Remote Sensing Study of Mobile Networks: An Assessment of TechnologicalChallenges

The current era is witnessing a notable shift in the logistics and transportation sector due to the advent of Advanced Technologies (ATs). ATs, or smart technologies, encompass the use of artificial intelligence and data science methodologies, including machine learning and big data analysis, to establish cognitive comprehension and autonomous capabilities in relation to an entity. This study investigates the efficacy of remote sensing techniques in analyzing mobile network coverage for optimizing logistic applications. With the proliferation of mobile technologies, seamless connectivity has become integral for efficient logistical operations. Presently, numerous implementations of ATs have exhibited considerable potential in augmenting the efficiency and efficacy of diverse logistical operations and transportation systems. Moreover, the emergence of these innovative technologies presents significant modelling complexities for conventional optimization techniques, hence offering promising avenues for the exploration and development of novel optimization strategies within the realm of logistics and transportation research. The study aims to provide insights into areas with limited or inadequate network coverage, facilitating strategic planning for logistical operations. By integrating remote sensing findings with logistic frameworks, this research contributes to enhancing the efficiency, reliability, and responsiveness of logistical networks in regions with varying degrees of mobile network connectivity. The focus of our investigation is to thoroughly examine and engage in discourse regarding the technological challenges faced by researchers during the creation of optimization approaches as a result of the use of ATs.