scholarly journals Geometric Dilution of Precision for Received Signal Strength in the Wireless Sensor Networks

2019 ◽  
Vol E102.A (9) ◽  
pp. 1330-1332
Author(s):  
Wanchun LI ◽  
Yifan WEI ◽  
Ping WEI ◽  
Hengming TAI ◽  
Xiaoyan PENG ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Wireless Sensor ◽  
Dilution Of Precision ◽  
Geometric Dilution Of Precision

scholarly journals Considerations about the Signal Level Measurement in Wireless Sensor Networks for Node Position Estimation

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4179 ◽  
Author(s):  
Stelian Dolha ◽  
Paul Negirla ◽  
Florin Alexa ◽  
Ioan Silea
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Signal Strength ◽  
Data Access ◽  
Received Signal Strength ◽  
Position Estimation ◽  
Wireless Sensor ◽  
Node Localization ◽  
Received Signal Strength Indicator ◽  
Starting Point

Wireless Sensor Networks (WSN) are widely used in different monitoring systems. Given the distributed nature of WSN, a constantly increasing number of research studies are concentrated on some important aspects: maximizing network autonomy, node localization, and data access security. The node localization and distance estimation algorithms have, as their starting points, different information provided by the nodes. The level of signal strength is often such a starting point. A system for Received Signal Strength Indicator (RSSI) acquisition has been designed, implemented, and tested. In this paper, experiments in different operating environments have been conducted to show the variation of Received Signal Strength Indicator (RSSI) metric related to distance and geometrical orientation of the nodes and environment, both indoor and outdoor. Energy aware data transmission algorithms adjust the power consumed by the nodes according to the relative distance between the nodes. Experiments have been conducted to measure the current consumed by the node depending on the adjusted transmission power. In order to use the RSSI values as input for distance or location detection algorithms, the RSSI values can’t be used without intermediate processing steps to mitigate with the non-linearity of the measured values. The results of the measurements confirmed that the RSSI level varies with distance, geometrical orientation of the sensors, and environment characteristics.

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

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4698
Author(s):  
Xiaojun Mei ◽  
Huafeng Wu ◽  
Nasir Saeed ◽  
Teng Ma ◽  
Jiangfeng Xian ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Signal Strength ◽  
Measurement Model ◽  
Received Signal Strength ◽  
Wireless Sensor ◽  
Closed Form Expression ◽  
Absorption Loss ◽  
Underwater Wireless Sensor Networks ◽  
Mitigation Technique

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.

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