## 2 Literature Review
Here, multiple sensors were used to fuse the patterns for more precise detection. This prototype helped to detect careless driving events using MCS. [PERSON] et al. [11] provided a framework to select a task in MCS by combining the facts like location privacy preservation, efficient resource consumption, and high task profits. Here, there was freedom to select the path, altering their privacy requirements according to their convenience without disclosing the location information, and destination inference attack. This work needed further consideration of the task completion rate payment for the users. [PERSON] et al. [12] presented a framework to examine context-aware MCS. It specified three different viewpoints of concepts, functionalities, and context awareness. These constraints can be differentiated with diverse parameters that examine and categorize the present related works. This framework provides better insight in distinguishing the domain and cooperation type, context awareness, data sharing, incentive mechanisms, and other aspects of MCS. [PERSON] et al. [13] proposed people centric navigation (PCN), a positioning system that gives a local map of surrounding crowd. It computes the proximity among the people with the help of sensor data through Bluetooth RSS. This research work remarkably improved the position accuracy compared to other methods. [PERSON] et al. [14] presented a Fast-VPR (Vehicle Participant Recruitment) algorithm based on the study of a VPR problem. This work modeled the participant recruitment problem as a constrained maximization problem without an open cost constraint. Here, the recruiter utility is a linear combination of spatio-temporal coverage and recruiter cost. As these two parameters have different dimensional values, it needs to further consider the possible general issues. [PERSON] et al. [15] presented the strengths of large-scale sensing by the mobile users in MCS. It surpassed the participatory sensing by implied and obvious participation and crowd surround data from both mobile social networks and mobile sensing services. This work also gave insight into balancing human and machine intelligence in MCS design. [PERSON] et al. [16] proposed two task selection mechanisms for workers in mobile crowd sensing, based on the study on task bidding and task assignment for workers and platforms. This work achieved good results on better privacy reservation without much cost compared to earlier works. [PERSON] et al. [17] investigated the multitask allocation problem in which the different participants carry different devices and perform different tasks. Additionally, work is then accomplished by introducing a greedy discrete particle swarm optimization with a genetic algorithm. This algorithm assumes that the assigned tasks are static. [PERSON] et al. [18] presented some theoretical models for three possible selective data acquisition rules for MCS. These schemes were compared with non-selective but aware reputation data acquisition schemes. Additionally, they suggested that activation and deactivation of built-in sensors through mobile applications decrease platform utility. It was also suggested that no flexibility should be given to activate and deactivate some sensors by subscribing to the published content of the users in the surrounding areas. [PERSON] et al. [19] focused on privacy issues of MCS and carried out a complete literature systematic survey. Additionally, they analyzed the possible threats to the privacy of the people who are involved in crowd sensing. This work categorized the different privacy schemes and collected different possible existing solutions for privacy protection and evaluated them. [PERSON] et al. [20] proposed a solution to achieve better quality crowd sensing with the least cost. This work formulated the sensing quality assurance problem and proved it to be NP-Hard. It also proposed a polynomial time greedy approximation algorithm.