Convergence Of Internet Of Things Research Articles

INTERNET OF THINGS (IOT): A COMPREHENSIVE REVIEW, EMERGING TRENDS, AND FUTURE DIRECTIONS

This research paper conducts an extensive examination of the Internet of Things (IoT) ecosystem, encompassing its historical evolution, technological underpinnings, and diverse applications across industries. The study addresses challenges related to security, privacy, and interoperability, offering insights into potential solutions. Additionally, the paper explores emerging trends in IoT research, including advancements in edge computing, integration with machine learning, and the convergence of IoT with other transformative technologies. By providing a comprehensive overview of the current state of IoT, the research aims to inform discussions on scalability, sustainability, and societal implications. The findings contribute to the ongoing discourse, fostering a deeper understanding of IoT dynamics and guiding future research and development efforts.

Enhancing Data Preservation and Security in Industrial Control Systems through Integrated IOTA Implementation

Within the domain of industrial control systems, safeguarding data integrity stands as a pivotal endeavor, especially in light of the burgeoning menace posed by malicious tampering and potential data loss. Traditional data storage paradigms, tethered to physical hard disks, are fraught with inherent susceptibilities, underscoring the pressing need for the deployment of resilient preservation frameworks. This study delves into the transformative potential offered by distributed ledger technology (DLT), with a specific focus on IOTA, within the expansive landscape of the Internet of Things (IoT). Through a meticulous examination of the intricacies inherent to data transmission protocols, we present a novel paradigm aimed at fortifying data security. Our approach advocates for the strategic placement of IOTA nodes on lower-level devices, thereby streamlining the transmission pathway and curtailing vulnerabilities. This concerted effort ensures the seamless preservation of data confidentiality and integrity from inception to storage, bolstering trust in the convergence of IoT and DLT technologies. By embracing proactive measures, organizations can navigate the labyrinthine terrain of data management, effectively mitigate risks, and cultivate an environment conducive to innovation and progress.

Roles of Iot, big data and machine learning in precision agriculture: a systematic review

Precision agriculture utilizes information technologies to efficiently manage water and soil resources in agriculture. This technological revolution has been sparked by the big data analytics, machine learning and Internet of Things (IoT)in numerous industries. This game-changing technology has influence on houses, grids, smart cities, and health. Machine learning and data-intensive decision-making have been made possible, which has created new possibilities for efficiency and innovation in various fields. In several fields, the convergence of IoT, big data analytics, and machine learning has opened the way for cutting-edge solutions and enhanced results.The application of machine learning in the agricultural sector can increase production quantity and quality to meet increasing food demand. These advancements are transforming current agrarian approaches and generating new opportunities, despite some limitations. This paper presents a systematic appraisal of the role of IoT, big data analytics, and machine learning in precision agriculture, highlighting the potential benefits and challenges in this rapidly evolving field.

Modified graph-based algorithm to analyze security threats in IoT.

In recent years, the growing and widespread usage of Internet of Things (IoT) systems has led to the emergence of customized structures dependent on these systems. Industrial IoT (IIoT) is a subset of IoT in terms of applications and usage areas. IIoT presents many participants in various domains, such as healthcare, transportation, agriculture, and manufacturing. Besides the daily life benefits, IIoT technology provides major contributions via the Industrial Control System (ICS) and intelligent systems. The convergence of IoT and IIoT systems brings some integration and interoperability problems. In IIoT systems, devices interact with each other using information technologies (IT) and network space. However, these common usages and interoperability led to some security risks. To avoid security risks and vulnerabilities, different systems and protocols have been designed and published. Various public databases and programs identify and provide some of the security threats to make it easier for system administrators' missions. However, effective and long-term security detection mechanisms are needed. In the literature, there are numerous approaches to detecting security threats in IoT-based systems. This article presents two major contributions: First, a graph-based threat detection approach for IoT-based network systems is proposed. Threat path detection is one of the most critical steps in the security of IoT-based systems. To represent vulnerabilities, a directed acyclic graph (DAG) structure is constructed using threat weights. General threats are identified using Common Vulnerabilities and Exposures (CVE). The proposed threat pathfinding algorithm uses the depth first search (DFS) idea and discovers threat paths from the root to all leaf nodes. Therefore, all possible threat paths are detected in the threat graph. Second, threat path-reducing algorithms are proposed considering the total threat weight, hop length, and hot spot thresholds. In terms of available threat pathfinding and hot spot detecting procedures, the proposed reducing algorithms provide better running times. Therefore, all possible threat paths are founded and reduced by the constructed IoT-based DAG structure. Finally, simulation results are compared, and remarkable complexity performances are obtained.

