Battery-less Internet of Things (IoT) devices are emerging because it will be environmentally and economically unsustainable to power billions of IoT devices with batteries. Battery-less devices harvest energy from their environment, but the energy supplied by many such sources varies considerably over time, and there is hence a need for buffering surplus energy. This is enabled by the Energy Storage and Voltage Control Unit (ESVU), and the state-of-the-art ESVUs are REACT and CapDYN. We observe that an ideal ESVU (i) wastes minimal energy (efficiency), (ii) enables the application System-on-Chip (SoC) quickly when energy becomes available (responsiveness), (iii) can support a large variety of SoCs (applicability), and (iv) requires few or cheap components and occupies minimal area and volume (overhead). Through detailed circuit-level simulations, we demonstrate that no existing ESVU ticks all the boxes. More specifically, we find that CapDYN and REACT can provide high efficiency and responsiveness, but they fall short in applicability and overhead, respectively, and we thus propose Coulombix to fill this gap. To understand how ESVU design affects performance at the system level, we conduct a case study in which we implement hardware prototypes of REACT and Coulombix and measure the throughput and response time of a diverse set of IoT benchmarks with a solar energy harvester across three seasons. The case study supports the insights of the simulation-based study, while also exposing interesting second-order effects, highlighting that end-to-end analysis is critical when evaluating ESVUs.

The number of Internet of Things (IoT) devices is increasing exponentially, and it is environmentally and economically unsustainable to power all these devices with batteries. The key alternative is energy harvesting, but battery-less IoT systems require extensive evaluation to demonstrate that they are sufficiently performant across the full range of expected operating conditions. IoT developers thus need an evaluation platform that (i) ensures that each evaluated application and configuration is exposed to exactly the same energy environment and events, and (ii) provides a detailed account of what the application spends the harvested energy on. We therefore developed the EStacker evaluation platform which (i) enables fair and repeatable evaluation, and (ii) generates energy stacks. Energy stacks break down the total energy consumption of an application across hardware components and application activities, thereby explaining what the application specifically uses energy on. We augment EStacker with the ST-SP optimization which, in our experiments, reduces evaluation time by 6.3× on average while retaining the temporal behavior of the battery-less IoT system (average throughput error of 7.7%) by proportionally scaling time and power. We demonstrate the utility of EStacker through two case studies. In the first case study, we use energy stack profiles to identify a performance problem that, once addressed, improves performance by 3.3×. The second case study focuses on ST-SP, and we use it to explore the design space required to dimension the harvester and energy storage sizes of a smart parking application in roughly one week (7.7 days). Without ST-SP, sweeping this design space would have taken well over one month (41.7 days).

Constructing an accurate representation model of phenomena with fewer measurements is a fundamental challenge in the Internet of Things. Leveraging sparse sensing policies to select the most informative measurements is a prominent technique for addressing resource constraints. However, designing such sensing policies requires significant domain knowledge and involves manually fine-tuned heuristics that are task-specific and often non-adaptive. In this work, we propose reducing manual-engineering efforts in designing sensing policies by using an automated approach based on deep reinforcement learning. Guided by an uncertainty-aware prediction model, the sensors learn sensing behaviors autonomously by optimizing an application goal formulated in the reward function based on the measured peaks-over-threshold. We apply the proposed approach in two use cases of monitoring air quality and indoor noise and show the adaptability and transferability of the learned policies. Compared to conventional periodic sensing methods, our results achieve, on average, an increased detection in periods of interest by 78.5% and 357.3% while reducing energy expenditure by 14.3% and 7.6% for air quality and noise monitoring, respectively. Additionally, the resulting representation models are more credible, as measured by various metrics of probabilistic modeling.

Energy-harvesting wireless sensors require energy management due to volatile energy sources. Existing energy managers lack adaptability to changing utility requirements or often rely on manually defined utility profiles. To address this, we study an autonomous energy manager that learns utility profiles dynamically, without the need for prior data. The new energy manager ensures that devices adapt to evolving utility needs, extending their operational capabilities in changing environments.

IoT applications with their resource-constrained sensor devices can benefit from adjusting their operations to the phenomena they sense and the environments they operate in, leading to the paradigm of self-adaptive, autonomous, or cognitive IoT. On the other side, current AI deployment platforms focus on the provision and reuse of machine learning models through containers that can be wired together to build new applications. The challenge is that composition mechanisms of the AI platforms, albeit effective due to their simplicity, are in fact too simplistic to support cognitive IoT applications, in which sensor devices also benefit from the machine learning results. Our objective is to perform a gap analysis between the requirements of cognitive IoT applications on the one side and the current functionalities of AI deployment platforms on the other side. In this work, we provide an overview of the paradigms in AI deployment platforms and the requirements of cognitive IoT applications. We study a use case for person counting in a skiing area through camera sensors, and how this use case benefits from letting the IoT sensors have access to operational knowledge in the form of visual attention models. We describe the implementation of the IoT application using an AI deployment platform, analyze its shortcomings, and necessary workarounds. From the use case, we identify and generalize four gaps that limit the usage of deployment platforms: the transparent management of multiple instances of components, a more seamless integration with IoT devices, explicit definition of data flow triggers, and the availability of templates for cognitive IoT architectures and reuse below the top-level.

