Underwater Acoustic Sensor Node Scheduling using an Evolutionary Memetic Algorithm

• Autorzy: Sivakumar V. , Rekha D.

Identyfikatory

DOI

10.26636/jtit.2018.116217

• Języki publikacji: EN

Abstrakty

EN

Underwater Acoustic Sensor Networks (UWASNs) play an important role in monitoring the aqueous environment which has created a lot of interest for researchers and scientists. Utilization of underwater acoustic sensor node (UASN) scheduling for transmission remains, due to the limited acoustic bandwidth available, a challenge in such an environment. One of the methods to overcome this problem is to efficiently schedule UASN data using time division multiple access (TDMA) protocols the parallel transmissions, simultaneously avoiding interference. The paper shows how to optimize the utilization of acoustic sensor node bandwidth by maximizing the possible node transmissions in the TDMA frame and also by minimizing the node's turnaround wait time for its subsequent transmissions by using an evolutionary memetic algorithm (MA). The simulation of MA-TDMA proves that as the size of the network increases, every node in UWASN transmits with an average minimal turnaround transmission time. It also proves that as the TDMA cycle repeats, the overall network throughput gets maximized by increasing the possible node transmissions in the MA-TDMA frame.

Słowa kluczowe

EN

broadcast UASN scheduling; memetic algorithm; time division multiple access; underwater acoustic sensor network

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2018
• Tom: nr 1
• Strony: 88--94
• Opis fizyczny: Bibliogr. 28 poz., rys., tab.

Twórcy

autor

Sivakumar V.

• School of Computing Science and Engineering, VIT, Chennai, Tamil Nadu, India

autor

Rekha D.

• School of Computing Science and Engineering, VIT, Chennai, Tamil Nadu, India

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).