The advantages of smartphones such as integrated sensors, programmability, scalability and cloud servers have enabled low-cost and efficient public safety applications. However, designing such applications has to face daunting challenges, for instance, short battery life, low computing capability and lacking memory. To this end, lighter, faster, more efficient and scalable distributed data processing algorithms are necessary. In particular, new mechanisms are necessary for adaptive and opportunistic sensing within a cluster of smartphones. Furthermore, a distributed data mining algorithm is in need of proposition to run on each smartphone for information retrieval. The retrieved information also needs a new algorithm to be fused and even enhanced by more powerful devices such as sensor nodes in the infrastructure to obtain higher accuracy and reliability. For experimental validation, the new algorithms will be implemented on smartphones to collect and detect abnormalities from measured environmental elements such as sounds, pressure, temperature, light, etc., under various public safety scenarios including ambient noise. Currently, I am working from the scratch on a new idea that using a few preferable sensors to trigger others’ measurement for adaptive and opportunistic sensing. This saves considerable battery power and lessens interference with normal phone usage.