Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the Communications Societ

Cryptographic Primitives from Physical Variables

In this dissertation we explore a new paradigm emerging from the subtleties of cryptographic implementations and relating to theoretical aspects of cryptography. This new paradigm, namely physical variables (PVs), simply describes properties of physical objects designed to be identical but are not due to manufacturing variability. In the first part of this dissertation, we focus our attention on scenarios which require the unique identification of physical objects and we show how Gaussian PVs can be used to fulfill such a requirement. Using this framework we present and analyze a new technique for fingerprinting compact discs (CDs) using the manufacturing variability found in the length of the CDs\u27 lands and pits. Although the variability measured is on the order of 20 nm, the technique does not require the use of microscopes or any advanced equipment. Instead, the electrical signal produced by the photo-detector inside the CD reader will be sufficient to measure the desired variability. We thoroughly investigate the new technique by analyzing data collected from 100 identical CDs and show how to extract a unique fingerprint for each CD. In the second part, we shift our attention to physically parameterized functions (PPFs). Although all the constructions we provide are centered around delay-based physically unclonable functions (PUFs), we stress that the use of the term PUF could be misleading as most circuits labeled with the term PUF are in reality clonable on the protocol level. We argue that using a term like PPFs to describe functions parameterized by a PV is a more accurate description. Herein, we thoroughly analyze delay-PUFs and use a mathematical framework to construct two authentication protocols labeled PUF-HB and HB+PUF. Both these protocols merge the known HB authentication family with delay-based PUFs. The new protocols enjoy the security reduction put forth by the HB portion of the protocol and at the same time maintain a level of hardware security provided by the use of PUFs. We present a proof of concept implementation for HB+PUF which takes advantage of the PUF circuit in order to produce the random bits typically needed for an HB-based authentication scheme. The overall circuit is shown to occupy a few thousand gates. Finally, we present a new authentication protocol that uses 2-level PUF circuits and enables a security reduction which, unlike the previous two protocols, stems naturally from the usage of PVs

Body of Knowledge for Graphics Processing Units (GPUs)

Graphics Processing Units (GPU) have emerged as a proven technology that enables high performance computing and parallel processing in a small form factor. GPUs enhance the traditional computer paradigm by permitting acceleration of complex mathematics and providing the capability to perform weighted calculations, such as those in artificial intelligence systems. Despite the performance enhancements provided by this type of microprocessor, there exist tradeoffs in regards to reliability and radiation susceptibility, which may impact mission success. This report provides an insight into GPU architecture and its potential applications in space and other similar markets. It also discusses reliability, qualification, and radiation considerations for testing GPUs

Comprehensive Designs of Innovate Secure Hardware Devices against Machine Learning Attacks and Power Analysis Attacks

Hardware security is an innovate subject oriented from growing demands of cybersecurity and new information vulnerabilities from physical leakages on hardware devices. However, the mainstream of hardware manufacturing industry is still taking benefits of products and the performance of chips as priority, restricting the design of hardware secure countermeasures under a compromise to a finite expense of overheads. Consider the development trend of hardware industries and state-of-the-art researches of architecture designs, this dissertation proposes some new physical unclonable function (PUF) designs as countermeasures to side-channel attacks (SCA) and machine learning (ML) attacks simultaneously. Except for the joint consideration of hardware and software vulnerabilities, those designs also take efficiencies and overhead problems into consideration, making the new-style of PUF more possible to be merged into current chips as well as their design concepts. While the growth of artificial intelligence and machine-learning techniques dominate the researching trends of Internet of things (IoT) industry, some mainstream architectures of neural networks are implemented as hypothetical attacking model, whose results are used as references for further lifting the performance, the security level, and the efficiency in lateral studies. In addition, a study of implementation of neural networks on hardware designs is proposed, this realized the initial attempt to introduce AI techniques to the designs of voltage regulation (VR). All aforementioned works are demonstrated to be of robustness to threats with corresponding power attack tests or ML attack tests. Some conceptional models are proposed in the last of the dissertation as future plans so as to realize secure on-chip ML models and hardware countermeasures to hybrid threats

