Location Tracking Assessment

Authors

  • 1 Tejaswi , 2 A. Manoj, 3 K. Kishore, 4 P. Vikas Author

DOI:

https://doi.org/10.64751/

Abstract

Location tracking has emerged as a critical component of modern technological systems, playing a vital role in applications such as navigation, logistics, emergency response, smart transportation, mobile services, and location-based decision support systems. With the rapid growth of smartphones, wireless communication, and sensor technologies, accurate and reliable location tracking has become increasingly important. This paper presents a comprehensive assessment of location tracking systems, focusing on widely used techniques such as Global Positioning System (GPS), network-based localization, and smartphone assisted geolocation methods. The objective of this study is to evaluate the effectiveness, accuracy, and practical limitations of these techniques under varying environmental conditions.
The assessment considers both indoor and outdoor scenarios, where location tracking performance is influenced by factors such as signal strength, environmental obstacles, network availability, and device capabilities. GPS-based tracking is analyzed for its accuracy and reliability in open outdoor environments, while its limitations in indoor spaces and dense urban regions are highlighted. Network-based positioning techniques, including cellular and Wi-Fi based localization, are evaluated for their ability to provide approximate location information in areas where GPS signals are weak or unavailable. Additionally, the role of smartphone sensors in enhancing location accuracy through assisted tracking mechanisms is discussed.
The evaluation is carried out using performance parameters such as location accuracy, response time, reliability, and environmental dependency. Comparative analysis is used to identify the strengths and weaknesses of each tracking approach.

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Published

01-04-26

How to Cite

1 Tejaswi , 2 A. Manoj, 3 K. Kishore, 4 P. Vikas. (2026). Location Tracking Assessment . American Journal of AI Cyber Computing Management, 6(2), 66-74. https://doi.org/10.64751/