The Survey on Dynamic Allocation Methods of Edge Computing Resources in Smart Logistics Scenarios

Bingzi Li

doi:10.54097/9vakyk11

Authors

Bingzi Li

DOI:

https://doi.org/10.54097/9vakyk11

Keywords:

edge computing, smart logistics, dynamic resource allocation, deep learning.

Abstract

Smart logistics, driven by the Internet of Things (IoT), 5G, and artificial intelligence (AI), depends on real-time data consumption. But, with the need for real-time data processing at the centralized cloud computing layer, centralized cloud computing faces bottlenecks in latency, bandwidth, and privacy. Edge computing supports smart logistics functions by bringing computing close to the edge, but the success of edge computing depends on dynamic allocation of processes to make adjustments for different task loads, devices, and network conditions in logistics scenarios. This review paper will describe the state of research and advances needed for dynamic resource allocation with edge computing research for smart logistics. First, we review the body of work that brings together edge computing and smart logistics, focusing on in-building applications to logistics scenarios such as warehousing, transport, last-mile delivery, emergency logistics scheduling, cold chain logistics monitoring, and, distributed allocation of logistics resources. Next, we discuss key areas of the human-made edge logistics systems to think about a solution for, including resource sensing, prediction, and scheduling. By seeing examples and case studies, we give examples to demonstrate ways of using and improving resource allocations in practice. In addition, we review challenges in coupling heterogeneous edge digital devices and move from low-power sensors through to high-end servers, the urgent need for a few standards or guidelines to allow for seamless interoperable systems. Finally, we discuss the practical challenges of standardization, security, and heterogeneity, and try to highlight opportunities for AI-guided optimization and energy-aware resource allocations are future trends. This review paper will provide a holistic outline to researchers and practitioners with a framework to advance investigations into the management of a dynamic resource allocation system with edge-enabled smart logistics.

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