Internet Of Things Resources Research Articles

Multilevel agent negotiation on service bindings for efficient multi-tasking in IoT environments

In Internet-of-Things (IoT) environments, various IoT resources are utilized to provide the services necessary to accomplish users’ tasks. Therefore, IoT resources must be effectively and efficiently allocated so that service providers can successfully generate their service capabilities utilizing IoT resources. Additionally, conflicting resource allocation must be avoided for services supporting multiple user tasks. Furthermore, resource allocation and conflict resolution must be performed dynamically because users perform their tasks spontaneously. To address these issues, we propose an agent-based service-binding approach for efficient resource allocation and conflict resolution through negotiations among resources, service providers, and task agents. Negotiation occurs when a task agent attempts to use a service that is currently occupied by another task agent. The task agent that uses the service is determined in the negotiation process by considering the transfer fee based on the usage price of the service and the availability of a substitute service. Negotiations can occur at the task-service or service-resource levels to support flexible service binding. To evaluate the effectiveness of our approach, we compared its success rate with that of traditional resource allocation methods in terms of allocating resources successfully for multiple services. We also analyze the execution time to measure the overhead incurred by multilevel negotiation, which is an essential component of our approach. The proposed approach showed an average success rate of 79% which is 1.17%p lower than multi-objective optimization algorithms (MOOA), but the running time of the proposed approach was 98.3 s which was 56% of the running time of MOOA.

DTN-Assisted Dynamic Cooperative Slicing for Delay-Sensitive Service in MEC-Enabled IoT via Deep Deterministic Policy Gradient With Variable Action

Network Slicing (NS) provides customized services to users of the Internet of Things (IoT) by creating logical virtual networks, and NS combined with Multi-access Edge Computing (MEC) can significantly minimize the latency for delay-sensitive service. Therefore, it is important to research how to employ NS to achieve low latency for delay-sensitive service in MEC-enabled IoT. In this paper, we propose a paradigm of dynamic cooperative slicing based on Digital Twin Network (DTN) to achieve low latency for delay-sensitive service. Specifically, we first build a DTN for the MEC-enabled IoT, and build basic models and function models including prediction and decision-making in DTN. Then we realize dynamic cooperative slicing through the built basic models and function models. Secondly, with the assistance of the ubiquitous computing resources in MEC-enabled IoT based on DTN, we construct joint optimization problem of communication resources, computing resources, and collaboration proportion with the objective of ensuring low delay of delay-sensitive service while maximizing the long-term utility of operators. Thirdly, considering that the different MEC servers participating in the cooperation in each time slot lead to different action spaces in different time slots, we propose a Deep Deterministic Policy Gradient algorithm with Variable Action space, called VADDPG, which draws on the idea of action masking and introduces the action adjustor to realize the hard control of action space. Finally, a large number of simulations demonstrate that the proposed algorithm outperforms the benchmark algorithms in terms of both the long-term utility of operators and the delay obtained by slicing.

Business ecosystem model innovation based on Internet of Things big data

The research paper presents a novel big data technique for conceptual modelling of Internet of Things (IoT) networks. This method enables the development of models that incorporate IoT based devices into its activities to give a business for the company while taking into account the business operational activities. This study's major goal is to assist management in making quicker effective options that depend on the structured output from their input to develop IoT networks and resources. In the big data business ecological model based on the Internet of Things, 47.5% of the integrated enterprises started to innovate the enterprise ecological model, which can better help enterprises make decisions and plan. In order to include IoT into business model innovation programs to ensure that IoT is taken into account in the overall strategy business planning and decision-making at all levels, such that the modelling and formal descriptions must be used. A number of interviews with workers from various companies and with their experts, throughout such platform were done to further illuminate this research area. Future challenges will create a constant challenge to create and adopt fresh IoT-related business models for large manufacturing organisations. The high-level strategic steps of business model innovation (BMI) are an intriguing topic for current research. These results represent a first step in the exploration of IoT adoption and serve as a springboard for promising new research directions.

