Internet Of Things Entities Research Articles

An improved clock synchronization model for typical IoT applications

In today’s scenario, the Internet of Things (IoT) has transformed people’s lives by enabling data exchange among pervasive devices in various applications. However, clock synchronization is essential to ensure seamless transmission and synchronization among IoT entities involved in processing and communication. This paper proposes a clock synchronization algorithm based on linear-quadratic regression (LQR) to address synchronization-errors in IoT applications. The algorithm uses a linear model of skew and offsets to estimate clock parameters, and performance is evaluated in terms of R-square error (RSE) and Root Mean Square Error (RMSE). Our proposed algorithm outperformed traditional algorithms with an R-Square Error of 0.71% and RMSE of 0.379%. This paper also evaluated the stability of the proposed model using the correlation coefficient, which indicated a high correlation among the variables at 86%. The results below demonstrate the proposed algorithm’s effectiveness and goodness of fit in addressing clock synchronization errors for IoT applications.

Authentication, Authorization, Access Control, and Key Exchange in Internet of Things

The Internet of Things (IoT) is a dynamic network of devices and infrastructure supporting instances composed to platforms being based on cloud/fog and blockchain technologies. Its intervention in more and more sensitive areas requires IoT entities (devices and platform instances) to communicate with each other via secure channels generally established by using cryptographical methods. This needs an authentic key exchange, which in turn requires an authentication process. Moreover, it has to be ensured that client entities can access only authorized services provided by authorized server entities. Additionally, requirements specifically introduced by IoT complicate realizing these security goals even more. This article introduces a novel approach providing authentication, authorization, access control, and key exchange in instance-to-instance, device-to-instance, and device-to-device communications to handle cloud/fog-based and blockchain-based platforms. In contrast to related work, realizations of these security goals are not disjunct processes and are integrated with each other in our approach combining zero-knowledge and identity-based schemes while meeting the IoT security requirements. Thus, it does not require any public data pre-distribution or secret pre-sharing between communicating entities, and no entity has to hold any device-specific or instance-specific data to be used for authentication or authorization. While supporting the autonomous character of IoT, our approach is independent of application and platform types without requiring additional components or procedures. Moreover, it is resistant to active man-in-the-middle attacks and does not include costly cryptographic operations. This article also demonstrates the high performance of our approach with regard to multiple affecting factors.

Enhancing security mechanisms for robot-fog computing networks

<p>The evolution from conventional Internet usage to the internet of things (IoT) is reshaping communication norms significantly. Cloud computing, while prevalent, faces challenges like limited capacity, high latency, and network failures, especially when handling connected objects, leading to the emergence of fog computing as a more suitable approach for IoT. However, establishing secure connections among heterogeneous IoT entities is complex due to resource disparities and the unsuitability of existing security protocols for resource-constrained devices. This article explores fog computing's architecture, drawing comparisons with cloud computing while emphasizing its significance within the realm of IoT. Moreover, it delves into the practical application of fog computing within the context of the robot teacher project. Subsequently, our exploration introduces an advanced mutual authentication protocol, centered around hashed message authentication code (HMAC), aimed at enhancing the security infrastructure between the robot and the fog computing server.</p>

Machine to Machine Authenticated Key Agreement with Forward Secrecy for Internet of Things

Internet of things (IoT), is the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data. The communication is through the internet hence susceptible to security and privacy attacks. Consequently, authenticated key agreement (AKA) of communicating entities in IoT is of paramount importance as a security and privacy credential. However, IoT devices have resource-constrained feature, hence implementation of heavy security and privacy features becomes a challenge. Research on AKA in IoT has been done since year 2006. Current research trends on AKA are together with forward secrecy (FS) feasibility, which ensures that future SKs remain safe even if the long-term master keys get compromised. However, most of researches use public key cryptosystems to achieve FS, which requires heavy computations that is not good for the resource-constrained IoT environment. The main purpose of this Thesis is to devise a new machine AKA with FS for IoT, denoted as M2MAKA-FS. To design M2MAKA-FS, we devise a new lightweight FS framework first, which does not rely on the public key cryptosystem but based on a hash chain. The security and privacy building blocks of M2MAKA-FS and the FS framework are symmetric key cryptosystem, one-way hash function, fuzzy commitment and challenge-response mechanism. Results of formal security and privacy analysis show that M2MAKA-FS provides mutual authentication, SK agreement with FS, anonymity and unlinkability and is resilient against various active attacks. Performance analysis shows that M2MAKA-FS achieves the lightweight requirements for IoT environments compared to the related protocols.