Low Power Memory/Memristor Devices and Systems

This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within

Harnessing noise to enhance robustness vs. efficiency trade-off in machine learning

While deep nets have achieved human-comparable accuracy in various classification tasks, they fall short significantly in terms of the robustness and cost metrics. For example, tiny engineered corruptions in deep net inputs can reduce their accuracy to zero. Furthermore, deep nets also require millions of trainable parameters, resulting in significant training and inference costs. These robustness and cost challenges are well recognized today. In response, there have been a plethora of works focusing on improving either the accuracy vs. robustness trade-off, or the accuracy vs. cost trade-off. However, simultaneous consideration of accuracy, robustness, and cost metrics is largely absent today, in part, because far fewer works have explored the robustness vs. cost trade-off. This dissertation aims to fill this gap by focusing explicitly on the robustness vs. cost trade-off in the presence of data noise, as well as hardware noise. Specifically, we explore how to harness the noise in order to enhance this trade-off. We characterize and improve robustness vs. cost trade-offs across diverse problem settings, ranging from beyond-CMOS hardware implementations of machine learning (ML) classifiers to efficient training of deep nets that are robust to multiple types of corruptions in their inputs. This dissertation can be roughly divided into two part, one focusing on hardware noise and the other on data noise. In the first part, we start by focusing on harnessing noise in spintronic hardware implementations, where the logic gates become error prone when operated at lower switching energy/delay. We propose techniques to shape the resulting hardware noise distribution and to efficiently compensate it at the system-level output. As a result, we observe 1000x improvement intolerance to gate-level switching error rates, while keeping the area/energy overhead of compensation circuits to as low as 15%. These robustness enhancements further enable 3× reduction in iso-throughput energy consumption of a binary ML classifier employed for EEG-based seizure detection. Building on this work, we propose spintronic channel networks, exponential decay of spin current to efficiently realize multi-bit dot product computation. We employ error-prone nanomagnets as efficient stochastic slicers biased by spin currents proportional to the likelihood of the classification decision. We achieve 112x-to-22.5x and 14x-to-2.5x higher energy-efficiency over conventional spin-based and 20 nm CMOS designs, respectively, when realizing 10-to-100-dimensional binary classifiers. Furthermore, we also consider the impact of hardware noise originated from process variations and readout circuits in in-memory computing implementations employing non-volatile resistive crossbar arrays. Based on our analysis, we identify design configurations achieving the highest signal-to-noise ratio (SNR), and further estimate how such robustness trades off with the array energy consumption. In the second part, we switch gears to improve the robustness vs. cost trade-off for deep nets in the presence of data noise. Specifically, we focus on the impact of adversarial perturbations in the deep nets inputs. We propose and validate the hypotheses about orientations of dominant subspaces of adversarial perturbations. We demonstrate how changes in the curvature of decision boundary of the deep nets affects the orientations of the adversarial perturbations. Based on these insights we demonstrate how shaped noise can be introduced as a feature to enhance robustness vs. cost trade-off in deep nets. Specifically, we propose shaped noise augmented processing (SNAP), a method to efficiently train deep nets that are robust to multiple types of adversarial perturbations, simultaneously. SNAP prepends a deep net with a shaped noise augmentation layer whose distribution is learned along with the network parameters using any established robust training framework. Based on extensive comparisons with nine state-of-the-art (SOTA) robust training frameworks, we show that SNAP achieves the best robustness vs. training cost trade-off. In particular, it enables 4x reduction in the training cost compared to the SOTA approach published just this last year. Furthermore, thanks to the computational simplicity of SNAP, it is the first technique of its kind that is scalable to large datasets, such as ImageNet

