1,721,003 research outputs found
WoT on The Extreme Edge (WoTTEE): Enabling W3C Web of Things for Micro-controllers
Full text linkEdge computing has emerged as a viable approach to minimize the latency of time-critical Internet of Things (IoT) applications. The new frontier of research is to offload tasks directly on micro-controllers, i.e. on the same device generating the sensory data. However, this approach requires to cope with to the limited computational capabilities of the devices and consequently to adapt existing techniques and tools to the extreme edge. In this paper, we contribute to this research direction by investigating how to enable W3C Web of Things (WoT) capabilities on micro-controllers, for increased performance and interoperability purposes. Given the excessive complexity of the original proposal, we propose a revised architecture of the Web Thing (WT) that can fit the limited resources of constrained devices while maintaining the compatibility with the WoT standard. Then, we present the WoTTEE framework, a software suite that supports and facilitates the deployment, installation and monitoring of edge-oriented WTs. Finally, we validate the operations of WoTTEE in a small testbed and demonstrate the capability to support adaptive IoT systems where micro-controllers are able to dynamically switch among different IoT network protocols thanks to the Thing Description (TD) mechanism of the W3C WoT
Design and Development of a Mobile Dapp for Mobile Crowdsensing over EVM-enabled Blockchains
Full text linkMobile crowdsensing (MCS) is a valuable approach for data collection via personal devices, however, it faces challenges in data security and reward to end users. Blockchain is considered a viable addition to MCS, though few real integrations of this kind are deployed. In this paper, we explore the integration of blockchain into MCS, highlighting benefits and architectural adaptation challenges. We then present a novel mobile distributed application (MDapp) that serves crowdsourcers, workers, and verifiers in managing campaigns, data contribution and validation, as well as in the reward process. We also provide a quantitative analysis across different blockchains to highlight the feasibility and economic considerations of this integration
: Location-aware Publish-subscribe Communications for the Internet of Things
Full text linkNowadays, several Internet of Things (IoT) deployments use publish-subscribe paradigms to disseminate IoT data to a pool of
interested consumers. At the moment, the most widespread standard for such scenarios is MQTT. We also register an increasing
interest in IoT-enabled Location-Based Services, where data must be disseminated over a target area and its spatial relevance
as well as the current positions of the consumers must be taken into account. Unfortunately, the MQTT protocol does not
support location-awareness, hence it may result in notifying consumers that are geographically far from the data source, causing
increased network overhead and poor Quality of Service (QoS). We address the issue by proposing LA-MQTT, an extension to
standard MQTT supporting spatial-aware publish-subscribe communications on IoT scenarios. LA-MQTT is broker-agnostic
and fully backward compatible with standard MQTT. As monitoring the position of subscribers over time may cause privacy
concerns, LA-MQTT carefully supports location privacy preservation, for which the optimal trade-off with the QoS of the spatial
notifications is addressed via a learning-based algorithm. We demonstrate the effectiveness of LA-MQTT by experimentally
evaluating its features via large-scale hybrid simulations, including real and virtual components. Finally, we provide a Proof of
Concept real implementation of a LA-MQTT scenari
Supporting Resilient, Ethical, and Verifiable Anonymous Identities Through Blockchains
Full text linkIn recent years, anonymity on the internet has come under intense scrutiny for enabling criminal behaviors like cyberbullying, disinformation, child exploitation, and illicit financial activities. Nevertheless, strong advocates highlight its importance as a protective space for legitimate and ethical actions that individuals may prefer to keep separate from their real-world identities. This paper presents a protocol for authenticated anonymity, enabling anonymous usage that remains unlinkable to real identities unless criminal activity is detected. Blockchain offers a robust and secure framework to manage these needs. While existing solutions — e.g., self-sovereign identities — grant users full control over their disclosure, they lack proper accountability. To address this limitation, the proposed protocol employs a blockchain-driven mechanism that supports anonymous yet verifiable identities. De-anonymization is achieved exclusively through multi-party consensus on the blockchain, t riggered by explicit and non-repudiable requests. We provide the formal mathematical model of the protocol and offer some evaluations of its robustness and fault tolerance, even under large-scale identity management scenarios
An Over the Air Software Update System for IoT Microcontrollers based on WebAssembly
No full textIn recent years, the proliferation of Internet of Things (IoT) applications has been remarkable, characterized by integrating tasks across multiple devices, forming the so-called edge-cloud continuum. The ability to reconfigure the software behavior over all nodes of the continuum has become crucial to support applications with varying demands. However, located at the extreme edge of IoT deployments, IoT microcontrollers often have static behaviors. Although Over the Air (OTA) methodologies are widely supported by most devices, the reconfiguration process may involve potentially inefficient and hazardous updates of the entire firmware. In response, this paper proposes an OTA software update system for IoT microcontrollers leveraging portable WebAssembly (WASM) code. By separating the application logic from the rest of the firmware, our system ensures heterogeneity, safety, efficiency, and reliability. Furthermore, we propose an update procedure based on different network protocols (MQTT and HTTP) to facilitate dealing with groups of IoT microcontrollers in a single action. We implemented a Configuration and Administration Toolkit (CAT) to evaluate our proposal and conducted a comprehensive performance analysis using a small-scale IoT testbed. Our findings demonstrate that while the WASM logic updates notably outperform those of the entire firmware, achieving a 30x delay reduction, they incur a performance overhead that is lower than MicroPython when compared to native C++ development
