# Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario +
## 3 Materials and Methods
### Decentralized Edge-Based MCS Environment  
MCS takes advantage of the widespread availability of mobile devices to monitor a variety of phenomena of common interest in which citizens can act as both data sources and consumers. An MCS ecosystem consists of two categories of users who communicate through an MCS service: those who are interested in a specific data (_requesters_) and those who can create the data of interest (_workers_), where the interest in sensor data can be seen as an MCS _task_. We denote workers as data sources in the MCS ecosystem. Data sourcescan be both humans and devices, typically mobile, and change geographical location over time. The data produced by workers needs to be geo-tagged, either by a precise location determined by GPS or cell identifier (e.g., a mobile network cell or MGRS area identifier--Military Grid Reference System (MGRS) is the geo-coordinate standard used by the North Atlantic Treaty Organisation (NATO) for locating points on the Earth.) as location represents the essential contextual information of the worker's result (e.g., sensor data reading). At the same time, mobile devices can be used by task requesters, denoted as data consumers, to define new tasks and to receive the data of interest from the cloud. In general, entities in the MCS ecosystem interact as follows: A requester creates a new task, which is forwarded to the MCS service responsible for locating at least one eligible worker capable of collecting data for this task. Each task has related conditions (the geographical region of interest, required sensors, incentives, etc.) that must be met within a particular time frame, or the task will expire. The MCS service disseminates the task to all potential workers who can accept it and begin the sensing process if a good match exists between workers' available sensors and task requirements. After collecting data, workers transmit their results to the MCS service, which aggregates all incoming data and disseminates it to interested parties (i.e., task requesters) in near real-time. As mobile devices usually provide limited filtering and aggregation mechanisms, workers generally deliver all raw data readings to the MCS service, governing the data production process. In addition, worker mobility patterns often result in a dynamic sensing coverage, with some locations extensively covered by data readings and others suffering from a lack of available data. Furthermore, the demand for MCS data fluctuates throughout time, so certain places may not require data coverage at all. Therefore, in order to conduct the data acquisition process in an energy- and bandwidth-efficient manner while preserving MCS application quality requirements (in terms of required data accuracy and frequency of data readings), intelligent data acquisition methods need to be applied in the MCS ecosystem to optimize the number of collected data readings. In our previous [8] work, we have shown that MCS services can be decentralized by using multi-access edge computing (MEC) principles and moving computation in the proximity of mobile users. In such architecture, edge servers, located between physical sensing devices and the cloud, take responsibility for the management and coordination of MCS users in the geographical area under their supervision, as shown in Figure 1. Such distributed hierarchical edge-based MCS ecosystem consists of one cloud coordinator, several ME hosts deployed in different regions of interest, and multiple end-users acting as both workers and requesters in the system. The cloud coordinator has global knowledge about the MCS ecosystem because it knows all users and ME hosts connected to the system and all existing tasks. When a new task is submitted to the system, the coordinator forwards it to the corresponding ME host(s) based on its geographical area of interest, so an ME host knows all active tasks in its deployment area.