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.

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.

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.

Internet of Things (IoT) is a recent paradigm that envisions a near future, in which the objects of everyday life will communicate with one another and with the users, becoming an integral part of the Internet. The application of the IoT paradigm to an urban context is of particular interest, as it responds to the need to adopt ICT solutions in the city management, thus realizing the Smart City concept. Creating IoT and Smart City platforms poses many issues and challenges. Building suitable solutions that guarantee an interoperability of platform nodes and easy access, requires appropriate tools and approaches that allow to timely understand the effectiveness of solutions. This thesis investigates the above mentioned issues through two methodological approaches: mathematical modelling and experimenta- tion. On one hand, a mathematical model for multi-hop networks based on semi- Markov chains is presented, allowing to properly capture the behaviour of each node in the network while accounting for the dependencies among all links. On the other hand, a methodology for spatial downscaling of testbeds is proposed, implemented, and then exploited for experimental performance evaluation of proprietary but also standardised protocol solutions, considering smart lighting and smart building scenarios. The proposed downscaling procedure allows to create an indoor well-accessible testbed, such that experimentation conditions and performance on this testbed closely match the typical operating conditions and performance where the final solutions are expected to be deployed.

Detection, localization and tracking of non-collaborative objects moving inside an area is of great interest to many surveillance applications. An ultra- wideband (UWB) multistatic radar is considered as a good infrastructure for such anti-intruder systems, due to the high range resolution provided by the UWB impulse-radio and the spatial diversity achieved with a multistatic configuration. Detection of targets, which are typically human beings, is a challenging task due to reflections from unwanted objects in the area, shadowing, antenna cross-talks, low transmit power, and the blind zones arised from intrinsic peculiarities of UWB multistatic radars. Hence, we propose more effective detection, localization, as well as clutter removal techniques for these systems. However, the majority of the thesis effort is devoted to the tracking phase, which is an essential part for improving the localization accuracy, predicting the target position and filling out the missed detections. Since UWB radars are not linear Gaussian systems, the widely used tracking filters, such as the Kalman filter, are not expected to provide a satisfactory performance. Thus, we propose the Bayesian filter as an appropriate candidate for UWB radars. In particular, we develop tracking algorithms based on particle filtering, which is the most common approximation of Bayesian filtering, for both single and multiple target scenarios. Also, we propose some effective detection and tracking algorithms based on image processing tools. We evaluate the performance of our proposed approaches by numerical simulations. Moreover, we provide experimental results by channel measurements for tracking a person walking in an indoor area, with the presence of a significant clutter. We discuss the existing practical issues and address them by proposing more robust algorithms.

In a future perspective, the need of mapping an unknown indoor environment, of localizing and retrieving information from objects with zero costs and efforts could be satisfied by the adoption of next 5G technologies. Thanks to the mix of mmW and massive arrays technologies, it will be possible to achieve a higher indoor localization accuracy without relying on a dedicated infrastructure for localization but exploiting that designed for communication purposes. Besides users localization and navigation objectives, mapping and thus, the capability of reconstructing indoor scenarios, will be an important field of research with the possibility of sharing environmental information via crowd-sourcing mechanisms between users. Finally, in the Internet of Things vision, it is expected that people, objects and devices will be interconnected to each other with the possibility of exchanging the acquired and estimated data including those regarding objects identification, positioning and mapping contents. To this end, the merge of RFID, WSN and UWB technologies has demonstrated to be a promising solution. Stimulated by this framework, this work describes different technological and signal processing approaches to ameliorate the localization capabilities and the user awareness about the environment. From one side, it has been focused on the study of the localization and mapping capabilities of multi-antenna systems based on 5G technologies considering different technological issues, as for example those related to the existing available massive arrays. From the other side, UWB-RFID systems relying on passive communication schemes have been investigated in terms of localization coverage and by developing different techniques to improve the accuracy even in presence of NLOS conditions.

This thesis covers two main topics: the design and performance evaluation of Wireless Body Area Networks (WBANs), and the simulation and mathematical modeling of Delay Tolerant Networks (DTNs). Different Medium Access Control (MAC) protocols for WBANs are implemented on dedicated hardware in order to evaluate, through extensive measurement campaigns, the performance of the network in terms of packet loss rate, delay and energy consumption. Novel solutions to cope with body shadowing and to improve the coexistence with other wireless technologies, are presented and evaluated. An analytic model for the CSMA/CA protocol defined in the IEEE 802.15.6 standard is also presented. The benefits of offloading part of the traffic carried by a wireless backbone to a DTN composed of mobile nodes in a urban environment, is also investigated. A more analytic approach, mainly using tools from stochastic geometry and Markov chains theory, is used to develop a mathematical framework for the evaluation of the performance of routing rules for DTNs.

The Internet of Things promises to be a key-factor in the forthcoming industrial and social revolution. The Internet of Things concept rely on pervasive communications where ’things’ are ’always connected’. The focus of the thesis is on Heterogeneous Networks for Internet of Things and Machine Type Communications. Heterogeneous Networks are an enabling factor of paramount important in order to achieve the ’always connected’ paradigm. On the other hand, Machine Type Communications are deeply different from Human-to-Human communications both in terms of traffic patterns and requirements. This thesis investigate both concepts. In particular, here are studied short and long range solutions for Machine-to-machine applications. For this work a dual approach has been followed: for the short-range solutions analysis an experimental approach has been privileged; meanwhile for the long-range solutions analysis a theoretical and simulation approach has been preferred. In both case, a particular attention has been given to the feasibility of the solutions proposed, hence solutions based on products that already exist in the market have been privileged.

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