A Cost-Efficient Management Protocol for Mobile Crowd-Sensing in Urban Vehicular Scenarios

In urban-traffic scenarios, massive mobile terminals can provide more comprehensive and accurate environmental sensing data with various built-in sensors. Humans can also collect environmental information as task executors. Therefore, facing different sensing requirements from environmental monitoring, intelligent transportation, urban management, and other fields, if we make good use of the sensing abilities of mobile terminals and humans, we can provide more supporting data for them. This paper proposes a multitasking-oriented participant-recruitment algorithm for vehicular crowd sensing based on the Combine-S algorithm that could merge-common sensing requirements in the time and space dimensions to solve the problem of overlapping requirements among multiple tasks. Combine-S could also be integrated with a greedy recruitment algorithm to improve the sensing effect and reduce sensing costs. We used the OMNet++ simulators that are used for simulating urban transportation and IoTKeywords-Genetic Algorithm, NSL-KDD, Intrusion Detection system, Fast Learning Network.