Circuits and Systems for Energy Harvesting and Internet of Things Applications

The Internet of Things (IoT) continues its growing trend, while new “smart” objects are con-stantly being developed and commercialized in the market. Under this paradigm, every common object will be soon connected to the Internet: mobile and wearable devices, electric appliances, home electronics and even cars will have Internet connectivity. Not only that, but a variety of wireless sensors are being proposed for different consumer and industrial applications. With the possibility of having hundreds of billions of IoT objects deployed all around us in the coming years, the social implications and the economic impact of IoT technology needs to be seriously considered. There are still many challenges, however, awaiting a solution in order to realize this future vision of a connected world. A very important bottleneck is the limited lifetime of battery powered wireless devices. Fully depleted batteries need to be replaced, which in perspective would generate costly maintenance requirements and environmental pollution. However, a very plausible solution to this dilemma can be found in harvesting energy from the ambient. This dissertation focuses in the design of circuits and system for energy harvesting and Internet of Things applications. The first part of this dissertation introduces the research motivation and fundamentals of energy harvesting and power management units (PMUs). The architecture of IoT sensor nodes and PMUs is examined to observe the limitations of modern energy harvesting systems. Moreover, several architectures for multisource harvesting are reviewed, providing a background for the research presented here. Then, a new fully integrated system architecture for multisource energy harvesting is presented. The design methodology, implementation, trade-offs and measurement results of the proposed system are described. The second part of this dissertation focus on the design and implementation of low-power wireless sensor nodes for precision agriculture. First, a sensor node incorporating solar energy harvesting and a dynamic power management strategy is presented. The operation of a wireless sensor network for soil parameter estimation, consisting of four nodes is demonstrated. After that, a solar thermoelectric generator (STEG) prototype for powering a wireless sensor node is proposed. The implemented solar thermoelectric generator demonstrates to be an alternative way to harvest ambient energy, opening the possibility for its use in agricultural and environmental applications. The open problems in energy harvesting for IoT devices are discussed at the end, to delineate the possible future work to improve the performance of EH systems. For all the presented works, proof-of-concept prototypes were fabricated and tested. The measured results are used to verify their correct operation and performance

State-of-the-Art Sensors Technology in Spain 2015: Volume 1

This book provides a comprehensive overview of state-of-the-art sensors technology in specific leading areas. Industrial researchers, engineers and professionals can find information on the most advanced technologies and developments, together with data processing. Further research covers specific devices and technologies that capture and distribute data to be processed by applying dedicated techniques or procedures, which is where sensors play the most important role. The book provides insights and solutions for different problems covering a broad spectrum of possibilities, thanks to a set of applications and solutions based on sensory technologies. Topics include: • Signal analysis for spectral power • 3D precise measurements • Electromagnetic propagation • Drugs detection • e-health environments based on social sensor networks • Robots in wireless environments, navigation, teleoperation, object grasping, demining • Wireless sensor networks • Industrial IoT • Insights in smart cities • Voice recognition • FPGA interfaces • Flight mill device for measurements on insects • Optical systems: UV, LEDs, lasers, fiber optics • Machine vision • Power dissipation • Liquid level in fuel tanks • Parabolic solar tracker • Force sensors • Control for a twin roto

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Elastically-bounded flapping plates for flow-induced energy harvesting

This work concerns a novel concept for energy harvesting (EH) from fluid flows, based on the aeroelastic flutter of elastically-bounded plates immersed in laminar flow. The resulting flapping motions are investigated in order to support the development of centimetric-size EH devices exploiting low wind velocities, with potential application in the autonomous powering of low-power wireless sensor networks used, e.g., for remote environmental monitoring. The problem is studied combining three-dimensional direct numerical simulations exploiting a state-of-the-art immersed boundary method, wind-tunnel experiments on prototypal EH devices, and a reduced-order phenomenological model based on a set of ordinary differential equations. Three key features of the aeroelastic system are investigated: (i) we identify the critical condition for self-sustained flapping using a simple balance between characteristic timescales involved in the problem; (ii) we explore postcritical regimes characterized by regular limit-cycle oscillations, highlighting how to maximize their amplitude and/or frequency and in turns the potential energy extraction; (iii) we consider arrays of multiple devices, revealing for certain arrangements a constructive interference effect that leads to significant performance improvements. These findings lead to an improved characterization of the system and can be useful for the optimal design of EH devices. Moreover, we outline future research directions with the ultimate goal of realizing high-performance networks of numerous harvesters in real-world environmental conditions

Delivering IoT Services in Smart Cities and Environmental Monitoring through Collective Awareness, Mobile Crowdsensing and Open Data

