A survey on nature-inspired techniques for computation offloading and service placement in emerging edge technologies

Kumar, Dinesh; Baranwal, Gaurav; Shankar, Yamini; Vidyarthi, Deo Prakash

doi:10.1007/s11280-022-01053-y

Cited by 17 publications

(6 citation statements)

References 140 publications

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“…These papers are categorized based on the title, but three papers are classified [11][12][13] based on the abstract. Figure 2B based on the area of interest such as generic, 4,6-8,10,14-19 energy-efficient, 8,11,12,20 machine learning (ML)/AI and game theory (GT)-based, 9,[21][22][23][24] vehicle communication and computing, [25][26][27][28][29] and optimization techniques and algorithms 3,13,16,30,31 as shown in Figure 3.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 2B illustrates the distribution of papers based on the area of interest. An overview of related works on EC based on CO has been divided into five categories based on the area of interest such as generic, 4,6‐8,10,14‐19 energy‐efficient, 8,11,12,20 machine learning (ML)/AI and game theory (GT)‐based, 9,21‐24 vehicle communication and computing, 25‐29 and optimization techniques and algorithms 3,13,16,30,31 as shown in Figure 3.…”

Section: Introductionmentioning

confidence: 99%

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Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

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SummaryIn today's era, Internet of Things (IoT) devices generate a vast amount of data, which is typically stored in the cloud environment and can be accessed by edge and IoT devices. The data generated by these devices are offloaded through computation offloading (CO) techniques in an edge/cloud computing environment. This paper conducts a systematic literature review (SLR) to review the state‐of‐the‐art CO techniques in edge computing (EC) in the context of energy, Quality of Service (QoS), authentication and traceability. In this SLR, the evolution of offloading techniques is analyzed in detail. A total of 138 articles, spanning from 2016 to 2023 (till date), have been classified into QoS, energy, and authentication and traceability‐based CO techniques. The optimization‐based techniques are the most preferred choices to improve the QoS and reduce energy in the research field of CO in EC. In addition, this paper explores the significant issues and challenges that require further investigation. For future research, energy, QoS, and data provenance for dependent or dynamic task offloading in mobility scenarios can be explored further.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

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“…15 By mimicking biological and ecological principles, these algorithms contribute to the optimization of resource allocation, personalized treatment plans, and predictive analytics. 16 For instance, GA can be utilized to optimize feature selection inpatient data, while ACO can enhance routing efficiency in IoT networks within healthcare infrastructures. 17 Synthesizing mathematics, biology, computer science, chemistry, and statistics, this interdisciplinary field of study shapes a unique and integrated framework.…”

Section: Iot-based Healthcare Servicesmentioning

confidence: 99%

“…These algorithms, such as GAs, PSO, and ACO, can enhance the analysis of vast amounts of real‐time health data generated by interconnected devices and sensors 15 . By mimicking biological and ecological principles, these algorithms contribute to the optimization of resource allocation, personalized treatment plans, and predictive analytics 16 . For instance, GA can be utilized to optimize feature selection inpatient data, while ACO can enhance routing efficiency in IoT networks within healthcare infrastructures 17 .…”

Section: Basic Concepts and Corresponding Terminologymentioning

confidence: 99%

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Amiri,

Heidari,

Zavvar

et al. 2024

Trans Emerging Tel Tech

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Nature‐inspired algorithms revolve around the intersection of nature‐inspired algorithms and the IoT within the healthcare domain. This domain addresses the emerging trends and potential synergies between nature‐inspired computational approaches and IoT technologies for advancing healthcare services. Our research aims to fill gaps in addressing algorithmic integration challenges, real‐world implementation issues, and the efficacy of nature‐inspired algorithms in IoT‐based healthcare. We provide insights into the practical aspects and limitations of such applications through a systematic literature review. Specifically, we address the need for a comprehensive understanding of the applications of nature‐inspired algorithms in IoT‐based healthcare, identifying gaps such as the lack of standardized evaluation metrics and studies on integration challenges and security considerations. By bridging these gaps, our paper offers insights and directions for future research in this domain, exploring the diverse landscape of nature‐inspired algorithms in healthcare. Our chosen methodology is a Systematic Literature Review (SLR) to investigate related papers rigorously. Categorizing these algorithms into groups such as genetic algorithms, particle swarm optimization, cuckoo algorithms, ant colony optimization, other approaches, and hybrid methods, we employ meticulous classification based on critical criteria. MATLAB emerges as the predominant programming language, constituting 37.9% of cases, showcasing a prevalent choice among researchers. Our evaluation emphasizes adaptability as the paramount parameter, accounting for 18.4% of considerations. By shedding light on attributes, limitations, and potential directions for future research and development, this review aims to contribute to a comprehensive understanding of nature‐inspired algorithms in the dynamic landscape of IoT‐based healthcare services.

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“…The node deployment problem has been investigated with different networks and in different environments. It remains a challenging problem that has been proven to be NP-Hard [14]. In order to address these issues, meta-heuristic techniques have been developed, although they typically only provide optimal local solutions [15].…”

Section: Introductionmentioning

confidence: 99%

Efficient fog node placement using nature-inspired metaheuristic for IoT applications

Naouri,

Nouri,

Khelloufi

et al. 2024

Cluster Comput

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Managing the explosion of data from the edge to the cloud requires intelligent supervision, such as fog node deployments, which is an essential task to assess network operability. To ensure network operability, the deployment process must be carried out effectively regarding two main factors: connectivity and coverage. The network connectivity is based on fog node deployment, which determines the network’s physical topology, while the coverage determines the network accessibility. Both have a significant impact on network performance and guarantee the network quality of service. Determining an optimum fog node deployment method that minimizes cost, reduces computation and communication overhead, and provides a high degree of network connection coverage is extremely hard. Therefore, maximizing coverage and preserving network connectivity is a non-trivial problem. In this paper, we propose a fog deployment algorithm that can effectively connect the fog nodes and cover all edge devices. Firstly, we formulate fog deployment as an instance of multi-objective optimization problems with a large search space. Then, we leverage Marine Predator Algorithm (MPA) to tackle the deployment problem and prove that MPA is well-suited for fog node deployment due to its rapid convergence and low computational complexity, compared to other population-based algorithms. Finally, we evaluate the proposed algorithm on a different benchmark of generated instances with various fog scenario configurations. Our algorithm outperforms state-of-the-art methods, providing promising results for optimal fog node deployment. It demonstrates a 50% performance improvement compared to other algorithms, aligning with the No Free Lunch Theorem (NFL Theorem) Theorem’s assertion that no algorithm has a universal advantage across all problem domains. This underscores the significance of selecting tailored algorithms based on specific problem characteristics.

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A survey on nature-inspired techniques for computation offloading and service placement in emerging edge technologies

Cited by 17 publications

References 140 publications

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Efficient fog node placement using nature-inspired metaheuristic for IoT applications

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