Machine Learning based Energy Efficient Wireless Sensor Network

Lata, Sonam; Mehfuz, Shabana

doi:10.1109/icpeca47973.2019.8975526

Cited by 9 publications

(6 citation statements)

References 43 publications

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“…With the aim to achieve this balance, the routing protocols based on multiobjective optimization were proposed in [26][27][28]. Moreover, with the help of machine learning-based algorithms and the use of the operational experience data of WSN, a self-learning routing protocol was proposed in [29][30][31] to enhance the network's adaptive characteristics.…”

Section: Related Workmentioning

confidence: 99%

A Novel Energy-Efficient, Static Scenario-Oriented Routing Method of Wireless Sensor Network Based on Edge Computing

Liu

Sheng

et al. 2022

Wireless Communications and Mobile Computing

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In wireless sensor network (WSN), the energy of sensor nodes is limited. Designing efficient routing method for reducing energy consumption and extending the WSN’s lifetime is important. This paper proposes a novel energy-efficient, static scenario-oriented routing method of WSN based on edge computing named the NEER, in which WSN is divided into several areas according to the coverage of gateway (or base station), and each of the areas is regarded as an edge area network (EAN). Each edge area network is abstracted into a weighted undirected graph model combined with the residual energy of the sensor nodes. The base station (or a gateway) calculates the optimal energy consumption path for all sensor nodes within its coverage, and the nodes then perform data transmission through their suggested optimal paths. The proposed method is verified by the simulations, and the results show that the proposed method may consume about 37% less energy compared with the conventional WSN routing protocol and can also effectively extend the lifetime of WSN.

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Section: Related Workmentioning

confidence: 99%

A Novel Energy-Efficient, Static Scenario-Oriented Routing Method of Wireless Sensor Network Based on Edge Computing

Liu

Sheng

et al. 2022

Wireless Communications and Mobile Computing

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“…Machine learning is an excellent technique to overcome the computational complexity issue in any complicated engineering problem because it is a self-learner, and it does not need to be reprogrammed [ 32 , 33 , 34 , 35 ]. Based on background studies, there are three types of machine learning approaches (i.e., supervised, unsupervised, and reinforcement learning), which have been intelligently utilized for energy optimization.…”

Section: Related Workmentioning

confidence: 99%

“…They employed a multi-hop coordination strategy to decrease energy consumption. However, these types of unsupervised techniques are time-consuming to address because of the lack of available prior data labeling [ 34 ].…”

Section: Related Workmentioning

confidence: 99%

Energy Conservation for Internet of Things Tracking Applications Using Deep Reinforcement Learning

Sultan

Waleed

Pyun

et al. 2021

Sensors

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The Internet of Things (IoT)-based target tracking system is required for applications such as smart farm, smart factory, and smart city where many sensor devices are jointly connected to collect the moving target positions. Each sensor device continuously runs on battery-operated power, consuming energy while perceiving target information in a particular environment. To reduce sensor device energy consumption in real-time IoT tracking applications, many traditional methods such as clustering, information-driven, and other approaches have previously been utilized to select the best sensor. However, applying machine learning methods, particularly deep reinforcement learning (Deep RL), to address the problem of sensor selection in tracking applications is quite demanding because of the limited sensor node battery lifetime. In this study, we proposed a long short-term memory deep Q-network (DQN)-based Deep RL target tracking model to overcome the problem of energy consumption in IoT target applications. The proposed method is utilized to select the energy-efficient best sensor while tracking the target. The best sensor is defined by the minimum distance function (i.e., derived as the state), which leads to lower energy consumption. The simulation results show favorable features in terms of the best sensor selection and energy consumption.

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“…In addition, WSNs, by their nature, are dynamic environments in terms of both their structure and their operation. Therefore, the use of Machine Learning (ML) is an excellent alternative for the development of energy efficient routing protocols in WSNs because it provides the ability of self-learning from the experience gathered and thus self-adapt to the modifications occurring [198][199][200].…”

Section: Open Research Issuesmentioning

confidence: 99%

Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey

2020

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Wireless Sensor Networks (WSNs) are among the most emerging technologies, thanks to their great capabilities and their ever growing range of applications. However, the lifetime of WSNs is extremely restricted due to the delimited energy capacity of their sensor nodes. This is why energy conservation is considered as the most important research concern for WSNs. Radio communication is the utmost energy consuming function in a WSN. Thus, energy efficient routing is necessitated to save energy and thus prolong the lifetime of WSNs. For this reason, numerous protocols for energy efficient routing in WSNs have been proposed. This article offers an analytical and up to date survey on the protocols of this kind. The classic and modern protocols presented are categorized, depending on i) how the network is structured, ii) how data are exchanged, iii) whether location information is or not used, and iv) whether Quality of Service (QoS) or multiple paths are or not supported. In each distinct category, protocols are both described and compared in terms of specific performance metrics, while their advantages and disadvantages are discussed. Finally, the study findings are discussed, concluding remarks are drawn, and open research issues are indicated.

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Machine Learning based Energy Efficient Wireless Sensor Network

Cited by 9 publications

References 43 publications

A Novel Energy-Efficient, Static Scenario-Oriented Routing Method of Wireless Sensor Network Based on Edge Computing

A Novel Energy-Efficient, Static Scenario-Oriented Routing Method of Wireless Sensor Network Based on Edge Computing

Energy Conservation for Internet of Things Tracking Applications Using Deep Reinforcement Learning

Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey

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