IoT System for Sensing Condition of Roads Using IMU Sensors

Ugale, Hrishikesh; Patil, Pushpak; Chauhan, Shubham; Rao, Neeraj

doi:10.1109/icsccc51823.2021.9478080

Cited by 1 publication

(1 citation statement)

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“…Additionally, field testing showed that the NN model achieved an accuracy of 84%. Another IoT-based system was presented in [64], which employed Ard, IMU sensor, and GPS technologies to detect road conditions. The system consisted of multiple sensor nodes placed on vehicles, collecting data and transmitting it to a central server for analysis.…”

Section: ) Comparative Studiesmentioning

confidence: 99%

Crowdsensing for Road Pavement Condition Monitoring: Trends, Limitations, and Opportunities

Jan,

Khattak,

Khan

et al. 2023

IEEE Access

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RPCM forms a crucial element of preventive maintenance strategies, particularly in light of escalating vehicular pressures and the advent of extreme weather patterns. Consequently, there is a growing demand for cost-effective solutions that leverage emergent technologies such as the IoT, AI, and cloud computing. This research intends to articulate the evolutionary trajectory of road solutions while delineating the prevalent challenges and offering viable trajectories for future enhancements. To achieve this objective, a systematic literature review was executed using the Scopus and Web of Science databases, the aim of which was to discern the inherent challenges of existing solutions. Following a stringent elimination process of duplicates and irrelevant studies, a corpus of 74 research papers was assembled for review. Assessment criteria encompassed the sensing platforms and algorithms deployed, the variety of road deformities detected, and the overall accuracy of the proposed solutions. The analysis revealed a variety of methodologies applied to RPCM, each bearing distinct advantages and limitations. Notably, SPbased monitoring solutions utilizing ML techniques and improved data gathering methodologies exhibited superior outcomes relative to alternative approaches. To conclude, this research elucidates the wide-ranging methodologies in RPCM, critically examining their respective advantages and drawbacks. Among the methodologies surveyed, SP-based monitoring deploying ML techniques emerges as a compelling approach, demonstrating the potential for enhancing accuracy and data gathering techniques. These insights form a valuable foundation for the conception and development of future cost-effective and efficacious RPCM solutions.

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Section: ) Comparative Studiesmentioning

confidence: 99%