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Blockchain (BC) is increasingly applied to the Internet of Things (IoT) domains to realize decentralized IoT environments where reliable and anonymous activities can be carried out. BCs exploit a distributed ledger, which requires to be continuously synchronized and to maintain a high level of consistency. To allow BCs applied to the IoT of maintaining high levels of reliability in the network and resilience against malicious or fraudulent nodes, in the recent past has been proposed a Trust-based Optimum Neighbor Selection (TONS) algorithm able to find the Minimum Spanning Tree of the agent network with the purpose of selecting those agents which optimize communication and trustworthiness. In this paper, a new version of TONS, named TONS2, has been conceived for agent-based IoT environments to improve the overall efficiency of the TONS algorithm. The results ot the test we performed have confirmed that in terms of consumed resources TONS2 is appreciably more efficient than TONS.

Edge Computing is becoming a promising computing paradigm that addresses the limitations of cloud computing concerning latency, autonomy, and costs. To facilitate more intelligent applications made possible by the Edge Computing paradigm, it is essential to integrate intelligence and adaptability into devices located at the network’s edge. The paper explores the potential integration of Situation Awareness (SA) capabilities into the Cloud-Edge continuum. This integration aims to empower smarter applications while effectively managing challenges related to low latency, high autonomy, and cost-effective solutions. Within our illustrative example in healthcare, we showcase how the proposed SA cloud-edge continuum architecture enables efficient data processing and decision-making.

The Internet of Things (IoT) promises to change many aspects of our daily lives thanks to the opportunity of interconnecting a massive numbers of smart objects with increasing computational, storage, communication, and power capabilities, in such a way making “smart” and “interactive” most of the world around us. In other words, smart objects and humans will be involved together in pervasive, proactive, and collaborative activities to orchestrate and execute increasingly complex and sophisticated tasks. In such a scenario, thanks also to a greater ecological awareness of people, a promising field of application for IoT technology is the monitoring of natural habitats in automatic or semiautomatic mode. A potentially effective and efficient solution is to form composite teams joining human operators and IoT devices. In such teams, not only the kind of team members will be different, i.e., humans and IoT devices, but also the IoT devices will be heterogeneous among them in terms of characteristics and performance. However, a basic requirement for a good team is the existence of high levels of mutual trust among its members. In other words, for the formation of a good team, it becomes of primary relevance to know and adequately represent the trustworthiness of the individual team members. To this end, our contribution can be summarized as (i) introducing a trust measure that takes into account both the reputation of devices and the accuracy of their measures; (ii) designing a framework that based on the proposed trust measure forms temporary teams for environmental ecosystems made of humans and IoT devices; and (iii) testing the proposed framework by simulating a collaborative environmental monitoring activity. The simulation results confirmed the advantages of the proposed approach in terms of performance and appreciation of the composite temporary teams that have been formed in this way.

The blockchain paradigm is currently modifying all the major Internet of Things (IoT) application domains, giving the opportunity of constructing decentralized environments in which trustful and anonymous activities can be efficiently performed. Blockchain proposes an approach which assures data saved on a distributed ledger would be continuously synchronized and in such a way the ledger will remain consistent. The distributed ledger has the responsibility to maintain a higher level of consistency. Given a connected network, the Optimum Neighbor Selection (ONS) of paths can be obtained by finding the Minimum Spanning Tree of the network. However, none of past approaches proposed in the literature to construct ONS considered the problem of having nodes with low levels of reliability in the network and even malicious or fraudulent nodes, that is a situation very common in an IoT environment. In this paper, we propose an optimized blockchain ONS algorithm, called Trust-based ONS (TONS), which allows the miners to communicate with a globally-optimized selection of neighbors. The algorithm can also guarantee that these nodes are the most reliable miners. We also describe an experimental simulation campaign we have performed to evaluate the effectiveness and the efficiency of our approach.

