In Underwater Acoustic Sensor Network (UASN), routing and propagation delay is affected through various water column environment effects in each node such as temperature, salinity, depth, gases, divergent and rotational wind. High sound velocity increases the transmission rate of packets and high dissolved gases in the water environment increase sound velocity. High dissolved gases and sound velocity environment in water column provide high transmission rates among UASN nodes. In this paper, the Modified Mackenzie Sound equation calculates sound velocity in each node for energy-efficient routing. Golden Ratio Optimization Method (GROM) and Gaussian Process Regression (GPR) predict propagation delay of each node in UASN from dataset which consists of Mackenzie Sound equation calculated sound velocity based on temperature, salinity, depth, dissolved gases. Dissolved gases, rotational and divergent winds, and stress play major problems in UASN, increases propagation delay and energy consumption. Predicted values from GPR and GROM lead to node selection and among selected nodes Corona Virus Optimization Algorithm (CVOA) routing is performed. The proposed GPR-CVOA and GROM-CVOA algorithm solves the problem of propagation delay and consumes less energy in nodes based on appropriate tolerant delays in transmitting packets among nodes during high rotational and divergent winds. From simulation results, CVOA Algorithm performs better than traditional DF and LION algorithms.
Katika Underwater Acoustic Sensor Network (UASN), kuchelewesha kwa njia na kuenea huathiriwa na athari mbalimbali za mazingira ya safu ya maji katika kila node kama vile joto, chumvi, kina, gesi, upepo wa divergent na mzunguko. Kasi ya juu ya sauti huongeza kiwango cha usafirishaji wa vifurushi na gesi nyingi zilizomenyolewa katika mazingira ya maji huongeza kasi ya sauti. Gesi nyingi zilizomwagika na mazingira ya kasi ya sauti katika safu ya maji hutoa viwango vya juu vya usafirishaji kati ya nodes za UASN. Katika karatasi hii, Modified Mackenzie Sound equation mahesabu sauti kasi katika kila node kwa ajili ya nishati ya ufanisi routing. "Mfano wa ""Golden Ratio Optimization"" (GROM) na ""Gaussian Process Regression"" (GPR) ni njia za kutabiri muda wa kuenea kwa kila node katika UASM kutoka kwa seti ya data ambayo inajumuisha ""Mackenzie Sound Equation"" (MAC) na ""Sonic Speed"" kulingana na joto, chumvi, kina, na gesi zilizomwagika." Gesi zilizomwagika, upepo wa mzunguko na tofauti, na mkazo hucheza matatizo makubwa katika UASN, huongeza ucheleweshaji wa kuenea na matumizi ya nishati. "Hizi ni ""maadili ya utabiri kutoka kwa GPR na GROM"" ambayo husababisha uteuzi wa node na kati ya node zilizochaguliwa, algorithm ya optimization ya virusi vya corona (CVAO) inafanywa." GPR-CVA na GROM-CVA algorithms hupunguza muda wa kuenea na kutumia nishati kidogo katika nodes kulingana na ucheleweshaji wa kutosha katika kusambaza pakiti kati ya nodes wakati wa upepo wa juu wa mzunguko na tofauti. Kutokana na matokeo ya simulation, CVOA Algorithm hufanya vizuri zaidi kuliko jadi DF na LION algorithms.