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.
Highlights
The Internet of Things (IoT) reached the peak of expectations, according to Gartner in 2016 [1], and we are witnessing a consolidation of the developed technologies and paradigms beyond initial trials and prototype solutions
Mobile crowdsensing refers to a set of human-driven IoT applications that allow users to detect phenomena of personal, communal, or even societal importance by sharing sensor data about their surroundings while being mobile
Because mobile edge (ME) hosts are responsible only for workers and mobile crowdsensing (MCS) tasks inside their deployment area, such architecture allows parallelizing and segmenting the problem space based on the location
Summary
The Internet of Things (IoT) reached the peak of expectations, according to Gartner in 2016 [1], and we are witnessing a consolidation of the developed technologies and paradigms beyond initial trials and prototype solutions. The digitalization of our everyday environment resulted in a vast number of novel IoT services addressing the needs of citizens (e.g., monitoring of personal pollution exposure or live traffic data). This has evolved into a concept called mobile crowdsensing (MCS), which utilizes many users to create knowledge from data generated by moving devices with various sensing capabilities without requiring the deployment of a particular physical infrastructure. 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. It 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
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