CACHE-IT: A distributed architecture for proactive edge caching in heterogeneous IoT scenarios
Full text linkThe Cloud-to-Things (C2T) continuum combines the proximity of edge infrastructure to the devices with cloud resources to optimize data processing and response time in the Internet of Things (IoT). Proactive edge caching is a potential solution for meeting latency constraints in C2T environments, enabling efficient data processing and storage while reducing redundant computation and cost. However, while 5G/6G infrastructural aspects and caching strategies are extensively studied, no frameworks facilitate the design and deployment of caching strategies or address IoT’s unique requirements. This paper proposes CACHE-IT, a proactive edge caching framework that decouples the caching strategy algorithm from the underlying architecture, enabling customization based on application-specific requirements. Through extensive simulations, we analyzed the impact of different configurations on system metrics and verified that the CACHE-IT positively impacts the system latency and hit rate. By implementing a scenario-specific caching strategy, we illustrate the CACHE-IT deployment in a real-world Structure Health Monitoring (SHM) system. The evaluation demonstrates that CACHE-IT impacts positively in terms of latency, hit rate, and the number of requests sent to data providers
Designing a Hybrid Push-Pull Architecture for Mobile Crowdsensing using the Web of Things
Full text linkMobile crowdsensing (MCS) is an emerging paradigm that leverages the pervasive presence of mobile devices to collect and analyze data from the environment. However, the choice of a push- or pull-based architecture for MCS can result in a loss of flexibility and limitations for the creators of the campaigns (crowdsourcers). To address this issue, we propose a hybrid push-pull architecture for MCS campaigns that leverages the W3C Web of Things (WoT) to standardize the interfaces and interactions of devices through well-consolidated Web technologies. Furthermore, we present the design and implementation of a WoT-enabled Android application for MCS. We evaluate our proposal through simulations in a vehicular scenario based on a real dataset, showing that the hybrid architecture provides greater flexibility to crowdsourcers, supporting simultaneously the push and pull paradigms
Temperature Compensation in Vibration-Based Structural Health Monitoring Using Neural Network Regression
Full text linkVibration-based structural health monitoring (SHM) systems continuously estimate modal parameters to detect structural anomalies. The modal data corresponding to a healthy state are stored in a database during a training period, forming a baseline for comparison. However, variations in modal frequencies due to environmental and operational factors can lead to larger false positive rates and decrease the sensitivity of system to small damages, reducing the probability of damage detection. To mitigate these challenges, temperature compensation techniques are commonly employed to reduce variations in recorded modal data. In this paper, we propose a temperature compensation technique using neural network regression models. Unlike commonly used multivariate linear regression (MVLR), neural networks can capture the nonlinear relationship between temperature and modal frequencies effectively. The results of the numerical simulation in the present work demonstrate the superiority of the neural network-based compensation over the MVLR approach
Relativistic Digital Twin: Bringing the IoT to the Future
Full text linkComplex IoT ecosystems often require the usage of Digital Twins (DTs) of
their physical assets in order to perform predictive analytics and simulate
what-if scenarios. DTs are able to replicate IoT devices and adapt over time to
their behavioral changes. However, DTs in IoT are typically tailored to a
specific use case, without the possibility to seamlessly adapt to different
scenarios. Further, the fragmentation of IoT poses additional challenges on how
to deploy DTs in heterogeneous scenarios characterized by the usage of multiple
data formats and IoT network protocols. In this paper, we propose the
Relativistic Digital Twin (RDT) framework, through which we automatically
generate general-purpose DTs of IoT entities and tune their behavioral models
over time by constantly observing their real counterparts. The framework relies
on the object representation via the Web of Things (WoT), to offer a
standardized interface to each of the IoT devices as well as to their DTs. To
this purpose, we extended the W3C WoT standard in order to encompass the
concept of behavioral model and define it in the Thing Description (TD) through
a new vocabulary. Finally, we evaluated the RDT framework over two disjoint use
cases to assess its correctness and learning performance, i.e., the DT of a
simulated smart home scenario with the capability of forecasting the indoor
temperature, and the DT of a real-world drone with the capability of
forecasting its trajectory in an outdoor scenario. Experiments show that the
generated DT can estimate the behavior of its real counterpart after an
observation stage, regardless of the considered scenario.Comment: 18 pages, 10 figures, 4 tables, 6 listing
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