An Absorption Mitigation Technique for Received Signal Strength-Based Target Localization in Underwater Wireless Sensor Networks

Abstract

Localization is an indispensable technology for underwater wireless sensor networks (UWSNs). In what concerns UWSNs, the accurate location information is not only the requirement of the marine field applications but also the basis of the other corresponding research, for instance, network routing and topology control. Recently, an astonishing surge of interest has been drawn in the received signal strength (RSS)-based scheme due to cost-effectiveness and synchronization-free compared with others. However, unlike the terrestrial wireless sensor networks (WSNs), the acoustic signal may suffer the absorption loss in the underwater environment besides the path loss, which degrades the localization accuracy and limits the capability of the RSS-based technology in UWSNs. In this context, a robust localization method with an absorption mitigation technique (AMT) is developed. First, an RSS-based analytically tractable measurement model is conducted, where the maximum likelihood estimator (MLE) is derived. Nevertheless, it is quite challenging to solve the problem using MLE under a non-convex expression. Therefore, by exploiting certain approximations, the considered localization problem is converted into an optimization expression with a maximum absorption loss involved. A min–max strategy is then presented, with which the problem is turned to minimize the worst situation of the absorption loss. After a simple manipulation, the problem is further investigated as a generalized trust region sub-problem (GTRS) framework. Although the GTRS is a non-convex scheme, the solution can be obtained through an iteration method by introducing a multiplier. In addition, the closed-form expression of the Cramer–Rao lower bound (CRLB) of the analytically tractable measurement model is derived. Numerical simulations demonstrate the effectiveness of the proposed method compared with the state-of-the-art approaches in different scenarios.

An ameliorated localization algorithm for compensating stratification effect based on improved underwater salp swarm optimization technique

“…The algorithm determines the position of the unknown node considering the path loss and absorption loss in the underwater environment. In terms of localization accuracy and computing performance, the BPPL algorithm beats the previous technique 19 . Gray wolf optimizer based on hunting step size (GWO‐HSS) and prediction based on localization approach (MP‐LBG) in UWSN are proposed.…”

“…In terms of localization accuracy and computing performance, the BPPL algorithm beats the previous technique. 19 Gray wolf optimizer based on hunting step size (GWO-HSS) and prediction based on localization approach (MP-LBG) in UWSN are proposed. Based on TOA and backtracking, the location of the sensor node is evaluated.…”

WiFi Signal Strength Degradation Over Different Building Materials

“…But it is also good if we avoid factors that can reduce the strength of WiFi signals, such as (1) human activity (Damodaran, N., et al, 2020;Thewan, T., et al, 2019), (2) electromagnetic waves (Valadares, D. C., et al, 2020), (3) magnetic fields (Wu, Y., et al, 2019), (4) distance between signal transmitter and receiver (Brinkhoff, J. & Hornbuckle, J., 2018;Zhang, S., et al, 2018), ( 5) water (Mei, X. et al, 2020). ( 6) Obstacles such as walls, floors, ceilings, doors, or windows (Suherman, S., 2018;Suherman, S., et al, 2018;Din, Z. U., & Bernold, L. E., 2017;Lee, H., et al, 2019;Mathisen, A., et al, 2016;Rath, H. K., et al, 2017;UmaMaheswara, M., & Kadaru, B.…”

The underwater dynamic environment RSSI ranging filtering algorithm

“…As a result, ensuring the concealment and deployment convenience of AUVs cannot be guaranteed. Although RSSI ranging technology has the benefits of not requiring clock synchronization and being easily implemented, its ranging accuracy is often affected by noise [10] [11]. The traditional RSSI ranging correction filtering algorithm is ineffective in correcting ranging outliers when the AUV is moving [12][13].…”