A blockchain-based group formation strategy for optimizing the social reputation capital of an IoT scenario

doi:10.1016/j.simpat.2020.102261

Simulation Modelling Practice and Theory

Volume 108, April 2021, 102261

https://doi.org/10.1016/j.simpat.2020.102261 Get rights and content

Abstract

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.

Introduction

The main actors in the “Internet of Things” (IoT) [1] are the IoT “smart” objects (i.e., things), that are physical (or virtual) entities with embedded computational, sensing and communication abilities [2], where each object is identifiable and traceable in space and time. In order to reach their goals, these smart objects need to be provided with human-like capabilities. In our previous research [3], we have highlighted that the features above can be improved by introducing complex forms of collaboration between smart objects. This consideration leaded us to propose the association of the Multi-Agent Systems technology with the IoT devices. The convergence of IoT and MAS allows ubiquitous and heterogeneous devices to exploit the needed services in a scalable and pervasive way, thanks to the agent’s social attitude to interact and collaborate. In this scenario, the possibility of constructing a wide network composed by heterogeneous smart objects becomes very interesting, since this type of objects are capable of living, acting as prosumers and moving among different, federated environments.

In [3] we already focused on the issue of accurately choosing the best partners for cooperation, in a scenario where IoT devices migrate among different federated environments, where the IoT objects will be unreferenced in terms of its trustworthiness. To this aim, in [3] we proposed a reputation model for characterizing the social reputation of each agent. We have proved that the proposed model leads to detect agents having unreliable or misleading behaviors, designing a strategy by which these agents pay higher costs for obtaining services in the IoT environment, with respect to more reliable and honest agents. However, the cooperation among objects, also small and low-cost, implies their capability of having social interactions with other devices. Then, as preliminary result, we have proved that the designed model can also be profitably used for forming groups of agents that cooperate for improving the effectiveness of their tasks.

In this work, we further delve into the significant aspect of group formation. In particular, we argue that in practical situations the group formation strategy should be adaptive with respect to the characteristics of the global IoT network [4], [5]. This leads us to introduce, with respect to our past researches, a group formation algorithm which is particularly suitable to also extend the reputation model already introduced in [3] to take into account the perspective above.

With respect to [3], in this work we extend the idea of modeling the reputation of each agent by using a sort of “personal capital” represented by the sum of the feedback received by the agents during the interactions with the other agents. Furthermore, we also propose to employ blockchain technology to maintain information about agents trustworthiness (i.e, the personal capital). Blockchain technology can ensure trust and data integrity to anonymous entities through decentralized, distributed P2P network [6], and the use of cryptographic validation techniques, representing the safe replacement of third parties or centralized authorities to certify such a reputation. Then we extend the original idea of using blockchain to support a trust and reputation model, that we have presented in [7].

While in that paper we only focused on the reputation model, and presented a first version of an algorithm to form groups of agents, in this new paper we particularly deal with the group formation strategy. This new strategy is based on a two-phases algorithm that, differently from the approach presented in [7] fixing the number of the groups and the threshold of reputation for joining with a group, gives instead the possibility to compute the aforementioned parameters based on the characteristics of the IoT objects. Moreover, a new, large experimental campaign has been here implemented to validate the advantage introduced by our approach.

Then, as the core of our contribution, we introduce a new algorithm to support the group formation based on the trustworthiness stored in the blockchain. In particular, this new technical contribution consists of proposing an approach based on a competitive IoT scenario, able to enhance the whole social capital of the community, by implementing a strategy conceived to prevent possible collusive and misleading activities to incorrectly increasing the personal capital of the agents. Moreover, the new strategy of group formation we have introduced implies that an agent desiring to join with a group having a high average reputation, needs to first obtain a high personal capital of reputation.

As further contribution with respect to the previous work, we will present the results of a large experimental campaign aimed at verifying the good performance of the group formation procedure in terms of group composition; in particular we will show that the group formation procedure is capable to increase the overall reputation capital of the IoT community.

The paper is organized as follows. In Section 2, we survey the related work of the recent literature, while in Section 3 the competitive IoT scenario is presented. Section 4 illustrates the reputation-blockchain mechanism. Section 6 presents our group formation algorithm and Section 7 describes and discusses the results of our experimental campaign and in Section 8 the conclusions are carried out.

