Cognitive-LPWAN: Towards Intelligent Wireless Services in Hybrid Low Power Wide Area Networks
Min Chen, Yiming Miao, Xin Jian, Xiaofei Wang, Iztok Humar

TL;DR
This paper proposes Cognitive-LPWAN, an AI-enabled architecture for heterogeneous IoT networks that intelligently manages wireless communication technologies to optimize delay and energy consumption, demonstrated through an emotion interaction system.
Contribution
It introduces a novel Cognitive-LPWAN architecture and an AI-driven hybrid method for intelligent management of LPWA technologies in IoT networks.
Findings
The scheme meets communication delay requirements.
Cognitive-LPWAN improves interaction experience.
Effective AI-based traffic control in heterogeneous networks.
Abstract
The relentless development of the Internet of Things (IoT) communication technologies and the gradual maturity of Artificial Intelligence (AI) have led to a powerful cognitive computing ability. Users can now access efficient and convenient smart services in smart-city, green-IoT and heterogeneous networks. AI has been applied in various areas, including the intelligent household, advanced health-care, automatic driving and emotional interactions. This paper focuses on current wireless-communication technologies, including cellular-communication technologies (4G, 5G), low-power wide-area (LPWA) technologies with an unlicensed spectrum (LoRa, SigFox), and other LPWA technologies supported by 3GPP working with an authorized spectrum (EC-GSM, LTE-M, NB-IoT). We put forward a cognitive low-power wide-area-network (Cognitive-LPWAN) architecture to safeguard stable and efficient…
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