Application of reinforcement learning to medium access control for wireless sensor networks

Chu, Yi; Koşunalp, Selahattin; Mitchell, Paul; Grace, David; Clarke, Tim

doi:10.1016/j.engappai.2015.08.004

Cited by 60 publications

(62 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reference [11] proposes an energy-efficient cluster adaptive time division multiple access protocol EA-TDMA, which is a communication protocol between sensors in railway transportation system. This protocol improves energy efficiency by collecting information about future data packets rather than dispatching data packet exchanges in the competition stage [12], it is especially suitable for high-flow load characteristics of train operation [13], but its universality needs further verification.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

A Heterogeneous Energy Wireless Sensor Network Clustering Protocol

Meng-jia

Huang

Zheng

et al. 2019

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The Low-Efficiency Adaptive Clustering Hierarchical (LEACH) protocol, a hierarchical routing protocol, has the advantage of simple implementation and can effectively balance network loads. However, to date there has been a lack of consideration for its use in heterogeneous energy network environments. To solve this problem, the Energy-Coverage Ratio Clustering Protocol (E-CRCP) is proposed, which is based on reducing the energy consumption of the system and utilizing the regional coverage ratio. First, the energy model is designed. The optimal number of clusters is determined based on the principle of "minimum energy consumption", and the cluster head selection is based on the principle of "regional coverage maximization". In order to balance the network load as much as possible, in the next iteration of cluster head selection, the cluster head with the lowest residual energy and the highest energy consumption is replaced to prolong the network's life. Our simulated results demonstrate that the proposed method has some advantages in terms of longer network life, load balancing, and overall energy consumption in the environment of a heterogeneous energy wireless sensor network.

show abstract

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

A Heterogeneous Energy Wireless Sensor Network Clustering Protocol

Meng-jia

Huang

Zheng

et al. 2019

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…The approach presented in [19] for ALOHA-Q provides the convergence time of a whole network through an analytical model. In this model, a state transition probability matrix, P, which is a sparse matrix, is considered.…”

Section: Loss Of Convergence Time Estimationmentioning

confidence: 99%

Practical Implementation and Stability Analysis of ALOHA-Q for Wireless Sensor Networks

Koşunalp

Mitchell

Grace

et al. 2016

ETRI J

Self Cite

View full text Add to dashboard Cite

This paper presents the description, practical implementation, and stability analysis of a recently proposed, energy‐efficient, medium access control protocol for wireless sensor networks, ALOHA‐Q, which employs a reinforcement‐learning framework as an intelligent transmission strategy. The channel performance is evaluated through a simulation and experiments conducted using a real‐world test‐bed. The stability of the system against possible changes in the environment and changing channel conditions is studied with a discussion on the resilience level of the system. A Markov model is derived to represent the system behavior and estimate the time in which the system loses its operation. A novel scheme is also proposed to protect the lifetime of the system when the environment and channel conditions do not sufficiently maintain the system operation.

show abstract

“…The amount of energy required to send j packets from one sensor to another E T (j, k, d). is computed as in [3,33].…”

Section: Energy Cost Modelmentioning

confidence: 99%

“…Therefore, the proposed algorithms focus on minimizing the communication cost. We assume a scheduling scheme such as ALOHA-Q [33,34], is used to minimize the energy cost during rebroadcasting information and idle listening; therefore, the resulting energy cost from idle listening is minimal and can be ignored. The amount of energy required to send j packets from one sensor to another E T (j, k, d).…”

Section: Energy Cost Modelmentioning

confidence: 99%

Efficient Data Collection by Mobile Sink to Detect Phenomena in Internet of Things

2017

View full text Add to dashboard Cite

Abstract:With the rapid development of Internet of Things (IoT), more and more static and mobile sensors are being deployed for sensing and tracking environmental phenomena, such as fire, oil spills and air pollution. As these sensors are usually battery-powered, energy-efficient algorithms are required to extend the sensors' lifetime. Moreover, forwarding sensed data towards a static sink causes quick battery depletion of the sinks' nearby sensors. Therefore, in this paper, we propose a distributed energy-efficient algorithm, called the Hilbert-order Collection Strategy (HCS), which uses a mobile sink (e.g., drone) to collect data from a mobile wireless sensor network (mWSN) and detect environmental phenomena. The mWSN consists of mobile sensors that sense environmental data. These mobile sensors self-organize themselves into groups. The sensors of each group elect a group head (GH), which collects data from the mobile sensors in its group. Periodically, a mobile sink passes by the locations of the GHs (data collection path) to collect their data. The collected data are aggregated to discover a global phenomenon. To shorten the data collection path, which results in reducing the energy cost, the mobile sink establishes the path based on the order of Hilbert values of the GHs' locations. Furthermore, the paper proposes two optimization techniques for data collection to further reduce the energy cost of mWSN and reduce the data loss.

show abstract

Application of reinforcement learning to medium access control for wireless sensor networks

Cited by 60 publications

References 50 publications

A Heterogeneous Energy Wireless Sensor Network Clustering Protocol

A Heterogeneous Energy Wireless Sensor Network Clustering Protocol

Practical Implementation and Stability Analysis of ALOHA-Q for Wireless Sensor Networks

Efficient Data Collection by Mobile Sink to Detect Phenomena in Internet of Things

Contact Info

Product

Resources

About