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

Jin, Wenquan; Kim, Do‐Hyeun

doi:10.3390/electronics8030311

Cited by 10 publications

(5 citation statements)

References 41 publications

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“…The IoT is a system that is comprised of heterogeneous elements to provide autonomous and intelligent services in various industrial domains. For developing the communication between the EG and IoT device, we use the IoTivity that is a framework to develop a light-weight IoT system based on CoAP [35][36][37]. IoTivity is implemented through Open Connectivity Foundation (OCF) is an IoT standard specification including the definition of connectivity, management, functions, and security [38][39][40].…”

Section: Related Workmentioning

confidence: 99%

Decision-making of IoT device operation based on intelligent-task offloading for improving environmental optimization

Jin

Lim

Woo

et al. 2022

Complex Intell. Syst.

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Computation offloading enables intensive computational tasks to be separated into multiple computing resources for overcoming hardware limitations. Leveraging cloud computing, edge computing can be enabled to apply not only large-scale and personalized data but also intelligent algorithms based on offloading the intelligent models to high-performance servers for working with huge volumes of data in the cloud. In this paper, we propose a getaway-centric Internet of Things (IoT) system to enable the intelligent and autonomous operation of IoT devices in edge computing. In the proposed edge computing, IoT devices are operated by a decision-making model that selects an optimal control factor from multiple intelligent services and applies it to the device. The intelligent services are provided based on offloading multiple intelligent and optimization approaches to the intelligent service engine in the cloud. Therefore, the decision-making model in the gateway is enabled to select the best solution from the candidates. Also, the proposed IoT system provides monitoring and visualization to users through device management based on resource virtualization using the gateway. Furthermore, the gateway interprets scenario profiles to interact with intelligent services dynamically and apply the optimal control factor to the actual device through the virtual resource. For implementing the improved energy optimization using the proposed IoT system, we propose two intelligent models to learn parameters of a user’s residential environment using deep learning and derive the inference models to deploy in the intelligent service engine. The inference models are used for predicting a heater energy consumption that is applied to the heater. The heater updates the environment parameters to reach the user-desired values. Moreover, based on two energy consumption values, the decision-making model brings a smaller value to operate the heater to enable reducing the energy consumption as well as providing a user-desired environment.

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Section: Related Workmentioning

confidence: 99%

Decision-making of IoT device operation based on intelligent-task offloading for improving environmental optimization

Jin

Lim

Woo

et al. 2022

Complex Intell. Syst.

Self Cite

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“…To overcome such issues, OCF enables the resourceconstrained devices deployed in the smart greenhouse to communicate securely and reliably with enhanced accessibility and seamless connectivity. The open source software framework IoTivity implementing OCF provides a customized protocol for the constrained hardware, ensuring lower round trip time and smaller packet size employing constrained application protocols [9]. Hence there is a dire need to develop an agricultural IoT system that can optimally control the greenhouse environment through efficient utilization of resources.…”

Section: Introductionmentioning

confidence: 99%

Optimal Environment Control Mechanism Based on OCF Connectivity for Efficient Energy Consumption in Greenhouse

Rizwan

Khan

Ahmad

et al. 2023

IEEE Internet Things J.

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Greenhouses are a productive system that allows us to respond to the growing global demand for fresh and healthy food throughout the year, but the greenhouse environment is not easily controlled because its climate parameters are interrelated. However, the numbers of the actuator are operated parallelly to maintain the greenhouse environment; as a result, the energy consumption of greenhouses is high. In this study, we presented the optimization module by considering the outdoor environment with the aim of minimum energy consumption. Metaheuristicbased Differential Evaluation (DE) is used to optimize the climate parameters by considering indoor and outdoor environmental constraints. Furthermore, the Long Short-Term Memory (LSTM) based inference model is offloaded on the Internet of Things (IoT) device to predict the next environmental situation. The objective function selects the optimal parameters within user preferences with minimum energy consumption based on the inferred parameter value. The open-source software framework IoTivity, implementing Open Connectivity Foundation (OCF) technical standards, is used for the real-time connection between IoT devices and the IoT platform. Greenhouse owners can set the preferences based on the requirements of plants in the greenhouse by using a smart and remotely accessible Androidbased interface. A fuzzy logic-based control module operates on an IoT device that maps the optimized parameters with the actuator and operates accordingly. The proposed model is analyzed, and the performance is evaluated in terms of energy consumption for each climate parameter and actuator in the greenhouse. The results show that the proposed mechanism saves 36% of energy.

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“…The edge gateway is a device that provides edge computing functions to interact with IoT devices and cloud elements from the entry of the network edge [ 12 , 13 , 14 ]. The edge gateway provides management functionality through monitoring the environment and devices, registering the information of devices for discovery by web clients and forwarding messages to different protocols and networks [ 15 , 16 , 17 , 18 ]. Additionally, the intelligent services are provided based on the sufficient computational ability of the edge gateway [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Inference Approach Based on Rules Engine in Intelligent Edge Computing for Building Environment Control

Jin

Lim

et al. 2021

Sensors

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Computation offloading enables intensive computational tasks in edge computing to be separated into multiple computing resources of the server to overcome hardware limitations. Deep learning derives the inference approach based on the learning approach with a volume of data using a sufficient computing resource. However, deploying the domain-specific inference approaches to edge computing provides intelligent services close to the edge of the networks. In this paper, we propose intelligent edge computing by providing a dynamic inference approach for building environment control. The dynamic inference approach is provided based on the rules engine that is deployed on the edge gateway to select an inference function by the triggered rule. The edge gateway is deployed in the entry of a network edge and provides comprehensive functions, including device management, device proxy, client service, intelligent service and rules engine. The functions are provided by microservices provider modules that enable flexibility, extensibility and light weight for offloading domain-specific solutions to the edge gateway. Additionally, the intelligent services can be updated through offloading the microservices provider module with the inference models. Then, using the rules engine, the edge gateway operates an intelligent scenario based on the deployed rule profile by requesting the inference model of the intelligent service provider. The inference models are derived by training the building user data with the deep learning model using the edge server, which provides a high-performance computing resource. The intelligent service provider includes inference models and provides intelligent functions in the edge gateway using a constrained hardware resource based on microservices. Moreover, for bridging the Internet of Things (IoT) device network to the Internet, the gateway provides device management and proxy to enable device access to web clients.

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

Cited by 10 publications

References 41 publications

Decision-making of IoT device operation based on intelligent-task offloading for improving environmental optimization

Decision-making of IoT device operation based on intelligent-task offloading for improving environmental optimization

Optimal Environment Control Mechanism Based on OCF Connectivity for Efficient Energy Consumption in Greenhouse

Dynamic Inference Approach Based on Rules Engine in Intelligent Edge Computing for Building Environment Control

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