Reinforcement learning based ALOHA for multi-hop wireless sensor networks with informed receiving

Chu, Yi; Mitchell, Paul; Grace, David

doi:10.1049/cp.2012.0582

Cited by 6 publications

(9 citation statements)

References 6 publications

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“…Owing to low-computational requirements, Q-learning is widely-studied in terrestrial MAC protocols to solve issues such as time-slot scheduling [32], data transmission scheduling [33], and active and sleep time adjustment for duty cycling [34]. At present, very few studies have been conducted on using Q-learning-based protocols in underwater.…”

Section: Related Workmentioning

confidence: 99%

A Time-Slotted Data Gathering Medium Access Control Protocol Using Q-Learning for Underwater Acoustic Sensor Networks

Ahmed

Cho

2021

IEEE Access

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Section: Related Workmentioning

confidence: 99%

A Time-Slotted Data Gathering Medium Access Control Protocol Using Q-Learning for Underwater Acoustic Sensor Networks

Ahmed

Cho

2021

IEEE Access

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“…A.1 Medium access control (MAC) MAC protocols coordinate channel access among multiple nodes in a single-hop transmission to reduce collisions. Two main functions are sleep-wake scheduler [7][8][9] and transceiver selector [10] as follows: A.1.1 Sleep-wake scheduler arranges the transmission, reception, idle and sleeping time durations. During the idle mode, sensor nodes listen for potential packet transmissions and the energy consumption is almost identical to that of receive mode.…”

Section: Application Schemes Of Wireless Sensor Networkmentioning

confidence: 99%

“…The waking time duration may increase with network traffic load [8,9] or Quality of Service (QoS) requirements [11]. RL has been applied to minimize collisions and energy consumption in slot assignment [7], as well as to estimate traffic arrivals from neighboring nodes in order to adjust the sleeping and waking time durations [8,9]. Secondly, a mobile data collector node moves within an area to collect sensing outcomes from static sensor nodes [12].…”

Section: Application Schemes Of Wireless Sensor Networkmentioning

confidence: 99%

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Application of reinforcement learning to wireless sensor networks: models and algorithms

et al. 2014

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Wireless sensor network (WSN) consists of a large number of sensors and sink nodes which are used to monitor events or environmental parameters, such as movement, temperature, humidity, etc. Reinforcement learning (RL) has been applied in a wide range of schemes in WSNs, such as cooperative communication, routing and rate control, so that the sensors and sink nodes are able to observe and carry out optimal actions on their respective operating environment for network and application performance enhancements. This article provides an extensive review on the application of RL to WSNs. This covers many components and features of RL, such as state, action and reward. This article presents how most schemes in WSNs have been approached using the traditional and enhanced RL models and algorithms. It also presents performance enhancements brought about by the RL algorithms, and open issues associated with the application of RL in WSNs. This article aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented

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“…A frame-based back-off strategy (FB-QL-RA CH) is also applied to QL-RACH to further improve its performance. Chu et al [13], [14] use a Qlearning slot selection strategy and show that it is a better scheme than blind transmission in wireless sensor networks (WSNs). Our recent work in [15], [16] developed a simulation model of the QL-RACH and FB-QL-RA CH schemes.…”

Section: Introductionmentioning

confidence: 99%

Intelligent RACH Access Techniques to Support M2M Traffic in Cellular Networks

Bello

Mitchell

Grace

2018

IEEE Trans. Veh. Technol.

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ReuseItems deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Index Terms-M achine-to-machine, medium access control, cellular networks, Q-learning, ALOHA, RACH.I. INTRODUCTION ecent technological developments are changing the perception of wireless communication from the traditional human-centric view towards human independent communications. This is due to the increase in the number of devices that require connection to wireless networks. It has been argued in [1] that there are more electrical/electronic or mechanical objects in the world than people. It is estimated in [2], [3] that by 2020, up to 50 billion devices will require access to a communication network for industrial and domestic applications. This number is significant compared to the estimated human population of 8.3 billion. With this large difference in the ratio of the number of machines to humans, it will be difficult for such devices to be directly controlled by humans, and hence there is a need for them to communicate amongst themselves, with or without human intervention. This can be achieved through what is commonly referred to as Copyright (c) M2M communication will enable interaction between various devices with or without human intervention. M2M devices may be sensors, actuators, embedded processors, radio frequency identification (RFID) tags, smart meters, etc., [7]. The devices may be connected using wired or wireless access networks. Although wired networks are considered to be reliable and secure, they are very expensive to roll out and are not very flexible. As a result, many standards are not considering wired networks as an option for M2M communication. On the other hand, a wireless network is capable of providing excellent coverage, flexibility, mobility , and roaming capability. Hence, wireless access networks, which may be short range or long range (e.g. cellular), are considered as the most suitable option to deploy M2M communication [6].To realise cellular M2M communication, different wireless communication standardisation bodies , including 3GPP, are actively involved in research to provide global standards. Initial access to a cellular network is through the random access channel (RACH) which has a limited capacity. One of the majo r challenges identified by 3GPP in supporting M2M communication is the potential for RACH overload, due to the significant increase in traffic load that will arise from large numbers of M2M devices....

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Reinforcement learning based ALOHA for multi-hop wireless sensor networks with informed receiving

Cited by 6 publications

References 6 publications

A Time-Slotted Data Gathering Medium Access Control Protocol Using Q-Learning for Underwater Acoustic Sensor Networks

A Time-Slotted Data Gathering Medium Access Control Protocol Using Q-Learning for Underwater Acoustic Sensor Networks

Application of reinforcement learning to wireless sensor networks: models and algorithms

Intelligent RACH Access Techniques to Support M2M Traffic in Cellular Networks

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