Distributed Multi-Agent Approach for Achieving Energy Efficiency and Computational Offloading in MECNs Using Asynchronous Advantage Actor-Critic

Mobile edge computing networks (MECNs) based on hierarchical cloud computing have the ability to provide abundant resources to support the next-generation internet of things (IoT) network, which relies on artificial intelligence (AI). To address the instantaneous service and computation demands of IoT entities, AI-based solutions, particularly the deep reinforcement learning (DRL) strategy, have been intensively studied in both the academic and industrial fields. However, there are still many open challenges, namely, the lengthening convergence phenomena of the agent, network dynamics, resource diversity, and mode selection, which need to be tackled. A mixed integer non-linear fractional programming (MINLFP) problem is formulated to maximize computing and radio resources while maintaining quality of service (QoS) for every user’s equipment. We adopt the advanced asynchronous advantage actor-critic (A3C) approach to take full advantage of distributed multi-agent-based solutions for achieving energy efficiency in MECNs. The proposed approach, which employs A3C for computing offloading and resource allocation, is shown through numerical results to significantly reduce energy consumption and improve energy efficiency. This method’s effectiveness is further shown by comparing it to other benchmarks.

Efficient and Privacy-Preserving Search Over Edge–Cloud Collaborative Entity in IoT

Limited by the storage and computing capacity of Internet of Things (IoT) devices, outsourcing encrypted entity data has become a prevalent trend. The existing IoT entity search methods lack the integration and utilization of both edge and cloud resources and the protection of user privacy. Besides, the traditional searchable encryption mode is inapplicable to state-time-varying entities in IoT. Therefore, in this article, an edge–cloud collaborative entity search method with privacy protection in IoT is proposed, fusing the advantages of edge and cloud resources to fulfill users’ needs for real-time search and privacy protection. Specifically, a secure search architecture and search method for edge–cloud collaboration is designed to support various needs of users, such as real-time search and global search. Then, an adaptive discrimination method for similar interested entities through attribute analysis and feature extraction is proposed to construct attribute-distinguished encrypted index and query vector groups, enabling efficient entity search meanwhile ensuring fast index update. Simulation results demonstrate that the proposed method with privacy protection can effectively improve the efficiency of entity search in IoT while safeguarding user privacy.

Enhancing IoT Data and Semantic Interoperability Based on Entity Tree Embedding Under an Edge–Cloud Framework

Internet of Things (IoT) devices and services have become increasingly ubiquitous in recent years as they greatly facilitate our daily life. To promote IoT data and semantic interoperability, we propose an edge–cloud framework. The edge end is responsible for handling customized data processing tasks and transferring the processed data, while the cloud end deals with semantic information processing. Additionally, we present an entity tree embedding algorithm at the cloud end to convert IoT entities and attributes into embedding vectors in a tree-structured way. Consequently, entity embeddings could reflect the semantic information at both Class and Property levels, which ameliorates our previous entity embedding method, leading to better embedding results and clustering effects. Finally, the entity tree embedding algorithm and the corresponding compression algorithm are evaluated. Results indicate that the Whitening algorithm is the best method to compress embedding vectors. More importantly, the entity tree embedding algorithm captures both the semantic and structural information of entities and attributes. Additionally, the clustering experiments show that the proposed embedding algorithm achieves better clustering results compared with the original entity embeddings and the uncompressed averaging method.