The introduction of the IoT technology and its pervasive penetration in our daily life implies that IoT smart objects can often participate in social events, and in this case their paradigm of interaction is commonly denoted as social IoT (SIoT). To make reliable transactions into a SIoT scenario, in this article, we introduce a multi-agent SIoT architecture, which integrates a reputation system based on a clustering of the SOs. In our framework, when a SO looks for a resource and detects a reliable partner having that resource, then the two SOs can interact to make a transaction, and at the end of the transaction each of them provides a feedback about the partner to the local reputation system of the edge server, to update the SOs' reputation scores. Moreover, each edge server sends these reputation scores to the cloud, which updates the associated reputation values of the SOs, deriving from experiences coming from all the edge domains. This architecture provides a given object, moving from an edge domain to another one, with the possibility to have an updated value of its reputation, represented by the value stored in the cloud. We have validated our approach by a campaign of simulations, whose results seem particularly promising.

In this work we introduce a clustering reputation-based approach for edge-based IoT (Internet of Things) environments where it’s possible to process and save data on edge servers. In particular, once in an edge domain two Smart IoT Objects (SOs) exchanged a service, supported by their associated agents, they will release a feedback about their respective counterparts to the edge server managing their current domain to update the counterparts’ reputation score. However, even though in the proposed systems the reputation is managed and hosted in the Edge, the SOs’ reputation scores are also periodically saved on the Cloud. In this way, when a SO moves itself from an edge domain to another edge domain its reputation score will be available on the Cloud. Moreover, each edge server provides to cluster its managed SOs on the basis of their reputation scores to strengthen the competitiveness of SOs into the domain. This paper describes the proposed distributed clustering reputation-based framework for heterogeneous SOs and its performance in a simulated scenario.

More data are generated through interaction between cyber space, physical space, and social space thanks to mobile network technology, giving birth to the so-called cyber–physical social intelligent ecosystem (C&P-SIE). This survey studies the development of physical social intelligence. First, it classifies and discusses the behavior modeling, learning, and adaptation applications of C&P-SIE from intelligent transportation, healthcare, public service, economy, and social networking. Then, it prospects the application of behavior modeling in the C&P-SIE from the perspectives of information security, data-driven techniques, and modeling learning under cooperative artificial intelligence technologies. The research provides a theoretical basis and new opportunities for the digital and intelligent development of smart cities and social systems. IEEE, Export Date: 13 April 2023; Article

The “Internet of Things” (IoT) provide humans and smart objects with attractive services, based on the advanced features of the IoT devices, like high sensing, real-time acting and reasoning. In our previous research we have highlighted that these features can be improved by promoting cooperation between smart objects, and we introduced the association between Multi-Agent Systems and IoT devices. In that context, we focused on the issue of accurately choosing the best partners for cooperation, in a scenario composed by several federations. We proposed a reputation model and we have shown that the model leads to detect agents having unreliable or misleading behaviors and that the model itself can be profitably used to form groups of agents that mutually cooperate for improving the effectiveness of their tasks. In this further contribution, we focus on the important issue of the group formation, by arguing that in practical IoT situations it is necessary to improve the group formation strategy to provide it with greater adaptability. To this end we introduce – in a particular IoT context described in this work – a two-phase group formation algorithm to support the reputation model. Experimental results prove that the adoption of the group formation algorithm, along with the proposed reputation model provides a few benefits to the whole IoT ecosystem.

In competitive Multi-Agent Systems (MASs) for IoT, an important task is to form friendships and groups for mutual cooperation. The existing proposals try to improve the profit of individual agent or group but, on the other hand, the most aggressive agents may take advantages from these approaches by means of malicious behaviors. In this work we model a MAS framework of non cooperative games by taking into account i) the reliability of agents relationships through a trust model and ii) the community ability to furnish a good environment to its members through its social capital. Thus, we designed an algorithm that maximizes the social capital. We emphasize two main findings: i) the result is a Nash equilibrium and ii) the agents are awarded only if they behave correctly.

In a massive IoT systems, large amount of data are collected and stored in clouds, edge devices, and terminals, but the data are mostly isolated. For many new demands of various intelligent applications, self-organized collaborated learning on those data to achieve group decisions has been a new trend. However, in order to reach the goal of group decisions, trust problems on data fusion and model fusion should be solved since the participants may not be trusted. We propose a consistent and trust fusion method with the consortium chain to reach a consensus, and complete the self-organized trusted decentralized collaborated learning. In each consensus process, consensus candidates check others’ trust levels to ensure that they tends to fuse consensus with users with high trust, where the trust levels are evaluated by scores according to their historical behaviors in the past consensus process and stored in the public ledger of blockchain. A trust rewards and punishments method is designed to realize trust incentive consensus, the candidates with higher trust levels have more rights and reputation in the consensus. Simulation results and security analysis show that the method can effectively defend malicious users and data, improve the trust perception performance of the whole federated learning network, and make the federated learning more trusted and stable.

