Relevant node discovery and selection approach for the Internet of Things based on neural networks and ant colony optimization

Zannou, Abderrahim; Boulaalam, Abdelhak; Nfaoui, El Habib

doi:10.1016/j.pmcj.2020.101311

Cited by 22 publications

(4 citation statements)

References 27 publications

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“…Step 4: The probability of selecting ij path by ant k is calculated due to the pheromone and service’s score [ 74 ]. The probability of selecting a particular path (combination of services) by an ant is calculated based on the pheromone level on the path and the Q score of the services in the path.…”

Section: Proposed Methodsmentioning

confidence: 99%

A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm

Hamzei,

Khandagh,

Jafari Navimipour

2023

Sensors

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The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%.

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Section: Proposed Methodsmentioning

confidence: 99%

A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm

Hamzei,

Khandagh,

Jafari Navimipour

2023

Sensors

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“…The main downside to the hybrid and centralized mechanism is limited to scalability and mobility. Researchers may use fog or edge platforms near the end-user side to solve them (Zannou and Boulaalam, 2020). Also, mobile cloud computing supports services and mobile users seamlessly, transparently, securely and effectively.…”

Section: Open Issuesmentioning

confidence: 99%

Service discovery mechanisms in cloud computing: a comprehensive and systematic literature review

Heidari

Navimipour

2021

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PurposeThe main goal of this paper is to study the cloud service discovery mechanisms. In this paper, the discovery mechanisms are ranked in three major classes: centralized, decentralized, and hybrid. Moreover, in this classification, the peer-to-peer (P2P) and agent-based mechanisms are considered the parts of the decentralized mechanism. This paper investigates the main improvements in these three main categories and outlines new challenges. Moreover, the other goals are analyzing the current challenges in a range of problem areas related to cloud discovery mechanisms and summarizing the discussed service discovery techniques.Design/methodology/approachSystematic literature review (SLR) is utilized to detect, evaluate and combine findings from related investigations. The SLR consists of two key stages in this paper: question formalization and article selection processes. The latter includes three steps: automated search, article selection and analysis of publication. These investigations solved one or more service discovery research issues and performed a general study of an experimental examination on cloud service discovery challenges.FindingsIn this paper, a parametric comparison of the discovery methods is suggested. It also demonstrates future directions and research opportunities for cloud service discovery. This survey will help researchers understand the advances made in cloud service discovery directly. Furthermore, the performed evaluations have shown that some criteria such as security, robustness and reliability attained low attention in the previous studies. The results also showed that the number of cloud service discovery–related articles rose significantly in 2020.Research limitations/implicationsThis research aimed to be comprehensive, but there were some constraints. The limitations that the authors have faced in this article are divided into three parts. Articles in which service discovery was not the primary purpose and their title did not include the related terms to cloud service discovery were also removed. Also, non-English articles and conference papers have not been reviewed. Besides, the local articles have not been considered.Practical implicationsOne of the most critical cloud computing topics is finding appropriate services depending on consumer demand in real-world scenarios. Effective discovery, finding and selection of relevant services are necessary to gain the best efficiency. Practitioners can thus readily understand various perspectives relevant to cloud service discovery mechanisms. This paper's findings will also benefit academicians and provide insights into future study areas in this field. Besides, the drawbacks and benefits of the analyzed mechanisms have been analyzed, which causes the development of more efficient and practical mechanisms for service discovery in cloud environments in the future.Originality/valueThis survey will assist academics and practical professionals directly in their understanding of developments in service discovery mechanisms. It is a unique paper investigating the current and important cloud discovery methods based on a logical categorization to the best of the authors’ knowledge.

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“…IoT oriented various platforms and applications that exploit communication among heterogeneous and nonheterogeneous devices to execute tasks and provide real-time services [7,8,9,10,11,12]. It creates a new type…”

Section: Introductionmentioning

confidence: 99%

Data Flow Optimization in the Internet of Things

Zannou

Boulaalam

Nfaoui

2022

Stat., optim. inf. comput.

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The Internet of Things (IoT) is constituted of an important number of constrained nodes limited in terms of power energy, computation capacity, storage capacity. They produce a considerable amount of data, which increases the data flflow in the network. The ineffificient transmission of data via constrained nodes makes the network unstable, the energy consumption increases rapidly, and the data delay increases strictly. To overcome these limitations, we propose a new approach that allows nodes to select the effificient path to transmit data from source nodes to base stations (BSs) to optimize the data flflow in the constrained network. First, we grouped nodes using a density peaks (DP) clustering algorithm based on the coordinate’s location of these nodes. Second, using the group nodes, the assignment of nodes to BSs that are considered as the collectors of data is performed. Third, the nodes make a dynamic and automated path plan to optimize the data flflow in the constrained network. Simulation results on a real network data set demonstrate that our proposal outperforms the state-of-the-art approaches in terms of the number of hops to achieve the cluster head (CH) node, the data delay, the network lifetime, and the number of the alive nodes.

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Relevant node discovery and selection approach for the Internet of Things based on neural networks and ant colony optimization

Cited by 22 publications

References 27 publications

A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm

A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm

Service discovery mechanisms in cloud computing: a comprehensive and systematic literature review

Data Flow Optimization in the Internet of Things

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