ABSTRACT Long‐term and time‐critical aquatic monitoring applications account for major challenges in the design and deployment of underwater wireless sensor networks (UWSNs). In these types of applications, the biggest challenge is to build a comprehensive software‐defined network (SDN) that includes accurate estimation of channel modeling parameters that is, received signal strength indicator (RSSI) and noise contribute to make decisions using the SDN control panel. In this research work, we developed an SDN for underwater sensor node position (d) estimation mechanism using real‐time observed data of ocean parameters, temperature (T) and salinity (S) parameters and estimated conductivity () and permittivity to compute channel losses. Our proposed work utilizes real‐time observed oceanic data, which is more beneficial in the accurate computation of the underwater channel loss (absorption and scattering) , resulting in a more accurate software‐defined d estimation of the underwater sensor nodes; compared to the existing software‐defined multimodal system communication scheme (SDMM) for d estimation. Real‐time observed ocean data not only assist in the computation of transceiver power , and d but also reduce the hardware portion to minimize overall cost using the SDN controller. Within a 2–40 GHz frequency range, absorption loss and spreading loss are computed through various depths from the surface to 5500 m, and 41088 Lat/Long points across the five oceans. Here, and the estimated position of the sensor node assist in the software‐based calculations of underwater loss and received signal strength indicator , which reduces the hardware portion of the transceiver and shifts transmission to a more software‐defined approach to minimize the overall cost. At the level of the SDN control panel, by feeding the sensor nodes' position, we can accurately compute channel losses along with updated d considering oceanic waves traveling at the speed of 5–6 km/h w.r.t time. At the data link layer, this SDN approach will help to access the channel using the MAC protocol at a suitable time, along with routing between the courier node and sensors made feasible by taking decisions at the SDN panel. For frequently changing underwater environments; proposed SDN‐based position estimation uses an open‐loop for input parameters like ocean T and S, while a closed‐loop feedback process is used to reduce control packets overhead and also reduces computations for d estimation if no change is detected in environmental parameters. Simulation results show that the proposed scheme effectively increases the packet reception ratio (PRR) up to 90% and 96.15%, reduces overall network energy consumption up to 1 , and reduces the hardware portion for computation of RSSI and to minimize cost compared to existing schemes SDMM and LoLo‐AUV, respectively. For the proposed SDN‐based sensor node position estimation, we see coverage probability enhancement between 10% and 20% that depicts increased performance based on the SDN control panel.
Tahir et al. (Wed,) studied this question.