Distributed Intelligence in Wireless Networks
Xiaolan Liu, Jiadong Yu, Yuanwei Liu, Yue Gao, Toktam, Mahmoodi, Sangarapillai Lambotharan, Danny H. K. Tsang

TL;DR
This paper reviews recent advances in integrating AI and edge computing for distributed intelligence in wireless networks, emphasizing privacy, efficiency, and the design of novel architectures.
Contribution
It provides a comprehensive overview of native-AI wireless networks, highlighting hybrid distributed learning architectures and discussing future research challenges and opportunities.
Findings
Hybrid distributed learning architectures outperform traditional methods.
Edge computing enhances AI application performance in wireless networks.
Communication-efficient technologies are crucial for scalable distributed learning.
Abstract
The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, security and privacy concerns caused by billions of connected wireless devices and typically zillions bytes of data they produce at the network edge. A blend of edge computing and Artificial Intelligence (AI) techniques could optimally shift the resourceful computation servers closer to the network edge, which provides the support for advanced AI applications (e.g., video/audio surveillance and personal recommendation system) by enabling intelligent decision making on computing at the point of data generation as and when it is needed, and distributed Machine Learning (ML) with its potential to avoid the transmission of large dataset and possible compromise of privacy that may exist in cloud-based centralized learning. Therefore, AI is envisioned to become native and…
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Taxonomy
TopicsEnergy Efficient Wireless Sensor Networks · Indoor and Outdoor Localization Technologies · Distributed Sensor Networks and Detection Algorithms
