Ranging Explosion Events using Smartphones


In this paper, we address the problem of ranging explosion events from sensing corresponding accelerometer readings from stationary smartphones. First, we statically emplaced a number of smartphones with built-in accelerometers at various locations in the vicinity of real explosions (conducted at a university training facility). An app was installed in 4 off-the-shelf smartphones to collect accelerometer readings continuously, and effectively retaining only those readings that correspond to an explosion event (while filtering out the rest). As a result, a total of 52 data-sets from 4 individual explosion blast-experiments (with Dynamite acting as the explosive charge) were collected. Using these data-sets, we developed a non linear regression model to estimate the distance of the source of an explosion event, and the intensity of the explosion (measured in terms of charge weight of the explosive material) based on extracting a number of statistical features from the accelerometer sensor readings in three dimensions (lateral (x), longitudinal (y), and vertical (z) directions) from smartphones. We are able to range the explosion event, with an average case error of 12.86% in our experiments. We were also able to estimate the intensity of the explosion event with a high accuracy, with an average case error of 11.26%. To the best of our knowledge, this is the first work that attempts to range explosion events leveraging sensor readings from smartphones.

Meeting Name

2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2015 (2015: Oct. 19-21, Dhabi, United Arab Emirates)


Computer Science

Keywords and Phrases

Accelerometers; Explosives; Knowledge Management; Linear Regression; Mobile Computing; Regression Analysis; Smartphones; Accelerometer Sensor; Average Case Error; Blast Experiments; Explosive Charges; Explosive Materials; Non-Linear Regression; Statistical Features; Training Facility; Explosions

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International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version


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