Routing in wireless sensor networks using machine learning techniques: Challenges and opportunities

Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes.To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.

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“…Recently, with advances in artificial intelligence, researchers have applied machine learning (ML) to WSNs [42]- [45].…”

Section: Machine Learning For Wsnsmentioning

confidence: 99%

“…It increases the lifespan of the WSNs by reducing the number of dead nodes and minimizing the moving distance of the mobile charger. Nayak et al [45] examined the application scenarios of ML in WSNs. In particular, they focused on routing, which is the area where ML is most often applied.…”

Section: Machine Learning For Wsnsmentioning

confidence: 99%

Adaptive Data Collection Using UAV With Wireless Power Transfer for Wireless Rechargeable Sensor Networks

Yoon

Noh

2022

IEEE Access

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“…MATLAB was used to carry out the research. Fuzzy logic and Ann techniques [18] were both found to be effective in that their findings matched the characteristic of a real solar PV array [3]. These will be put to use to forecast and test the overall reliability of the solar power plant.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Machine-learning techniques are wide applied to several fields related to data-driven issues. Machinelearning techniques embrace several knowledge domain areas, like statistics, arithmetic, Ann, data processing, optimization, and computation are all terms that come to mind when thinking about artificial neural networks [18].…”

Section: Introductionmentioning

confidence: 99%

Survey Analysis of Solar Power Generation Forecasting

Erlapally¹,

Anuradha²,

Karuna³

et al. 2021

E3S Web Conf.

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Solar power is the conversion of sunlight into electricity using solar photovoltaic cells as a source of energy. There are various applications for solar power; here is information on PV cell generation. We seek to understand the behavior of solar power plants through the data generated by the photovoltaic modules and the power generation in different weather conditions in India. The goal of this survey is to give a thorough assessment and study of machine learning, deep learning and artificial intelligence. Artificial intelligence (AI) models as well as information preprocessing techniques, parameter selection algorithms and predictive performance evaluations are used in machine learning and deep learning models for predicting renewable energies. But in case of time series data we can predict only the errors using a linear regression model, we can also calculate things like root mean square error (RMSE), mean absolute error (MSE), mean bias error (MBE) and mean absolute percentage error (MAPE). By the analysis of weather condition also we can predict the consumption of current by solar for every 15 minutes, 1day, and 1week or even for 1 month and find the accuracy.

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“…Considering machine learning techniques, 35 numerous protocols are proposed such as Mehmood et al 36 and Huang et al 37 using artificial neural networks and deep learning, respectively.…”

Section: Introductionmentioning

confidence: 99%

ACOMAR: ACO‐based MAC‐aware routing protocol

Mechta

Mahgoun

Harous

2021

Int J Communication

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In this work, we present a new nature-inspired cross-layer routing protocol named ACO-based MAC-aware routing protocol (ACOMAR). An enhancement of time division multiple access (TDMA) algorithm is proposed where slots allocation algorithm considers the geographical distance between nodes and the sink. The nodes that are far away from the sink have better chance to be scheduled first. This strategy will result in more efficiency than a usual random TDMA schedule. This algorithm uses a metric based on the pheromone's value of Ant Colony Optimization (ACO) to choose the best next hop with the aim of reducing latency and energy and extending the network lifetime. Comparisons are made between ACOMAR, MAC-aware routing (MAR) and the latest cross-layer protocol latency and energy MAC-aware routing (LEMAR). The experimental results show that ACOMAR outperforms considerably LEMAR by 80%, 20%, 81%, and 54% in latency, energy, hop count, and throughput, respectively. Consequently, ACOMAR is a suitable solution for healthcare applications where WSN-based critical IoT devices interact quickly in emergence cases (high rate in latency optimization 80%).

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Routing in wireless sensor networks using machine learning techniques: Challenges and opportunities

Cited by 95 publications

References 112 publications

Adaptive Data Collection Using UAV With Wireless Power Transfer for Wireless Rechargeable Sensor Networks

Adaptive Data Collection Using UAV With Wireless Power Transfer for Wireless Rechargeable Sensor Networks

Survey Analysis of Solar Power Generation Forecasting

ACOMAR: ACO‐based MAC‐aware routing protocol

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