a flexible design of inductors and transformers with high inductance and high turns ratio. Additionally, it supports the development of magnetics on top of the IC active circuitry for package and wafer level integrations, thus enabling the design of high density power components. This makes possible the evolution of PwrSiP and PwrSoC with reliable highly efficient magnetics.

This thesis is the result of a Ph.D. program in Alto Apprendistato carried out at the Dipartimento di Informatica - Scienza e Ingegneria (DISI) of the University of Bologna and at the company devDept Software. With regard to the professional side of my Individual Training Project, I developed technical and scientific skills in 3D geometry of curves and surfaces, CAD, and Finite Element Analysis (FEA). Regarding the academic side, I investigated CAD aspects in the field of Isogeometric Analysis (IGA) on both single and hybrid multipatch physical domains. Simulations are performed in classical FEA systems, which require the conversion of designs, made by CAD systems, into finite element meshes. IGA is a new approach that aims to unify the worlds of CAD and FEA by using the same geometry for analysis as what is used for modeling. That is, the same set of basis functions are adopted both to describe the computational geometry in the CAD tool, and to span the solution space for FEA. The traditional FEA pipeline works on meshes and the most advanced IGA systems work on NURBS or T-spline geometries. Hybrid geometric models (i.e., models in which mesh and NURBS entities coexist), are an emergent way to represent a solid object, but in most CAD systems mesh and NURBS geometries cannot interact with each other, and conversions to a common representation are often needed. In this thesis, we investigate how IGA can be applied on 2D and 3D hybrid models made by both mesh and NURBS entities without requiring laborious and time consuming conversion processes.

This thesis is focused on the study and design of techniques able to optimize resources in distributed mobile platforms. It is related to a smart city environment, in order to enhance quality, performance and interactivity of urban services. The subject is the computation offloading, intended as the delegation of certain computing tasks to an external platform, such as a cloud or a cluster of devices. Offloading the computation tasks can effectively expand the usability of mobile devices beyond their physical limits and may be necessary due to limitations of a system handling a particular task on its own. The computation offloading within an ecosystem as a urban community, where a large amount of users are connected towards even multiple devices, is a challenging subject. In a very close future, smart cities will be peculiar sources of intensive computing tasks, since they are conceived as systems where e-governance will be not only transparent and fast, but also oriented to energy and water conservation, efficient waste disposal, city automation, seamless facilities to travel and affordable access to health management systems. Also traffic will need to be monitored intelligently, emergencies foreseen and resolved quickly, homes and citizens provided with a wide series of control and security devices. All these ambitious aspirations will require the deployment of infrastructures and systems where devices will generate massive data and should be orchestrated in a collective way. In this context, the computation offloading is an operation dealing with the optimization of urban services, in order to reduce costs and consumption of resources and to improve the connection between citizens and government. This dissertation is organized in three main parts, dealing with the optimization of the resources in a smart city from different points of view.

In this thesis the problem of trajectory planning for automatic machines is addressed, considering in particular the problem of vibration suppression and perfect tracking. Two different trajectory generators based on dynamic filters are developed and implemented. These novel trajectory planners are designed respectively for residual vibrations suppression and perfect tracking of periodic trajectories. Both solutions are very effective and easy to implement, exploiting the realization of dynamic filters by means of FIR filters. In the first part of the thesis the problem of residual vibrations in motion control of robots is addressed. In a review fashion the most widespread feed-forward techniques for residual vibration suppression are analyzed in detail and implemented in simulation. Moreover all the analysis are performed with a control system perspective in order to give a unified point of view allowing to compare all the solutions despite their differences. In Chapter 5 a novel trajectory generator based on Exponential Filters is presented, analyzed and compared with the most commonly used feed-forward techniques for vibration suppression, proving comparable performances with the state of the art. The analysis and comparison procedure is performed both in simulation and in experimental activities. This new method is developed first for simple SISO LTI systems and then extended to MIMO systems. In Chapter 6 a new repetitive control scheme based on B-Spline Trajectory Generator that exploits dynamic filters is presented. The novel scheme integrates the trajectory generator and the repetitive controller in a single discrete time feedback loop achieving perfect tracking for periodic motions. In appendix, the development of a 6-axis Force/Torque sensor for underwater activities based on optoelectronic components is reported. The description covers the entire process from the concept to the development of a simulation model and finally to the prototype realization, along with an extensive experimental activity.

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.

As human beings, we trust our five senses, that allow us to experience the world and communicate. Since our birth, the amount of data that every day we can acquire is impressive and such a richness reflects the complexity of humankind in arts, technology, etc. The advent of computers and the consequent progress in Data Science and Artificial Intelligence showed how large amounts of data can contain some sort of “intelligence” themselves. Machines learn and create a superimposed layer of reality. How data generated by humans and machines are related today? To give an answer we will present three projects in the context of “Mixed Reality”, the ideal place where Reality, Virtual Reality and Augmented Reality are increasingly connected as long as data enhance the digital experiences, making them more “real”. We will start with BRAVO, a tool that exploits the brain activity to improve the user’s learning process in real time by means of a Brain-Computer Interface that acquires EEG data. Then we will see AUGMENTED GRAPHICS, a framework for detecting objects in the reality that can be captured easily and inserted in any digital scenario. Based on the moments invariants theory, it looks particularly designed for mobile devices, as it assumes a light concept of object detection and it works without any training set. As third work, GLOVR, a wearable hand controller that uses inertial sensors to offer directional controls and to recognize gestures, particularly suitable for Virtual Reality applications. It features a microphone to record voice sequences that then are translated in tasks by means of a natural language web service. For each project we will summarize the main results and we will trace some future directions of research and development.

Body-centric and cooperative communications are new trends in telecommunications field. Being concerned with human behaviour, body-centric communication networks, also known as Wireless Body Area Networks (WBANs), are suitable for a wide variety of applications. The advances in the miniaturisation of embedded devices to be placed on or around the body, foster the diffusion of these systems, where the human body is the key element defining communication characteristics. Cooperative communications paradigm, on the other hand, is one of the emerging technologies that promises significantly higher reliability and spectral efficiency in wireless networks. This thesis investigates possible applications of the cooperative communication paradigm to body-centric networks and, more generally, to Wireless Sensor Networks (WSNs). Firstly, communication protocols for WBANs are in the spotlight. Performance achieved by different approaches is evaluated and compared through experimentation providing guidelines for choosing appropriate protocol and setting protocol parameters to meet application requirements. Secondly, a cooperative Multiple Input Multiple Output (MIMO) scheme for WBANs is presented. The scheme, named B-MIMO, exploits the natural heterogeneity of the WBAN propagation channel to improve energy efficiency of the system. Finally, a WSN scenario is considered, where sensor nodes cooperate to establish a massive MIMO-like system. The analysis and subsequent optimisation show the advantages of cooperation in terms of energy efficiency and provide insights on how many nodes should be deployed in such a scenario.