Hierarchical Aerial Computing for Internet of Things via Cooperation of HAPs and UAVs

With the explosive increment of computation requirements, the multiaccess edge computing (MEC) paradigm appears as an effective mechanism. Besides, as for the Internet of Things (IoT) in disasters or remote areas requiring MEC services, unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs) are available to provide aerial computing services for these IoT devices. In this article, we develop the hierarchical aerial computing framework composed of HAPs and UAVs, to provide MEC services for various IoT applications. In particular, the problem is formulated to maximize the total IoT data computed by the aerial MEC platforms, restricted by the delay requirement of IoT and multiple resource constraints of UAVs and HAPs, which is an integer programming problem and intractable to solve. Due to the prohibitive complexity of the exhaustive search, we handle the problem by presenting the matching game theory-based algorithm to deal with the offloading decisions from IoT devices to UAVs, as well as a heuristic algorithm for the offloading decisions between UAVs and HAPs. The external effect affected by the interplay of different IoT devices in the matching is tackled by the externality elimination mechanism. Besides, an adjustment algorithm is also proposed to make the best of aerial resources. The complexity of proposed algorithms is analyzed and extensive simulation results verify the efficiency of the proposed algorithms, and the system performances are also analyzed by the numerical results.

Towards an Integrated Methodology and Toolchain for Machine Learning-Based Intrusion Detection in Urban IoT Networks and Platforms

The constant increase in volume and wide variety of available Internet of Things (IoT) devices leads to highly diverse software and hardware stacks, which opens new avenues for exploiting previously unknown vulnerabilities. The ensuing risks are amplified by the inherent IoT resource constraints both in terms of performance and energy expenditure. At the same time, IoT devices often generate or collect sensitive, real-time data used in critical application scenarios (e.g., health monitoring, transportation, smart energy, etc.). All these factors combined make IoT networks a primary target and potential victim of malicious actors. In this paper, we presented a brief overview of existing attacks and defense strategies and used this as motivation for proposing an integrated methodology for developing protection mechanisms for smart city IoT networks. The goal of this work was to lay out a theoretical plan and a corresponding pipeline of steps, i.e., a development and implementation process, for the design and application of cybersecurity solutions for urban IoT networks. The end goal of following the proposed process is the deployment and continuous improvement of appropriate IoT security measures in real-world urban IoT infrastructures. The application of the methodology was exemplified on an OMNET++-simulated scenario, which was developed in collaboration with industrial partners and a municipality.

Federated Learning-Based Resource Management with Blockchain Trust Assurance in Smart IoT

Resource management is a key issue that needs to be addressed in the future smart Internet of Things (IoT). This paper focuses on a Federated Learning (FL)-based resource management mechanism in IoT. It incorporates blockchain technology to guarantee the security of the FL model parameters exchange. We propose an IoT resource management framework incorporating blockchain and federated learning technologies; then, a specific FL-based resource management with a blockchain trust assurance algorithm is given. We use a Support Vector Machine (SVM) classifier to detect malicious nodes in order to avoid the impact on the performance of the FL-based algorithm. Finally, we perform simulation to verify the SVM classification effect and the proposed algorithm performance. The results show that the SVM-based malicious node identification accuracy can be acceptable. Moreover, the proposed algorithm obtains better performance when malicious nodes are excluded from the FL selected participant.

Efficient hybrid centralized and blockchain-based authentication architecture for heterogeneous IoT systems

With the rapid increase in the number of Internet of Things (IoT) devices in recent years, massive amounts of sensitive IoT data are being generated and transmitted over the Internet. Despite its growing adoption in various fields, IoT security remains a major challenge requiring further research. IoT authentication is an essential security mechanism for building trust in IoT systems. However, conventional authentication approaches use expensive cryptographic primitives that do not align with the resource-constrained nature of IoT devices. Furthermore, centralized authentication schemes have proven to be inapplicable for cross-domain authentication and do not limit the scalability of IoT networks. Recently, blockchain technology has been applied to building decentralized authentication between IoT devices. Nevertheless, most existing blockchain-based authentication approaches incur high overhead in IoT computation, storage, and energy consumption. Authentication time is another critical issue in real-time IoT systems. When numerous IoT authentication requests are transferred to the blockchain, an additional time delay is imposed, in addition to the high computational cost of the blockchain caused by the consensus mechanism. This study proposes a hybrid centralized and blockchain-based authentication architecture for IoT systems. Edge servers are deployed to provide centralized authentication for associated IoT devices. A blockchain network of centralized edge servers is then established to ensure decentralized authentication and verification of IoT devices that belong to different and heterogeneous IoT systems. Lightweight cryptographic methods are implemented to achieve efficient authentication, in which limiting the consumption of IoT resources is required. The architecture is demonstrated using a local Ethereum blockchain network. The results indicate that the proposed method achieves significant improvements in terms of computation cost, execution time, and power consumption for IoT compared with centralized and blockchain-based authentication schemes. A security analysis proves the ability of our architecture to mitigate attacks and satisfy the IoT security requirements.