Remote Attacks on FPGA Hardware

Immer mehr Computersysteme sind weltweit miteinander verbunden und über das Internet zugänglich, was auch die Sicherheitsanforderungen an diese erhöht. Eine neuere Technologie, die zunehmend als Rechenbeschleuniger sowohl für eingebettete Systeme als auch in der Cloud verwendet wird, sind Field-Programmable Gate Arrays (FPGAs). Sie sind sehr flexible Mikrochips, die per Software konfiguriert und programmiert werden können, um beliebige digitale Schaltungen zu implementieren. Wie auch andere integrierte Schaltkreise basieren FPGAs auf modernen Halbleitertechnologien, die von Fertigungstoleranzen und verschiedenen Laufzeitschwankungen betroffen sind. Es ist bereits bekannt, dass diese Variationen die Zuverlässigkeit eines Systems beeinflussen, aber ihre Auswirkungen auf die Sicherheit wurden nicht umfassend untersucht. Diese Doktorarbeit befasst sich mit einem Querschnitt dieser Themen: Sicherheitsprobleme die dadurch entstehen wenn FPGAs von mehreren Benutzern benutzt werden, oder über das Internet zugänglich sind, in Kombination mit physikalischen Schwankungen in modernen Halbleitertechnologien. Der erste Beitrag in dieser Arbeit identifiziert transiente Spannungsschwankungen als eine der stärksten Auswirkungen auf die FPGA-Leistung und analysiert experimentell wie sich verschiedene Arbeitslasten des FPGAs darauf auswirken. In der restlichen Arbeit werden dann die Auswirkungen dieser Spannungsschwankungen auf die Sicherheit untersucht. Die Arbeit zeigt, dass verschiedene Angriffe möglich sind, von denen früher angenommen wurde, dass sie physischen Zugriff auf den Chip und die Verwendung spezieller und teurer Test- und Messgeräte erfordern. Dies zeigt, dass bekannte Isolationsmaßnahmen innerhalb FPGAs von böswilligen Benutzern umgangen werden können, um andere Benutzer im selben FPGA oder sogar das gesamte System anzugreifen. Unter Verwendung von Schaltkreisen zur Beeinflussung der Spannung innerhalb eines FPGAs zeigt diese Arbeit aktive Angriffe, die Fehler (Faults) in anderen Teilen des Systems verursachen können. Auf diese Weise sind Denial-of-Service Angriffe möglich, als auch Fault-Angriffe um geheime Schlüsselinformationen aus dem System zu extrahieren. Darüber hinaus werden passive Angriffe gezeigt, die indirekt die Spannungsschwankungen auf dem Chip messen. Diese Messungen reichen aus, um geheime Schlüsselinformationen durch Power Analysis Seitenkanalangriffe zu extrahieren. In einer weiteren Eskalationsstufe können sich diese Angriffe auch auf andere Chips auswirken die an dasselbe Netzteil angeschlossen sind wie der FPGA. Um zu beweisen, dass vergleichbare Angriffe nicht nur innerhalb FPGAs möglich sind, wird gezeigt, dass auch kleine IoT-Geräte anfällig für Angriffe sind welche die gemeinsame Spannungsversorgung innerhalb eines Chips ausnutzen. Insgesamt zeigt diese Arbeit, dass grundlegende physikalische Variationen in integrierten Schaltkreisen die Sicherheit eines gesamten Systems untergraben können, selbst wenn der Angreifer keinen direkten Zugriff auf das Gerät hat. Für FPGAs in ihrer aktuellen Form müssen diese Probleme zuerst gelöst werden, bevor man sie mit mehreren Benutzern oder mit Zugriff von Drittanbietern sicher verwenden kann. In Veröffentlichungen die nicht Teil dieser Arbeit sind wurden bereits einige erste Gegenmaßnahmen untersucht

