Most of the data gathering devices used for monitoring driver’s behavior require large storage, strong cellular signals, and unlimited internet. Touching mobile devices, during driving, is prohibited by many law enforcement agencies. There are situations, especially in developing countries, where people get stuck on roads with a low battery, low device-memory, and no mobile network. The drivers in such situations are not able to report against reckless drivers effectively.This paper proposes the framework of the “citizen reporting program” (CRP) aided with mobile apps to reduce reckless driving in such resource-constrained situations (RCS). A mobile app was designed, developed, and tested as a tool for this purpose. It could convert speech to text without a cellular network, capture the nearest geolocation, and send data to a server on the network or internet availability. We tested its reliability for converting speech to text and got a “word error rate” (WER) of less than 5%. We tested its functional usability and got a score of more than 71% on the system usability scale (SUS). The survey showed a favorable response of 70 plus % in reducing reckless driving via CRP in RCS if aided with mobile apps.

With high paced growth in biometrics, and its easy availability to capture various biometric features, it is emerging as one of the most valuable technologies for multifactor authentication to verify a user’s identity, for data security.Organizations encourage their members to use biometrics, but they are hesitant to use due to perceived security risks. Because of its low usage rate, many medium and small segment organizations find it unfeasible to deploy robust biometric systems.We propose a “server-specific add-on biometric security layer model,” to enhance confidence in the usage of biometrics. We tested this model via a biometric mobile app, and the survey showed a favorable response of 80%. The innovative mobile app was tested for its usability and got a score of more than 71%. For test tool reliability, we examined the equal error rate (EER) of the app and got a reasonably low score of 6%. The results show a good potential of this framework to enhance users’ confidence-level in the usage of biometrics. Higher usage rates may make deployment of biometrics more cost-effective for many organizations to decrease their information security risk.