Survey on security and privacy in Internet of Things‐based eHealth applications: Challenges, architectures, and future directions

AbstractThe integration of Internet of Things (IoT) technology into electronic health (eHealth) applications has revolutionized the healthcare landscape, enabling real‐time patient monitoring, personalized care, and improved patient outcomes. However, this convergence of IoT and healthcare also introduces critical security and privacy challenges, needing careful consideration. This survey comprehensively explores the multifaceted realm of security and privacy issues in IoT‐based eHealth applications. First, we taxonomize the diverse security threats that arise due to the interconnected nature of IoT medical devices. Additionally, we highlight privacy concerns stemming from the collection and sharing of personal health information, while reconciling them with the need for accessible and collaborative healthcare ecosystems. Second, we synthesize functional, ethical, and regulatory perspectives to pick up the major requirements needed in the context of eHealth data during their whole lifecycle, from creation to destruction. Third, we identify emerging research strategies employed to address security and privacy concerns, such as cloud‐based solutions, decentralized technologies such as blockchain technology and InterPlanetary File System (IPFS), cryptographic approaches, fine‐grained access control strategies, and so forth. Additionally, we examine the impact of these approaches on computational efficiency, latency, and energy consumption, critically evaluating their suitability in the healthcare context. Building upon this comprehensive assessment, we outline potential future research directions aimed at advancing security and privacy measures in IoT‐based eHealth applications.

Effective Risk Assessment and Management with IoT in Supply Chains

Supply chain risk management is a critical area in today’s globalized economy, and with the development of Internet of Things (IoT) technology, revolutionary changes are taking place in risk assessment and management methods. This paper aims to explore effective risk assessment and management with IoT in supply chains in China, emphasizing the crucial role of IoT technology in real-time data collection, predictive analytics, and traceability. We delve into the limitations of traditional risk assessment methods and how IoT enhances supply chain visibility and transparency. Furthermore, we speculate on the future applications of IoT technology in risk management, including autonomous vehicles, smart packaging, and supply chain simulations. This paper also focuses on the convergence of IoT with emerging technologies and the role of government and industry in shaping IoT adoption in supply chain risk management. Through this paper, we encourage organizations to harness IoT technology to enhance supply chain resilience and mitigate potential risks.

The Convergence of IoT, Big Data, and International Logistics: Enhancing Supply Chain Efficiency

This paper explores the transformative convergence of the Internet of Things (IoT) and Big Data within the realm of international logistics, focusing on their collective potential to enhance supply chain efficiency. We delve into the IoT's foundational technology, its role in international logistics, and its adoption within China's logistics networks. Additionally, we examine the significance of Big Data analytics, its applications in supply chain management, and its specific utilization in China's international logistics operations. The paper also highlights the convergence of these technologies, emphasizing their impact on supply chain efficiency and data-driven decision-making. Privacy and security concerns, the cost of technology adoption, workforce training, and international cooperation are addressed as critical challenges. Finally, the paper presents policy recommendations and a forward-looking perspective on the future development of IoT, Big Data, and international logistics.

Energy Efficient Node Selection in Edge-Fog-Cloud Layered IoT Architecture

Internet of Things (IoT) architectures generally focus on providing consistent performance and reliable communications. The convergence of IoT, edge, fog, and cloud aims to improve the quality of service of applications, which does not typically emphasize energy efficiency. Considering energy in IoT architectures would reduce the energy impact from billions of IoT devices. The research presented in this paper proposes an optimization framework that considers energy consumption of nodes when selecting a node for processing an IoT request in edge-fog-cloud layered architecture. The IoT use cases considered in this paper include smart grid, autonomous vehicles, and eHealth. The proposed framework is evaluated using CPLEX simulations. The results provide insights into mechanisms that can be used to select nodes energy-efficiently whilst meeting the application requirements and other network constraints in multi-layered IoT architectures.