Smart Workshops are experiencing the need of a mobile intelligence for mining both learning patterns and knowledge from the wide sea of data generated by both mobile users and mobile technologies. Indeed, mobile intelligence would represent the ideal substratum for providing "agentified" robots with a plethora of advanced capabilities (e.g., visual recognition, fault detection, self-recovery) and, hence, with high-level functionalities, like production line control, asset movement, connectivity restore. Besides the operational plane, however, mobile intelligence can be successfully exploited also in organizational tasks, like the formation of temporary, ad-hoc teams for accomplishing a given target. The complexity of some industrial operations, indeed, often demands the involvement of several, heterogeneous group of robots and the adequate representation of the reciprocal trustworthiness represents a key pre-requisite. It holds particularly for the Automated Guided Vehicles (AGVs) which are increasingly involved in collaborative activities aimed to optimise storage, picking, and transport functions in a wide variety of workshop areas. Therefore, in this paper we define a trustworthiness model for agentified AGVs based on the mix of their reputation and reliability and we present an agent-based framework implementing the related team formation strategy. The improvements obtained in terms of effectiveness and efficiency from the AGV team are observed and measured through a simulation activity, in which realistic settings for an industrial applications have been considered.

In this paper, we are interested in answering the following research question: Is it possible to form effective groups in virtual communities by exploiting trust information without significant overhead, similarly to real user communities? In order to answer this question, instead of adopting the largely used approach of exploiting the opinions provided by all the users of the community (called global reputation), we propose to use a particular form of reputation, called local reputation. We also propose an algorithm for group formation able to implement the proposed procedure to form effective groups in virtual communities. Another interesting question is how to measure the effectiveness of groups in virtual communities. To this aim we introduce the Gk index in a measure of the effectiveness of the group formation. We tested our algorithm by realizing some experimental trials on real data from the real world EPINIONS and CIAO communities, showing the significant advantages of our procedure w.r.t. another prominent approach based on traditional global reputation.

In Smart Factories, automated guided vehicles (AGVs) accomplish heterogeneous tasks as moving objects, restoring connectivity, or performing different manufacturing activities into production lines. These kinds of devices combine several capabilities, as artificial intelligence (visual and speech recognition, automatic fault detecting, proactive behavior) and mobility, into the so-called 'mobile intelligence.' A typical scenario is represented by a workshop with a large number of mobile intelligent devices with associated agents, mutually interacting on their behalf. Here, to reach a given target by contemporary satisfying some basic requirements like effectiveness and efficiency, it is often necessary to organize ad hoc teams of free-moving vehicles, sensors, and smart devices. Therefore, a specific issue is the adequate representation of the reciprocal agent/device trustworthiness for advantaging such team formation processes within a smart factory environment. To this end, in this article, first, we define a trust measure based on reliability and reputation of AGVs, which are computed based on the feedbacks released for the AGVs activities in the factory; second, we design a trust framework exploiting the defined measures to support the formation of virtual, temporary, and trust-based teams of mobile intelligent devices; and third, we present a set of experimental results highlighting that the proposed trust framework can improve the workshop performance in terms of effectiveness and efficiency.

The purpose of the next internet of things IoT is that of making available myriad of services to people by high sensing intelligent devices capable of reasoning and real time acting. The convergence of IoT and multi-agent systems MAS provides the opportunity to benefit from the social attitude of agents in order to perform machine-to-machine M2M cooperation among smart entities. However, the selection of reliable partners for cooperation represents a hard task in a mobile and federated context, especially because the trustworthiness of devices is largely unreferenced. The issues discussed above can be synthesized by recalling the well known concept of social resilience in IoT systems, i.e., the capability of an IoT network to resist to possible attacks by malicious agent that potentially could infect large areas of the network, spamming unreliable information and or assuming unfair behaviors. In this sense, social resilience is devoted to face malicious activities of software agents in their social interactions, and do not deal with the correct working of the sensors and other information devices. In this setting, the use of a reputation model can be a practicable and effective solution to form local communities of agents on the basis of their social capabilities. In this paper, we propose a framework for agents operating in an IoT environment, called ResIoT, where the formation of communities for collaborative purposes is performed on the basis of agent reputation. In order to validate our approach, we performed an experimental campaign by means of a simulated framework, which allowed us to verify that, by our approach, devices have not any economic convenience to performs misleading behaviors. Moreover, further experimental results have shown that our approach is able to detect the nature of the active agents in the systems i.e., honest and malicious , with an accuracy of not less than 11 compared to the best competitor tested and highlighting a high resilience wi