An approach for managing the Internet of things’ resources to optimize the energy consumption using a nature-inspired optimization algorithm and Markov model

The Internet of Things (IoT) is a developed communication idea in which connectivity and intelligence are added to nearly every device in a real-world context. From a technological standpoint, IoT provides a computational and communications capability to an item to connect to the Internet. Resource management policies are integral to IoT systems to ensure service quality and prevent energy loss and resource fragmentation. The resource management architecture includes some parts that coherently control resources in a way that minimizes resource loss in addition to offsetting the application service quality limitations. To this aim, planning and scheduling components are critical in determining the condition of available resources and the best candidates for hosting a program module. We proposed a method to handle IoT's resources utilizing a nature-inspired optimization algorithm and a Markov model to solve these restrictions. The performance of the proposed method was evaluated to current IoT access management systems in this article. In comparison to earlier approaches, the results demonstrated the efficacy of the new approach in terms of execution time and energy consumption.

An Ontology Integrating the Open Standards of City Models and Internet of Things for Smart-City Applications

Smart city applications integrate the human, physical, and digital systems in a built environment with Internet of Things (IoT) resources, city models, and domain models. However, existing methods for the integration are suitable for individual applications and lack interoperability among application modules. This study analyzed existing integration strategies and developed an ontology for integrating the data modeling standards of the Open Geospatial Consortium (OGC) CityGML, IndoorGML, and SensorThings API. To cope with the broad definition of “things” in the IoT, the proposed ontology supports multiple views of things, including the a-building-as-a-thing, a-room-as-a-thing, an-opening-as-a-thing, and a-device-as-a-thing views. Thus, the proposed ontology relates information from these three standards and supports semantic queries. We demonstrated the proposed solution in smart home, smart security, smart health care, and fire evacuation systems. Overall, the proposed solution can facilitate the integration of standard-based IoT resources and city models to support smart city applications.

An Internet of Things-Based Empirical Study on the Coordination between Rural Economic Development and Ecological Environment Conflict for Luoyang

At present, key elements of the Internet of Things (IoT) are becoming increasingly affordable, making the technology incredibly attractive to many countries, particularly China. Collecting data from multiple IoT resources and combining them with data from other sources lead to significant rural economic development, ecological environment, and ecological and environmental ramifications in these nations. The most visible effects of the IoT in these nations include advances in rural economic growth, environmental conflict, and the efficiency of critical sectors. Furthermore, the IoT has the potential to revolutionize the industrial and commercial sectors, notably in terms of data confidentiality and information security. It is required to conduct an empirical study on the coordinated interaction between rural economic growth and ecological environmental conflict in Luoyang using an IoT environment. Because the IoT plays a key role in economic growth and the ecological environment, this study examines the coordination of rural economic development and ecological environment conflict in Luoyang’s IoT environment. To analyze the original data for the establishment of index weight, it first employs the enhanced entropy approach by building the coordinated assessment index scheme of rural financial development and environmental surroundings conflict in the IoT environment of Luoyang. Second, it calculates the benefits of rural financial growth and the environmental surroundings benefit index of Luoyang. Then, it carried out the Pearson correlation analysis of the abovementioned calculation results using SPSS software that uses the layered architecture of the IoT, and the degree of conflict and coordination among rural financial growth and the ecological environment in Luoyang is judged by referring to relevant standards from 2010 to 2019. The experimental result of this study indicates that the conflict coordination state between rural financial growth and the ecological environment in Luoyang has gradually enhanced. Through this study, local decision makers can give additional consideration to the balance between economic development and the coordination of ecological environmental conflicts, especially those indicators that have a great influence on the coordinated development of the two.

