# Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario +
## 2 Related Work
### Bloom Filter  
In this paper, we focus on the applicability of BF data structure in the MCS domain, i.e., to determine whether a data item is already submitted to the system or not. The BF structure is well-known and widely used among different domains, especially for efficient testing of whether an item exists in a set. For example, Bloom filters are extensively used in spell-checking software to determine if the word is a member of the particular language [31], or they can be used for bar code recognition and processing [32]. Furthermore, they are applied for distributed search and caching in the web [33] and P2P environments [34], where the main goal is to find the exact location of resources, while we aim to determine whether the data is being produced on some of the devices at a specific location and time. In addition, one interesting approach is presented in [35], where BF is used to ensure efficient and secure matching of sensing users and tasks on end-user and fog nodes, while we want to ensure that end-user devices can autonomously decide to transmit only valuable data through the network. [PERSON] et al. [36] tried to achieve secure and fine-grained MCS data sharing in which forward secrecy is based on the BF data structure. Similarly, [PERSON] et al. [37] dealt with the MCS data privacy and proposed a BF-based user-union matching scheme to protect it, while [PERSON] et al. [38] proposed a BF-based secure data provenance mechanism suitable for resource-constrained devices in IoT networks. A secure cloud storage service for an IoT environment is presented in [39], where BF is used to verify the integrity of data saved in the cloud. The context-aware addressing and routing method described in [40] uses BFs along the routing protocol to express context information inside the IoT domain. In [41], the authors proposed a fog-based new technique for distributing certificate revocation information across IoT devices in which BF is used to reduce the size of the revocation list while maintaining an acceptable overhead. Another approach closely related to ours is presented in [42], where the authors employed BF to minimize the amount of data transmitted during the tag identification in large-scale RFID-based IoT systems. [PERSON] et al. [43] proposed a distributed naming service for the IoT that relies on BFs to generate compact names from node descriptions, while [PERSON] et al. [44] proposed the Accommodative Bloom filter approach to deal with massive data streaming from IoT sensor devices. In [45], the authors deal with an efficient broadcasting mechanism in IoT networks. In particular, they used BFs to prevent IoT nodes from being flooded with unwanted packets previously received. The concept most similar to ours is presented in [46], where BFs are used to identify the items for data dissemination in wireless sensor networks without global topology information. Additionally, BFs are used for packet routing and forwarding to improve network router performance [47], to increase network security (e.g., virus detection [48]), and in many other applications.