IoT devices dealing with complex tasks usually require powerful hardware capabilities or, as a possible alternative, to get on the Cloud those resources they need. When an IoT device is 'virtualized' on the Cloud, it can take benefit from relying on one or more software agents and their social skills to mutually interact and cooperate. In particular, in a Cloud of Things scenario, where agents cooperate to perform complex tasks, the choice of a partner is a sensitive question. In such a context, when an agent is not capable to perform a reliable choice then, like real social communities, it can ask information to other agents it considers as trustworthy. In order to support agents in their partner choices, we conceived a local trust model, based on reliability and reputation measures coming from its ego-network, adopted to partition the agents in groups by exploiting trust relationships to allow agents to be associated with the most reliable partners. To this aim, we designed an algorithm to form agent groups by exploiting available local trust measures and the results obtained in a simulated scenario confirmed the potential advantages of this approach.

In the Internet of Things (IoT) age, the majority of real environments will become smart through the massive spread of novel devices empowered with cyber-physical abilities. Provided with increasing computation capabilities and pervasively deployed, smart IoT devices (SDs) will exhibit pro-Active behaviors and will perform more and more complex tasks, thus enabling the provision of advanced cyber-physical services which make the environment smarter and smarter. Such technology advancements together with an increased environmental awareness suggested the exploitation of these SDs for natural environments monitoring purposes. However, in presence of many and heterogeneous SDs, the formation of good teams requires high levels of trustworthiness among the members and, therefore, it is necessary to adequately represent their mutual trustworthiness. To this aim, the contribution provided by this paper consists in (i) defining a trust measure combining the reputation of SDs and the precision of their sensory data; (ii) designing a framework which adopts such measures as main criteria for the formation of temporary teams of humans and SDs; (iii) testing the proposed trust-based framework on a case study simulating the collaborative monitoring of a natural environment. The obtained results confirmed the potential improvements in the teams composition in terms of both performance and appreciation.

Recent years have witnessed an increasing popularity of wireless body area network (WBAN), with which continuous collection of physiological signals can be conveniently performed for healthcare monitoring. Energy consumption is a critical issue because it directly affects the duration of the equipped sensors. In this paper, we propose a low-cost and confidential electrocardiogram (ECG) acquisition approach for WBAN. The compressed sensing (CS) is employed for low-cost signal acquisition, and its cryptographic features are exploited for promoting the framework's confidentiality. In particular, the RIPless measurement matrix is used to give CS the resistance against plaintext attack, while the first-order $\Sigma \Delta$ quantizer is employed to embed the cryptographic diffusion feature into the whole system. Two chaotic systems are employed for generating the required secret elements for the acquisition and encryption. Experiment results well demonstrate the signal reconstruction and security performance of the proposed framework., Export Date: 29 December 2022; Article; CODEN: ITIBF

In this paper we consider a CoT (Cloud of Things) scenario where agents cooperate to perform complex tasks. Agents have to select reliable partners and, in some cases, they don't have enough information about their peers. In order to support agents in their choice and to maximize the benefits during their cooperation, we combined several contributions. First of all, we designed a trust model which exploits the recommendations coming from the ego networks of the agents. Secondly, we propose to partition the agents in groups by exploiting trust relationships to allow agents to interact with the most reliable partners. To this aim, we designed an algorithm named DAGA (Distributed Agent Grouping Algorithm) to form agent groups by exploiting available reliability and reputation and the results obtained in a simulated scenario confirmed its potential advantages.

