A Collaborative Mobile Crowdsensing System for Smart Cities

Nowadays words like Smart City, Internet of Things, Environmental Awareness surround us with the growing interest of Computer Science and Engineering communities. Services supporting these paradigms are definitely based on large amounts of sensed data, which, once obtained and gathered, need to be analyzed in order to build maps, infer patterns, extract useful information. Everything is done in order to achieve a better quality of life. Traditional sensing techniques, like Wired or Wireless Sensor Network, need an intensive usage of distributed sensors to acquire real-world conditions. We propose SenSquare, a Crowdsensing approach based on smartphones and a central coordination server for time-and-space homogeneous data collecting. SenSquare relies on technologies such as CoAP lightweight protocol, Geofencing and the Military Grid Reference System.

Simulazione di protocolli di gossip in territori a bassa connettività

I mondi di Smart City e Internet-of-Things si stanno ampliando notevolmente grazie all'evoluzione continua delle tecnologie. Tuttavia risultano escluse dall'ambito di interesse le zone rurali e decentralizzate perché spesso prive di un'infrastruttura di rete ben definita. A fronte di questo problema, i dispositivi che si trovano in queste zone potrebbero auto-organizzarsi per comunicare instaurando collegmenti di tipo peer-to-peer e utilizzando protocolli di disseminazione di informazioni basati su gossip. In questa tesi sono trattate le seguenti questioni e mediante alcune simulazioni al calcolatore sono riprodotti alcuni scenari per valutare le prestazioni degli algoritmi di Gossip with Fixed Probability e Conditional Broadcast e la diffusione delle informazioni tra i nodi all'interno di una rete creata in maniera opportunistica.

Acumen : an open-source testbed for cyber-physical systems research

Developing Cyber-Physical Systems requires methods and tools to support simulation and verification of hybrid (both continuous and discrete) models. The Acumen modeling and simulation language is an open source testbed for exploring the design space of what rigorousbut- practical next-generation tools can deliver to developers of Cyber- Physical Systems. Like verification tools, a design goal for Acumen is to provide rigorous results. Like simulation tools, it aims to be intuitive, practical, and scalable. However, it is far from evident whether these two goals can be achieved simultaneously. This paper explains the primary design goals for Acumen, the core challenges that must be addressed in order to achieve these goals, the “agile research method” taken by the project, the steps taken to realize these goals, the key lessons learned, and the emerging language design.

Frequency adjustable MEMS vibration energy harvester

Ambient mechanical vibrations offer an attractive solution for powering the wireless sensor nodes of the emerging "Internet-of-Things". However, the wide-ranging variability of the ambient vibration frequencies pose a significant challenge to the efficient transduction of vibration into usable electrical energy. This work reports the development of a MEMS electromagnetic vibration energy harvester where the resonance frequency of the oscillator can be adjusted or tuned to adapt to the ambient vibrational frequency. Micro-fabricated silicon spring and double layer planar micro-coils along with sintered NdFeB micro-magnets are used to construct the electromagnetic transduction mechanism. Furthermore, another NdFeB magnet is adjustably assembled to induce variable magnetic interaction with the transducing magnet, leading to significant change in the spring stiffness and resonance frequency. Finite element analysis and numerical simulations exhibit substantial frequency tuning range (25% of natural resonance frequency) by appropriate adjustment of the repulsive magnetic interaction between the tuning and transducing magnet pair. This demonstrated method of frequency adjustment or tuning have potential applications in other MEMS vibration energy harvesters and micromechanical oscillators.

Privacy matters: issues within mechatronics

As mechatronic devices and components become increasingly integrated with and within wider systems concepts such as Cyber-Physical Systems and the Internet of Things, designer engineers are faced with new sets of challenges in areas such as privacy. The paper looks at the current, and potential future, of privacy legislation, regulations and standards and considers how these are likely to impact on the way in which mechatronics is perceived and viewed. The emphasis is not therefore on technical issues, though these are brought into consideration where relevant, but on the soft, or human centred, issues associated with achieving user privacy.

Seudonimización de datos en el entorno sanitario

Los problemas de privacidad están aumentando con el uso masivo de dispositivos IoT (Internet of Things) en el ámbito de la Salud y el Bienestar. Estos dispositivos manejan gran cantidad de datos relativos a la salud del individuo que son almacenados y transferidos a través de diferentes dispositivos y plataformas propiedad de empresas privadas. El tratamiento confidencial de estos datos es, por tanto, fundamental para mantener la privacidad de los usuarios. En este trabajo se ha realizado una extensa revisión de la legislación internacional y trabajos de investigación publicados con respecto a los datos que deben protegerse en el ámbito de la salud. El propósito es garantizar la protección de los datos de las personas que hacen uso de estos servicios. Se ha clasificado y optimizado la colección de atributos obtenida con esta revisión a través de la identificación de duplicados y eliminación de aquellos no utilizados. Posteriormente, el conjunto de datos obtenido se ha sometido al criterio de expertos utilizando diferentes técnicas como entrevistas y cuestionarios con el objetivo de obtener el conjunto de datos que debe ser objeto de estudio en cada caso concreto para su protección en el ámbito de la Salud. Los datos finales obtenidos se presentan en este trabajo.