Design and Analysis of Security Schemes for Low-cost RFID Systems

With the remarkable progress in microelectronics and low-power semiconductor technologies, Radio Frequency IDentification technology (RFID) has moved from obscurity into mainstream applications, which essentially provides an indispensable foundation to realize ubiquitous computing and machine perception. However, the catching and exclusive characteristics of RFID systems introduce growing security and privacy concerns. To address these issues are particularly challenging for low-cost RFID systems, where tags are extremely constrained in resources, power and cost. The primary reasons are: (1) the security requirements of low-cost RFID systems are even more rigorous due to large operation range and mass deployment; and (2) the passive tags' modest capabilities and the necessity to keep their prices low present a novel problem that goes beyond the well-studied problems of traditional cryptography. This thesis presents our research results on the design and the analysis of security schemes for low-cost RFID systems. Motivated by the recent attention on exploiting physical layer resources in the design of security schemes, we investigate how to solve the eavesdropping, modification and one particular type of relay attacks toward the tag-to-reader communication in passive RFID systems without requiring lightweight ciphers. To this end, we propose a novel physical layer scheme, called Backscatter modulation- and Uncoordinated frequency hopping-assisted Physical Layer Enhancement (BUPLE). The idea behind it is to use the amplitude of the carrier to transmit messages as normal, while to utilize its periodically varied frequency to hide the transmission from the eavesdropper/relayer and to exploit a random sequence modulated to the carrier's phase to defeat malicious modifications. We further improve its eavesdropping resistance through the coding in the physical layer, since BUPLE ensures that the tag-to-eavesdropper channel is strictly noisier than the tag-to-reader channel. Three practical Wiretap Channel Codes (WCCs) for passive tags are then proposed: two of them are constructed from linear error correcting codes, and the other one is constructed from a resilient vector Boolean function. The security and usability of BUPLE in conjunction with WCCs are further confirmed by our proof-of-concept implementation and testing. Eavesdropping the communication between a legitimate reader and a victim tag to obtain raw data is a basic tool for the adversary. However, given the fundamentality of eavesdropping attacks, there are limited prior work investigating its intension and extension for passive RFID systems. To this end, we firstly identified a brand-new attack, working at physical layer, against backscattered RFID communications, called unidirectional active eavesdropping, which defeats the customary impression that eavesdropping is a ``passive" attack. To launch this attack, the adversary transmits an un-modulated carrier (called blank carrier) at a certain frequency while a valid reader and a tag interacts at another frequency channel. Once the tag modulates the amplitude of reader's signal, it causes fluctuations on the blank carrier as well. By carefully examining the amplitude of the backscattered versions of the blank carrier and the reader's carrier, the adversary could intercept the ongoing reader-tag communication with either significantly lower bit error rate or from a significantly greater distance away. Our concept is demonstrated and empirically analyzed towards a popular low-cost RFID system, i.e., EPC Gen2. Although active eavesdropping in general is not trivial to be prohibited, for a particular type of active eavesdropper, namely a greedy proactive eavesdropper, we propose a simple countermeasure without introducing extra cost to current RFID systems. The needs of cryptographic primitives on constraint devices keep increasing with the growing pervasiveness of these devices. One recent design of the lightweight block cipher is Hummingbird-2. We study its cryptographic strength under a novel technique we developed, called Differential Sequence Attack (DSA), and present the first cryptanalytic result on this cipher. In particular, our full attack can be divided into two phases: preparation phase and key recovery phase. During the key recovery phase, we exploit the fact that the differential sequence for the last round of Hummingbird-2 can be retrieved by querying the full cipher, due to which, the search space of the secret key can be significantly reduced. Thus, by attacking the encryption (decryption resp.) of Hummingbird-2, our algorithm recovers 36-bit (another 28-bit resp.) out of 128-bit key with $2^{68}$ ($2^{60}$ resp.) time complexity if particular differential conditions of the internal states and of the keys at one round can be imposed. Additionally, the rest 64-bit of the key can be exhaustively searched and the overall time complexity is dominated by $2^{68}$. During the preparation phase, by investing $2^{81}$ effort in time, the adversary is able to create the differential conditions required in the key recovery phase with at least 0.5 probability. As an additional effort, we examine the cryptanalytic strength of another lightweight candidate known as A2U2, which is the most lightweight cryptographic primitive proposed so far for low-cost tags. Our chosen-plaintext-attack fully breaks this cipher by recovering its secret key with only querying the encryption twice on the victim tag and solving 32 sparse systems of linear equations (where each system has 56 unknowns and around 28 unknowns can be directly obtained without computation) in the worst case, which takes around 0.16 second on a Thinkpad T410 laptop

Smart Wireless Sensor Networks

The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society