The Internet of Things (IoT) is the paradigm that allows us to interact with the real world by means of networking-enabled devices and convert physical phenomena into valuable digital knowledge. Such a rapidly evolving field leveraged the explosion of a number of technologies, standards and platforms. Consequently, different IoT ecosystems behave as closed islands and do not interoperate with each other, thus the potential of the number of connected objects in the world is far from being totally unleashed. Typically, research efforts in tackling such challenge tend to propose a new IoT platforms or standards, however, such solutions find obstacles in keeping up the pace at which the field is evolving. Our work is different, in that it originates from the following observation: in use cases that depend on common phenomena such as Smart Cities or environmental monitoring a lot of useful data for applications is already in place somewhere or devices capable of collecting such data are already deployed. For such scenarios, we propose and study the use of Collective Awareness Paradigms (CAP), which offload data collection to a crowd of participants. We bring three main contributions: we study the feasibility of using Open Data coming from heterogeneous sources, focusing particularly on crowdsourced and user-contributed data that has the drawback of being incomplete and we then propose a State-of-the-Art algorith that automatically classifies raw crowdsourced sensor data; we design a data collection framework that uses Mobile Crowdsensing (MCS) and puts the participants and the stakeholders in a coordinated interaction together with a distributed data collection algorithm that prevents the users from collecting too much or too less data; (3) we design a Service Oriented Architecture that constitutes a unique interface to the raw data collected through CAPs through their aggregation into ad-hoc services, moreover, we provide a prototype implementation

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

A Scalable and Secure System Architecture for Smart Buildings

Recent years has seen profound changes in building technologies both in Europe and worldwide. With the emergence of Smart Grid and Smart City concepts, the Smart Building has attracted considerable attention and rapid development. The introduction of novel information and communication technologies (ICT) enables an optimized resource utilization while improving the building performance and occupants' satisfaction over a broad spectrum of operations. However, literature and industry have drawn attention to certain barriers and challenges that inhibit its universal adoption. The Smart Building is a cyber-physical system, which as a whole is more than the sum of its parts. The heterogeneous combination of systems, processes, and practices requires a multidisciplinary research. This work proposes and validates a systems engineering approach to the investigation of the identified challenges and the development of a viable architecture for the future Smart Building. Firstly, a data model for the building management system (BMS) enables a semantic abstraction of both the ICT and the building construction. A high-level application programming interface (API) facilitates the creation of generic management algorithms and external applications, independent from each Smart Building instance, promoting the intelligence portability and lowering the cost. Moreover, the proposed architecture ensures the scalability regardless of the occupant activities and the complexity of the optimization algorithms. Secondly, a real-time message-oriented middleware, as a distributed embedded architecture within the building, empowers the interoperability of the ICT devices and networks and their integration into the BMS. The middleware scales to any building construction regardless of the devices' performance and connectivity limitations, while a secure architecture ensures the integrity of data and operations. An extensive performance and energy efficiency study validates the proposed design. A "building-in-the-loop" emulation system, based on discrete-event simulation, virtualizes the Smart Building elements (e.g., loads, storage, generation, sensors, actuators, users, etc.). The high integration with the message-oriented middleware keeps the BMS agnostic to the virtual nature of the emulated instances. Its cooperative multitasking and immerse parallelism allow the concurrent emulation of hundreds of elements in real time. The virtualization facilitates the development of energy management strategies and financial viability studies on the exact building and occupant activities without a prior investment in the necessary infrastructure. This work concludes with a holistic system evaluation using a case study of a university building as a practical retrofitting estimation. It illustrates the system deployment, and highlights how a currently under development energy management system utilizes the BMS and its data analytics for demand-side management applications

