  Ad hoc architectures have emerged as a valuable alternative to centralized
participatory sensing systems due to their infrastructureless nature, which
ensures good availability, easy maintenance and direct user communication. As a
result, they need to incorporate content-aware assessment mechanisms to deal
with a common problem in participatory sensing: information assessment. Easy
contribution encourages users participation and improves the sensing task but
may result in large amounts of data, which may not be valid or relevant.
Currently, prioritization is the only totally ad hoc scheme to assess
user-generated alerts. This strategy prevents duplicates from congesting the
network. However, it does not include the assessment of every generated alert
and does not deal with low-quality or irrelevant alerts. In order to ensure
users receive only interesting alerts and the network is not compromised, we
propose two collaborative alert assessment mechanisms that, while keeping the
network flat, provide an effective message filter. Both of them rely on
opportunistic collaboration with nearby peers. By simulating their behavior in
a real urban area, we have proved them able to decrease network load while
maintaining alert delivery ratio.
