Resource Management In Internet Of Things Research Articles

IoT Resources and Their Practical Application, A Comprehensive Study

The Internet of Things (IoT) has become a paradigm shifter, connecting an enormous number of smart devices and facilitating seamless data exchange for a diverse array of applications. The availability and effective use of the IoT ecosystem's resources are key factors in determining how its practical applications will develop as they mature. The IoT resources and their practical application across several areas are thoroughly explored in this paper. The paper begins by classifying and describing the various sensor types, their applications in various fields, and IoT resources, highlighting their contributions to real-time data collection, processing, and transmission. It then goes on to demonstrate a wide range of real-world uses for these resources, such as smart cities, education, agriculture, business, healthcare, environment monitoring, transportation, and industrial automation. However, utilizing IoT resources effectively is not without difficulties. Critical difficulties such as resource allocation, scalability, security, interoperability, and privacy concerns are identified and discussed in the paper. Furthermore, the paper also highlights future directions and emerging trends in IoT resource management, including edge computing, cloud computing, human machine integration, and compatibility with other systems. These developments aim to increase the dependability of IoT applications in diverse settings and optimize resource allocation. This paper's conclusion highlights the crucial role that IoT resources play in advancing real-world applications across a variety of areas. Researchers, practitioners, policymakers, and other stakeholders may collaborate together to effectively leverage the full potential of IoT resources to build intelligent, effective ecosystems that meet the needs of contemporary society by solving difficulties and utilizing developing trends.

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.

Service-oriented architecture for Internet of Things: A semantic approach

The Internet of Things (IoT) aids an interconnection between systems, humans, and services to develop computation-intensive autonomous applications. Service-oriented architecture (SOA) concepts have been used as defacto software architecture to develop IoT-based systems. Though SOA facilitates various benefits, several challenges exist for integration with IoT-based systems, including configurability, interoperability, and manageability. The ontology-based approach provides a semantically enriched and rigorous platform for interoperating different heterogeneous IoT-based systems and applications. The semantic modelling research has focused on IoT resource management without concerning the method of accessing and utilizing IoT information. In this context, an IoT-based large-scale SOA (IoT-LSS) ontology is proposed that supports interoperability, heterogeneity, flexibility, manageability, extendibility, scalability. This ontology integrates the concept of large-scale SOA (LSS) with semantic sensor network (SSN) ontology. Clinical decision support system (CDSS) ontology has considered as a case study to illustrate the proposed ontology. A detailed comparative analysis between the proposed system and several existing IoT-based service ontologies has been performed based on different characteristics. This analysis shows that IoT-LSS supports dynamic features and exposes to service with its different mechanisms. This article presents an ontological framework for the IoT service-based environment to efficiently use for service composition mechanisms in the large service domain.

Feedback-based fuzzy resource management in IoT using fog computing

The goal of Internet of Things (IoT) is to make “things” (wearable devices, smart cameras, sensors and smart home appliances) connect to internet. Large storage is required to store huge volume of data that is generated, data processing need to be carried out between IoT devices and the massive number of applications. This process can be made effectively with the help of cloud computing technology. Resources can be effectively utilized with the help of cloud, and IoT plays a significant role in managing the tasks that are to be offloaded to the cloud. The performance of the application is to be enhanced by providing Quality of Service (QoS) and the performance is evaluated in terms of QoS parameters like Power utilization, Makespan and Execution Time. The tasks are allocated based on priority. Fog computing paradigm is used in the proposed model to decrease the makespan of time. The projected mechanism is tested and compared with different present systems and is shown that proposed methodology produced effective results.

Machine Learning and Prediction-Based Resource Management in IoT Considering Qos

Internet of Things (IoT) is one of the fast-growing technology paradigms used in every sectors, where in the Quality of Service (QoS) is a critical component in such systems and usage perspective with respect to ProSumers (producer and consumers). Most of the recent research works on QoS in IoT have used Machine Learning (ML) techniques as one of the computing methods for improved performance and solutions. The adoption of Machine Learning and its methodologies have become a common trend and need in every technologies and domain areas, such as open source frameworks, task specific algorithms and using AI and ML techniques. In this work we propose an ML based prediction model for resource optimization in the IoT environment for QoS provisioning. The proposed methodology is implemented by using a multi-layer neural network (MNN) for Long Short Term Memory (LSTM) learning in layered IoT environment. Here the model considers the resources like bandwidth and energy as QoS parameters and provides the required QoS by efficient utilization of the resources in the IoT environment. The performance of the proposed model is evaluated in a real field implementation by considering a civil construction project, where in the real data is collected by using video sensors and mobile devices as edge nodes. Performance of the prediction model is observed that there is an improved bandwidth and energy utilization in turn providing the required QoS in the IoT environment.

Resource Management Based on OCF for Device Self-Registration and Status Detection in IoT Networks

Recently, there are heterogeneous devices that connect to the Internet to provide ubiquitous and intelligent services based on sensors and actuators in the network of the Internet of Things (IoT). The resources of IoT represent the physical entities on the Internet to expose functions through services. Resource management is necessary to enable a massive amount of IoT-connected devices to be discoverable and accessible in the network of IoT. In this paper, we propose an IoT resource management to provide schemes of device self-registration and status detection for devices based on the Open Connectivity Foundation (OCF) standard. This device self-registration scheme is based on an agent that is proposed for registering devices itself which deployed in the OCF network. The devices host the OCF resources to provide IoT services such as sensing and controlling through the sensors and actuators. For a group of devices, an agent-based self-registration is proposed to register the resources. Through the proposed self-registration, the information of IoT devices is published using profile and saved in the management platform that enables the clients to discover the resources and access the services. For accessing the IoT resources in the OCF network, an interworking proxy is proposed to support the communications between web clients and devices over Hypertext Transfer Protocol (HTTP) and Constrained Application Protocol (CoAP) based on OCF. Furthermore, through the interoperability of the resources using the registered information, a real-time monitoring scheme is proposed based on periodic request and response for the status detection of deployed devices.

Need for Resource Management in IoT

Internet of Things (IoT) is a technical revolution which represents the future of communications and computing. In IoT many devices are connected through network and they communicate by the Internet and send data or secret information on the web. The next huge step in the IoT field is to recognize a virtual computing platform that provides access to heterogeneous group of device present in our living environment. It is nonlinear connection between resource information of smart device or client and quality of services (Qos) are built to manage the universal or limited network resource management. KeywordsManagement, Internet of Things, CoAP

Knowledge Representation in the Internet of Things: Semantic Modelling and its Applications

Semantic modelling provides a potential basis for interoperating among different systems and applications in the Internet of Things (IoT). However, current work has mostly focused on IoT resource management while not on the access and utilisation of information generated by the “Things”. We present the design of a comprehensive and lightweight semantic description model for knowledge representation in the IoT domain. The design follows the widely recognised best practices in knowledge engineering and ontology modelling. Users are allowed to extend the model by linking to external ontologies, knowledge bases or existing linked data. Scalable access to IoT services and resources is achieved through a distributed, semantic storage design. The usefulness of the model is also illustrated through an IoT service discovery method.