critical issue for small and low-cost Internet of Things (IoT) devices facing multiple complex, advanced and interactive tasks trying to save their power resources. To reach these goals IoT devicescan use the capabilities of nearby devices having suitable resources, given that they make their resources available for free or with a determinedcost. In such a context, IoT devices can take significant benefits by exploiting the social attitude of software agents to mutually interact and cooperate with other agents they consider as trustworthy. However, in wide communities it is common that a lot of members are unreferenced with respect to the own trustworthiness and, therefore, the task of carrying out a reliable choice about a potential partner can be very difficult. To tackle such an issue, we propose an agent framework where each IoTdevice is associated with an agent that helps its device in choosing reliable partners for its tasks. To this aim, we designed a reputation modelimplementing some countermeasures against malicious IoT devices. Toverify the efficiency and effctiveness of our proposal, we carried out some experiments in a simulated scenario, which confirmed the potential advantages deriving by its adoption.

In the Cloud of Things, data are stored and processed in the cloud and the results are sent to IoT Smart Objects (SOs) with, generally, a network latency overhead due to the distances between the cloud and the local IoT networks. Moreover, trusting inappropriate counterparts can expose SOs to threats and to mitigate them we propose a clustering reputation-based approach for IoT Edge-based platform processing and storing data on the “edge”, nearby the SOs. Whenever SOs interact for services, a feedback is sent to an Edge server to calculate their reputation scores. In this way, the reputation systems is moved from the cloud to the edge servers, but available on the cloud if a SO will change edge domain. To this aim, we designed a distributed Trusted Object Framework (TOF) where heterogeneous OSs host and exploit the assistance of associated software agents and verified its performance in a simulated scenario which confirmed the potential advantages of TOF.

The need of managing complex and interactive activities is becoming a key challenge in the “Internet of Things” (IoT) and leads to request large hardware and power resources. A possibility of facing such a problem is represented by the possibility of virtualizing physical IoT environments over the so called Cloud-of-Things (CoT), where each device is associated with one or more software agents working in the Cloud on its behalf. In this open and heterogeneous context, IoT devices obtain significant advantages by the social cooperation of software agents, and the selection of the most trustworthy partners for cooperating becomes a crucial issue, making necessary to use a suitable trust model. The cooperation activity can be further improved by clustering agents in different groups on the basis of trust measures, allowing each agent will to interact with the agents belonging to its own group. To this purpose, we designed an algorithm to form agent groups on the basis of information about reliability and reputation collected by the agents. In order to validate both the efficiency and effectiveness of our approach, we performed some experiments in a simulated scenario, which showed significant advantages introduces by the use of the trust measures.

Nowadays, a challenge for the ‘‘Internet of Things’’ (IoT) world is represented by the necessity of facing very complex and interactive tasks, such that IoT devices have to be equipped with hardware having very powerful capabilities. All this becomes particularly critical in presence of small and low-cost IoT devices. A way to deal with such problems is represented by the possible virtualization of the IoT environments over the cloud, the so called Cloud-of-Things (CoT), and then to associate each device with one or more software agents working in the Cloud environment. Moreover, the convergence of these technologies allows IoT devices to take significant benefits also by the social attitude of software agents to interact and cooperate. In this context, based on Machine-to-Machine (M2M) interactions, the choice of the partner for cooperating is a sensitive question, particularly in open and heterogeneous environments. If an agent does not hold suitable information to carry out a reliable choice then, similarly to real communities, it can ask information to other agents it considers as trustworthy. In this context, agents cooperation must be supported by a proper trust model which helps to select potential partners. This process can be further improved by partitioning the agents in different groups based on trust relationships. This way, each agent has the possibility to prefer the interactions with the agents belonging to its group that are, from its viewpoint, the most reliable for avoiding malicious behaviors and threats of different nature. To this purpose, we designed an algorithm, named CoTAG (CoT Agent Grouping algorithm), to form agent groups on the basis of information about reliability and reputation collected by the agents. To verify the efficiency and effectiveness of this algorithm, we carried out some experimentations in a simulated scenario. The results confirm the potential advantages deriving by the adoption of our proposal.