QSec-RPL: Detection of version number attacks in RPL based mobile IoT using Q-Learning

Internet of Things (IoT) has revolutionized the networking by connecting the real world entities to the Internet. IoT connects the communication devices and has an incredible impact on perspective analytics on the massive volume of data produced every day. An attacker may exploit vulnerabilities of IoT entities and compromise users’ security and privacy. Development of solutions to address security and privacy issues of IoT is in premature stage and considered as challenge. This challenge becomes more critical when the devices in the network are resource-constrained in terms of energy, processing and memory. The IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) has emerged in recent years as an adaptation layer to carry IPv6 packets over IEEE 802.15.4. Many IoT applications use Routing Protocol for Low Power and Lossy Networks (RPL) as a network layer protocol developed for routing in 6LoWPAN. Security is challenging in resource constrained environment where encryption may not be a viable solution. Version number attack is one of the most common network layer attacks against RPL based 6LoWPAN. The RPL specification does not address the integrity of the version number and therefore leaves version number mechanism as a weak point in terms of security. This paper investigates the impact of version number attack in RPL networks while considering mobility of the sensor nodes. We propose a solution that utilizes Q-Learning strategy to detect the malicious nodes that are performing version number attack. The proposed approach detects malicious nodes with reasonable accuracy while imposing significantly less overhead on the nodes of low power and lossy networks. There are other approaches too like Message Authentication Codes (MAC) based on symmetric keys but these techniques have memory and communication overhead. So we propose different approach Q-Learning to detect the attacker nodes.

An Energy-Efficient Multilevel Secure Routing Protocol in IoT Networks

In Internet of Things (IoT) applications with multihop networking, not only traditional energy efficiency but also many distinct features should be considered when designing routing protocols, including different security requirements, heterogeneity, and scalability. In this article, an energy-efficient multilevel secure routing (EEMSR) protocol in IoT networks is proposed. Considering that clustering is a reasonable solution of conserving energy, a cluster-based multihop routing protocol is utilized to reduce the high communication overhead due to the scalability of IoT networks. In particular, more reasonable analytic hierarchy process and genetic algorithms are adopted to assign accurate weight and optimize intercluster routing in which heterogeneous IoT networks are considered to support large amount of heterogeneous IoT entities and services. Moreover, multiple trust levels are adopted to defend the different attacks by calculating the trust factor on the clustering and routing, including data perception trust, data fusion trust, and communication trust. It is shown that the proposed algorithm outperforms the existing algorithms in terms of network lifetime, throughput, packet delivery ratio, network energy balance, and adaptability.

A Sustainable Solution for IoT Semantic Interoperability: Dataspaces Model via Distributed Approaches

In the past few decades, the prevalence of Internet of Things (IoT) applications has brought both opportunities and challenges of different categories. One of the main concerns nowadays is semantic interoperability. Although plenty of augmented ontologies and resource description frameworks have been designed to achieve semantic interoperation, there is still no sustainable solution to interconnect the increasing number of heterogeneous devices and corresponding data. Aiming at this problem, this article presents a dataspaces model utilizing distributed approaches to represent semantic information as a sustainable solution for IoT semantic interoperability. In this model, an attention-based entity embedding approach is designed to convert IoT entities into low-dimensional dense vectors, then calculations, including entities, relations, etc., can be further conducted. Consequently, the entity embeddings could reflect the semantic information of Class equivalences among entities. To recognize entity relations, the generated entity vectors and the arithmetic results of two entities are combined as input features. By feeding the input features to a well-trained Tensor-train modified DNN, the relation between two entities could be recognized. Finally, experiments are conducted on two data sets to evaluate the proposed approaches. Results indicate that the generated entity vectors could effectively reflect the semantic similarity between entities. More importantly, while achieving parameter compression and better generalizing ability, the relation recognition approach improves the relation recognition accuracy on new entities compared with the state-of-art models.

