Non-Cooperative Game Theory Based Congestion Control in Lossy WSN

Chowdhury, Srijit; Giri, Chandan

doi:10.1109/glocom.2018.8647723

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…In [72] Michopoulos et al developed the Duty Cycle-Aware Congestion Control (DCCC6) method to reduce congestion in 6LoWPAN networks. This method determines the radio duty cycle and changes the operation as needed.…”

Section: Game Theory-based Algorithms For Congestion Mitigation In Wsnmentioning

confidence: 99%

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

Umar,

Khalid,

Ali

et al. 2023

Applied Sciences

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The Internet of Things (IoT) and wireless sensor networks (WSNs) have evolved rapidly due to technological breakthroughs. WSNs generate high traffic due to the growing number of sensor nodes. Congestion is one of several problems caused by the huge amount of data in WSNs. When wireless network resources are limited and IoT devices require more and more resources, congestion occurs in extremely dense WSN-based IoT networks. Reduced throughput, reduced network capacity, and reduced energy efficiency within WSNs are all effects of congestion. These consequences eventually lead to network outages due to underutilized network resources, increased network operating costs, and significantly degraded quality of service (QoS). Therefore, it is critical to deal with congestion in WSN-based IoT networks. Researchers have developed a number of approaches to address this problem, with new solutions based on artificial intelligence (AI) standing out. This research examines how new AI-based algorithms contribute to congestion mitigation in WSN-based IoT networks and the various congestion mitigation strategies that have helped reduce congestion. This study also highlights the limitations of AI-based solutions, including where and why they are used in WSNs, and a comparative study of the current literature that makes this study novel. The study concludes with a discussion of its significance and potential future study topics. The topic of congestion reduction in ultra-dense WSN-based IoT networks, as well as the current state of the art and emerging future solutions, demonstrates their significant expertise in reducing WSN congestion. These solutions contribute to network optimization, throughput enhancement, quality of service improvement, network capacity expansion, and overall WSN efficiency improvement.

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Section: Game Theory-based Algorithms For Congestion Mitigation In Wsnmentioning

confidence: 99%