Advanced Energy and Industrial IoT

The Internet of Things (IoT) has been enforced in different industries, including the electrical sector and the broader industrial landscape. IoT offers immense potential for enhanced electrical system monitoring, control, and optimization by seamlessly connecting devices, sensors, and equipment to the Internet. This issue explores IoT's current realization and implementation in electrical and industrial sectors and its impact on them. Additionally, it delves into the crucial connection between IoT, relay protection, and automation, highlighting the benefits of this integration in terms of efficiency, reliability, and safety. Furthermore, this issue explores perspectives and adaptations of IoT in the energy and industry sectors. The realization of IoT in electrical systems and the industry sector holds significant promise. As IoT technology continues to evolve, it will enable the seamless integration of various components within electrical systems and industrial processes. This integration will lead to intelligent automation, streamlined workflows, and real-time decision-making, improving operational efficiency, productivity, and resource optimization. The convergence of IoT, electrical systems, and the industry sector will create an intelligent ecosystem that redefines traditional practices and establishes a foundation for sustainable growth and competitiveness.

A Review of IoT Convergence in Healthcare and Smart Cities: Challenges, Innovations, and Future Perspectives

This in depth analysis explores the transformative implications of the convergence of the Internet of Things (IoT) in healthcare and smart cities. By examining the current state, challenges and innovations within these sectors, existing literature highlights how IoT plays a crucial role in reshaping healthcare through remote patient monitoring, personalized treatments and improved healthcare delivery. It also discusses how IoT impacts smart cities by optimizing urban services and resource management. While challenges such as security, interoperability and data governance persist in both sectors, there are promising innovations in secure IoT frameworks and robust governance policies that provide solutions. The analysis identifies similarities in challenges and benefits across different sectors while acknowledging sector specific impacts. To shape the future of healthcare and smart city development effectively, it suggests focusing on fortified security measures, interoperable standards, collaborative efforts among stakeholders, as well as integrating IoT with artificial intelligence (AI) and machine learning (ML) for enhanced decision making. The study emphasizes the importance of user centric design, infrastructure investments and seamless integration of IoT to fully realize its potential in shaping the future of healthcare and smart city development.

Service Home Identification of Multiple-Source IoT Applications in Edge Computing

The real-time communication requirement of the Internet of Things (IoT) applications promotes the convergence of IoT and Mobile Edge Computing (MEC). The MEC paradigm greatly shortens the IoT service delay by leveraging cloudlets (edge servers) of MEC in the proximity of IoT devices. Considering limited computing and storage resources in an MEC network, it is challenging to provide efficient IoT-enabled service provisioning in such a network. In this article, we study the service home identification problem of service provisioning for multi-source IoT applications in an MEC network, by identifying a service home (cloudlet) of each multi-source IoT application for its data processing, querying and storage. Each multi-source IoT application consists of multiple sources located at different geographical locations and each source uploads its data stream via a gateway (its nearby access point) to the MEC network and the uploaded data then is aggregated with the stream data of the other sources of the IoT application at the service home. We here focus on two novel service home identification problems: the service operational cost minimization problem with the aim to minimize the total service operational cost by admitting as many multi-source IoT applications as possible, and the online throughput maximization problem with the aim to maximize the number of multi-source IoT application requests admitted. We first show that both the problems are NP-hard. We then formulate an Integer Linear Programming (ILP) solution to the service operational cost minimization problem, and propose a randomized algorithm with high probability and a deterministic approximation algorithm respectively, at moderate resource capacity violations. We third develop an efficient heuristic algorithm for the problem without any resource violation. Furthermore, we deal with the online throughput maximization problem under an assumption that multi-source IoT application requests arrive one by one without the knowledge of future arrivals, for which we formulate an Integer Linear Programming (ILP) solution to its offline version, followed by devising an online algorithm with competitive ratio. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising, and outperform their comparison counterparts.

