Abstract
In this study’s scheme, the objective is to employ the robot to implement the location estimation process for sensor nodes with which it interacts relative to the radio messages’ signal strengths, having received the signals from the sensor nodes. The implication is that the central purpose of the study lies in the elimination of the static sensor nodes’ processing constraints. Imperative to highlight is that the study’s mathematical contribution lies in the utilization of the REKF-based state estimator to analyze the localization problem. Deviating from Kalman Filter, it is worth noting that REKF exhibits computational efficiency and robustness. Indeed, the localization scheme is implemented on a hybrid network test bed.
Highlights
In the recent past, sensor network research has increased dramatically [1, 2]
The implication is that the central purpose of the study lies in the elimination of the static sensor nodes’ processing constraints
Deviating from Kalman Filter, it is worth noting that REKF exhibits computational efficiency and robustness
Summary
Sensor network research has increased dramatically [1, 2]. The wide range of possible applications has motivated the research efforts, including the condition-based maintenance of aircrafts and environmental monitoring [3, 4]. The sensor networks involve extremely limited processing capability, memory, and end-node power, and are at large scale [5]. Some environmental monitoring applications do not necessarily call for the use of uniformly distributed and fully connected sensor networks [6]. These applications do not necessarily call for the use of real-time sensor information [7, 8]. The majority rely on sensor data that has been obtained for a significant period, examples being long-term coastline monitoring and the monitoring of cane toads [9, 10]
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