Fuzzy Link Cost Estimation based Adaptive Tree Algorithm for Routing Optimization in Wireless Sensor Networks using Reinforcement Learning
Results and Conclusion: The simulation results reveal that Fuzzy AT algorithm is most appropriate reinforcement learning based routing algorithm among all five algorithms for ensuring energy efficient, reliable, error-free, real-time compatible and QoS aware routing in dynamically changing, asymmetric and unreliable wireless environment of Wireless Sensor Networks.
Keywords: Adaptive tree; fuzzy logic; internet of things; link cost; reinforcement learning; routing; wireless sensor networks
Document Type: Review Article
Publication date: 01 December 2018
The International Journal of Sensors, Wireless Communication and Control publishes timely research articles, reviews and communications on these three strongly related areas, with emphasis on networked control systems whose sensors are interconnected via wireless communication networks.
The emergence of high speed wireless network technologies allows a cluster of devices to be linked together economically to form a distributed system. Wireless communication is playing an increasingly important role in such distributed systems. Transmitting sensor measurements and control commands over wireless links allows rapid deployment, flexible installation, fully mobile operation and prevents the cable wear and tear problem in industrial automation, healthcare and environmental assessment. Wireless networked systems has raised and continues to raise fundamental challenges in the fields of science, engineering and industrial applications, hence, more new modelling techniques, problem formulations and solutions are required.
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