Applicazione di tecniche AI per la progettazione di un sistema a supporto del paziente iperteso

In questa tesi si è cercato di trovare le soluzioni più efficaci a supporto delle questioni legate all'ipertensione di seguito descritte attraverso l'uso di tecniche riguardanti l'intelligenza artificiale e l'Internet of Things. Uno tra i compiti dei medici che si occupano di curare i malati di ipertensione è quello di elaborare protocolli per quanto riguarda la prevenzione e la cura di questa malattia, i quali vengono periodicamente aggiornati. Per supportare ciò, il primo progetto sviluppato è consistito in un'analisi dei dati sul dataset ottenuto a partire dall'elaborazione delle risposte date ai questionari che sono stati distribuiti durante la Giornata Mondiale dell'Ipertensione. A partire da questo, si è cercato di evidenziare la classe di persone che con più probabilità sono malate di ipertensione in modo tale che le linee guida aggiornate si concentrino maggiormente su costoro. La seconda questione affrontata è che non sempre le cure che vengono prescritte sono efficaci, talvolta a causa del medico, talvolta a causa del paziente. Si rende perciò necessario fornire ai pazienti degli strumenti che li aiutino direttamente nella cura della loro malattia. Devono avere anche lo scopo di aiutare il medico nel suo lavoro di monitoraggio periodico delle condizioni di salute del paziente, perché possa avere realmente il polso della situazione. Per fare questo, il secondo progetto ha riguardato lo sviluppo di un chatbot disponibile sulla piattaforma di messaggistica istantanea Telegram ad uso dei malati di ipertensione. Questo assistente virtuale permette loro di registrare le misurazioni di pressione che settimanalmente devono effettuare e ricorda loro di farlo quando passa troppo tempo dall'ultima misurazione. Il sistema permette inoltre di visualizzare medie e grafici delle misurazioni che sono state raccolte cosicché il medico può affidarsi ad uno strumento più evoluto del semplice libretto diario in cui il paziente annota tutte le misurazioni.

Sviluppo di YouCrowd: una piattaforma per campagne mobile crowdsensing on-demand

Con il termine "crowdsensing" si intende una tecnica in cui un folto gruppo di individui aventi dispositivi mobili acquisiscono e condividono dati di natura diversa in maniera collettiva, al fine di estrarre informazioni utili. Il concetto di Mobile Crowdsensing è molto recente e derivante dalle ultime innovazioni tecnologiche in materia di connettività online e cattura di dati di vario genere; pertanto non si trova attualmente una vera e propria applicazione in campo reale, la modellazione solo teorica e fin troppo specifica pone un limite alla conoscenza di un ambito che può rivelarsi molto utile ai fini di ricerca. YouCrowd è un piattaforma web che va ad implementare un sistema di crowdsourcing completo, in grado di leggere dati dai numerosi sensori di uno smartphone e condividerli, al fine di ottenere una remunerazione per gli utenti che completano una campagna. La web application vede la sua implementazione di base supportata da NodeJS e si configura come una piattaforma dinamica che varia la propria interfaccia con l'utente in base alle richieste di dati da parte degli administrators. Il test di YouCrowd ha coinvolto un buon numero di partecipanti più o meno esperti nell'utilizzo degli strumenti informatici, rivelando delle buone prestazioni in relazione alla difficoltà del task e alle prestazioni del device in test.

High performance and energy-efficient instruction cache design and optimisation for ultra-low-power multi-core clusters

High Energy efficiency and high performance are the key regiments for Internet of Things (IoT) end-nodes. Exploiting cluster of multiple programmable processors has recently emerged as a suitable solution to address this challenge. However, one of the main bottlenecks for multi-core architectures is the instruction cache. While private caches fall into data replication and wasting area, fully shared caches lack scalability and form a bottleneck for the operating frequency. Hence we propose a hybrid solution where a larger shared cache (L1.5) is shared by multiple cores connected through a low-latency interconnect to small private caches (L1). However, it is still limited by large capacity miss with a small L1. Thus, we propose a sequential prefetch from L1 to L1.5 to improve the performance with little area overhead. Moreover, to cut the critical path for better timing, we optimized the core instruction fetch stage with non-blocking transfer by adopting a 4 x 32-bit ring buffer FIFO and adding a pipeline for the conditional branch. We present a detailed comparison of different instruction cache architectures' performance and energy efficiency recently proposed for Parallel Ultra-Low-Power clusters. On average, when executing a set of real-life IoT applications, our two-level cache improves the performance by up to 20% and loses 7% energy efficiency with respect to the private cache. Compared to a shared cache system, it improves performance by up to 17% and keeps the same energy efficiency. In the end, up to 20% timing (maximum frequency) improvement and software control enable the two-level instruction cache with prefetch adapt to various battery-powered usage cases to balance high performance and energy efficiency.