Section snippets

Related work

Distributed Systems (DS) are subject to a greater number of threats for malicious and/or disliked behaviors [8] than Centralized ones. This problem becomes critical in the case of open and competitive DSs, but cryptographic techniques [9], trust and reputation systems [10], [11], can reduce risks and support user (i.e., agents) activities.

In particular, cryptographic techniques guarantee protection from external attacks by assuring privacy and counterparts authentication [12]. Conversely, trust

The underlying IoT scenario

We suppose that our scenario contains a number $N$ of heterogeneous IoT objects (see Fig. 1), each of them supported by a software agent. In such a scenario, the agents can interact in the execution of a task on the basis of smart contracts.

Let $A$ denote the set of software agents, and $G N$ the Global Network consisting of several federated Local Networks (say $L N$ ). A particular agent, called Local Network Administrator ( $L N A_{m}$ ), is delegated to administrate each local network by providing some basic

The reputation model

Here we will introduce the notion of Reputation Capital. In this approach, the reliability of a consumer is guaranteed by the blockchain (in this case, it must pay the service to another agent), while the reputation capital of a provider witnesses its ability to provide quality service.

The Reputation Capital ( $R C$ ) is represented by a numerical value – a real positive number – computed on the basis of the past interactions among agents on the basis of the following requirements:

•
the more recent

Using smart contracts for managing the reputation capital

It is well known that security for distributed IoT can be supported by the blockchain technology, as discussed in Section 2. In particular, the blockchain [46] ensures trust and data integrity to unknown and anonymous entities (permissionless), through decentralized, distributed, open, and unchangeable ledger saving data across a peer-to-peer (P2P) network by using cryptographic technologies to identify source and sink of the data, support transactions and management of complex digital assets,

The group formation procedure

Now, we will illustrate the procedure designed to be executed by each $L N A$ to form groups of objects on the basis of their $R C$ values. In this context, the role of a group is that of offering the possibility to implement a form of collaboration among the smart objects, whenever a service $s$ is free only if the consumer agent belongs to the same group of its provider.

The approach for group formation is composed by two temporal phases, as described in Sections 6.1 Phase 1: Determining number of

Experiments

An experimental campaign has been carried out to evaluate the performance of our approach also with respect to the two phases of the group formation algorithm. In particular, the experiments have been performed by adopting different values for both the horizon parameter ( $h$ ) and the number of active malicious devices living in the IoT environment. In such a way, we have investigated on: (i) the effectiveness in identifying malicious actors by implementing several types of attacks at the same

Conclusions

In this work, we deal with Federations of IoT networks comprises myriad of heterogeneous, smart IoT devices. In our context, devices shift among local networks, and cooperate to attain own targets with their peers.

The level of “satisfaction” of the single device must be fairly high at the end of interactions, therefore it is important to select reliable collaborators. This is complex problem when device interactions embroil critical and/or complex activities (like, the release of resources). In

Acknowledgments

This work has been partially supported by the University of Catania, Italy, Piano per la Ricerca 2016–2018 - Linea Di Intervento 1 “CHANCE” II Edizione - and PIA.CE.RI. 2020–2022 (University of Catania) and by the Network and Complex Systems (NeCS) Laboratory at the University Mediterranea of Reggio Calabria, Department of Civil, Energy and Materials Engineering (DICEAM), Italy .

