Multihop Transmission Strategy Using Dijkstra Algorithm to Improve Energy Efficiency in WSNs

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

doi:10.1007/978-3-030-02849-7_9

Cited by 4 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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]. FIS SP-61 by FIS, O3-3E1F by CityTechnology, AirSensEUR v.2 by LiberaIntentio, and S-500 by Aeroqual, and AirSensEUR used a built-in AlphaSense OX-A431 limited to O3 [9][10][11]. The PMS1003 and PMS3003 by Plantower; DC1100 PRO and DC1700 by Dylos; OPC-N2 by AlphaSense had only sensing support for particulate matter (PM) [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…FIS SP-61 by FIS, O3-3E1F by CityTechnology, AirSensEUR v.2 by LiberaIntentio, and S-500 by Aeroqual, and AirSensEUR used a built-in AlphaSense OX-A431 limited to O3 [9][10][11]. The PMS1003 and PMS3003 by Plantower; DC1100 PRO and DC1700 by Dylos; OPC-N2 by AlphaSense had only sensing support for particulate matter (PM) [11][12][13]. The CO-3E300 by City Technology; CO-B4 by Alphasense, MICS-4515 by and SGX Sensortech, Smart Citizen Kit by Acrobotic and the RAMP had only abilities to measure carbon mono-oxide [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real-time Gradient-Aware Indigenous AQI Estimation IoT Platform

Tariq¹,

Abdaoui²,

Touati³

et al. 2020

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

Self Cite

View full text Add to dashboard Cite

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).

show abstract