Journal ArticleDOI
ELDC: An Artificial Neural Network Based Energy-Efficient and Robust Routing Scheme for Pollution Monitoring in WSNs
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TLDR
This work proposes an artificial neural network based energy-efficient and robust routing scheme for WSNs called ELDC, which outperforms LEACH protocol by 42 percent, and other state-of-the-art protocols by more than 30 percent.Abstract:
The range of applications of Wireless Sensor Networks (WSNs) is increasing continuously despite of their serious constraints of the sensor nodes’ resources such as storage, processing capacity, communication range and energy. The main issues in WSN are the energy consumption and the delay in relaying data to the Sink node. This becomes extremely important when deploying a big number of nodes, like the case of industry pollution monitoring. We propose an artificial neural network based energy-efficient and robust routing scheme for WSNs called ELDC. In this technique, the network is trained on huge data set containing almost all scenarios to make the network more reliable and adaptive to the environment. Additionally, it uses group based methodology to increase the life-span of the overall network, where groups may have different sizes. An artificial neural network provides an efficient threshold values for the selection of a group's CN and a cluster head based on back propagation technique and allows intelligent, efficient, and robust group organization. Thus, our proposed technique is highly energy-efficient capable to increase sensor nodes’ lifetime. Simulation results show that it outperforms LEACH protocol by 42 percent, and other state-of-the-art protocols by more than 30 percent.read more
Citations
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Deep Learning in Mobile and Wireless Networking: A Survey
TL;DR: This paper bridges the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas, and provides an encyclopedic review of mobile and Wireless networking research based on deep learning, which is categorize by different domains.
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Machine learning algorithms for wireless sensor networks: A survey
TL;DR: This survey presents various ML-based algorithms for WSNs with their advantages, drawbacks, and parameters effecting the network lifetime, covering the period from 2014–March 2018.
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The rise of traffic classification in IoT networks: A survey
TL;DR: A taxonomy of the current network traffic classification within the IoT context is presented and commercial and real-world use cases of the IoT traffic classification are exposed and open research issues and challenges in this domain are outlined.
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Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey
TL;DR: This is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities and shows that the supervised learning algorithms have been most widely used as compared to reinforcement learning and unsupervised learning for smart city applications.
Journal ArticleDOI
A Trust-Based Active Detection for Cyber-Physical Security in Industrial Environments
TL;DR: Results illustrate that the proposed trust-based active detection (TBAD) scheme can greatly improve the efficiency and security of data routing in CPS.
References
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An application-specific protocol architecture for wireless microsensor networks
TL;DR: This work develops and analyzes low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality.
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A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Dervis Karaboga,Celal Ozturk +1 more
TL;DR: Simulation results indicate that ABC algorithm can efficiently be used for multivariate data clustering and is compared with Particle Swarm Optimization (PSO) algorithm and other nine classification techniques from the literature.
Proceedings ArticleDOI
EECS: an energy efficient clustering scheme in wireless sensor networks
TL;DR: This paper proposes a novel clustering schema EECS for wireless sensor networks, which better suits the periodical data gathering applications and elects cluster heads with more residual energy through local radio communication while achieving well cluster head distribution.
Proceedings ArticleDOI
An energy-efficient unequal clustering mechanism for wireless sensor networks
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