Energy-Efficient Transmission Technique based on Dijkstra Algorithm for decreasing energy consumption in WSNs

Abderrahim, Maha; Hakim, Hela; Boujemâa, Hatem; Touati, Farid

doi:10.1109/sta.2019.8717210

Cited by 12 publications

(7 citation statements)

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“…It also contains the recommendation for future research of several relevant, open-ended issues. In this paper [2] we propose a multi-hop transmission technology based on the Dijkstra algorithm for the Wireless Sensor Networks (WSN). A WSN of N sensor nodes is considered.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Our proposed algorithm for relay selection is based on Dijkstra. The paper is mainly aiming at defining a new transmission strategy which improves the results of our previous work [2] by reducing power consumption more effectively. This paper [3] examines WSNs' energy-efficient cooperative communication across frequency-selective fading channels.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(3) The LEACH algorithm is used to have the same energy model. The energy consumption per bit of the transmitter and receiver model is displayed as the energy used to transfer the l-bit packet over distance d in Equation (1) and in Equation (2) with Eelec for receipt. The parameters ∈fs and ∈mpare the power amps in the free space (2 loss), and the multipath (4 loss) as per the distance from the equation (3).…”

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confidence: 99%

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Clustering Algorithm for Energy- Efficient Wireless Sensor Network

Rawat¹

2021

TURCOMAT

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Wireless networks data aggregation allows in-network processing, reduces packet transmission and data redundancy, and thus helps extend wireless sensor systems to the full duration of their lives. There have been many ways of dividing the network into clusters, collecting information from nodes and adding it to the base station, to extend wireless sensor network life. Certain cluster algorithms consider the residual energy of the nodes when selecting clusterheads and others regularly rotate the selection head of the cluster. However, we seldom investigate the network density or local distance. In this report we present an energy-efficient clustering algorithm that selects the best cluster heads of the system after dividing the network into clusters. The cluster head selection depends on the distance between the base station nodes and the remaining power of this approach.Each node's residual energy is compared to the node count. Our results show that the solution proposed more efficiently extends the life of the wireless sensor network. The algorithm prolongs the life and effectiveness of the Wireless Sensor Network.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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confidence: 99%

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Clustering Algorithm for Energy- Efficient Wireless Sensor Network

Rawat¹

2021

TURCOMAT

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“…There is much work done on the network routing 8‐17 . From this aspect, an energy‐efficient multihop transmission technique for wireless sensor networks (WSN) based on Dijkstra algorithm is proposed 8 .…”

Section: Related Workmentioning

confidence: 99%

Adaptive energy saving algorithms for Internet of Things devices integrating end and edge strategies

Wang

Yang

Wan

et al. 2020

Trans Emerging Tel Tech

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By taking into account not only the node specifics but also the benefits of mobile edge computing, an integrated strategy for energy saving of Internet of Things (IoT) devices is proposed in this article. This strategy consists of two algorithms at both the end and the edge. Considering the changeable battery level and downlink communication traffic of the battery‐powered wireless nodes, an energy efficient automatic mode switching algorithm is designed at the end. Three different kinds of working modes are designed based on the features of the end nodes and the various application requirements. This algorithm tends to enable the end nodes to automatically select and switch to the proper working mode according to the actual conditions. At the edge server with much stronger processing and computing capabilities which belongs to a higher layer, the dynamic sampling rate adjustment algorithm is designed. It can adaptively adjust the sampling frequencies of the end nodes and thus reduce their working durations. The proposed integrated solution aims to decrease the energy consumption of IoT devices as much as possible and thus prolong their battery lives. The simulation results have shown both the effectiveness and the efficiency of the proposed end and edge integrated strategy in terms of energy consumption.

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“…An increase of 41% in the design and development of gas and pollutant sensing systems was observed in the environmental market since 2001 [5]. The top contributions AirNut, PA-I and PA-II, Egg, PATS+, and S-500, CairClip, Portable ASLUNG, AirSensEUR, Met One, AQY v0.5, Vaisala AQT410, 2B Tech, and AQMesh V3.0 systems had measurement capabilities in specific pollutants and gases presented in [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Real-time Gradient-Aware Indigenous AQI Estimation IoT Platform

Tariq¹,

Abdaoui²,

Touati³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Environmental monitoring has gained significant importance in outdoor air quality measurement and assessment for fundamental survival as well as ambient assisted living. In real-time outdoor urban scale, instantaneous air quality index estimation, the electrochemical sensors warm-up time, cross-sensitivity computation-error, geo-location typography, instantaneous capacity or back up time; and energy efficiency are the six major challenges. These challenges lead to real-time gradient anomalies that effect the accuracy and pro-longed lags in air quality index mapping campaigns for state and environmental/meteorological agencies. In this work, a gradient-aware, multi-variable air quality sensing node is proposed with event-triggered sensing based on position, gas magnitudes, and cross-sensitivity interpolation. In this approach, temperature, humidity, pressure, geo-position, photovoltaic power, volatile organic compounds, particulate matter (2.5), ozone, Carbon mono-oxide, Nitrogen dioxide, and Sulphur dioxide are the principle variables. Results have shown that the proposed system optimized the real-time air quality monitoring for the chosen geo-spatial cluster (Qatar University).

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Energy-Efficient Transmission Technique based on Dijkstra Algorithm for decreasing energy consumption in WSNs

Cited by 12 publications

References 0 publications

Clustering Algorithm for Energy- Efficient Wireless Sensor Network

Clustering Algorithm for Energy- Efficient Wireless Sensor Network

Adaptive energy saving algorithms for Internet of Things devices integrating end and edge strategies

Real-time Gradient-Aware Indigenous AQI Estimation IoT Platform

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