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The authors mentioned that the management of trust in the said domain is vital for improved user privacy and data security. Additionally, the trust and reputation of any system are greatly dependent upon the social attitude of the involved entities (Fortino et al., 2019a, 2021). The devices communicate with each other for providing services to the end user and spread information about the device’s reputation.
The impetuous expansion of the Internet of Things (IoT) network has resulted in a noticeable increase in the production of sensitive user data. With this, to meet the demand for real-time response, a processing layer is introduced near the user end which is known as the fog computing layer. The fog layer lies in the user’s vicinity and thus highly attracts malicious and/or curious intruders. As a result, the trust of the network gets negatively impacted. Motivated by the aforementioned issue, the authors consider Reputation-based trust and propose a RepuTE Framework in the Fog-IoT domain. The given framework consists of a soft voting ensemble learning model that classifies and predicts two popular reputation-based attacks namely, DoS/ DDoS and Sybil attacks. Furthermore, a novel feature selection technique is also presented that selects the most relevant features well in advance. The performance evaluation is done on NSL-KDD, CICDDOS2019, IoTID20, NBaIoT2018, TON_IoT, and UNSW_NB15 benchmarked IoT and network traffic datasets. The comprehensive performance analysis depicts that the proposed model attains 99.99% accuracy and outperforms other recent state-of-the-art works. This indicates the potential of the proposed approach for reputation-based attack filtration in the IoT domain.
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Finally, reputation values of agents are stored in an Ethereum-based blockchain. In another work, Fortino et al. (2021) proposed a two-phase group formation algorithm to support their reputation model in IoT environment. In this work, the idea of modeling the reputation of each agent was extended, so that the number of groups and the reputation threshold for joining with a group are determined based on the characteristics of the IoT objects instead of a fixed value.
Fair selection of community members’ transactions in blockchain-based technologies, especially in finance, is essential and vital. Therefore, in this paper, a novel consensus model for fair selection of community members’ transactions through reputation models is presented as fair reputation-based consensus model (FRCM), so that all transactions of community members have the same weight and transactions are selected regardless of the transaction fee or the assets or computing power but based on the reputation of the trustees who are responsible for placing the transactions in the block. For this purpose, a FRCM prototype has been designed and implemented based on the proposed algorithms. Experimental and theoretical evaluations and simulation results show that the FRCM can select the transactions of community members fairly while reducing the average response time effectively. In addition, comparative analysis with other consensus mechanisms proved that FRCM model has lower consensus latency than other mechanisms, while improving throughput and scalability.
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Many hostile behaviors may exist, attempting to damage vehicular social networks, e.g., some malicious vehicles may send bogus or improper messages uninterruptedly to undermine the trustiness of vehicles towards each other. Against this background, many reputation-based countermeasures have been adopted to solve the trustiness-related issues in IoV and acquired satisfactory effects such as [6–10]. On the other hand, however, researchers seldom apply reputation-based mechanisms to service provisioning in VFC.
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Some of these research works used the blockchain for grouping agents using a reputation-based strategy. In Giancarlo et al. (2021) and Fortino et al. (2019), the social reputation capital in IoT was optimized using a blockchain-based group formation strategy. The optimization allows for effective cooperation and coordination between the smart IoT devices.
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Eventually, this will result in users and agents forming a trust network whose complexity requires reliable analytic methods and mathematical models to study the underlying dynamics similar to those in computational trust (Marsh, 1994). Besides user-agent trust, inter-agent trust plays an essential role in multi-agent systems and is, for instance, the subject of various recent studies on trust management within the Internet of Things (IoT) (Din et al., 2021; Fortino et al., 2020, 2021; Hriez et al., 2021). The primary focus here lies on modeling inter-node (i.e., inter-agent) trust to maximize the performance and efficiency in an IoT system consisting of multiple edge devices.
Intelligent autonomous agents (IAA) are proliferating and rapidly evolving due to the exponential growth in computational power and recent advances, for instance, in artificial intelligence research. Ranging from chatbots, over personal virtual assistants and medical decision-aiding systems, to self-driving or self-piloting systems, whether unbeknownst to the users or not, IAA are increasingly integrated into many aspects of daily life. Despite this technological development, many people remain skeptical of such agents. Conversely, others might have excessive confidence in them. Therefore, establishing an appropriate level of trust is crucial to the successful deployment of IAA in everyday contexts. Virtual Reality (VR) is another domain where IAA play a significant role, yet its experiential and immersive character particularly allows for new ways of interaction and tackling trust-related issues. In this article, we provide an overview of the numerous factors involved in establishing trust between users and IAA, spanning scientific disciplines as diverse as psychology, philosophy, sociology, computer science, and economics. Focusing on VR, we discuss the different types and definitions of trust and identify foundational factors classified into three interrelated dimensions: Human-Technology, Human-System, and Interpersonal. Based on this taxonomy, we identify open issues and a research agenda towards facilitating the study of trustful interaction and collaboration between users and IAA in VR settings.
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Giancarlo Fortino is full Professor of Computer Engineering at the Dept. of Informatics, Modeling, Electronics, and Systems of the University of Calabria (Unical), Italy. He received a Ph.D. in Computer Engineering from Unical, in 1995 and 2000, respectively. He is also adjunct professor at Wuhan University of Technology (Wuhan, China) and senior research fellow at the Italian National Research Council ICAR Institute. His research interests include agent-based computing, wireless (body) sensor networks, and Internet of Things. He is author of over 350 papers in int’l journals, conferences and books. He is cofounder and CEO of SenSysCal S.r.l., a Unical spinoff focused on innovative IoT systems. Fortino is currently member of the IEEE SMCS BoG and chair of the IEEE SMCS Italian Chapter.