Wireless Power Link Design for Both High-Power Inductive Coupling and Smart Metasurfaces Exploitation

As future technologies are going to be autonomous under the umbrella of the Internet of things (IoT) we can expect WPT to be the solution for intelligent devices. WPT has many industrial and medical applications both in the near-field and far-field domains. Considering the impact of WPT, this thesis is an attempt to design and realize both near-field and far-field WPT solutions for different application scenarios. A 27 MHz high frequency inductive wireless power link has been designed together with the Class-E switching inverter to compensate for the efficiency loss because of the varying weak coupling between transmitter and receiver because of their mutual misalignment. Then a system of three coils was introduced for SWIPT. The outer coil for WPT and the inner two coils were designed to fulfil the purpose of communication and testing, operating at frequencies different from the WPT coil. In addition to that, a trapping filter technique has also been adopted to ensure the EM isolation of the coils. Moreover, a split ring resonator-based dual polarization converter has been designed with good efficiency over a wide frequency range. The gap or cuts have been introduced in the adjacent sides of the square ring to make it a dual-polarization converter. The converter is also stable over a wide range of incident angles. Furthermore, a meta-element based intelligent surface has been designed to work in the reflection mode at 5 GHz. In this research activity, interdigital capacitors (IDCs) instead of ICs are introduced and a thin layer of the HfZrO between substrate and meta elements is placed whose response can be tuned and controlled with the applied voltage to achieve IRS.

Surveillance risks in IoT applied to smart cities

Nowadays, cities deal with unprecedented pollution and overpopulation problems, and Internet of Things (IoT) technologies are supporting them in facing these issues and becoming increasingly smart. IoT sensors embedded in public infrastructure can provide granular data on the urban environment, and help public authorities to make their cities more sustainable and efficient. Nonetheless, this pervasive data collection also raises high surveillance risks, jeopardizing privacy and data protection rights. Against this backdrop, this thesis addresses how IoT surveillance technologies can be implemented in a legally compliant and ethically acceptable fashion in smart cities. An interdisciplinary approach is embraced to investigate this question, combining doctrinal legal research (on privacy, data protection, criminal procedure) with insights from philosophy, governance, and urban studies. The fundamental normative argument of this work is that surveillance constitutes a necessary feature of modern information societies. Nonetheless, as the complexity of surveillance phenomena increases, there emerges a need to develop more fine-attuned proportionality assessments to ensure a legitimate implementation of monitoring technologies. This research tackles this gap from different perspectives, analyzing the EU data protection legislation and the United States and European case law on privacy expectations and surveillance. Specifically, a coherent multi-factor test assessing privacy expectations in public IoT environments and a surveillance taxonomy are proposed to inform proportionality assessments of surveillance initiatives in smart cities. These insights are also applied to four use cases: facial recognition technologies, drones, environmental policing, and smart nudging. Lastly, the investigation examines competing data governance models in the digital domain and the smart city, reviewing the EU upcoming data governance framework. It is argued that, despite the stated policy goals, the balance of interests may often favor corporate strategies in data sharing, to the detriment of common good uses of data in the urban context.

QoS-aware architectures, technologies, and middleware for the cloud continuum

The recent trend of moving Cloud Computing capabilities to the Edge of the network is reshaping how applications and their middleware supports are designed, deployed, and operated. This new model envisions a continuum of virtual resources between the traditional cloud and the network edge, which is potentially more suitable to meet the heterogeneous Quality of Service (QoS) requirements of diverse application domains and next-generation applications. Several classes of advanced Internet of Things (IoT) applications, e.g., in the industrial manufacturing domain, are expected to serve a wide range of applications with heterogeneous QoS requirements and call for QoS management systems to guarantee/control performance indicators, even in the presence of real-world factors such as limited bandwidth and concurrent virtual resource utilization. The present dissertation proposes a comprehensive QoS-aware architecture that addresses the challenges of integrating cloud infrastructure with edge nodes in IoT applications. The architecture provides end-to-end QoS support by incorporating several components for managing physical and virtual resources. The proposed architecture features: i) a multilevel middleware for resolving the convergence between Operational Technology (OT) and Information Technology (IT), ii) an end-to-end QoS management approach compliant with the Time-Sensitive Networking (TSN) standard, iii) new approaches for virtualized network environments, such as running TSN-based applications under Ultra-low Latency (ULL) constraints in virtual and 5G environments, and iv) an accelerated and deterministic container overlay network architecture. Additionally, the QoS-aware architecture includes two novel middlewares: i) a middleware that transparently integrates multiple acceleration technologies in heterogeneous Edge contexts and ii) a QoS-aware middleware for Serverless platforms that leverages coordination of various QoS mechanisms and virtualized Function-as-a-Service (FaaS) invocation stack to manage end-to-end QoS metrics. Finally, all architecture components were tested and evaluated by leveraging realistic testbeds, demonstrating the efficacy of the proposed solutions.

