1,721,013 research outputs found
Trust-related Attacks and their Detection: a Trust Management Model for the Social IoT
Full text linkThe integration of social networking concepts into the Internet of Things (IoT) has led to the so called Social Internet of Things paradigm, according to which the objects are capable of establishing social relationships in an autonomous way with respect to their owners. Within this scenario“, things" interact opportunistically with their peers to seek needed services. However, attacks and malfunctions in the IoT can outweigh any of its benefits if not handled adequately. In this paper, we focus on the possible types of trust attacks that can affect the IoT and propose a trust management model able to overcome all the analyzed attacks. Simulations show how the proposed model can effectively isolate almost any malicious nodes in the network at the expense of an increase in the number of transactions needed for the model to converge
An Evaluation of Service Discovery Mechanisms for a Network of Social Digital Twins
No full textDue to the continuous expansion of the Internet of Things (IoT) and its related applications, service discovery nowadays represents a crucial mechanism that enables devices to look efficiently for the desired services. In this regard, a new paradigm, namely Social IoT, has been recently introduced according to which the devices are capable of establishing social relationships in an autonomous way with respect to the rules set by their owners. Within this scenario, 'things' interact opportunistically with their peers to provide composite services for the benefit of human beings. In this sense, this paper proposes an exhaustive analysis of the main parameters needed to implement service discovery mechanisms for the Social IoT and studies their relative importance based on a dataset of real objects. On the basis of the parameters' importance, then an efficient service discovery algorithm is proposed, and experiment evaluations are conducted to show its performance in comparison to traditional approaches. Final simulations prove that the proposed mechanism can discover desired services in a fast and autonomous manner
Crowdsensing and Trusted Digital Twins for Environmental Noise Monitoring
No full textThis paper introduces an innovative Mobile Crowd Sensing (MCS) system and the related architecture to aggregate data with the goal of monitoring the noise within an indoor environment. Two of the most common problems with MCS systems are related to measurement localization and their trustworthiness. While GPS data is commonly used for outdoor MCS tasks, indoor environments present challenges for location-based measurements. In this sense, the proposed system makes use of Bluetooth beaconing to identify the rooms, while students’ smartphones act as sensors for noise evaluation. Moreover, to ensure data reliability and weed out malicious contributions by students, a trust management system is implemented, isolating users with anomalous measurements without completely excluding them from participation. The proposed solution revolves around the concept of digital twins (DTs), where physical objects and individuals are represented virtually. The key contributions of the research include the development of a crowdsensing system for the monitoring of environmental noise through trusted digital twins and a performance analysis conducted in a real-world scenario involving three university offices
A Cognitive Social IoT Approach for Smart Energy Management in a Real Environment
Full text linkEnergy usage inside buildings is a critical problem, especially considering high loads such as Heating, Ventilation and Air Conditioning (HVAC) systems: around 50% of the buildings’ energy demand resides in HVAC usage which causes a significant waste of energy resources due to improper uses. Usage awareness and efficient management have the potential to reduce related costs. However, strict saving policies may contrast with users’ comfort. In this sense, this paper proposes a multi-user multi-room smart energy management approach where a trade-off between the energy cost and the users’ thermal comfort is achieved. The proposed user-centric approach takes advantage of the novel paradigm of the Social Internet of Things to leverage a social consciousness and allow automated interactions between objects. Accordingly, the system automatically obtains the thermal profiles of both rooms and users. All these profiles are continuously updated based on the system experience and are then analysed through an optimization model to drive the selection of the most appropriate working times for HVACs. Experimental results in a real environment demonstrated the cognitive behaviour of the system which can adapt to users’ needs and ensure an acceptable comfort level while at the same time reducing energy costs compared to traditional usage
IoT for the users: Thermal comfort and cost saving
Full text linkInterconnection of objects via the Internet of Things (IoT) is playing a key role in tackling problems related to energy consumption, which affect environment and sustainability. In particular over 30% of the global energy consumption resides in Heating, Ventilation and Air Conditioning (HVAC) usage inside buildings. Usage awareness and efficient management of HVAC have the potential to significantly reduce related costs. Nevertheless, strict saving policies may contrast with users' comfort: people will accept changes in their habits only if these changes do not affect their comfort. This paper proposes a smart system consisting of 5 HVAC distributed over 5 rooms and a solar photovoltaic farm. The goal of the system is to propose a user-centric approach, which is able to find the most appropriate working times for the 5 HVAC systems so to have a trade-off between the thermal comfort for all the users in the rooms and its related cost, taking into account information inferred from the context, such as room occupancy and external temperature. The system is based on the Social Internet of Things (SIoT) paradigm to augment real world objects with a virtual counterpart that leverages social consciousness to interact with other objects. Experimental results show how the implemented system is able to learn users' habits and to allow significant financial savings without sacrificing user comfort
