Partial distance strategy analysis on city characteristics to improve reliable smart cities services

Safitri, Wiwi; Ikhwan, Muhammad; Firmansyah, Dedy; Rusdiana, Sa’ida; Rahayu, Listya; Akhdansyah, T.

doi:10.1049/icp.2021.0933

Cited by 4 publications

(3 citation statements)

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“…Her approach involves first grouping the dataset to get the relevant periods throughout the day, and then predicting the data in these clusters [ 19 ]. Safitri et al [ 20 ] conducted a cluster analysis of smart city regions in Banda Aceh, Indonesia. This study identified the similarities in the characteristics of each object in the Aceh regions.…”

Section: Literature Reviewmentioning

confidence: 99%

Discovering smart cities’ potential in Kazakhstan: A cluster analysis

Urdabayev,

Kireyeva,

Vasa

et al. 2024

PLoS ONE

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The potential for developing smart cities in Kazakhstan is evaluated using cluster analysis. Built on previous research focused on clustering the regions of Kazakhstan, this study applies the same method to the cities of the country. The analysis uses indicators related to human capital, infrastructure, education, information technology, production, and other factors to assess the potential of each city. The clustering is performed using Single Linkage, Complete Linkage, and Ward’s methods. The results show that Almaty and Astana are the cities with the highest potential for becoming smart cities. Aktobe is identified as a city with distinctive features that may help or hinder its development as a smart city. The remaining cities are clustered into two groups, with one group having the potential to catch up and maintain the trend of developing smart cities, while the other group is less suitable for starting smart city projects and may require more investment per capita. The study highlights the deep regional inequality affecting the potential to successfully develop and manage smart cities in Kazakhstan. The analysis also reveals some limitations and challenges in the data and variables used, including the lack of data for some variables and the difficulties in "translating" some factors and indicators into quantitative variables for clustering. The study concludes that future research should address these challenges and consider clustering inside certain regions to focus on their unique features. The study recommends launching pilot projects in small cities, with the most successful practices then scaled and implemented in the core smart cities and possibly Aktobe, if it manages to use its advantages to compensate for risks. Overall, this study provides insights into the potential of smart city development in Kazakhstan and can inform policymakers in their efforts to support smart city projects in the country.

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Section: Literature Reviewmentioning

confidence: 99%

Discovering smart cities’ potential in Kazakhstan: A cluster analysis

Urdabayev,

Kireyeva,

Vasa

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

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“…The effectiveness of our proposed pruning principle (PP) is evaluated by comparing the Jain's fairness index value of TFLAN with PP and without PP (referred as random). The Jain's fairness index for the utility rate of base stations W is measured as: 2 , where W refers to the total number of base stations in the environment (Section III-C). Similarly, the fairness index for the EC, J (M ), and for the BC, J (N ), respectively, is also measured.…”

Section: Performance Analysismentioning

confidence: 99%

“…Over the years, the concept of smart city has gained attention of the research communities to utilize the technological infrastructure for societies to meet their rapidly growing needs for a high quality of life [1]. Smart cities are expected to transform the way of living by advancing the healthcare [2]; the city management services (e.g., smart traffic, electricity…”

Section: Introductionmentioning

confidence: 99%

Deep-Deterministic Policy Gradient Based Multi-Resource Allocation in Edge-Cloud System: A Distributed Approach

Qadeer

Lee

2023

IEEE Access

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Edge Cloud (EC) empowers the beyond 5G (B5G) wireless networks to cope with large-scale and real-time traffics of Internet-of-Things (IoT) by minimizing the latency and providing compute power at the edge of the network. Due to a limited amount of resources at the EC compared to the backend cloud (BC), intelligent resource management techniques become imperative. This paper studies the problem of multi-resource allocation (MRA) in terms of compute and wireless resources in an integrated EC and BC environment. Machine learning-based approaches are emerging to solve such optimization problems. However, it is challenging to adopt traditional discrete action space-based methods due to their high dimensionality issue. To this end, we propose a deep-deterministic policy gradient (DDPG) based temporal feature learning attentional network (TFLAN) model to address the MRA problem. TFLAN combines convolution, gated recurrent unit and attention layers together to mine local and long term temporal information from the task sequences for excellent function approximation. A novel heuristic-based priority experience replay (hPER) method is formulated to accelerate the convergence speed. Further, a pruning principle helps the TFLAN agent to significantly reduce the computational complexity and balance the load among base stations and servers to minimize the rejection-rate. Lastly, data parallelism technique is adopted for distributed training to meet the needs of a high-volume of IoT traffic in the EC environment. Experimental results demonstrate that the distributed training approach suites well to the problem scale and can magnify the speed of the learning process. We validate the proposed framework by comparing with five state-of-theart RL agents. Our proposed agent converges fast and achieves up to 28% and 72% reduction in operational cost and rejection-rate, and achieves up to 32% gain in the quality of experience on average, compared to the most advanced DDPG agent.

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HRL-Edge-Cloud: Multi-Resource Allocation in Edge-Cloud based Smart-StreetScape System using Heuristic Reinforcement Learning

Qadeer

Lee

2023

Inf Syst Front

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Partial distance strategy analysis on city characteristics to improve reliable smart cities services

Cited by 4 publications

References 0 publications

Discovering smart cities’ potential in Kazakhstan: A cluster analysis

Discovering smart cities’ potential in Kazakhstan: A cluster analysis

Deep-Deterministic Policy Gradient Based Multi-Resource Allocation in Edge-Cloud System: A Distributed Approach

HRL-Edge-Cloud: Multi-Resource Allocation in Edge-Cloud based Smart-StreetScape System using Heuristic Reinforcement Learning

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