Mobile crowdsensing (MCS) paradigm enables users equipped with energy-constrained smart devices to participate in sensing and reporting of assigned tasks. To achieve seamless communication as well as effective energy and resource management, we leveraged the fog computing platform to propose a centralized, energy-efficient and robust data collection framework, called bioMCS, based on the topological properties of a biological network called transcriptional regulatory network. However, since MCS platforms may potentially entail a high number of mobile users and massive volumes of data traffic, we extend the current work under the name bioMCS 2.0 to conceive a distributed energy-aware data forwarding mechanism where the fog devices function as task data relay nodes. bioMCS 2.0 combines energy-awareness, abundance of subgraphs (called motifs) in the fog network and proximity to the base station to perform efficient task sensing and forwarding in a dynamic scenario where fog devices are both energy constrained and mobile. It also ensures quality of information by accepting task data from reliable smart devices. Extensive simulation on the map of New York City and realistic mobility models suggests that bioMCS 2.0 exhibits comparable performance in terms of data delivery, latency and energy efficiency in comparison with both random next hop (fog node) selection as well as centralized forwarding technique that rely on global network knowledge.

移动人群感知（MCS）范例使配备了能量受限智能设备的用户能够参与感知和报告已分配的任务。为了实现无缝通信以及有效的能源和资源管理，我们利用雾计算平台基于称为转录调控网络的生物网络的拓扑特性，提出了一个集中化，节能高效的健壮数据收集框架，称为bioMCS。但是，由于MCS平台可能会带来大量的移动用户和大量的数据流量，因此我们将当前的工作扩展为bioMCS 2.0设想一种分布式的能量感知数据转发机制，其中雾设备充当任务数据中继节点。bioMCS 2.0结合了能源意识，雾网络中大量子图（称为主题）以及靠近基站的位置，可以在雾设备既受能量限制又可移动的动态场景中执行有效的任务感测和转发。它还通过接受来自可靠智能设备的任务数据来确保信息质量。在纽约市地图上的大量模拟和真实的流动性模型表明，bioMCS 2.0 与随机下一跳（雾节点）选择以及依赖于全球网络知识的集中转发技术相比，该产品在数据传输，延迟和能源效率方面均具有可比的性能。