Markov decision process‐based routing algorithm in hybrid Satellites/UAVs disruption‐tolerant sensing networks

Yuan, Peng; Wang, Ye; Su, Min; Yang, Zhihua; Zhang, Qinyu

doi:10.1049/iet-com.2018.5932

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…Simulations were conducted using the NS2 tool and results showed efficiency of the proposed approach. In another work [31], a Markov chain process and an energy-efficient routing protocol was presented for Unmanned Aerial Vehicles (UAV) in IoT environments. A probabilistic communication graph method was proposed to support semi-deterministic communications between UAV nodes with the aim of reducing delay ratio and energy consumption using the proposed routing protocol.…”

Section: Energy Efficient Routingmentioning

confidence: 99%

PriNergy: a priority-based energy-efficient routing method for IoT systems

et al. 2020

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The Internet of Things (IoT) devices gather a plethora of data by sensing and monitoring the surrounding environment. Transmission of collected data from the IoT devices to the cloud through relay nodes is one of the many challenges that arise from IoT systems. Fault tolerance, security, energy consumption, and load balancing are all examples of issues revolving around data transmissions. This paper focuses on energy consumption, where a priority-based and energyefficient routing (PriNergy) method is proposed. The method is based on the Routing Protocol for Low-Power and Lossy Networks (RPL) model, which determines routing through contents. Each network slot uses timing patterns when sending data to the destination, while considering network traffic, audio and image data. This technique increases the robustness of the routing protocol and ultimately prevents congestion. Experimental results demonstrate that the proposed PriNergy method reduces overhead on the mesh, end-to-end delay, and energy consumption. Moreover, it outperforms one of the most successful routing methods in an IoT environment, namely, the Quality of Service RPL (QRPL).

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Section: Energy Efficient Routingmentioning

confidence: 99%

PriNergy: a priority-based energy-efficient routing method for IoT systems

et al. 2020

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“…Due to the complexity of customer service structure and the uncertainty of customer demand in power enterprises, a cyclic neural network with strong fuzzy data processing capability was designed [22] to realize intelligent decision support for customer service in power enterprises. Proper fine-tuning of the weights allows you to decrease the error rates and make the model reliable by increasing its generalization [23].…”

Section: Proposed Modelmentioning

confidence: 99%

Intelligent Assistant Decision-Making Method for Power Enterprise Customer Service Based on IoT Data Acquisition

Long

Liu

et al. 2021

Mobile Information Systems

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The prevailing era of the Internet of Things (IoT) has renewed all fields of life in general, but, especially with the advent of artificial intelligence (AI), has drawn the attention of researchers into a new paradigm of life standards. This revolution has been accepted around the world for making life comfortable with the use of intelligent devices. AI-enabled machines are more intelligent and capable of completing a specific task which saves a lot of time and resources. Currently, diverse methods are available in the existing literature to handle different issues of real life based on AI and IoT systems. The role of decision-making has its prominence in the AI-enabled and IoT systems. In this article, an AI- and IoT-based intelligent assistant decision-making method is presented for power enterprise customer service. An intelligent model of the customer service data network is designed, and the method of collecting data from IoT to assist decision-making is presented. Then, the semantic relationship of customer service data is defined, and the sharing scope of data transmission and resources are determined to realize intelligent assistant decision-making of customer service in power enterprises. Simulation results show that the proposed method improves the decision data transmission speed and shortens the transmission delay, and the network performance of data interaction is better than that of the existing methods.

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“…Due to the benefits of mobility, flexibility and manoeuvrability, unmanned aerial vehicles (UAVs) can be deployed as a kind of portable wireless communication system associated with other terrestrial base stations (TBSs) or aerial vehicles in public [1–3]. In the past decade, UAV has been emerging as a promising technology for various purposes, such as real‐time monitoring, search and rescue, security and surveillance, wireless relays, and many others [4].…”

Section: Introductionmentioning

confidence: 99%