Lidia Fotia received her MsC Degree in Telecommunication Engineering from the University Mediterranea of Reggio Calabria in 2010 and a Ph.D. in Information Engineering from the University Mediterranea of Reggio Calabria in 2014. Currently she is Post-doc fellow at the University of Calabria (Unical), Italy. Her research interests include social network and social internetworking analysis, privacy, security, trust and reputation, intelligent agents.

Fabrizio Messina received his Ph.D. in Computer Science from the Department of Mathematics and Informatics at the University of Catania, Italy in 2009. He is currently serving as assistant professor in the same department. His research interest includes Distributed systems, Complex Networks, Simulation systems, Trust. Contact him at [email protected].

Domenico Rosaci is Associated Professor of Computer Science at the Department of Information, Infrastructures and Sustainable Energy Engineering at the University Mediterranea of Reggio Calabria, Italy. In 1999, he took the Ph.D. in Electronic Engineering. His research interests include distributed artificial intelligence, multi-agent systems, trust and reputation in social communities. He is a member of a number of conference PCs and he is Associate Editor of Journal of Universal Computer Science (Springer). Contact him at [email protected].

Giuseppe M. L. Sarné is Associated Professor of Computer Science at the Department of Psychology at the University of Milano Bicocca, Italy. His main research interests include distributed artificial intelligence, multi-agent systems, trust and reputation systems. He is a member of a number of conference PCs and he is Associate Editor of E-Commerce Research and Applications (Elsevier) and of Big Data and Cognitive Computing (MDPI). Contact him at [email protected].

View full text

A blockchain-based group formation strategy for optimizing the social reputation capital of an IoT scenario

Abstract

Introduction

Section snippets

Related work

The underlying IoT scenario

The reputation model

Using smart contracts for managing the reputation capital

The group formation procedure

Experiments

Conclusions

Acknowledgments

Comput. Netw.

Int. J. Approx. Reason.

Comput. Hum. Behav.

Inf. Syst.

Comput. Netw.

Inform. Sci.

J. Econ. Behav. Organ.

Future Gener. Comput. Syst.

Knowl.-Based Syst.

J. Netw. Comput. Appl.

Comput. Netw.

Future Gener. Comput. Syst.

Future Gener. Comput. Syst.

Comput. Statist. Data Anal.

That’ internet of things’ thing. rfid journal, june

Middlewares for smart objects and smart environments: overview and comparison

Using blockchain for reputation-based cooperation in federated iot domains

Smart objects as building blocks for the internet of things

IEEE Internet Comput.

Agent-oriented cooperative smart objects: From iot system design to implementation

IEEE Trans. Syst. Man Cybern.: Syst.

Distributed Systems: Concepts and Design

Using blockchain in a reputation-based model for grouping agents in the internet of things

IEEE Trans. Syst. Man Cybern.: Syst.

A survey on current key issues and status in cryptography

A survey of trust and reputation management systems in wireless communications

Proc. IEEE

Security and privacy issues in internet of things

An integrated trust and reputation model for open multi-agent system

Auton. Agent Multi Agent Syst.

Integrating trust measures in multiagent systems

Int. J. Intell. Syst.

Supporting reputation-based trust for peer-to-peer electronic communities

IEEE Trans. Knowl. Data Eng.

Social computing: the state of the art

Int. J. Soc. Comput. Cyber-Phys. Syst.

Towards a corporate governance trust agent scoring model for collaborative virtual organisations

Int. J. Grid Utility Comput.

An integrated trust and reputation model for open multi-agent systems

Auton. Agents Multi-Agent Syst.

Reputation systems

Commun. ACM

Survey of trust models in different network domains

Grouptrust: Finding trust-based group structures in social communities