Exploring the Role of IoT in Smart Logistics for Future using Machine Learning

Smart Logistics Powered by IoT and Machine
Learning By employing real-time data and predictive
analytics to minimize delays, optimize routes, and improve
transportation performance, route optimization increases
supply chain efficiency. Accuracy, scalability, and rapid
decision-making in AI-driven logistics route optimization are
significantly impacted by maintaining high-quality real-time
data and managing the computational demands of AI models.
RFID tags, GPS sensors, and environmental monitors are
examples of IoT devices that collect data and give continuous
data streams to manufacturers, suppliers, and logistics
companies at different stages of the supply chain.
Preprocessing is necessary for efficient analysis, data
cleaning, and the elimination of noise and redundant entries
from unprocessed IoT data streams. The integration of new
technologies, particularly blockchain, the Internet of Things
(IoT), and artificial intelligence (AI), is the only way to
achieve increasingly critical aspects like flexibility,
adaptability, and traceability. Without specifically searching
for knowledge and data patterns, machine learning (ML)
enables the Internet of Things (IoT) to become genuinely
pervasive and extract hidden insights from the wealth of
observed data. IoT's main objective is to sense what is going
on around us and enable intelligent ways to automate
decision-making that will resemble human decision-making.
IoT is made up of four important technologies: security,
actuators, connectivity, and sensing. The world continues to
struggle with connectivity. Internet access and mobile
connectivity are barriers, especially in low-income nations.
Another issue is the current IoT platform's lack of crossplatform functionality, which contributes to its sluggish
adoption. Additionally, these cutting-edge technologies pose
significant modeling problems to conventional optimization
techniques, opening up a wealth of new research opportunities
for the development of novel optimization techniques in the
field of logistics and transportation studies. Therefore, our
goal is to carry out a thorough analysis of significant
contributions made in the uses of STs in enhancing logistics
operations and transportation network efficiency.