RL-Sleep: Temperature Adaptive Sleep Scheduling using Reinforcement Learning for Sustainable Connectivity in Wireless Sensor Networks

Banerjee, Paula; Mandal, Satyendra Nath; De, Debashis; Maiti, Biswajit

doi:10.1016/j.suscom.2020.100380

Cited by 16 publications

(17 citation statements)

References 16 publications

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“…The resultant temperature 𝑇 𝑖 of a sensor node i is given as 𝑇 𝑖 = 𝑇 𝑡+∆𝑡 𝑖 = 𝑇 + 𝛥𝑇 𝑖 . Foregoing in view, the solar radiation pattern for a day can be represented as 𝑆 𝑆𝑈𝑁 (𝑡) = 𝑆 𝑆𝑈𝑁 𝑚𝑎𝑥 𝑒𝑥𝑝 −(𝑡−𝜌) 2 2𝜎 2 , 0 ≤ t ≤ 2ρ where S SUN max is the peak value of the solar radiation during the day [7]. It has been found that the identical sensor nodes behave differently to the temperature variations due to solar radiation exposure, traffic flow and relative position of the sensor node [8].…”

Section: 𝛥𝑇 𝑖 =mentioning

confidence: 99%

“…The normalized attribute values after data standardization in the hesitant fuzzy set are converted to linguistic term set S using triangular membership function as depicted from Fig 1 . A contextfree grammar 𝐺 𝐶𝐹 [7] has been used to produce linguistic terms for the alternatives against different values of the criteria. These HFS membership values are then transformed into HFLTS using a transformation function 𝐸 𝐺𝐶𝐹 as shown in equation ( 19) and (20).…”

Section: Hesitant Fuzzy Linguistic Term Set (Hflts) Analysismentioning

confidence: 99%

“…Conversely, a node enters its sleeping state for an interval predefined or calculated according to contemporary environmental conditions. Researchers have also worked on temperature adaptive sleep/awake scheduling techniques [7][8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Thermal entropy based hesitant fuzzy linguistic term set analysis in energy efficient opportunistic clustering

Anees¹,

Zhang²

2021

Natural Language Computing

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Limited energy resources and sensor nodes’ adaptability with the surrounding environment play a significant role in the sustainable Wireless Sensor Networks. This paper proposes a novel, dynamic, self-organizing opportunistic clustering using Hesitant Fuzzy Linguistic Term Analysis- based Multi-Criteria Decision Modeling methodology in order to overcome the CH decision making problems and network lifetime bottlenecks. The asynchronous sleep/awake cycle strategy could be exploited to make an opportunistic connection between sensor nodes using opportunistic connection random graph. Every node in the network observe the node gain degree, energy welfare, relative thermal entropy, link connectivity, expected optimal hop, link quality factor etc. to form the criteria for Hesitant Fuzzy Linguistic Term Set. It makes the node to evaluate its current state and make the decision about the required action (‘CH’, ‘CM’ or ‘relay’). The simulation results reveal that our proposed scheme leads to an improvement in network lifetime, packet delivery ratio and overall energy consumption against existing benchmarks.

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Section: 𝛥𝑇 𝑖 =mentioning

confidence: 99%

Section: Hesitant Fuzzy Linguistic Term Set (Hflts) Analysismentioning

confidence: 99%

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Thermal entropy based hesitant fuzzy linguistic term set analysis in energy efficient opportunistic clustering

Anees¹,

Zhang²

2021

Natural Language Computing

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“…Q-learning (QL) is a representative RL algorithm that has been used in various fields [11], [13], [14]. It is particularly suitable for applications that require online learning as the policy can be improved every time an agent performs an action within an episode.…”

Section: B Rlmentioning

confidence: 99%

Multi-Agent Reinforcement-Learning-Based Time-Slotted Channel Hopping Medium Access Control Scheduling Scheme

Park

Kim

et al. 2020

IEEE Access

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Time-slotted channel hopping (TSCH) is a medium access control technology that realizes collision-free wireless network communication by coordinating the media access time and channel of network devices. Although existing TSCH schedulers have suitable application scenarios for each, they are less versatile. Scheduling without collisions inevitably lowers the throughput, whereas contention-based scheduling achieves high-throughput but it may induces to frequent collisions in densely deployed networks. Therefore, a TSCH scheduler that can be used universally, regardless of the topology and data collection characteristics of the application scenario, is required to overcome these shortcomings. To this end, a multiagent reinforcement learning (RL)-based TSCH scheduling scheme that allows contention but minimizes collisions is proposed in this study. RL is a machine-learning method that gradually improves actions to solve problems. One specific RL method, Q-Learning (QL), was used in the scheme to enable the TSCH scheduler to become a QL agent that learns the best transmission slot. To improve the QL performance, reward functions tailored for the TSCH scheduler were developed. Because the QL agent runs on multiple nodes concurrently, changes in the TSCH schedule of one node also affect the performance of the TSCH schedules of other nodes. The use of action peeking is proposed to overcome this non-stationarity problem in a multi-agent environment. The experimental results indicate that the TSCH scheduler consistently performs well in various types of applications, compared to other schedulers.INDEX TERMS Internet of Things (IoT), Time-Slotted Channel Hopping (TSCH) scheduling, wireless sensor networks.

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“…Therefore, a few researchers have used RL algorithm to resolve the routing-related problems faces by WSN. This section describes the state-of-art performed by various researchers by utilizing the RL approach in WSN [32].…”

Section: Reinforcement Learning In Wsnmentioning

confidence: 99%

An Energy Efficient Reinforcement Learning Based Clustering Approach for Wireless Sensor Network

Kaur

Aulakh

2018

ICST Transactions on Scalable Information Systems

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Clustering is known to conserve energy and enhance the network lifetime of Wireless Sensor Network (WSN). Although, the topic of energy efficiency has been well researched in conventional WSN, but it has not been extensively studied. In the research, Reinforcement Learning (RL) based energy-aware clustering algorithm is proposed by which the neighboring nodes in the cluster selects an appropriate Cluster Head (CH) by observing the environmental conditions like as energy consumption and coverage that is computed as distance from the CH to the Base Station (BS). An optimal cluster is selected by each neighboring node, which minimized the energy consumption and network lifetime. The problem of selecting an optimal CH is resolved using the RL approach. Using the RL approach, the CH having the highest reward point is selected for data communication. The results show that energy saving of 7.41%, 3.27%, 4.03%, and 2.79 % is achieved for 100, 200, 300, and 400 deployed nodes, respectively.

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RL-Sleep: Temperature Adaptive Sleep Scheduling using Reinforcement Learning for Sustainable Connectivity in Wireless Sensor Networks

Cited by 16 publications

References 16 publications

Thermal entropy based hesitant fuzzy linguistic term set analysis in energy efficient opportunistic clustering

Thermal entropy based hesitant fuzzy linguistic term set analysis in energy efficient opportunistic clustering

Multi-Agent Reinforcement-Learning-Based Time-Slotted Channel Hopping Medium Access Control Scheduling Scheme

An Energy Efficient Reinforcement Learning Based Clustering Approach for Wireless Sensor Network

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