The use of Recommender Systems (RSs) to support customers and sellers in Business-to-Consumer activities is emerged in the last years and several RSs have been proposed on different e-Commerce platforms to provide customers with automatic and personalized suggestions. However, the information such tools catch in supporting B2C customers in their Web activities then are unused to support them on the traditional commerce. In other words, these two environments operate separately without implementing synergistic actions to share knowledge and experiences between these two modality of commerce. In this paper, we propose a distributed RS, called ICR-IoT, based on a multi-tiered agent architecture, conceived to realize such a synergy. The key of our idea is that of using a tier, based on the Internet-of-Things technology, designed to catch information about customers of traditional markets in order to generate very effective suggestions to support commercial activities both on a traditional store as well as on an e-Commerce site.

[EN] This work proposes an innovative solution in the field of Edge AI in order to efficiently exploit new hardware components available on the market at low cost. Edge AI means that algorithms are processed locally on a hardware device. The algorithms use data (sensor data or signals) that are created on the own device. The idea of this paper focuses on demonstrating the validity of the proposed solution by implementing an autonomous driving system that exploits communication between intelligent agents. In this case, our self-driving cars are equipped with a low-cost device that allows you to recognise objects along the way and consequently take actions by running a machine learning model. The presence of a machine learning model also allows the developer to modify it by extending the flexibility and application possibilities of the proposed solution.

The complex and opportunistic environment in which edge computing systems operate, poses a fundamental challenge for online edge system orchestration, resource provisioning and real-time responsiveness in response to user movement. Such a challenge needs to addressed throughout the edge system lifecycle, starting from the software development methodologies. In this paper, we propose a novel development process for modeling opportunistic edge computing services, which rely on (i) ETSI MEC reference architecture and Opportunistic Internet of Things Service modeling for the early stage of system analysis and design, i.e. domain model and service metamodel; and on (ii) feature engineering for evaluating those opportunistic aspects with data analysis. To address the identified opportunistic properties, at the service design phase we construct (both automatically and through domain expertise) Opportunistic Feature Vectors for Edge, containing the numerical representations of those properties. Such vectors enable further data analysis and machine learning techniques in the development of distributed, effective and efficient edge computing systems. Lastly, we exemplify the integrated process with a microservice-based user mobility management service, based on a real-world data set, for online analysis in MEC systems.

The Internet of Things (IoT) makes available attractive services to smart objects and humans. To this aim, IoT devices need of high sensing, reasoning and real time acting capabilities that can be also obtained by promoting adaptive forms of cooperation Machine-to-Machine among smart objects. The convergence of IoT and Multi-Agent Systems also relies on the association between software agents with IoT devices for exploiting their social attitude of interacting and cooperating for services. However, the choice of reliable partners for cooperation can be very difficult when IoT devices migrate across different environments, where the most part of their members will be unreferenced with respect to their trustworthiness. It is well known that agents reputation can be a viable aspect to consider to form social groups, therefore a possible solution to this problem is to form groups of agents in each IoT environment, based on their social capabilities. In this respect, the first contribution of this paper is represented by a reputation model focused on building the reputation capital of each agent. Secondly, an algorithm capable to form groups of agents in IoT environments on the basis of their reputation capital was designed. Finally, since in this contest it is important to spread reliable and certified information about the device/agent reputation in a distributed environment, the third contribution is represented by the adoption of the blockchain technology to certify the reputation capital. Some experiments we have performed show that the model is capable to detect almost all the misleading agents if their percentage is under a high enough threshold, and that good results in term of group composition are obtained. Moreover, the simulations show that, adopting our model, malicious devices always pay for services significantly more than honest ones. We argue that the individual reputation capital of devices and, consequently, the overall reputation capital of the IoT co

This paper deals with the principal aspects emerging from the application of trust techniques to the IoT domains with respect to the particular viewpoint of an IoT environment. We consider intelligent agents technology to add social behavior to the community of the smart objects, and we analyze the fundamental role of the concepts of trust and reputation in this perspective. Also, we analyze the existing architectures for IoT and explain how to integrate IoT with fog/edge computing. Besides discussing the main proposals present in the literature, the key contribution of this paper consists of providing a comparative study of the main current architectures for modeling trust in IoT environments. In this setting, we propose a comparison based on the important characteristics of the IoT layer and the architecture type. Such an analysis allows us to highlight both advantages and limitations of each approach, and to discuss the emerging research challenges.