SUACC-IoT: secure unified authentication and access control system based on capability for IoT

With the widespread use of Internet of Things (IoT) in various applications and several security vulnerabilities reported in them, the security requirements have become an integral part of an IoT system. Authentication and access control are the two principal security requirements for ensuring authorized and restricted accesses to limited and essential resources in IoT. The built-in authentication mechanism in IoT devices is not reliable, because several security vulnerabilities are revealed in the firmware implementation of authentication protocols in IoT. On the other hand, the current authentication approaches for IoT that are not firmware are vulnerable to some security attacks prevalent in IoT. Moreover, the recent access control approaches for IoT have limitations in context-awareness, scalability, interoperability, and security. To mitigate these limitations, there is a need for a robust authentication and access control system to safeguard the rapidly growing number of IoT devices. Consequently, in this paper, we propose a new secure unified authentication and access control system for IoT, called SUACC-IoT. The proposed system is based around the notion of capability, where a capability is considered as a token containing the access rights for authorized entities in the network. In the proposed system, the capability token is used to ensure authorized and controlled access to limited resources in IoT. The system uses only lightweight Elliptic Curve Diffie-Hellman Ephemeral (ECDHE), symmetric key encryption/decryption, message authentication code and cryptographic hash primitives. SUACC-IoT is proved to be secure against probabilistic polynomial-time adversaries and various attacks prevalent in IoT. The experimental results demonstrate that the proposed protocol’s maximum CPU usage is 29.35%, maximum memory usage is 2.79% and computational overhead is 744.5 ms which are quite acceptable. Additionally, in SUACC-IoT, a reasonable communication cost of 872 bits is incurred for the longest message exchanged.

A novel fault-tolerant scheduling approach for collaborative workflows in an edge-IoT environment

As a newly emerging computing paradigm, edge computing shows great capability in supporting and boosting 5G and Internet-of-Things (IoT) oriented applications, e.g., scientific workflows with low-latency, elastic, and on-demand provisioning of computational resources. However, the geographically distributed IoT resources are usually interconnected with each other through unreliable communications and ever-changing contexts, which brings in strong heterogeneity, potential vulnerability, and instability of computing infrastructures at different levels. It thus remains a challenge to enforce high fault-tolerance of edge-IoT scientific computing task flows, especially when the supporting computing infrastructures are deployed in a collaborative, distributed, and dynamic environment that is prone to faults and failures. This work proposes a novel fault-tolerant scheduling approach for edge-IoT collaborative workflows. The proposed approach first conducts a dependency-based task allocation analysis, then leverages a Primary-Backup (PB) strategy for tolerating task failures that occur at edge nodes, and finally designs a deep Q-learning algorithm for identifying the near-optimal workflow task scheduling scheme. We conduct extensive simulative case studies on multiple randomly-generated workflow and real-world edge-IoT server position datasets. Results clearly suggest that our proposed method outperforms the state-of-the-art competitors in terms of task completion ratio, server active time, and resource utilization.

User Authentication Techniques of the Internet of Things

Internet of Things (IoT) is a growing technology that has got the attention of researchers from different fields. As more of IoT resources increasing, the number of users' sensitive data also increasing. Authentication process is one of the security factors which used to protect the users' sensitive data and identify whether the user is legitimate or not. IoT facing many authentication challenges due to its complicated environment it contains a massive number of connected devices, different layers, big data, cloud computing, and the open channels. The truly important challenge of the user authentication process for IoT resources is how to do it more securely and easily utilize for computer-illiterate users. In general, the password is the first defense line of user confidentiality, but the use of alphanumeric-based passwords is still widely, even with the existence of alternatives designed to overcome its weakness. Graphical-based Password is one of the practical alternatives which is developed to improve the security of the user authentication. This paper aims to explore the various authentication aspects that can be used in the IoT technology. Also, an overview of the graphical passwords which can be used as an alternative of the weak alphanumeric-based passwords in IoT technology.

