Energy Harvesting Networked Nodes: Measurements, Algorithms, and Prototyping

Recent advances in ultra-low-power wireless communications and in energy harvesting will soon enable energetically self-sustainable wireless devices. Networks of such devices will serve as building blocks for different Internet of Things (IoT) applications, such as searching for an object on a network of objects and continuous monitoring of object configurations. Yet, numerous challenges need to be addressed for the IoT vision to be fully realized. This thesis considers several challenges related to ultra-low-power energy harvesting networked nodes: energy source characterization, algorithm design, and node design and prototyping. Additionally, the thesis contributes to engineering education, specifically to project-based learning. We summarize our contributions to light and kinetic (motion) energy characterization for energy harvesting nodes. To characterize light energy, we conducted a first-of-its kind 16 month-long indoor light energy measurements campaign. To characterize energy of motion, we collected over 200 hours of human and object motion traces. We also analyzed traces previously collected in a study with over 40 participants. We summarize our insights, including light and motion energy budgets, variability, and influencing factors. These insights are useful for designing energy harvesting nodes and energy harvesting adaptive algorithms. We shared with the community our light energy traces, which can be used as energy inputs to system and algorithm simulators and emulators. We also discuss resource allocation problems we considered for energy harvesting nodes. Inspired by the needs of tracking and monitoring IoT applications, we formulated and studied resource allocation problems aimed at allocating the nodes' time-varying resources in a uniform way with respect to time. We mainly considered deterministic energy profile and stochastic environmental energy models, and focused on single node and link scenarios. We formulated optimization problems using utility maximization and lexicographic maximization frameworks, and introduced algorithms for solving the formulated problems. For several settings, we provided low-complexity solution algorithms. We also examined many simple policies. We demonstrated, analytically and via simulations, that in many settings simple policies perform well. We also summarize our design and prototyping efforts for a new class of ultra-low-power nodes - Energy Harvesting Active Networked Tags (EnHANTs). Future EnHANTs will be wireless nodes that can be attached to commonplace objects (books, furniture, clothing). We describe the EnHANTs prototypes and the EnHANTs testbed that we developed, in collaboration with other research groups, over the last 4 years in 6 integration phases. The prototypes harvest energy of the indoor light, communicate with each other via ultra-low-power transceivers, form small multihop networks, and adapt their communications and networking to their energy harvesting states. The EnHANTs testbed can expose the prototypes to light conditions based on real-world light energy traces. Using the testbed and our light energy traces, we evaluated some of our energy harvesting adaptive policies. Our insights into node design and performance evaluations may apply beyond EnHANTs to networks of various energy harvesting nodes. Finally, we present our contributions to engineering education. Over the last 4 years, we engaged high school, undergraduate, and M.S. students in more than 100 research projects within the EnHANTs project. We summarize our approaches to facilitating student learning, and discuss the results of evaluation surveys that demonstrate the effectiveness of our approaches

Methods and Tools for Battery-free Wireless Networks

Embedding small wireless sensors into the environment allows for monitoring physical processes with high spatio-temporal resolutions. Today, these devices are equipped with a battery to supply them with power. Despite technological advances, the high maintenance cost and environmental impact of batteries prevent the widespread adoption of wireless sensors. Battery-free devices that store energy harvested from light, vibrations, and other ambient sources in a capacitor promise to overcome the drawbacks of (rechargeable) batteries, such as bulkiness, wear-out and toxicity. Because of low energy input and low storage capacity, battery-free devices operate intermittently; they are forced to remain inactive for most of the time charging their capacitor before being able to operate for a short time. While it is known how to deal with intermittency on a single device, the coordination and communication among groups of multiple battery-free devices remain largely unexplored. For the first time, the present thesis addresses this problem by proposing new methods and tools to investigate and overcome several fundamental challenges

Towards self-powered wireless sensor networks

Ubiquitous computing aims at creating smart environments in which computational and communication capabilities permeate the word at all scales, improving the human experience and quality of life in a totally unobtrusive yet completely reliable manner. According to this vision, an huge variety of smart devices and products (e.g., wireless sensor nodes, mobile phones, cameras, sensors, home appliances and industrial machines) are interconnected to realize a network of distributed agents that continuously collect, process, share and transport information. The impact of such technologies in our everyday life is expected to be massive, as it will enable innovative applications that will profoundly change the world around us. Remotely monitoring the conditions of patients and elderly people inside hospitals and at home, preventing catastrophic failures of buildings and critical structures, realizing smart cities with sustainable management of traffic and automatic monitoring of pollution levels, early detecting earthquake and forest fires, monitoring water quality and detecting water leakages, preventing landslides and avalanches are just some examples of life-enhancing applications made possible by smart ubiquitous computing systems. To turn this vision into a reality, however, new raising challenges have to be addressed, overcoming the limits that currently prevent the pervasive deployment of smart devices that are long lasting, trusted, and fully autonomous. In particular, the most critical factor currently limiting the realization of ubiquitous computing is energy provisioning. In fact, embedded devices are typically powered by short-lived batteries that severely affect their lifespan and reliability, often requiring expensive and invasive maintenance. In this PhD thesis, we investigate the use of energy-harvesting techniques to overcome the energy bottleneck problem suffered by embedded devices, particularly focusing on Wireless Sensor Networks (WSNs), which are one of the key enablers of pervasive computing systems. Energy harvesting allows to use energy readily available from the environment (e.g., from solar light, wind, body movements, etc.) to significantly extend the typical lifetime of low-power devices, enabling ubiquitous computing systems that can last virtually forever. However, the design challenges posed both at the hardware and at the software levels by the design of energy-autonomous devices are many. This thesis addresses some of the most challenging problems of this emerging research area, such as devising mechanisms for energy prediction and management, improving the efficiency of the energy scavenging process, developing protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support. %, including the design of mechanisms for energy prediction and management, improving the efficiency of the energy harvesting process, the develop of protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support

Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems

Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed

IoT-MQTT based denial of service attack modelling and detection

Internet of Things (IoT) is poised to transform the quality of life and provide new business opportunities with its wide range of applications. However, the bene_ts of this emerging paradigm are coupled with serious cyber security issues. The lack of strong cyber security measures in protecting IoT systems can result in cyber attacks targeting all the layers of IoT architecture which includes the IoT devices, the IoT communication protocols and the services accessing the IoT data. Various IoT malware such as Mirai, BASHLITE and BrickBot show an already rising IoT device based attacks as well as the usage of infected IoT devices to launch other cyber attacks. However, as sustained IoT deployment and functionality are heavily reliant on the use of e_ective data communication protocols, the attacks on other layers of IoT architecture are anticipated to increase. In the IoT landscape, the publish/- subscribe based Message Queuing Telemetry Transport (MQTT) protocol is widely popular. Hence, cyber security threats against the MQTT protocol are projected to rise at par with its increasing use by IoT manufacturers. In particular, the Internet exposed MQTT brokers are vulnerable to protocolbased Application Layer Denial of Service (DoS) attacks, which have been known to cause wide spread service disruptions in legacy systems. In this thesis, we propose Application Layer based DoS attacks that target the authentication and authorisation mechanism of the the MQTT protocol. In addition, we also propose an MQTT protocol attack detection framework based on machine learning. Through extensive experiments, we demonstrate the impact of authentication and authorisation DoS attacks on three opensource MQTT brokers. Based on the proposed DoS attack scenarios, an IoT-MQTT attack dataset was generated to evaluate the e_ectiveness of the proposed framework to detect these malicious attacks. The DoS attack evaluation results obtained indicate that such attacks can overwhelm the MQTT brokers resources even when legitimate access to it was denied and resources were restricted. The evaluations also indicate that the proposed DoS attack scenarios can signi_cantly increase the MQTT message delay, especially in QoS2 messages causing heavy tail latencies. In addition, the proposed MQTT features showed high attack detection accuracy compared to simply using TCP based features to detect MQTT based attacks. It was also observed that the protocol _eld size and length based features drastically reduced the false positive rates and hence, are suitable for detecting IoT based attacks

Comment générer des traces applicatives avec FIT IoT-LAB pour la science ouverte

International audienceL'essor récent de l'apprentissage automatique et l'intérêt grandissant porté à l'intelligence artificielle a généré une demande de données de plus en plus importante. Le mouvement de la science ouverte, et plus particulièrement les données ouvertes, apportent une réponse à cette demande en offrant à tous un accès et un usage libre à des jeux de données. Dans cet article, nous proposons une méthodologie pour générer des traces correspondant à différentes applications IoT. Pour cela, nous caractérisons les différents types de trafic, comme leur fréquence de communication et la taille des paquets échangés. Puis, nous simulons ces applications sur la plateforme FIT IoT-LAB pour générer les traces. Les paramètres des simulations, tels que le nombre de noeuds employés, sont choisis selon les caractéristiques de l'application simulée. Les traces ainsi générées sont enrichies de plusieurs données qui permettent de déduire des métriques utiles, telles que le taux de livraison des paquets et le délai de bout en bout. Nous partageons en accès ouvert les traces obtenues ainsi qu'une base de code pour générer, manipuler et analyser les données obtenues de FIT IoT-LAB

Energy-Aware Decentralised Medium Access Control for Wireless Sensor Networks

The success of future Internet-of-Things (IoT) based application deployments depends on the ability of wireless sensor platforms to sustain uninterrupted operation based on: (i) environmental energy harvesting and optimised coupling with the platform’s energy consumption when processing and transmitting/receiving data; (ii) spontaneous adaptation to changes in the local network topology without requiring central coordination. To address the first aspect, starting from practical deployments of a multi-transducer platform for photovoltaic and piezoelectric energy harvesting and the associated modelling and analysis, data-driven probability models are derived to facilitate the optimal coupling of energy production and consumption when processing and transmitting data. To address the second aspect (adaptability), the new concept of decentralised time-synchronised channel swapping (DT-SCS) is proposed – a novel protocol for the medium access control (MAC) layer of IEEE 802.15.4-based wireless sensor networks (WSNs). Simulation results reveal that DT-SCS comprises an excellent candidate for completely decentralised MAC layer coordination in WSNs by providing quick convergence to steady state, high bandwidth utilisation, high connectivity, robustness to interference and low energy consumption. Moreover, performance results via a Contiki-OS based deployment on TelosB motes reveal that DT-SCS comprises an excellent candidate for a decentralised multichannel MAC layer