LAUSR

In this article, we consider the problem of distributed offloading in mobile crowdsensing (MCS) by the means of mobile edge computing(MEC). Deploying MEC in MCS can help address many challenges the centralized MCS solutions are facing such as delays in answering real-time requirements due to the centralized nature of the solution, discovering and selecting non-connected devices in the Area of Interest (AoI), and dealing with the complexity of data computation. Specifically, we propose to improve the selection of crowdsourced workers by opting for a distributed mechanism, where the selection is partially offloaded to the Local Edge Nodes (LENs). The proposed framework, OffSEC, relies on a) a Mobile edge computing architecture that defines the Main Edge Node (MEN) and LENs responsible for selecting the local workers available in the AoI and b) a two-layer selection mechanism that helps to offload the selection of crowdsourced workers to the identified LENs. To do this, nodes in the area of interest are first clustered based on their locations, and then for each cluster, one LEN is identified based on the closeness metrics. MEN is then nominated based on a greedy selection. Finally, LENs discover the available nodes in their cluster, including heterogeneous IoT nodes and workers that are not necessarily connected to the Edge server, and select the final list of workers that maximize the quality of service (QoS). The process of selection is dynamic as it is updated according to the requested task. The proposed OffSEC is evaluated using a real dataset and is compared to a centralized approach. The results show that OffSEC outperforms the benchmark by maximizing the QoS of the sensing activities and improving the quality of the collected data readings (QoDR).