Конвергенція інтернету речей та систем ситуаційного управління

Quick and accurate provision of information in the processes of situational management ac-quires special importance in the critical areas of application – the prevention and elimination of the consequences of catastrophic events, the management of military operations, medical activi-ties, the management of energy systems, etc. The goals and objectives of activities in these are-as also determine the specifics of building appropriate situation management systems for them. The functioning of such systems is based on obtaining accurate and current information about the environment in which situational management is carried out. The convergence of situation management systems with the means of wireless sensor networks (Wireless Sensor Networks, WSN) and the Internet of Things (IoT) ensures obtaining such information. The article analyzes the means of the Internet of Things from the point of view of their ability to be combined in sit-uation management systems to solve management problems in various spheres of activity. The construction features and advantages of using situational systems based on IoT in various spheres of activity are considered. The convergence of IoT in situational systems of various types requires the solution of IoT-typical tasks related to the organization of a heterogeneous network environment with controlled energy consumption and the need to process large vol-umes of data. The paper demonstrates that the scope of application determines the specific re-quirements for the construction of situation management systems, and in each specific case the system of requirements is focused on the support of a critical requirement. One of the main problems of the convergence of IoT and situation management systems is the difficulty of standardizing the operating environment. The multiplicity and variety of options for possible solutions in the development of such systems determine the need for the use of intelligent tech-nologies for the development of systems with the use of artificial intelligence and knowledge models of the target subject area.

Blockchain Integration for IoT-Enabled V2X Communications: A Comprehensive Survey, Security Issues and Challenges

In V2X (vehicle-to-everything) communication, there is a two-way communication among the vehicle(s) and other Internet of Things (IoT)-enabled smart devices around it that may change how we need to drive. Due to the advancement of Information and Communications Technology (ICT) and the rapid development of IoT in transportation, traditional applications are converted to intelligent applications. In V2X communications, the collected information from the IoT smart devices and other sources passes through low-latency, high-bandwidth, high-reliability links. With the future adoption of the 5th generation mobile network (5G) and beyond networks, V2X continues to produce a huge volume of data. However, collecting and storing data securely in blockchain-based storage are extremely needed for immutability and transparency. In this survey article, the convergence of IoT, V2X and blockchain technologies, and various security challenges and their countermeasures are discussed. Next, we discuss various V2X applications and their respective services. Moreover, IoT-V2X architecture and its enabling technologies are discussed in this article. In addition, we also provide a comprehensive analysis of various security mechanisms. Finally, we provide some important challenges and issues of Blockchain for Intelligent Transportation System (BITS).

A Reliable and Lightweight Trust Inference Model for Service Recommendation in SIoT

In the era of Internet of Things (IoT), millions of heterogeneous IoT devices generate an explosion of data and services waiting to be discovered. The convergence of IoT with social networks (SIoT) interconnects multiple IoT applications and alleviates the common data sparsity and cold start problems in traditional recommendation systems. However, the social trust relationships may also be very sparse, which affects the accuracy of trust-based recommendation systems. Meanwhile, mobile devices have limited resources and are more vulnerable to malicious attacks in the IoT environment. In order to complete the trust relationship and further improve the trust-based recommendation performance, we propose a reliable and lightweight trust inference model for service recommendation in SIoT, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TIRec</i> . First, we obtain a comprehensive weighted centrality metric (LGWC) considering both local and global contexts. Based on this, we propose a corresponding lightweight trust path selection algorithm. Then, we present a reliable trust inference calculation algorithm consist of trust propagation and aggregation strategy, which can efficiently resist two common malicious attacks. Finally, we incorporate the rating, direct trust, and indirect trust together into the matrix factorization model, and integrate the influence of truster and trustee to obtain the synthetic model for rating predication. To the best of our knowledge, this article is the first to integrate trust inference algorithm into the trust-based recommendation systems. The extensive experiments are conducted on three real-world data sets, and the results show that our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TIRec</i> model performs better than other advanced recommendation models in both “all users” view and “cold start users” view.

