# Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario +
###### Abstract  
Mobile crowdsensing (MCS) is a sensing paradigm that allows ordinary citizens to use mobile and wearable technologies and become active observers of their surroundings. MCS services generate a massive amount of data due to the vast number of devices engaging in MCS tasks, and the intrinsic mobility of users can quickly make information obsolete, requiring efficient data processing. Our previous work shows that the Bloom filter (BF) is a promising technique to reduce the quantity of redundant data in a hierarchical edge-based MCS ecosystem, allowing users engaging in MCS tasks to make autonomous informed decisions on whether or not to transmit data. This paper extends the proposed BF algorithm to accept multiple data readings of the same type at an exact location if the MCS task requires such functionality. In addition, we thoroughly evaluate the overall behavior of our approach by taking into account the overhead generated in communication between edge servers and end-user devices on a real-world dataset. Our results indicate that using the proposed algorithm makes it possible to significantly reduce the amount of transmitted data and achieve energy savings up to 62% compared to a baseline approach. mobile crowdsensing; Bloom filter; multi-access edge computing; mobile edge computing 202  
that rely on the power of the crowd to collectively sense the environment and share data of common interest have been used across several domains, from environmental and traffic monitoring to healthcare and social networks [4]. The main goal of such applications is to extract knowledge about the sensed phenomena by using different data analytic techniques and informing citizens about their surroundings, which can affect their decisions and behavior while being mobile. Due to the large number of devices that can be active in parallel, the MCS ecosystem must efficiently process data. At the same time, it also has to quickly disseminate information to interested users, as their context changes frequently, and information can become stale if it is not delivered as soon as it becomes available. Furthermore, users' inherent mobility leads to dynamic sensing coverage of geographical areas, where some areas may be densely covered with measurements. In contrast, others may remain unexplored or with insufficient data readings, posing additional challenges for data acquisition and device management [5]. Due to the volatile volumes of sensor data, in the beginning, a cloud infrastructure seemed suitable for the implementation of an elastic, scalable, and modular MCS ecosystem. All components responsible for processing integrated data streams were located in the cloud, while various data sources and consumers were deployed on mobile devices with connections to the Internet, as presented in [6]. Even though cloud-based architecture provides enormous computing capabilities and storage capacity, continuous processing of incoming data streams might become relatively inefficient in large-scale MCS ecosystems when there are numerous concurrent connections between user devices and cloud servers. Data processing and business intelligence are eventually brought closer to end-users to resolve these issues. With the help of 5G technology, which adds powerful processing capabilities to the radio access network [7], the MCS ecosystem design nowadays shifts to a distributed architecture where the cloud service is only responsible for coordinating processing components deployed at the network edge. Multi-access edge computing (MEC), previously known as mobile edge computing, has already been acknowledged as a critical enabler of improved context-aware services that move computationally demanding tasks previously executed on the central server in mobile users' proximity, thus lowering data propagation latency and bandwidth consumption. In our previous work [8], we proposed an edge-based architecture for MCS.