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.

翻译：临时架构因其无基础设施的特性，确保了良好的可用性、易于维护和直接用户通信，已成为集中式参与式感知系统的宝贵替代方案。因此，它们需要结合内容感知评估机制来应对参与式感知中的常见问题：信息评估。便捷的贡献激励用户参与并提升感知任务效果，但可能导致大量数据，其中部分数据可能无效或不相关。目前，优先级排序是唯一完全临时的用户生成警报评估方案。该策略可阻止重复警报阻塞网络，但它未涵盖所有生成警报的评估，且无法处理低质量或不相关的警报。为确保用户仅接收感兴趣警报且网络不受影响，我们提出了两种协作式警报评估机制，在保持网络扁平结构的同时提供有效的消息过滤。这两种机制均依赖于与附近对等节点的机会主义协作。通过在真实城市场景中模拟其行为，我们证明它们能在维持警报交付率的同时降低网络负载。