Post‐quantum cryptography techniques for secure communication in resource‐constrained Internet of Things devices: A comprehensive survey

AbstractAs the number and characteristics of smart devices change, the concept of the Internet of Things (IoT) emerges. The IoT provides the connected devices with a variety of resources that enable effective communication. At this point, several security issues arise to get the sensitive information behind every communication in the IoT. To provide users with security and privacy, cryptographic schemes are adopted, the most popular being public key cryptographic systems (PKC). However, with the advent of quantum computing, the level of security that can be provided by the PKC schemes is a big question. Another important issue is that the IoT environment is resource‐constrained, which necessitates the implementation of lightweight cryptographic algorithms for better security. In response to these issues, the post‐quantum cryptographic (PQC) schemes are one of the significant developments contributing to IoT security in the post‐quantum world. This article examines the key security issues in the IoT environment and examines the effective solutions found in the literature. The problems in IoT in the quantum era are discussed and appropriate solutions by PKC schemes under limited resources in IoT are focused. As the lattice‐based cryptosystems are more effective, the importance of these schemes in the resource‐constrained IoT is highlighted. This survey also leads to feasible future directions that can support developers and researchers in this field.

Throughput Enhancement in FD- and SWIPT-Enabled IoT Networks Over Nonidentical Rayleigh Fading Channels

Simultaneous wireless information and power transfer (SWIPT) and full-duplex (FD) communications have emerged as prominent technologies in overcoming the limited energy resources in Internet of Things (IoT) networks and improving their spectral efficiency (SE). This article investigates the outage and throughput performance for a decode-and-forward (DF) relay SWIPT system, which consists of one source, multiple relays, and one destination. The relay nodes in this system can harvest energy from the source’s signal and operate in the FD mode. A suboptimal, low-complexity, yet efficient relay selection scheme is also proposed. Specifically, a single relay is selected to convey information from a source to a destination so that it achieves the best channel from the source to the relays. An analysis of outage probability (OP) and throughput performed on two relaying strategies, termed static power splitting-based relaying (SPSR) and optimal dynamic power splitting-based relaying (ODPSR), is presented. Notably, we considered independent and nonidentically distributed (i.n.i.d.) Rayleigh fading channels, which pose new challenges in obtaining analytical expressions. In this context, we derived exact closed-form expressions of the OP and throughput of both SPSR and ODPSR schemes. We also obtained the optimal power splitting ratio of ODPSR for maximizing the achievable capacity at the destination. Finally, we present extensive numerical and simulation results to confirm our analytical findings. Both simulation and analytical results show the superiority of ODPSR over SPSR.

A Two-layer Fog-Cloud Intrusion Detection Model for IoT Networks

The Internet of Things (IoT) and its applications are becoming ubiquitous in our life. However, the open deployment environment and the limited resources of IoT devices make them vulnerable to cyber threats. In this paper, we investigate intrusion detection techniques to mitigate attacks that exploit IoT security vulnerabilities. We propose a machine learning-based two-layer hierarchical intrusion detection mechanism that can effectively detect intrusions in IoT networks while satisfying the IoT resource constraints. Specifically, the proposed model effectively utilizes the resources in the fog layer of the IoT network by efficiently deploying multi-layered feedforward neural networks in the fog-cloud infrastructure for detecting network attacks. With a fog layer into the picture, analysis is dynamically distributed across the fog and cloud layer thus enabling real-time analytics of traffic data closer to IoT devices and end-users. We have performed extensive experiments using two publicly available datasets to test the proposed approach. Test results show that the proposed approach outperforms existing approaches in multiple performance metrics such as accuracy, precision, recall, and F1-score. Moreover, experiments also justified the proposed model in terms of improved service time, lower delay, and optimal energy utilization.

