Energy-Efficient Localization Techniques for Wireless Sensor Networks in Indoor IoT Environments
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Abstract
For Wireless Sensor Networks (WSNs) to operate as efficiently as possible in Indoor Internet of Things (IoT) environments, energy-efficient localization approaches are essential. We investigate several localization approaches, such as trilateration based on Received Signal Strength Indicator (RSSI), Proximity Based Technique, Inertial Navigation, Ultrasound-based, and Magnetic Field-based approaches, in the context of energy efficiency. RSSI-based trilateration, which provides good accuracy with little energy consumption, uses measurements of signal intensity to infer device positions. In cases where there are limitations on line of sight, technologies based on ultrasound measure signal travel durations. Although calibration and sensitivity to interference are taken into account, magnetic field-based approaches use magnetic field anomalies to determine positions. Accuracy, energy usage, scalability, robustness, and calibration effort are some of the factors that these techniques are evaluated against in order to fulfil the demands of indoor IoT environments. A thoughtful choice of localization methods can increase energy efficiency, increase the lifespan of sensor networks, and enable precise location-aware IoT applications. In order to meet the increasing demand for energy-efficient localisation in Indoor IoT environments, more research in this field is still being conducted.
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