Internet of Things application in Indian agriculture, challenges and effect on the extension advisory services – a review

PurposeThe emerging technologies of the Fourth Industrial Revolution are transforming various industries, including agriculture. Unaware, young male and female farmers leave the agriculture profession as they perform unsustainable practices. Precision agriculture using the Internet of Things (IoT) is a solution to sustainable agriculture. Extension professionals are at the heart of disseminating agricultural advisory agricultural services in India. The discourse on the IoT is entering the space of extension advisory services (EASs) and social sciences. Thus, the present paper seeks to review the application of IoT in Indian agriculture, its challenges and its effect on EASs. The conceptual framework is drawn from disruptive and surveillance capitalist theories.Design/methodology/approachOnline literature review was conducted on electronic e-book Ebsco, Google scholar, PubMed, Jane, j gate, research4life, springer journal and Mendeley databases for full-text repositories, textbook, thesis, web articles, newspaper articles, reports, blogs for the year 1990 to May 2021 using keywords “IoT application in agriculture,” “emerging technologies in agriculture,” “challenges in IoT application,” “extension advisory services sources of information,” “big data and extension advisory, “IoT and extension advisory in India.” Only publications in the English language were included.FindingsIoT aids progressive farmers and small farmers alike. Drones, robotics, precision irrigation, livestock tracking and crop disease surveillance are examples of IoT applications in agriculture. Only large corporations and governments access IoT, and for them, big data storage is an issue. Privacy and security concerns demand upgrades in IoT systems. Solutions to the convergence of IoT with the cloud will leverage agricultural EASs, resulting in fast computing, precise and proactive up-to-date problem solving. Hence, the need for communication between firms and clients has ceased. Thus, the jobs of extension agents are replaced.Research limitations/implicationsThe competence of future human extension agents lies in reskilling as a “knowledge broker” of relationships and expertise, as s/he cannot have all multidisciplinary knowledge.Originality/valueAlthough IoT applications in agriculture are available from a technological standpoint, there remains an awareness gap regarding the impact of IoT applications in agricultural EASs. This study will aid in a better comprehension of IoT applications from current and prospective EASs.

Towards Convergence of IoT and Blockchain for Secure Supply Chain Transaction

Supply chain management (SCM) is essential for a company’s faster, efficient, and effective product life cycle. However, the current SCM systems are insufficient to provide product legitimacy, transaction privacy, and security. Therefore, this research proposes a secure SCM system for the authenticity of the products based on the Internet of Things (IoT) and blockchain technology. The IoT-enabled Quick Response (QR) scanner and the blockchain-integrated distributed system will allow all the SCM stakeholders to begin secure and private transactions for their products or services. Resulting, the consumer will receive an authentic and genuine product from the original producer. A lightweight asymmetric key encryption technique, i.e., elliptic curve cryptography (ECC) and Hyperledger Fabric-based blockchain technology with on-chain smart contracts are applied for distributed IoT devices to make the authentication process faster and lighter. Each SCM stakeholder is registered by the service provider and receives corresponding public and private keys, which will be used for the authentication process of the participants and IoT devices. The authenticated QR scanner records all transactions on the blockchain. Consequently, there will be no human intervention for the SCM transactions. The security and scalability analysis demonstrates that the proposed system is more secure and robust than other state-of-the-art techniques.

Conceptualization and scalable execution of big data workflows using domain-specific languages and software containers

Big Data processing, especially with the increasing proliferation of Internet of Things (IoT) technologies and convergence of IoT, edge and cloud computing technologies, involves handling massive and complex data sets on heterogeneous resources and incorporating different tools, frameworks, and processes to help organizations make sense of their data collected from various sources. This set of operations, referred to as Big Data workflows, requires taking advantage of Cloud infrastructures’ elasticity for scalability. In this article, we present the design and prototype implementation of a Big Data workflow approach based on the use of software container technologies, message-oriented middleware (MOM), and a domain-specific language (DSL) to enable highly scalable workflow execution and abstract workflow definition. We demonstrate our system in a use case and a set of experiments that show the practical applicability of the proposed approach for the specification and scalable execution of Big Data workflows. Furthermore, we compare our proposed approach’s scalability with that of Argo Workflows – one of the most prominent tools in the area of Big Data workflows – and provide a qualitative evaluation of the proposed DSL and overall approach with respect to the existing literature.

