Random node deployment and route establishment in receiver based routing protocol for WSNs

Pramod, M S; Shankar, Shiva; Mohammed, Khurram J

doi:10.1109/rteict.2016.7807786

Cited by 13 publications

(3 citation statements)

References 8 publications

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“…The routing of data packets in WSN-IoT is considered a classification problem in the domain of machine learning. The routing optimization algorithms such as genetic algorithms [82] and classification algorithms such as Markov decision process (MDP) in decision tree [83], random forest [84] and Q-learning (QELAR) [85] have been used.…”

Section: Summary Of Literature Survey Of ML Techniques For Wsn-iotmentioning

confidence: 99%

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Sharma¹,

2021

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Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications.

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Section: Summary Of Literature Survey Of ML Techniques For Wsn-iotmentioning

confidence: 99%

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Sharma¹,

2021

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“…Micro cell path loss [22] model is a dual slope path loss model, where the propagation characterization considers two different path loss exponents and the breakpoint distances. The breakpoints are maintained at the distance of few meters.…”

Section: Micro Cell Path Loss Modelmentioning

confidence: 99%

“…The number of nodes chosen is 100 and the topology area is considered to be 1000m X 1000m. The nodes are deployed in random [22]. The protocol is executed for 5 iterations with each node having transmission range of 200m.…”

Section: Performance Analysismentioning

confidence: 99%

Performance of EEDR Routing Protocol under Various Path Loss Models for WSNs

Pramod¹,

Shankar²

2018

IJET

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Path loss is the power variation between the receiver power and the transmitter power. The attenuation of the signal takes place in Wireless Sensor Networks (WSNs) because of path loss. There are various kinds of path loss due to absorption, reflection, refraction and many other factors under diverse environments which are all defined under path loss models. This research paper provides a brief study on different path loss models and their mathematical evaluation. The objective of the proposed work is to evaluate the Energy Efficient Distributed Receiver (EEDR) based routing protocol under the various path loss models and check the performance variation in different environments. The simulation results shows the effect of path loss on end-to-end delay, number of hops, energy consumption and the number of alive nodes in WSNs topology.

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