Proximity detection protocols for IoT devices

In recent years, we have witnessed the growth of the Internet of Things paradigm, with its increased pervasiveness in our everyday lives. The possible applications are diverse: from a smartwatch able to measure heartbeat and communicate it to the cloud, to the device that triggers an event when we approach an exhibit in a museum. Present in many of these applications is the Proximity Detection task: for instance the heartbeat could be measured only when the wearer is near to a well defined location for medical purposes or the touristic attraction must be triggered only if someone is very close to it. Indeed, the ability of an IoT device to sense the presence of other devices nearby and calculate the distance to them can be considered the cornerstone of various applications, motivating research on this fundamental topic. The energy constraints of the IoT devices are often in contrast with the needs of continuous operations to sense the environment and to achieve high accurate distance measurements from the neighbors, thus making the design of Proximity Detection protocols a challenging task.

NB-IoT via non terrestrial networks

Massive Internet of Things is expected to play a crucial role in Beyond 5G (B5G) wireless communication systems, offering seamless connectivity among heterogeneous devices without human intervention. However, the exponential proliferation of smart devices and IoT networks, relying solely on terrestrial networks, may not fully meet the demanding IoT requirements in terms of bandwidth and connectivity, especially in areas where terrestrial infrastructures are not economically viable. To unleash the full potential of 5G and B5G networks and enable seamless connectivity everywhere, the 3GPP envisions the integration of Non-Terrestrial Networks (NTNs) into the terrestrial ones starting from Release 17. However, this integration process requires modifications to the 5G standard to ensure reliable communications despite typical satellite channel impairments. In this framework, this thesis aims at proposing techniques at the Physical and Medium Access Control layers that require minimal adaptations in the current NB-IoT standard via NTN. Thus, firstly the satellite impairments are evaluated and, then, a detailed link budget analysis is provided. Following, analyses at the link and the system levels are conducted. In the former case, a novel algorithm leveraging time-frequency analysis is proposed to detect orthogonal preambles and estimate the signals’ arrival time. Besides, the effects of collisions on the detection probability and Bit Error Rate are investigated and Non-Orthogonal Multiple Access approaches are proposed in the random access and data phases. The system analysis evaluates the performance of random access in case of congestion. Various access parameters are tested in different satellite scenarios, and the performance is measured in terms of access probability and time required to complete the procedure. Finally, a heuristic algorithm is proposed to jointly design the access and data phases, determining the number of satellite passages, the Random Access Periodicity, and the number of uplink repetitions that maximize the system's spectral efficiency.

Adaptable security in the cloud-to-thing continuum

Recent technological advancements have played a key role in seamlessly integrating cloud, edge, and Internet of Things (IoT) technologies, giving rise to the Cloud-to-Thing Continuum paradigm. This cloud model connects many heterogeneous resources that generate a large amount of data and collaborate to deliver next-generation services. While it has the potential to reshape several application domains, the number of connected entities remarkably broadens the security attack surface. One of the main problems is the lack of security measures to adapt to the dynamic and evolving conditions of the Cloud-To-Thing Continuum. To address this challenge, this dissertation proposes novel adaptable security mechanisms. Adaptable security is the capability of security controls, systems, and protocols to dynamically adjust to changing conditions and scenarios. However, since the design and development of novel security mechanisms can be explored from different perspectives and levels, we place our attention on threat modeling and access control. The contributions of the thesis can be summarized as follows. First, we introduce a model-based methodology that secures the design of edge and cyber-physical systems. This solution identifies threats, security controls, and moving target defense techniques based on system features. Then, we focus on access control management. Since access control policies are subject to modifications, we evaluate how they can be efficiently shared among distributed areas, highlighting the effectiveness of distributed ledger technologies. Furthermore, we propose a risk-based authorization middleware, adjusting permissions based on real-time data, and a federated learning framework that enhances trustworthiness by weighting each client's contributions according to the quality of their partial models. Finally, since authorization revocation is another critical concern, we present an efficient revocation scheme for verifiable credentials in IoT networks, featuring decentralization, demanding minimum storage and computing capabilities. All the mechanisms have been evaluated in different conditions, proving their adaptability to the Cloud-to-Thing Continuum landscape.