A New Decision-Making Method for Service Discovery and Selection in the Internet of Things Using Flower Pollination Algorithm

Tabrizi, Sara Ghiasi; Navimipour, Nima Jafari; Danesh, Amir Seyed; Yalçın, Şenay

doi:10.1007/s11277-022-09604-2

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Cloud computing is used to store and process data instead of personal computers, which comes with the basic requirement of an Internet connection. 30,31 The next level of cloud computing is Edge Computing. The main difference between edge and cloud is where the data is stored and processed.…”

Section: System Modelmentioning

confidence: 99%

Dynamic service function chains placement based on parallelized requests in edge computing environment

Guo,

Rezaeipanah

2023

Trans Emerging Tel Tech

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The advent of Network Function Virtualization (NFV) technology brings flexible traffic engineering to edge computing environments. Online services in NFV are chained as Service Function Chains (SFCs), which consist of ordered sequences of Virtual Network Functions (VNFs). The SFC Placement (SFCP) problem is solved under Quality of Service (QoS) requirements and limited resource availability by directing traffic to the required VNFs. However, SFC assembly leads to high latency and network congestion by increasing the count of VNFs, which parallelized SFC can overcome this problem. With parallelizing an SFC request, independent VNFs are activated simultaneously and computational acceleration is realized by reducing the SFC length. Any pair of VNFs that do not conflict with traffic can be activated simultaneously. Most VNFs are deployed on distributed servers for load balancing, which makes SFC parallelization challenging. Meanwhile, the cost of merging/duplicating packets for parallelized SFCs between different servers is not negligible. Hence, in this article, Distributed Parallel Chaining (DPC) is proposed which is an algorithm based on Deep Reinforcement Learning (DRL) approaches. The DPC algorithm solves the SFCP problem to maximize the Long‐Term Expected Cumulative Reward (LTECR). DPC incorporates an Asynchronous Advantage Actor‐Critic (A3C) algorithm as a new approach based on DRL to increase the admission ability of future SFC requests by maximizing LTECR. The evaluation results show the effectiveness of the proposed algorithm from different aspects. Specifically, compared to the best existing approaches, DPC can reduce SFC latency by 8.7%.

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Section: System Modelmentioning

confidence: 99%

Dynamic service function chains placement based on parallelized requests in edge computing environment

Guo,

Rezaeipanah

2023

Trans Emerging Tel Tech

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A New Feature‐Based Biometric Identification System in IoT‐Powered Smart Cities Using a Hybrid Optimization Algorithm

Darbandi,

Ehsani,

Bahrami

et al. 2024

Security and Privacy

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The pervasive expansion of the Internet of Things (IoT) year after year has facilitated widespread sensing capabilities across various domains. Consequently, heightened concerns have arisen regarding authentication and security measures. There is a growing focus on biometrics in the realm of human identification applications, particularly in the context of advancing biometric‐enhanced IoT applications. This trend is garnering increasing attention as it unfolds. As new technologies have developed, biometric‐based identification has been seen as an effective way to automatically identify people because of its uniqueness and impossibility of fabricating it. Biometric identity systems make secure authentication and access management possible. However, due to their almost identical physical traits, one of the biggest problems with conventional systems is being able to tell between identical twins. This fact frequently results in high false acceptance rates, putting the system's security at risk. Thus, a solution is addressed in this work by applying a multi‐biometric identification method based on unique feature levels. Moreover, the accuracy and robustness of the biometric identifications are further enhanced both with Real Coded Genetic Algorithm (RCGA) and Fish Swarm Algorithm (FSA). RCGA is employed as a global search to explore the promising solution space and guide the solution toward the global optimal region. The algorithm exploits the capability of AFSA, serving as a local search to secure the final optimal solution. Besides, the proposed method enhances the system's discriminative power, enabling an identification more precise and trustworthy. Therefore, this work greatly contributes to the advancement of biometric identification systems and the increase of accuracy in various fields.

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A new energy-aware technique to improve the network lifetime of wireless Internet of Things using a most valuable player algorithm

Xiao,

Voronkova

2024

Cluster Comput

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A New Decision-Making Method for Service Discovery and Selection in the Internet of Things Using Flower Pollination Algorithm

Cited by 3 publications

References 35 publications

Dynamic service function chains placement based on parallelized requests in edge computing environment

Dynamic service function chains placement based on parallelized requests in edge computing environment

A New Feature‐Based Biometric Identification System in IoT‐Powered Smart Cities Using a Hybrid Optimization Algorithm

A new energy-aware technique to improve the network lifetime of wireless Internet of Things using a most valuable player algorithm

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