Research on autonomous management for large-scale deployments of constrained devices is still a maturing field in the Internet of Things (IoT). Although much research has been conducted on how to achieve autonomous management in specific cases, there is a need for literature investigating which mechanisms can achieve such behavior in a generalized way. In this review, we present a comprehensive and structured study of the mechanisms for autonomous device management of constrained IoT devices in the light of management tasks, operational environment, network topology, resource constraints, scalability and management categories. Data extracted from 32 relevant cases are first organized and analyzed according to a synthesized taxonomy of observed adaptation mechanisms, and then combined with the state-of-the-art models of autonomous operations, identifying common patterns for autonomous management. Based on our findings we substantiate best practices for designing and implementing solutions around adaptation mechanisms. We then present a generalized model for autonomous device management that describes and explains the processes required for autonomous operation, unifying the insights from previous works as one cohesive archetype.

For sustainable Internet-of-Things (IoT) systems, the solar power prediction is an essential element to optimize performance, allowing devices to schedule energy-intensive tasks in periods with excess energy. In regions with volatile weather, this requires taking the weather forecast into account. The problem is how to provide such solar energy predictions with high accuracy for large-scale IoT systems with various devices in an autonomous way, without manual adaptation effort. We present a detailed study on machine-learning approaches for the prediction of solar power intake for large-scale IoT systems. We examine which machine learning models, feature sets, and sampling rates gain the best results for a medium-term forecasting horizon. We also explore an operational setting in which devices are deployed without prior data and machine learning models are retrained for each sensor continuously as a form of online learning. Our results show that prediction errors can be reduced by 20 % compared to the state of the art, despite strong weather volatility.

Cities worldwide aim to reduce their greenhouse gas emissions and improve air quality for their citizens. Therefore, there is a need to implement smart city approaches to monitor, model, and understand local emissions to better guide these actions. We present our approach that deploys a number of low-cost sensors through a wireless Internet of Things (IoT) backbone and is thus capable of collecting high-granular data. Based on a flexible architecture, we built an ecosystem of data management and data analytics including processing, integration, analysis, and visualization as well as decision-support systems for cities to better understand their emissions. Our prototype system has so far been tested in two Scandinavian cities. We present this system and demonstrate how to collect, integrate, analyze, and visualize real-time air quality data.

The validation and verification of reliable systems is a difficult and complex task, mainly for two reasons: First, it is difficult to precisely state which formal properties a system needs to fulfil to be of high quality. Second, it is complex to automatically verify such properties, due to the size of the analysis state space which grows exponentially with the number of components. We tackle these problems by a tool-supported method which embeds application functionality in building blocks that use UML activities to describe their internal behaviour. To describe their externally visible behaviour, we use a combination of complementary interface contracts, so-called ESMs and EESMs. In this paper, we present an extension of the interface contracts, External Reliability Contracts (ERCs), that capture failure behaviour. This separation of different behavioural aspects in separate descriptions facilitates a two-step analysis, in which the first step is completely automated and the second step is facilitated by an automatic translation of the models to the input syntax of the model checker TLC. Further, the cascade of contracts is used to separate the work of domain and reliability experts. The concepts are proposed with the background of a real industry case, and we demonstrate how the use of interface contracts leads to significantly smaller state spaces in the analysis.

We describe a method based on UML activities for the unified specification of collaborative service behavior and local user interfaces. The method enables a model-driven development process, which effectively combines the need to express service collaborations involving several components with the need to provide detailed operations for user interfaces. Our service models use activities as the primary building blocks that encapsulate self-contained functionalities. We show, how a complete distributed system can be decomposed into such building blocks, how this decomposition leads to a separation of user interface concerns from service collaboration concerns, and how they may be combined with an event-driven composition mechanism based on activity parameter nodes. We also demonstrate how different UI frameworks can be supported, and illustrate the method with a case study of a situated collaborative learning service.

We describe a complete and largely automated method for the development of systems from Web services, which comprises the encapsulation of services, as well as their composition, verification and subsequent implementation in a model-driven manner. The paper follows the steps of the method: In a first phase, we import WSDL descriptions automatically as UML~2.x activities and provide them as building blocks, with some optional, manual adaptations. In a second phase, these building blocks can be used to compose an application that orchestrates Web services. The building blocks have behavioral contracts that enable automated, incremental verification based on compositional model checking. We demonstrate the approach by a subscription-based service to receive SMS messages.