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Road safety remains a casualty in India, with potholes wrecking asphalt pavements by the dozens. A study in 2017 recorded that potholes caused the budget for road safety to increase by a whopping 100.4 per cent, and even doubled the death toll from that of the year prior. To address this situation, an effective solution is required that ensures the drivers’ safety and can prove beneficial for long term measures. This can be established by employing an apt pothole detection system which is simple yet functional. In this paper, the method for such a system is described which uses accelerometer and gyroscope, both built in the modern day smartphones, to sense potholes. Pothole induced vibrations can be measured on the axis reading, making them distinguishable. Our proposed Neural Network model is trained and evaluated on the data acquired from the sensors and classifies the potholes from the non-potholes. The neural network gives a classification accuracy of 94.78 per cent. It also presents a solid precision-recall trade-off with 0.71 precision and 0.81 recall, considerably high for a problem with class imbalance. The results indicate that the method is suitable for creating an accurate and sensitive supervised model for pothole detection.

A Wireless Body Area Network (WBAN) is considered as a special type of a Wireless Sensor Network (WSN) with its own challenges and requirements. A traditional WSN is designed to achieve higher efficiency, however, WBAN is designed to achieve maximum throughput, minimum delay and to maximize the energy efficiency, all together. Our analysis indicates that available protocols for WSN are not easily portable for WBAN, specifically Medium Access Control (MAC) Protocols. Thus, we have proposed a new WBAN MAC Protocol based on Aloha protocol which differentiates and preferred various types of traffic based on emergency case. The proposed protocol has been simulated using different scenarios and the obtained results are analyzed and compared using NS-2 Simulator.

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