EER-RL: Energy-Efficient Routing Based on Reinforcement Learning

Mutombo, Vially Kazadi; Lee, Seungyeon; Lee, Jusuk; Hong, Jiman

doi:10.1155/2021/5589145

Cited by 33 publications

(19 citation statements)

References 32 publications

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“…The base station then sends out a message to communicate its location coordinates. Individual nodes then keep the posi-tion of the base station after accepting the packet and use equations given to calculate the initial Q-value using residual, E min , E max , N H , and probabilistic parameter p. This work proposes a modest extension to [16] by introducing Shannon-entropy inspired modification in finding initial Q -value. We also assume that all nodes have varying degrees of energy.…”

Section: Network Initializationmentioning

confidence: 99%

“…The action space is defined as the collection of all feasible neighbours via which packets to the sink can be relayed, and the way the devices in the net network or the way the agents behave is defined as a policy. Mutombo et al [16] and Mutombo et al [17] suggested that the policy iteration is then used to evaluate and improve the given policy which maps the state-action pair, maximizing the long-term reward to get the best policy.…”

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confidence: 99%

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ReLeC: A Reinforcement Learning-Based Clustering-Enhanced Protocol for Efficient Energy Optimization in Wireless Sensor Networks

Sharma

Balyan²,

Nair

et al. 2022

Wireless Communications and Mobile Computing

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Wireless sensor networks (WSNs) are a widely studied area in the field of networked embedded computing. They are made up of several sensor nodes, which keep track of a variety of physical and environmental parameters, like temperature and humidity. The nodes are autonomous, self-configuring, and wireless. A significant problem in WSNs is that sensors in these networks consume a lot of energy. Energy consumption is a big issue when it comes to the deployment of sensor networks. The reason for this is the cost of operating a sensor node and the cost incurred due to energy consumption. Energy optimization is based on intelligent energy management. This paper presents a reinforcement learning-based and clustering-enhanced method. Reinforcement learning is a set of algorithms inspired by operant conditioning in animal behavior, and clustering-based methods have been extensively used for devising energy-efficient protocols. The proposed method is able to plan and schedule the nodes to ensure an extended network lifetime. In this work, we aim to assess and increase the efficiency of power consumption and reduce sensor node energy loss. The simulation results prove that the presented protocol effectively reduces the energy consumption of sensor nodes and ensures a prolonged lifetime of the sensor network.

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Section: Network Initializationmentioning

confidence: 99%

mentioning

confidence: 99%

ReLeC: A Reinforcement Learning-Based Clustering-Enhanced Protocol for Efficient Energy Optimization in Wireless Sensor Networks

Sharma

Balyan²,

Nair

et al. 2022

Wireless Communications and Mobile Computing

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“…ETALGOR achieves an overall network lifetime increase of 30.54% over LOADng. ETALGOR accomplishes an improvement of 7.69% better lifetime over Energy Efficient Routing based on Reinforcement Learning (EER-RL) (6) . Network lifetime improvement of 71.64% is attained by REERS (7) .…”

Section: Fig 8 Pdr Ratio Of Etalgormentioning

confidence: 99%

“…Thus, energy-efficient routing methods manage device energy usage and increase network lifespan. The demerit of this method is that it consumes energy, causing higher energy consumption during the data aggregation process every round (6) . Heterogeneous sensor network-enabled applications have diverse performance requirements like https://www.indjst.org/ low energy usage and low latency.…”

Section: Introductionmentioning

confidence: 99%

ETX-Aware Energy-Efficient Algorithm to Reduce Packet Retransmissions in the Internet of Things

Newton¹,

Felix²

2022

IJST

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Objectives: An energy-efficient optimum path selection to reduce the number of packet retransmissions in a path is proposed in this paper using a new metric New-Expected Transmission Count (N-ETX), Residual Energy (RE), and Path Objective Function (POF). Methods: Energy efficiency, packet retransmission, and RE is considered while making the best choice of the optimum path. The refined N-ETX metric is employed to measure the Packet Delivery Ratio (PDR) of a node. If the PDR ratio is higher, this node is included in the optimum path set. The proposed algorithm avoids the inclusion of critical nodes such as dead nodes if the RE of the nodes is lower than the predefined Energy Threshold (E T HR ). The Unicast message reply to strategy significantly decreases the quantity of control message overhead. The T-test's Degree of Variance and Degree of Independence is used to discover the energy-efficient optimum path. It is demonstrated through performance outcomes that the obtained Quality of Service (QoS) parameters for the proposed algorithm stands superior to the state-of-art protocols. Findings: An algorithm established on N-ETX and RE metrics is proposed, and the discussions have shown that this approach is energy efficient. This metric decreases the quantity of packet retransmission effectively for a path. As an outcome, the sensor's higher energy consumption is reduced. The proposed algorithm outperforms the current algorithm by decreasing energy consumption, reducing the quantity of packet retransmission, improving the PDR ratio, and enhancing the network life expectancy. The simulation outcome established that energy consumed is decreased by 19.07%, RE of the nodes is increased by 10.51%, PDR ratio is increased to 98%, and network lifespan is increased by 30.54%. Novelty: Internet of Things (IoT) gadgets are bound by hardware limits regarding computation, memory, and energy proficiency. To effectively transmit data packets, every IoT device must have a routing communications protocol that is easy to create. IoT devices necessitate the demand for self-adaptive routing algorithms. During network operation, packet energy dissipation during the broadcast and reception process is significantly higher when assessed to other https://www.indjst.org/ energy-consuming processes like sensing, data processing, etc.

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“…Additionally, individually modelling each task not only hampers the network's scalability but also restricts a generic model from simultaneously addressing multiple routing optimization tasks. Therefore, it is anticipated that the RL [22][23][24][25][26][27][28][29] will offer a fresh approach to solving this issue. Deep learning models [30][31][32][33] are often able to provide better performance, but it is not always necessary or appropriate to use them.…”

Section: Introductionmentioning

confidence: 99%

That Photograph: Serial Killer as Modern Celebrity

Cummins

Foley

King

2021

Critiquing Violent Crime in the Media

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Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.

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EER-RL: Energy-Efficient Routing Based on Reinforcement Learning

Cited by 33 publications

References 32 publications

ReLeC: A Reinforcement Learning-Based Clustering-Enhanced Protocol for Efficient Energy Optimization in Wireless Sensor Networks

ReLeC: A Reinforcement Learning-Based Clustering-Enhanced Protocol for Efficient Energy Optimization in Wireless Sensor Networks

ETX-Aware Energy-Efficient Algorithm to Reduce Packet Retransmissions in the Internet of Things

That Photograph: Serial Killer as Modern Celebrity

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