Jul 2026· Annual International Computer Software and Applications Conference· pp. 1333-1342· 0 citations· 62 references
Abstract
Internet of Things (IoT) and edge systems induce complex, heterogeneous, and distributed environments where interconnected physical devices and localized computing resources exhibit intricate behaviors. Specifying behavioral requirements in such systems is inherently challenging. Temporal logic, notably Linear Temporal Logic (LTL), is widely adopted for their formal specification, serving as a critical precursor to, e.g., verification or synthesis. This paper systematically investigates scientific literature spanning the past decade. We collect requirements and their corresponding LTL properties, analyzing trends in LTL specification within edge/IoT systems over time. Subsequently, we automatically extract defining features of these requirements and properties, including formula complexity and natural-language characteristics, and analyze their correlations. The insights we derive offer a valuable resource for researchers and practitioners engaged in reasoning within edge/IoT systems. Beyond serving as a reference corpus, the dataset can aid evaluation and validation of other approaches developed by the formal methods community, or serve as training data for machine learning techniques targeting the formalization process.
The study explores how decision continuity improves resilience, synchronization tolerance, predictive operations, and operational governance in real-time IoT systems, and further examines how distributed architectures can balance rapid edge responsiveness with deeper cloud intelligence without relying on rigid synchronization or centralized decision authority.
Ilker Kanatli· International Journal of Res...· 0 citations
Internet of Medical Things (IoMT) systems involve many interconnected devices that continuously collect, process, and share sensitive health data, creating significant privacy risks. Ensuring that these systems comply with GDPR is particularly challenging because legal requirements are complex, often timedependent, and difficult to validate at the level of individual data operations. This paper addresses this challenge by proposing a new formal verification approach that models the main IoMT operations, including data collection, transfer, storage, processing, and automated decision-making, as a GDPR-aware timed automaton. The proposed approach encodes legal requirements as formal rules and verifies compliance using a model checker through safety, liveness, and reachability properties. A remote cardiac monitoring scenario is presented to demonstrate typical data flows and interactions among smart medical devices, with business process models to represent the system. Experimental results show that the approach remains efficient as system complexity increases, providing a practical solution for developing IoMT applications that integrate privacy-by-design principles from the outset.
Masoud Barati· International Conference on...· 0 citations
IoT-enhanced business processes are characterized by high complexity due to heterogeneous actors, varying levels of autonomy among participating systems, continuously evolving execution contexts spanning the digital and physical worlds, and continuous event streams. In such settings, process behavior partially emerges only at runtime through complex interactions involving humans, IoT devices, physical objects, software systems, agents, and services. This complexity introduces partial observability, uncertainty, and runtime dynamics that are difficult to anticipate and that challenge traditional business process management (BPM) assumptions and systems. We discuss these challenges from three perspectives, addressing 1) uncertainty representation, 2) operationalization of IoT-enhanced processes, and 3) runtime management of emergent behavior. Based on a motivating scenario and an analysis of the state of the art, we identify open research gaps and outline short-, medium-, and long-term recommendations to shape a research agenda on emergent behavior in IoT-enhanced business processes.
M. Pegoraro, Sara Pettinari, Ivan Compagnucci et al.· 0 citations
The exponential growth of the Internet of Things (IoT) has generated massive data streams traditionally processed by centralized cloud architectures, which increasingly face latency, bandwidth, and privacy limitations. Shifting artificial intelligence to resource-constrained edge nodes, known as TinyML, offers a robust decentralized alternative, though it introduces severe memory, compute, and energy bottlenecks. To map this transition, a systematic literature review was conducted following PRISMA guidelines, analyzing peer-reviewed studies published between 2021 and 2026 across major databases. The analysis identifies primary architectural paradigms and evaluates the efficacy of state-of-the-art model compression techniques, such as quantization, pruning, and knowledge distillation. Furthermore, the findings reveal that hardware–software co-design and custom neural accelerators are crucial for overcoming operational bottlenecks, while also highlighting persistent security and privacy challenges in on-device learning. Ultimately, while deploying complex models on microcontrollers is increasingly viable, achieving optimal performance demands holistic optimization strategies. This review synthesizes current research gaps and provides a strategic roadmap to guide future interdisciplinary efforts toward resilient, energy-efficient, and secure next-generation intelligent edge systems.
Marco Fiore, Francesca Lanera· Electronics· 1 citation
This paper presents CRAFTER, an automated framework for designing and deploying self-adaptive IoT systems using Causal Reinforcement Learning (CRL). As IoT devices increasingly populate pervasive computing spaces, smart environments are enabled with advanced monitoring and interactive services. The dynamic nature of these environments, such as fluctuating workloads and evolving application demands, poses significant challenges in maintaining consistent Quality of Service (QoS) levels of IoT applications. While existing self-adaptation techniques offer adaptive capabilities, they are often designed to deal with specific application domains, hindering the design of self-adaptive solutions that can be re-used across multiple IoT verticals. In addition, there is a lack of automated pipelines that act on identifying key performance drivers to take effective adaptation decisions. CRAFTER addresses these issues by using Causality as a formal framework for performance analysis of IoT systems. CRAFTER generates causal graphs to uncover dependencies among system components and guide adaptation decisions based on cause–effect relationships. Then, adaptation agents can leverage this knowledge to take more effective adaptation decisions in dynamic situations. Our experimental evaluation demonstrates how CRAFTER enables deriving causal graphs spanning diverse IoT use cases. Furthermore, we showcase how CRAFTER improves self-adaptation performance by 25% compared to state-of-the-art Reinforcement Learning-based approaches.
Houssam Hajj Hassan, A. Kattepur, Denis Conan et al.· SEAMS@ICSE· 0 citations
The rapid expansion of the Internet of Things (IoT) has resulted in massive volumes of data being generated by interconnected devices across various domains. Traditional cloud-centric architectures often struggle with issues such as high latency, bandwidth constraints, and data privacy risks when processing this data. Edge computing has emerged as an effective solution by enabling data processing closer to the data source, thereby improving response time and reducing network dependency. In recent years, Generative Artificial Intelligence (GenAI) has gained significant attention for its ability to generate insights, predictions, and adaptive responses from complex and dynamic datasets. This paper examines the integration of Generative AI with IoT and edge computing to enhance intelligent edge systems capable of real-time analytics and autonomous decision-making. It explores architectural frameworks, potential applications in areas such as smart cities, healthcare, industrial automation, and autonomous systems, as well as the advantages of improved efficiency, scalability, and privacy preservation. Additionally, the paper discusses the technical challenges associated with deploying generative models at the edge, including resource constraints, model optimization, security, and data management. Finally, it outlines future research directions aimed at developing scalable, secure, and energy-efficient GenAI-enabled edge computing ecosystems.
Mr. L. S. Shendge, Mrs. S. N. Patel, Ms. D. V. Sharma· International Journal of Lat...· 0 citations
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