Clustering‐based resource discovery on Internet‐of‐Things

Bharti, M.; Kumar, Rajesh; Saxena, Sharad

doi:10.1002/dac.3501

Cited by 12 publications

(2 citation statements)

References 43 publications

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“…But according to our research, the mechanism suffers from rediscovery process because of ignoring the current status of target resources while resource discovery operation. There are also some recent works in resource discovery in other infrastructures like cloud computing and internet of things (IoT) (Djamaa et al, 2017;Ghebleh and Ghaffari, 2016;Ezugwu and Adewumi, 2017;Zarrin et al, 2017;Davoli et al, 2018;Bharti et al, 2018). For example in the (Djamaa et al, 2017) the proposed architecture employs the Constrained Application Protocol (CoAP) to achieve some characteristics like scalability, time and cost efficiency and adaptability.…”

Section: Related Studiesmentioning

confidence: 99%

A weighted resource discovery approach in grid computing

Sabamoniri

Souri

2019

IJPCC

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Purpose Grid computing is an effective environment for the execution of parallel applications that requires great computing power. This paper aims to present, based on the hierarchical architecture, an improved weighted resource discovery (WRD) algorithm to manage allocation of resources and minimize cost of communications between grid nodes. Design/methodology/approach A behavioral modeling method is addressed to prove the proposed method correctness. The behavioral model of the proposed algorithm is implemented by StarUML tool with two different model-checking mechanisms. Then, the resource discovery correctness is analyzed in terms of reachability condition, fairness condition and deadlock-free using NuSMV model checker. Findings The results show that WRD algorithm has better performance in requiring re-discovery process, the number of examined nodes in each request and discovering the free resources with high-bandwidth links. Originality/value To store information of resources, a new data structure called resource information table is proposed which facilitates resource finding of the algorithm. A behavioral modeling method is addressed to prove the proposed method correctness.

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Section: Related Studiesmentioning

confidence: 99%

A weighted resource discovery approach in grid computing

Sabamoniri

Souri

2019

IJPCC

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“…At present, various countries are conducting related theories and application research. As the development of the Internet of Things industry is included in our country's 12th Five-Year Development Plan, the development of the Internet of Things will usher in a new opportunity [6]. As a system that aims to realize the ubiquitous perception of things, the Internet of Things is composed of various sensor networks, radio frequency readers and other information acquisition equipment and communication systems [7].…”

Section: Introductionmentioning

confidence: 99%

Research on Data Security Detection Algorithm in IoT Based on K-means

Zhu

Huo

Ansari

et al. 2021

SCPE

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The development of the Internet of Things has prominently expanded the perception of human beings, but ensuing security issues have attracted people's attention. From the perspective of the relatively weak sensor network in the Internet of Things. Proposed method is aiming at the characteristics of diversification and heterogeneity of collected data in sensor networks; the data set is clustered and analyzed from the aspects of network delay and data flow to extract data characteristics. Then, according to the characteristics of different types of network attacks, a hybrid detection method for network attacks is established. An efficient data intrusion detection algorithm based on K-means clustering is proposed. This paper proposes a network node control method based on traffic constraints to improve the security level of the network. Simulation experiments show that compared with traditional password-based intrusion detection methods; the proposed method has a higher detection level and is suitable for data security protection in the Internet of Things. This paper proposes an efficient intrusion detection method for applications with Internet of Things.

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A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm

Zanbouri

Navimipour

2019

Int J Communication

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Summary One of the most critical issues in using service‐oriented technologies is the combination of services, which has become an important challenge in the present. There are some significant challenges in the service composition, most notable is the quality of service (QoS), which is more challenging due to changing circumstances in dynamic service environments. Also, trust value in the case of selection of more reliable services is another challenge in the service composition. Due to NP‐hard complexity of service composition, many metaheuristic algorithms have been used so far. Therefore, in this paper, the honeybee mating optimization algorithm as one of the powerful metaheuristic algorithms is used for achieving the desired goals. To improve the QoS, inspirations from the mating stages of the honeybee, the interactions between honeybees and queen bee mating and the selection of the new queen from the relevant optimization algorithm have been used. To address the trust challenge, a trust‐based clustering algorithm has also been used. The simulation results using C# language have shown that the proposed method in small scale problem acts better than particle swarm optimization algorithm, genetic algorithm, and discrete gbest‐guided artificial bee colony algorithm. With the clustering and reduction of the search space, the response time is improved; also, more trusted services are selected. The results of the simulation on a large‐scale problem have indicated that the proposed method is exhibited worse performance than the average results of previous works in computation time.

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Clustering‐based resource discovery on Internet‐of‐Things

Cited by 12 publications

References 43 publications

A weighted resource discovery approach in grid computing

A weighted resource discovery approach in grid computing

Research on Data Security Detection Algorithm in IoT Based on K-means

A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm

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