Engineering Self-Adaptive Collective Processes for Cyber-Physical Ecosystems

The pervasiveness of computing and networking is creating significant opportunities for building valuable socio-technical systems. However, the scale, density, heterogeneity, interdependence, and QoS constraints of many target systems pose severe operational and engineering challenges. Beyond individual smart devices, cyber-physical collectives can provide services or solve complex problems by leveraging a “system effect” while coordinating and adapting to context or environment change. Understanding and building systems exhibiting collective intelligence and autonomic capabilities represent a prominent research goal, partly covered, e.g., by the field of collective adaptive systems. Therefore, drawing inspiration from and building on the long-time research activity on coordination, multi-agent systems, autonomic/self-* systems, spatial computing, and especially on the recent aggregate computing paradigm, this thesis investigates concepts, methods, and tools for the engineering of possibly large-scale, heterogeneous ensembles of situated components that should be able to operate, adapt and self-organise in a decentralised fashion. The primary contribution of this thesis consists of four main parts. First, we define and implement an aggregate programming language (ScaFi), internal to the mainstream Scala programming language, for describing collective adaptive behaviour, based on field calculi. Second, we conceive of a “dynamic collective computation” abstraction, also called aggregate process, formalised by an extension to the field calculus, and implemented in ScaFi. Third, we characterise and provide a proof-of-concept implementation of a middleware for aggregate computing that enables the development of aggregate systems according to multiple architectural styles. Fourth, we apply and evaluate aggregate computing techniques to edge computing scenarios, and characterise a design pattern, called Self-organising Coordination Regions (SCR), that supports adjustable, decentralised decision-making and activity in dynamic environments.Con lo sviluppo di informatica e intelligenza artificiale, la diffusione pervasiva di device computazionali e la crescente interconnessione tra elementi fisici e digitali, emergono innumerevoli opportunità per la costruzione di sistemi socio-tecnici di nuova generazione. Tuttavia, l'ingegneria di tali sistemi presenta notevoli sfide, data la loro complessità—si pensi ai livelli, scale, eterogeneità, e interdipendenze coinvolti. Oltre a dispositivi smart individuali, collettivi cyber-fisici possono fornire servizi o risolvere problemi complessi con un “effetto sistema” che emerge dalla coordinazione e l'adattamento di componenti fra loro, l'ambiente e il contesto. Comprendere e costruire sistemi in grado di esibire intelligenza collettiva e capacità autonomiche è un importante problema di ricerca studiato, ad esempio, nel campo dei sistemi collettivi adattativi. Perciò, traendo ispirazione e partendo dall'attività di ricerca su coordinazione, sistemi multiagente e self-*, modelli di computazione spazio-temporali e, specialmente, sul recente paradigma di programmazione aggregata, questa tesi tratta concetti, metodi, e strumenti per l'ingegneria di ensemble di elementi situati eterogenei che devono essere in grado di lavorare, adattarsi, e auto-organizzarsi in modo decentralizzato. Il contributo di questa tesi consiste in quattro parti principali. In primo luogo, viene definito e implementato un linguaggio di programmazione aggregata (ScaFi), interno al linguaggio Scala, per descrivere comportamenti collettivi e adattativi secondo l'approccio dei campi computazionali. In secondo luogo, si propone e caratterizza l'astrazione di processo aggregato per rappresentare computazioni collettive dinamiche concorrenti, formalizzata come estensione al field calculus e implementata in ScaFi. Inoltre, si analizza e implementa un prototipo di middleware per sistemi aggregati, in grado di supportare più stili architetturali. Infine, si applicano e valutano tecniche di programmazione aggregata in scenari di edge computing, e si propone un pattern, Self-Organising Coordination Regions, per supportare, in modo decentralizzato, attività decisionali e di regolazione in ambienti dinamici

Deep Learning -Powered Computational Intelligence for Cyber-Attacks Detection and Mitigation in 5G-Enabled Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has various cyber-attack vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. Therefore, proactively monitoring, detecting, and defending against these attacks is very important. The state-of-the-art approaches are not agile and intelligent enough to detect, mitigate, and defend against various cyber-physical attacks in the EVCS system. To overcome these limitations, this dissertation primarily designs, develops, implements, and tests the data-driven deep learning-powered computational intelligence to detect and mitigate cyber-physical attacks at the network and physical layers of 5G-enabled EVCS infrastructure. Also, the 5G slicing application to ensure the security and service level agreement (SLA) in the EVCS ecosystem has been studied. Various cyber-attacks such as distributed denial of services (DDoS), False data injection (FDI), advanced persistent threats (APT), and ransomware attacks on the network in a standalone 5G-enabled EVCS environment have been considered. Mathematical models for the mentioned cyber-attacks have been developed. The impact of cyber-attacks on the EVCS operation has been analyzed. Various deep learning-powered intrusion detection systems have been proposed to detect attacks using local electrical and network fingerprints. Furthermore, a novel detection framework has been designed and developed to deal with ransomware threats in high-speed, high-dimensional, multimodal data and assets from eccentric stakeholders of the connected automated vehicle (CAV) ecosystem. To mitigate the adverse effects of cyber-attacks on EVCS controllers, novel data-driven digital clones based on Twin Delayed Deep Deterministic Policy Gradient (TD3) Deep Reinforcement Learning (DRL) has been developed. Also, various Bruteforce, Controller clones-based methods have been devised and tested to aid the defense and mitigation of the impact of the attacks of the EVCS operation. The performance of the proposed mitigation method has been compared with that of a benchmark Deep Deterministic Policy Gradient (DDPG)-based digital clones approach. Simulation results obtained from the Python, Matlab/Simulink, and NetSim software demonstrate that the cyber-attacks are disruptive and detrimental to the operation of EVCS. The proposed detection and mitigation methods are effective and perform better than the conventional and benchmark techniques for the 5G-enabled EVCS