# Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario +
## 2 Related Work
### Edge-Based MCS Environments  
In recent years, edge computing has been recognized as a promising approach to facilitate data processing and reduce the amount of raw data transmitted to the cloud servers in distributed MCS environments. One of the early works presented in [11] is Mobile Edge Capture and Analytics (MECA), a middleware for data collection from mobile devices, in which an edge layer is used to receive requirements from the back-end servers, manage the data collection among a subset of local devices, and run edge analytics for primitive data processing. RedEdge is a novel big data processing solution that enables the processing of big data streams near the data source in mobile edge cloud computing environments [12]. In contrast, ref. [13] focuses on preserving privacy and dealing with malicious participants in edge computing enhanced MCS systems. [PERSON] et al. proposed Fog Computing [14], a hierarchical and distributed platform that provides computational resources and storage to enable new services at the network edge. At the same time,Jayaraman et al. presented a distributed data analytics platform for MCS applications in which a fog layer is responsible for local computing and data storage [15]. [PERSON] et al. [16] presented a three-tier Mobile-Fog-Cloud architecture that deploys highly virtualized computing and communication facilities at the proximity of mobile users. In contrast, [PERSON] et al. [17] proposed a four-layer hierarchical distributed fog computing architecture that enables parallel data processing at the edge of the network and is, therefore, suitable for deploying smart city services. Another relevant approach described in [18] proposed an IoT-enabled MCS framework based on the oneM2M standard architecture. In contrast, in [19], [PERSON] et al. proposed human-driven edge computing (HEC) as a new model to ease the provisioning and extend traditional MEC coverage solutions. They further extended their work in [20] by introducing the concept of social MEC (SMEC) proxies in the MCS environment, i.e., they add SMEC nodes between other devices and cloud, based on the incentives and centrality measures concerning other people in the group. In [21], the authors focused on the synergy between MCS and MEC and considered a possible extension of the HEC architecture by introducing mobile edge nodes operated by the users' devices that can be selected as substitutes for fixed edge servers. In particular, they proposed a probabilistic model to estimate the number of nodes that need to be promoted as mobile MEC nodes to assist the MCS data gathering. [PERSON] et al. [22] proposed a three-layer MCS architecture in which edge servers are used to process raw data, protect users' privacy and improve response time, and propose a pricing incentive mechanism to address the problem that users may not actively participate in completing tasks by maximizing social welfare. Similarly, in [23], the authors proposed ParticipAct, an edge-enabled MCS platform that uses edge nodes to identify possible emergency crowd scenarios and deal with users' rewards using a federated blockchain network. In contrast to the identified papers, which mainly deal with data processing at the network edge, in this paper, we focus on sensing process management to reduce the amount of redundant data transmitted over the network without the need for additional user actions.