Enhanced Authentication Protocol for the Internet of Things Environment

The Internet of Things (IoT) is among the most promising technologies of the future, and its development has garnered attention worldwide. However, the rise of the IoT has been accompanied by a proportionate increase in security concerns regarding communication between IoT entities. Recently, Alzahrani et al. proposed an identity-based authentication and key agreement protocol for an IoT environment, wherein a physically unclonable function was employed. They claimed that their protocol can resist various types of attacks; however, after thorough analysis, we determined it to be ineffective against privileged internal attacks, physical IoT device capture attacks, stolen-verifier attacks, and known temporary information exposure attacks. To resolve these security weaknesses, we propose a new authentication and key agreement protocol. In addition, we demonstrate that the proposed protocol is provably secure in real-or-random (ROR) model and Burrows–Abadi–Needham (BAN) logic, resisting known attacks while incurring low communication and computation costs.

A Domain-Specific Language for Modeling IoT System Architectures That Support Monitoring

The Internet of Things (IoT) is a technological paradigm involved in a diversity of domains with favorable impacts on people’s daily lives and the development of industry and cities. Nowadays, one of the most critical challenges is developing software for IoT systems since the traditional Software Engineering methodologies and tools are unproductive in the face of the complex requirements resulting from the highly distributed, heterogeneous, and dynamic scenarios in which these systems operate. Model-Driven Engineering (MDE) emerges as an appropriate approach to abstract the complexity of IoT systems. However, there are no domain-specific languages (DSLs) aligned to standardized reference architectures for IoT. Furthermore, existing DSLs have an incomplete language to represent the IoT entities that may be needed at the edge, fog, and cloud layers to monitor IoT environments. Therefore, this paper proposes a domain-specific language named Monitor-IoT, which supports developers in designing multi-layer monitoring architectures for IoT systems with high abstraction, expressiveness, and flexibility. Monitor-IoT consists of a high-level visual modeling language and a metamodel aligned with the ISO/IEC 30141:2018 reference architecture. In addition, it provides a language capable of modeling architectures with a wide variety of digital entities and dataflows (synchronous and asynchronous) between them across the edge, fog, and cloud layers to support the monitoring of a diversity of IoT scenarios. The empirical evaluation of Monitor-IoT through the application of an experiment, which contemplates the use of the Technology Acceptance Model (TAM), demonstrates the intention of the participants to use this tool in the future since they consider it easy to use and useful.

Incremental Learning Implementations and Vision for Cyber Risk Detection in IoT

The Internet of Things (IoT) has emerged as an Internet-based extension and considerably changed our world. A tremendous amount of IoT applications has highly eased people's daily lives, and improved allocations and usage of a broad range of resources (power bank sharing, bike sharing, etc.). However, the ingrained openness of underlying wireless systems renders these IoT entities vulnerable to a broad range of cyber risks, such as vulnerabilities of spectrum that can be a source of adversarial inference. Furthermore, as our reliance on wireless/connected devices and radios has also been dramatically increasing, public safety, business operations, socializing, and navigation as well as critical national communication infrastructures have become more vulnerable to cyber threats. In addition, the stream of data from IoT devices are also prone to cyber risks, which is of major concern for end users, and could also mislead users and ML models with wrong information during the training and learning phases. Hence, policymakers and industries have recently begun to perceive that the expansion of connected devices and their cyber susceptibility results in a large malicious and inferential risk. As a result, there is a need to identify and comprehend these risks in order to elaborate efficient security solutions. Moreover, the economic impact of IoT devices and associated security vulnerabilities are further growing in accordance with artificial intelligence (AI) integration into human-computer interaction (HCI), including banking, insurance, and so on. Consequently, cyber risks are growing in terms of both frequency and acuteness. Therefore, this article discusses the evolution of common real-time machine learning (RTML) approaches, which are considered as enablers for ensemble learning-based use cases. We discuss a cyber risk detection framework that can be built effectively using Boosting- and Bagging-based ensemble learning approaches.

