Adapting food supply chains in Smart Cities to address the impacts of COVID19 a case study from Guadalajara metropolitan area

Baena, Barbara; Cobian, Cecilia; Larios, Victor M.; Orizaga, José; Maciel, Rocío; Cisneros, Marco Pérez; Beltran-Ramirez, J. Raul

doi:10.1109/isc251055.2020.9239076

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Liang-Bi Chen . consumption and waste management) [3], [4]; and many other industries [5], [6]. The prevalent beyond 5G (B5G) wireless communications and edge computing technologies aim to accelerate the development of smart cities to brace the hyper-local awareness and intelligence, allowing realtime, and next-generation mobile & commercial applications to ameliorate the streetscapes [1], [3], [7], [8].…”

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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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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Back to a resilient future: Digital technologies for a sustainable supply chain

Winkelmann,

Guennoun,

Möller

et al. 2024

Inf Syst E-Bus Manage

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Economic, political, and societal pressure forces companies to act more sustainably. New legislation and regulation such as the Supply Chain Act, mandates companies to take responsibility for their actions as well as those of their supply chain collaborators. Meeting these requirements demands transparency. Digital technologies along the supply chain have the potential to collect diverse types of data and raise awareness for sustainability purposes. For example, data obtained from multiple supply chain participants can be used to estimate CO2 emissions or optimize resource utilization. Following guidelines for systematically reviewing a body of literature, this paper therefore investigates the distribution of digital technologies in supply chains for sustainability. Based on a collected corpus of 70 articles, we identify seven key digital technologies and indicate how they can be employed to contribute to economic, environmental, and social sustainability. The results show that (1) blockchain is the most used technology for sustainability within the supply chain and (2) the majority of articles on digital technologies in our corpus address the social dimension of sustainability. Our findings advance the understanding of how digitalization can transform supply chains into more sustainable entities and help practitioners decide which technology fits a certain sustainability dimension.

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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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Adapting food supply chains in Smart Cities to address the impacts of COVID19 a case study from Guadalajara metropolitan area

Cited by 5 publications

References 3 publications

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

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

Back to a resilient future: Digital technologies for a sustainable supply chain

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

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