A Bio-Inspired Self-Learning Coevolutionary Dynamic Multiobjective Optimization Algorithm for Internet of Things Services

Yang, Zhen; Jin, Yaochu; Hao, Kuangrong

doi:10.1109/tevc.2018.2880458

Cited by 48 publications

(24 citation statements)

References 58 publications

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“…When the accident disaster situation expands, immediately report to the emergency agencies of the next level to be responsible for the implementation. There will be both administrative orders and coordination [ 25 ]. The United States takes each local government as the node and adopts a flat emergency network.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Remote Public Medical Emergency Management System with Low Delay Based on Internet of Things

Feng

Zhong-yu

2021

Journal of Healthcare Engineering

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Due to the limitation of economic geography condition, the existing health resources distribution is uneven, the emergence of remote medical disciplines perfectly solved this difficult problem, using computer and network communication network on audio video information transmissions, querying, display, storage, and backup and realizing the network of outpatient service, remote consultation, health advice, and other functions. Telemedicine enables the limited available medical resources to be shared and fully utilized and also enables many economically underdeveloped provinces to enjoy a higher level of medical sharing services. Public health emergency management system on the overall design of the low latency according to system function will be based on the Internet of things. The remote public health emergency management system is divided into three subsystems low latency modules, basic subsystems of platform, application platform, and specific application subsystems, and designs the structure of the various modules. The implementation process is given. In the realization of the system, this paper describes in detail how to realize the functions of the public medical low delay emergency management system, and, in the end, the realization process of the system is reasonably summarized. The application of Internet of things technology in regional emergency rescue can realize the identification and real-time positioning of material personnel, the collection and transmission of the wounded’s physiological information, real-time information transmission, and interaction based on mobile handheld devices, as well as the integration of emergency rescue resources, information integration, and command decision-making, so as to assist rescue operations and improve rescue efficiency.

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Section: Introductionmentioning

confidence: 99%

Optimization of Remote Public Medical Emergency Management System with Low Delay Based on Internet of Things

Feng

Zhong-yu

2021

Journal of Healthcare Engineering

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“…This function is extended with each mapping on the octagon diagonals. Finally, the consequence of continuous levels predicts the nearest spot [64].…”

Section: "Division C" With Iot Framework For Console Assembly Withmentioning

confidence: 96%

An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

Sanjeevi

Kumar

Prasanna

et al. 2020

Complex Intell. Syst.

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Constituting the agriculture solid substance manufacture, the post-harvest sector processing schema is direct to preventing reduce the losses in intelligent agriculture. Many processing schemata will be preventing post-harvest losses on the agriculture solid substance manufacture, especially sekai-ichi apple is the regularly used fruit also used to make active in human-related activities of the sensory and control function consisting of an agricultural industry. Sekai-ichi apple is being a definite number of diseases induce, but it is to the highest degree of wastage involving in the Post-Harvest process. Especially sekai-ichi apple count loss is an unsafe many time because it not critically post-harvest. Regardless of consideration, the existing hierarchical model specified post-harvest losses prevention research has deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. This paper suggests a "Hierarchical Model within Ontology Enabled IoT" for distinguishable healthy separation of sekai-ichi apple by using Boosted Continuous Non-spatial whole Attribute Extraction (BCNAE). Sekai-ichi apple count loss is always safe on critically post-harvest. Proposed Post-Harvest hierarchical model specified post-harvest losses prevention and deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. In these suggestions, the separation cognitive operation takes the three levels of processing schemes such as lower level, middle level, and higher level. Firstly, the lower level is express agreements with the dynamic functioning for maintaining the definite number of manual induces. This lower level showing an absorption with the activity of manual separation by the human reliability determination. Secondly, the middle level is an express arrangement with the dynamic functioning for reducing the overfitting and accommodate to fitting the right shape deliberation. Middle level is establishing being generalized by concentrating the time-varying features in the occurrence of a change for the worse identification. Finally, the upper level is express for features refining with the help of the function of sekai-ichi apple image segmentation connection. This interpretability process helps to make the proven position of a prominent classification in a particular fruit on the agriculture solid substance. These three processing flow constructs the ontology structure with manually collected sekai-ichi apple images from a 3D sensor. The observational consequences express that the proposed BCNAE framework recognizes a detection performance carrying out with an optimized-separation ratio for time-variant of the separation process.

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“…A hybrid memetic approach is proposed in Reference 26 to solve QoS‐aware Web service composition, combining elements of NSGA‐II and MOEA/D 27 to independently optimise quality attributes. Zhen et al 28 developed a bio‐inspired self‐learning co‐evolutionary algorithm for dynamic multi‐objective optimization of IoT services to reduce energy consumption and service time. However, these algorithms require more computational time and more space requirements.…”

Section: Related Workmentioning

confidence: 99%

QoS‐aware big service composition using distributed co‐evolutionary algorithm

Dutta

Jatoth

Gangadharan

et al. 2021

Concurrency and Computation

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Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service-aware big service composition methodology using a distributed co-evolutionary algorithm. In our proposed model, we develop a distributed NSGA-III for finding the optimal Pareto front and a distributed multi-objective Jaya algorithm for enhancing the diversity of solutions. The distributed co-evolutionary algorithm finds the near-optimal solution in a fast and scalable way. K E Y W O R D Sbig services, distributed co-evolutionary algorithm, distributed Jaya, distributed NSGA-III, quality of service, service composition INTRODUCTIONWith the onset of cloud computing, Internet of Things (IoT), and machine learning/ deep learning, manufacturing industries are undergoing a drastic change. Modern industries are incorporating these new technologies to achieve better efficiency in manufacturing products and services, with an improvement in quality control and a reduction in production costs. The Fourth Industrial Revolution (or Industry 4.0) is the ongoing automation of traditional manufacturing and industrial practices, using these modern smart technologies. Big services are collections of interrelated web services across the virtual and physical domains of Industry 4.0, processing Big Data. 1,2 Big services are integrated from various domains to develop a composite service that addresses the requirements of a customer. 3 Consider a fictitious company that manufactures electric vehicles. The company uses IoT, cloud computing, and machine learning/ deep learning to test for and predict possible failures and for tweaking or designing new engine parts, suspension units, and many more. Let us consider that the company has two divisions: design analysis unit and production unit. The design analysis unit analyzes the performances of the electric vehicles and suggests new design models. The lessons learned from the design analysis unit are incorporated in the design and manufacturing of electric vehicles by the production unit. Assume that both the design analysis unit and the production unit use a similar workflow. We can imagine two separate sets of service composition requirements: The design analysis unit requires high performance in terms of response time and throughput, high security, and with no restrictions on cost. The production unit has to keep the cost low and maintain the desirable quality of service (QoS) parameters and changes with respect to supply and demand throughout the year.In this scenario, we need to execute the service composition process repeatedly whenever there is a change in the requirements, with certain changes in the fitness function. This becomes computationally expensive as the number of services grows, being classified as NP-hard. 4,5 Co-evolutionary approaches offer promising solutions in this case and man...

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A Bio-Inspired Self-Learning Coevolutionary Dynamic Multiobjective Optimization Algorithm for Internet of Things Services

Cited by 48 publications

References 58 publications

Optimization of Remote Public Medical Emergency Management System with Low Delay Based on Internet of Things

Optimization of Remote Public Medical Emergency Management System with Low Delay Based on Internet of Things

An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

QoS‐aware big service composition using distributed co‐evolutionary algorithm

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