Towards a new semantic middleware for service description, discovery, selection, and composition in the Internet of Things

AbstractThe emergence of the Internet of Things (IoT) allows the integration of everyday devices such as tags, sensors, and actuators into the Internet. IoT also provides handy and automatic techniques to interoperate ad‐hoc, heterogeneous, and mobile networks and simplify data sharing among devices by abstracting their capabilities as services. Nevertheless, the large number and heterogeneity of encompassed networks/devices and the diversity of the offered resources/services make the IoT service description, discovery, selection, and composition a challenging task. To deal with these issues, we introduce, in this article, a semantic middleware for IoT applications based on service composition. Such a solution supports the semantic description of IoT resources, including services and user requests, and provides a modular, end‐to‐end, and loosely coupled request resolution process that comprises a context‐aware service discovery, a semantic service selection, and an automatic lightweight service composition. The proposal is illustrated and extensively evaluated on a restricted smart‐city scenario. Obtained results show that the proposed service discovery, selection, and composition sub‐processes improve the scalability of equivalent state‐of‐the‐art solutions by around 15, 20, and 40, respectively.

A Privacy-Aware Approach for Managing the Energy of Cloud-Based IoT Resources Using an Improved Optimization Algorithm

Cloud-based Internet of Things (IoT) is a network of physical things controlled and monitored via the Internet to develop numerous smart systems. In this network, numerous physical things with different types of sensors are created that can be linked to the Internet. IoT devices need to work with limited energy sources for a long time, and wireless communications are the primary source of energy usage. As a result, the lifespan of IoT devices can be extended by minimizing radio communications. Because of the great capability of the IoT, the issue of resource energy consumption is raised. On the other hand, privacy concerns must be considered when designing cloud-based IoT applications. The current article introduces programming formulations of the mentioned issues using an improved ant colony optimization (IACO) algorithm. The method is combined with a new kind of local search algorithm to obtain higher-quality responses. Also, for responding to privacy concerns, a privacy-preserving method has been suggested. The suggested approach has been tested widely, and the outcomes have been compared to the genetic and particle swarm optimization (PSO) algorithms. The results indicated the good ability of the proposed method in terms of network lifespan and energy consumption.

Resource identifier interoperability among heterogeneous IoT platforms

Many standards, projects, and platforms are being developed as the Internet of Things (IoT) is adopted in a wide range of fields. However, because each IoT platform is based on a different resource identifier (ID), it is difficult to identify each device and use the service among heterogeneous IoT platforms. To solve this problem, we propose an interoperability framework that includes an IoT resource name system (RNS) based on analysis of the resource IDs (i.e., device ID and resource request formats) of five selected IoT platforms: oneM2M, Oliot, Watson IoT, IoTivity, and FIWARE. The IoT RNS converts a specific resource path into a resource request format for each platform. The converted resource path is shared among IoT RNSs for each platform, and users can request services from other platforms using converted resource paths. We also present an example of interoperability scenario among heterogeneous IoT platforms using the proposed IoT RNS in a smart city. The scenario includes each stage, such as resource registration and deletion, sharing mapping tables, converting resource addresses, and service requests. Furthermore, to prove the aims of the proposed approach, we implemented the resource interoperability scenario between oneM2M and FIWARE. In the experiments, resources can interwork in the two platforms through resource path conversion. Based on the results, we performed a qualitative evaluation of the IoT RNS with the current studies. In conclusion, our proposal overcomes the issues of building an existing integrated platform or specific central ontology and duplicating resources inside the platform. In addition, we separate the functions of the root and local IoT RNSs to solve communication traffic and memory capability issues.

Recommendation platform in Internet of Things leveraging on a self-organizing multiagent approach

Identifying user requirements and preferences on the basis of the current context, is one of main challenges of the Internet of Things (IoT) paradigm. Users, services and applications interact maintaining, often unreliable, relations which need of smart management systems to satisfy their demands. Traditional information handling approaches in distributed systems are most often unsuitable for modern Smart Environments due to the huge amount and the extreme dynamism of the entities involved. This paper proposes NARIoT platform that allows building recommendation systems in IoT environment. The approach relies on vector representations of IoT resources obtained by using of a word embedding tool, the Doc2Vec neural model, which, starting from text documents describing the resources, provides real-valued vectors mapping them. The vectors are handled through intelligent agents, which self-organize themselves creating an ordered virtual structure, so enabling informed mechanisms of information filtering. In particular, an ordered overlay network emerges from the autonomous, parallel and decentralized work of intelligent agents, thus enabling efficient recommendation operations. The experimental validation confirms the effectiveness of the approach and provides very encouraging results.