IoT-Enabled Social Relationships Meet Artificial Social Intelligence

With the recent advances of the Internet of Things (IoT), and the increasing accessibility to ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as social relationships explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as artificial social intelligence (ASI) that has the potential to tackle the social relationships explosion problem. This article discusses the role of IoT in social relationships management, the problem of social relationships explosion in IoT, and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.

The MI-SDN System to Manage MQTT Data in an Interoperable IoT Wireless Network

The main challenge for the Internet of Things (IoT) is to ensure interoperability between heterogeneous IoT entities. To support the interaction, intercommunication, and interoperability between these devices several solutions are proposed in the literature. The SDN (Software-defined Network) is one of these solutions to resolve the problem of the heterogeneous network used in IoT. To guarantee network interoperability, the SDN uses a centralized controller, which handles the entire network. The role of end devices in IoT is only forwarding data. The MQTT (Message Queuing Telemetry Transport) protocol is another solution for granting interoperability in IoT. Which is a publish/subscribe based messaging protocol that avoids direct connection between devices by relaying data through a central server called the broker. Combination of these two solutions to manage IoT devices makes it easy to add new devices without touching or changing the existing infrastructure. The new devices only need to communicate with the broker. Moreover, the Controller SDN is responsible for handling networks. Consequently, smart devices added don’t need to be compatible with the others. In this paper, we present the design and the implementation of a new IoT architecture, which is a combination of SDN technology and MQTT protocol. That enables heterogeneous IoT devices to be interoperable and interact without any problems. Our system utilizes the lightweight protocol MQTT with a new mechanism using several slave brokers and one master. The slaves manage the group of the end devices in the wireless IoT network, and the master broker installed in the SDN controller supervises the integral network. The SDN controller uses a multicast system to send MQTTdata across the external wireless network. As a result, that reduces transmission delay between wireless IoT network compared with the using of a standard MQTT.

Provable Privacy Preserving Authentication Solution for Internet of Things Environment

The Internet of Things (IoT) has become an important technology which permits different devices and machines to interconnect with each other using heterogeneous networks. The integration of numerous techniques is expected to offer extraordinary growth in future and current promising applications of IoT. In these days, the secure communication among interconnected IoT components has become an important issue of concern. Therefore, it has become a foremost need to design such authentication protocol which can make the secure communication among IoT components. In this article, we proposed an identity-based authentication and key agreement protocol for the IoT environment in order to offer the secure communication between various IoT entities. The devised protocol utilizes the physically unclonable function which helps to robustly resist the physical attack on IoT components. We analyze the proposed protocol informally which clearly shows that the proposed protocol offers the perfect forward secrecy, device anonymity and untraceability and also resists the desynchronization, IoT node impersonation and server impersonation attacks. The security features of proposed protocol are also analyzed formally using well known Random Oracle Model (ROM). Moreover, the performance of the devised protocol has also been determined in terms of communication and computational overhead. The performance and security analysis shows the supremacy of the devised protocol over the various related protocols.

Attribute-Based Access Control for AWS Internet of Things and Secure Industries of the Future

Internet of Things (IoT) is revolutionizing and enhancing the quality of human lives in every aspect. With a disruption of IoT devices and applications, attackers are leveraging weak authentication and access control mechanisms on these IoT devices and applications to gain unauthorized access on user devices and data and cause them harm. Access control is a critical security mechanism to secure the IoT ecosystem which comprises cloud computing and edge computing services along with smart devices. Today major cloud and IoT service providers including Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure utilize some customized forms of Role-Based Access Control (RBAC) model along with specific authorization policies enabled by policy-based access control models. To enable fine-grained access control and overcome limitations of existing access control models, there is an imminent need to develop a flexible and dynamic access control model for securing smart devices, data and resources in the cloud-enabled IoT architecture. In this paper, we develop a formal attribute-based access control (ABAC) model for AWS IoT by building upon and extending previously developed access control model for AWS IoT, known as AWS-IoTAC model. We demonstrate the applicability of our proposed model through an industrial IoT use case and its implementation in the AWS IoT platform. Our proposed fine grained model for AWS IoT incorporates its existing capabilities and introduces new attributes for IoT entities and attribute-based policies for enabling expressive access control in AWS IoT. We also evaluate the performance of our model on the AWS cloud and IoT platform with the future smart industries use-case to depict the feasibility of our model in a real-world platform.