The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT)

The Internet of Things (IoT) tsunami, public embracement, and the ubiquitous adoption of smart devices are affecting virtually every industry, directly or indirectly. The success of the current and future landscape of IoT and connected devices requires service provision characterized by scalability, ubiquity, reliability, and high-performance, among others. In order to achieve this attribution, the integration of IoT and Cloud Computing (CC), known as cloud IoT, has emerged as a new paradigm providing advanced services specific to aggregating, storing, and processing data generated by IoT. While the convergence of IoT and Cloud brings opportunities, it suffers from specific limitations such as bandwidth, latency, and connectivity. The increasing need for supporting interaction between cloud and IoT led to Edge and Fog Computing (FC) in which computing and storage resources are located not only in the cloud but also at the edges near the source of data. The hierarchical and collaborative edge–fog–cloud architecture brings tremendous benefits as it enables us to distribute the intelligence and computation – including Artificial Intelligence (AI), Machine Learning (ML), and big data analytics – to achieve an optimal solution while satisfying the given constraints e.g., delay-energy tradeoff. Due to the hierarchical, cross-layer, and distributed nature of this model, achieving an osmotic and effective convergence of IoT, edge, fog, and cloud computing requires overcoming many challenges with respect to design and implementation, as well as deployment and evaluation. This paper provides a comprehensive insight into the edge-fog-cloud computing paradigm by providing a blend of discussions on all important aspects of the underlying technologies to offer opportunities for more holistic studies and to accelerate knowledge acquisition. To gain a deep understanding of edge–fog–cloud, we will begin this paper by providing an in-depth tutorial and presenting the main requirements, state-of-the-art reference architectures, building blocks, components, protocols, applications, and other similar computing paradigms, including their similarities and differences. Following this, a holistic reference architecture for edge–fog–cloud IoT is presented and the major corresponding design and deployment considerations (e.g., service models, infrastructure design, provisioning, resource allocation, offloading, service migration, performance evaluation, and security concerns) are discussed. Next, we will take a look at the role of privacy-preserving, distributed, and collaborative analytics as well as the interaction between edge, fog, and cloud computing. Finally, we will overview the main challenges in the field of edge–fog–cloud computing that need to be tackled to realize the full potential of IoT.

Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems

The recent advancements in Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI) transformed the conventional healthcare system into smart healthcare. By incorporating key technologies such as IoT and AI, medical services can be improved. The convergence of IoT and AI offers different opportunities in healthcare sector. In this view, the current research article presents a new AI and IoT convergence-based disease diagnosis model for smart healthcare system. The major goal of this article is to design a disease diagnosis model for heart disease and diabetes using AI and IoT convergence techniques. The presented model encompasses different stages namely, data acquisition, preprocessing, classification, and parameter tuning. IoT devices such as wearables and sensors permit seamless data collection while AI techniques utilize the data in disease diagnosis. The proposed method uses Crow Search Optimization algorithm-based Cascaded Long Short Term Memory (CSO-CLSTM) model for disease diagnosis. In order to achieve better classification of the medical data, CSO is applied to tune both `weights' and `bias' parameters of CLSTM model. Besides, isolation Forest (iForest) technique is employed in this research work to remove the outliers. The application of CSO helps in considerable improvement in the diagnostic outcomes of CLSTM model. The performance of CSO-LSTM model was validated using healthcare data. During the experimentation, the presented CSO-LSTM model accomplished the maximum accuracies of 96.16% and 97.26% in diagnosing heart disease and diabetes respectively. Therefore, the proposed CSO-LSTM model can be employed as an appropriate disease diagnosis tool for smart healthcare systems.

ResIoT: An IoT social framework resilient to malicious activities

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 with respect to some malicious activities.