Distributed QoS routing algorithm in large scale Wireless Sensor Networks

Kordafshari, Mohammad Sadegh; Pourkabirian, Azadeh; Meybodi, Mohammad Reza; Movaghar, Ali

doi:10.1109/isie.2012.6237195

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…The RL algorithm has its inherent advantages and is well suitable for dealing with distributed problems [17]. This paper proposes a RL-based algorithm to provide new ideas and incentives for addressing routing issue in WSNs.…”

Section: Discussionmentioning

confidence: 99%

An Intelligent Routing Algorithm in Wireless Sensor Networks Based on Reinforcement Learning

Guo

Yan

Gan

et al. 2014

AMM

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Lifetime enhancement has been a hot issue in Wireless Sensor Networks (WSNs). To prolong the network lifetime of WSNs, this paper proposes an intelligent routing algorithm named RLLO. RLLO makes uses of the superiority of reinforcement learning (RL) and considers residual energy and hop count to define the reward function. It is to uniformly distribute the energy consumption and improve the packet delivery without additional cost. This proposed algorithm has been compared with Energy Aware Routing (EAR) and improved EAR (I-EAR). Simulation results show that RLLO gains a significant improvement in terms of network lifetime and packet delivery over these two algorithms.

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Section: Discussionmentioning

confidence: 99%

An Intelligent Routing Algorithm in Wireless Sensor Networks Based on Reinforcement Learning

Guo

Yan

Gan

et al. 2014

AMM

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“…Our algorithm is compared with two different methods: i) basic routing protocol (DRLR) proposed in [13], which is without aggregation ii) ECHSSDA algorithm given in [5]. The results show that the proposed method significantly reduces energy consumption of DRLR with addition of the aggregation technique to DRLR and also it outperforms ECHSSDA, especially when the environment has low density.…”

Section: (Pj)mentioning

confidence: 99%

Aggregator election in wireless sensor networks: A distributed reinforcement learning approach

Hajishabani

Kordafshari

Meybodi

2013

2013 13th Iranian Conference on Fuzzy Systems (IFSC)

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Nowadays, artificial intelligence techniques are used in various fields of wireless sensor networks. Due to resource constraints in these types of networks, many studies focus on minimizing energy consumption and increasing the lifetime of the networks. Data aggregation is a powerful technique that it reduces the energy consumption in the network. In this paper, we've provided a distributed approach based on reinforcement learning and using learning automata for solving the problem of selection of aggregator in wireless sensor networks. We compared our method with DRLR and ECHSSDA algorithms. The results show that the proposed method significantly reduces energy consumption in DRLR and outperforms ECHSSDA, especially when the environment has low density.

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“…22,23 The RL algorithm has its inherent advantages and is well suitable for dealing with distributed problems. 24,25 In this algorithm, each possible action is assigned a Q-value which indicates the approximate goodness of the action. 26 In the learning process, according to the Q-value of each action, the agent selects one action.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing

Guo

Yan

2019

International Journal of Distributed Sensor Networks

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In wireless sensor networks, optimizing the network lifetime is an important issue. Most of the existing works define network lifetime as the time when the first sensor node exhausts all of its energy. However, such time is not necessarily important. This is because when a sensor node dies, the whole network is likely to work properly. In this article, we first make an overall consideration of the demand of applications and define the network lifetime in three aspects. Then, we construct a performance evaluation framework for routing protocols. To achieve the optimization of network lifetime in all defined aspects, we propose a reinforcement-learning-based routing protocol. Reinforcement-learning-based routing protocol takes advantage of the intelligent algorithm of reinforcement learning to search for the optimal routing path for data transmission. In the definition of reward function, factors such as link distance, residual energy, and hop count to the sink are taken into account to cut down the total energy consumption, balance the energy consumption, and improve the packet delivery. Simulation results demonstrate that compared with energy-aware routing, BEER, Q-Routing, and MRL-SCSO, reinforcement-learning-based routing protocol optimizes the network lifetime in three aspects and improves the energy efficiency.

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Distributed QoS routing algorithm in large scale Wireless Sensor Networks

Cited by 5 publications

References 19 publications

An Intelligent Routing Algorithm in Wireless Sensor Networks Based on Reinforcement Learning

An Intelligent Routing Algorithm in Wireless Sensor Networks Based on Reinforcement Learning

Aggregator election in wireless sensor networks: A distributed reinforcement learning approach

Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing

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