BorderChain: Blockchain-Based Access Control Framework for the Internet of Things Endpoint

The Internet of Things (IoT) providers serve better IoT services each year while producing more IoT gateways and devices to expand their services. However, the security of the IoT ecosystem remains an afterthought for most IoT providers. This action results in many cybersecurity breaches in the field, most likely due to the lack of access control mechanisms. In this paper, we propose BorderChain, an access control framework based on blockchain for IoT endpoints. The security protocol guarantees two properties. First, our proposal assures IoT users and services that they communicate with approved IoT gateways as endpoints, holding verified IoT devices that they need. Second, BorderChain also generates access tokens that the IoT service and users can use to query IoT resources legitimately inside the IoT domains. As a result, the protocol can convince IoT domain owners that the system will only authorize IoT requests that they approve. We realize our protocol in the form of a smart contract to allow many IoT entities such as IoT domain owners, IoT devices, IoT gateways, IoT vendors, IoT services, IoT users, and Internet Service Provider (ISP) to collaborate in a unified environment. We then implement entities in BorderChain as Node JS applications connecting to the Ethereum blockchain as our peer-to-peer platform. Based on our performance evaluation using several Raspberry Pi hardware and our private server, we show that BorderChain can process entities’ authentication and authorization requests efficiently using all hardware resources. Finally, we release BorderChain for public use.

A model-driven approach to ensure trust in the IoT

The Internet of Things (IoT) is a paradigm that permits smart entities to be interconnected anywhere and anyhow. IoT opens new opportunities but also rises new issues. In this dynamic environment, trust is useful to mitigate these issues. In fact, it is important that the smart entities could know and trust the other smart entities in order to collaborate with them. So far, there is a lack of research when considering trust through the whole System Development Life Cycle (SDLC) of a smart IoT entity. In this paper, we suggest a new approach that considers trust not only at the end of the SDLC but also at the start of it. More precisely, we explore the modeling phase proposing a model-driven approach extending UML and SysML considering trust and its related domains, such as security and privacy. We propose stereotypes for each diagram in order to give developers a way to represent trust elements in an effective way. Moreover, we propose two new diagrams that are very important for the IoT: a traceability diagram and a context diagram. This model-driven approach will help developers to model the smart IoT entities according to the requirements elicited in the previous phases of the SDLC. These models will be a fundamental input for the following and final phases of the SDLC.

Physical-Layer Authentication for Internet of Things via WFRFT-Based Gaussian Tag Embedding

Internet of Things (IoT) is regarded as the fundamental platform for many emerging services, such as smart city, smart home, and intelligent transportation systems. With ever-increasing penetration of IoT, it becomes of great importance to ensure the IoT security, as the security threats are extended from the cyber world to the physical world. In this article, we investigate physical-layer authentication to help verify the identity of IoT entities for preventing unauthorized access to information or service. Specifically, we propose a Gaussian-tag-embedded physical-layer authentication (GTEA) scheme by using a weighted fractional Fourier transform (WFRFT). Through the superimposition of a low-power Gaussian WFRFT tag onto the message signal, the legitimate receiver can verify the authenticity of the received signal at the physical layer, without being detected by adversaries. Moreover, security analysis shows that with the deliberately designed Gaussian tag, the GTEA scheme is robust against spoofing and replaying attacks. In addition, tradeoff analysis and simulation results are provided to demonstrate the capability of the GTEA scheme in achieving reliability of the message delivery, stealth of the embedded tag signal, and balancing the tradeoff among the robustness of user authentication. Moreover, a prototype is further developed using FPGA and experiments are conducted to demonstrate the effectiveness and performance improvement of the proposed GTEA scheme.