How to exploit the Social Internet of Things: Query Generation Model and Device Profiles’ Dataset
Full text linkThe future Internet of Things (IoT) will be characterized by an increasing number of object-to-object interactions for the implementation of distributed applications running in smart environments. The Social IoT (SIoT) is one of the possible paradigms that is proposed to make the objects’ interactions easier by facilitating the search of services and the management of objects’ trustworthiness. In this scenario, we address the issue of modeling the queries that are generated by the objects when fulfilling applications’ requests that could be provided by any of the peers in the SIoT. To this, the defined model takes into account the objects’ major features in terms of typology and associated functionalities, and the characteristics of the applications. We have then generated a dataset, by extracting objects’ information and positions from the city of Santander in Spain. We have classified all the available devices according to the FIWARE Data Models, so as to enable the portability of the dataset among different platforms. The dataset and the proposed query generation model are made available to the research community to study the navigability of the SIoT network, with an application also to other IoT networks. Experimental analyses have also been conducted, which give some key insights on the impact of the query model parameters on the average number of hops needed for each search
Design of an AI-driven Architecture with Cobots for Digital Transformation to Enhance Quality Control in the Food Industry
No full textIn recent years, the rapid evolution of smart technologies has spurred enterprises to undergo digital transformations, revolutionizing their business processes and operations. This shift, known as Digital Transformation, has permeated diverse sectors, particularly impacting production systems. Notably, Artificial Intelligence (AI) and robotic automation have emerged as pivotal drivers in this transformation, promising enhanced efficiency and innovation in industrial digitization. This paper presents a novel architecture designed to facilitate digital transformation within enterprises, harnessing the capabilities of advanced collaborative robots (cobots) and cutting-edge image segmentation techniques. Focused on a practical scenario within a food production environment, our proposed architecture aims to seamlessly integrate a cobot and a camera in an automatic system for efficient cardboard disposal. Specifically, our attention is drawn to the challenge of differentiating sections of food packaging suitable for disposal from those contaminated with stains or organic residues, a task with significant implications for waste management efficiency. By leveraging a cloud-based architecture and deploying AI algorithms for image segmentation, localization, and robot guidance, our study showcases the tangible benefits and practical applicability of these methodologies in real-world settings. This research not only highlights the potential of AI-driven solutions in addressing specific industrial challenges but also underscores the broader impact of digital transformation on optimizing operational processes and driving innovation across sectors
Composing Digital Twins for Internet of Everything Applications: A User-Centric Perspective
No full textDriven by the advancements in Artificial Intelligence (AI) and cutting-edge communication technologies, the interest in Digital Twins (DTs) has grown in recent years. Today, DTs are investigated in a variety of Internet of Everything (IoE) use cases, thanks to their capability to optimize and predict the behaviour of their physical counterpart. Several application-driven vertical architectures have been proposed for DTs, but a general-purpose modeling approach is lacking. Although the research community is promoting holistic DT representations, practical software deployments of complex physical entities will be likely achieved through multiple interconnected DTs that together provide composite advanced services. In this paper, we present a user-centric architecture to deploy DTs in a flexible, dynamic, and reusable fashion, according to the requirements of the user applications. In our vision, complex DTs can be composed by integrating digital replicas (DRs) of physical objects with an AI-driven Service Layer. DRs are implemented as representation models collecting real-time data from the physical counterpart and therefore enabling vertical intra-twin communications. In parallel, a Service Layer allows to share knowledge and services among DTs through horizontal inter-twin communications, i.e., between DTs in the virtual space, which can be exploited to anticipate users’ needs. A toy example is presented to showcase the conceived architecture
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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