Cloud, Edge, and Mobile Computing for Smart Cities

Liu, Qian; Gu, Juan; Yang, Jian; Li, Yun; Sha, Dexuan; Xu, Mengchao; Shams, Ishan; Yu, Manzhu; Yang, Chaowei

doi:10.1007/978-981-15-8983-6_41

Cited by 10 publications

(6 citation statements)

References 124 publications

(116 reference statements)

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“…The convergence factor A → , defined in relation (7), is based on the current iteration t and the maximum number of iterations t max . Unlike the traditional linear decrease of A → , the nonlinear decline introduced by [27] aims to determine the number a in the interval [−a, a] in which A → takes values as:…”

Section: Woa-based Offloading Techniquementioning

confidence: 99%

“…The edge computing paradigm enables offloading computational tasks from the cloud context closer to edge devices rather than only relying on the cloud for data processing. Such strategies can improve the smart grid operation, facilitating near real-time decision-making as it reduces the latency and usage of data network resources while addressing pressing issues related to the wide-area network capacity in transferring large quantities of data towards the cloud [7]. In addition, offloading computation reduces risks associated with data privacy and security breaches during data transit to the cloud.…”

Section: Introductionmentioning

confidence: 99%

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Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services

Arcas,

Cioara,

Anghel

2024

Biomimetics

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As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services’ computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time.

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Section: Woa-based Offloading Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services

Arcas,

Cioara,

Anghel

2024

Biomimetics

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“…In healthcare settings, Edge Computing facilitates remote patient monitoring, telemedicine, and medical device integration, enabling healthcare providers to deliver personalized care and enhance patient outcomes. Edge Computing powers smart city initiatives such as traffic management, public safety, and environmental monitoring, enabling city planners to make data-driven decisions and optimize urban infrastructure (Liu, et al, 2021;Bibri, 2023). In retail environments, Edge Computing enables personalized shopping experiences, inventory management, and supply chain optimization, enhancing customer satisfaction and driving revenue growth.…”

Section: Current State Of Edge Computing In Iotmentioning

confidence: 99%

Current state and prospects of edge computing within the Internet of Things (IoT) ecosystem

Enoch Oluwademilade Sodiya,

Uchenna Joseph Umoga,

Alexander Obaigbena

et al. 2024

Int. J. Sci. Res. Arch.

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The burgeoning growth of the Internet of Things (IoT) has prompted a paradigm shift in computing architectures, leading to the emergence and rapid evolution of edge computing. This review delves into the current state and prospects of edge computing within the IoT ecosystem, exploring its significance, challenges, and future potential. Edge computing, characterized by decentralized data processing at or near the source of data generation, has gained substantial traction owing to its ability to address critical concerns such as latency, bandwidth consumption, and privacy issues inherent in centralized cloud-based systems. By enabling data processing closer to the point of collection, edge computing minimizes latency and ensures real-time responses, making it indispensable for latency-sensitive applications like industrial automation, autonomous vehicles, and healthcare. The integration of edge computing with IoT devices has facilitated the creation of distributed computing architectures capable of handling massive data volumes generated by interconnected devices. This convergence enables efficient data aggregation, analysis, and decision-making at the network's edge, reducing the burden on centralized cloud infrastructure and optimizing resource utilization. Despite its numerous advantages, edge computing faces several challenges, including resource constraints, security vulnerabilities, and interoperability issues. Resource-constrained edge devices often lack the computational power and storage capacity required for complex analytics, necessitating innovative approaches to resource management and workload distribution. Moreover, the distributed nature of edge environments introduces new security risks, necessitating robust security measures to safeguard sensitive data and mitigate cyber threats. Looking ahead, the prospects of edge computing within the IoT ecosystem are promising, with advancements in edge hardware, software, and networking technologies driving innovation and adoption. Edge computing is poised to play a pivotal role in enabling the next wave of IoT applications, ranging from smart cities and autonomous systems to immersive experiences and personalized services. However, realizing the full potential of edge computing requires concerted efforts from industry stakeholders to address existing challenges and foster ecosystem-wide collaboration. The convergence of edge computing and IoT holds immense potential to revolutionize industries, reshape computing architectures, and empower a new era of intelligent, responsive, and decentralized systems.

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“…The concept of offering widespread, accessible, on-demand system access to a distributed pool of customizable computing services is known as "cloud computing." It allows for rapid deployment and release of these resources with much less organizational operations or service operator involvement [27]. A transition in the computing framework occurs when the responsibility of computing is transferred from personal desktop computers, single server applications, or personal data centers to a cloud of computers.…”

Section: Cloud Computingmentioning

confidence: 99%

AI-assisted Distributed Cloud Services Framework for Enhanced Safety in Urban Smart Cities Environment

Niveshitha,

Amsaad,

Sherif

et al. 2024

Preprint

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Smart cities have emerged to tackle life critical challenges that can thwart the overwhelming urbanization process, such as expensive health care, increasing energy demand, traffic jams, and environmental pollution. This paper proposes efficient and high-quality cloud-based machine-learning solutions for safe urban smart city environment. For that, supervised MLbased models, i.e., regression and classification, are developed utilizing cloud-based solutions to forecast high performance in execution time and enhanced quality of the solution in terms of the accuracy of the implemented cloud-based ML solution. To predict AQI, i.e. air quality index, ML models utilize pollutants in the air data sets. The mean absolute error, mean squared error, root means the squared error, R2 score are used to validate and test the designed models. As classification models, we perform the support vector machine and random forest algorithms, which are measured using the accuracy score and confusion matrix. Execution times and accuracy of the developed models are computed and contrasted with the times for the cloud-based versions of these models. The results show that among the regression algorithms, lasso regression has an r2 score of 80 percent, while linear regression has an r2 score of 75 percent. Furthermore, among the classification models, the random forest algorithm performs better with an accuracy of 99 percent than the support vector machine approach with 95 percent accuracy. In conclusion, our findings demonstrate that run-time is minimized when models are executed on a cloud platform compared to a desktop machine. Moreover, the accuracy of our models is maintained with reduced execution time.

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Cloud, Edge, and Mobile Computing for Smart Cities

Cited by 10 publications

References 124 publications

Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services

Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services

Current state and prospects of edge computing within the Internet of Things (IoT) ecosystem

AI-assisted Distributed Cloud Services Framework for Enhanced Safety in Urban Smart Cities Environment

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