## 2 Literature Review  
[PERSON] et al. [1] gathered and analyzed huge data sets related to mobile crowd sensing systems and they presented different challenges involved in crowd sensing and provided a detailed survey on current state-of-the-art works. It provides the detailed classifications of applications, methodologies, and architectures. It also gives an idea for future research directions and relationships with other research areas. [PERSON] et al. [2] proposed a four-stage life cycle for the MCS process and tried to utilize 4W1H to distinguish the research issues for every stage of the MCS life cycle. This work explained the different possible research issues of MCS in a systematic approach and helped many researchers to find multiple options in different directions. [PERSON] et al. [3] presented a framework for MCS that devised a model to characterize the mobile user's behavior and a novel incentive mechanism. This model describes the behavior of mobile users regarding their resource availability and dissimilar responses. The incentive mechanism proposed different values of incentives based on the user characteristics. However, this idea must be further considered for the real deployment case values to be within the limits of the total budget of the system. [PERSON] et al. [4] presented an application of the MCS paradigm for more favorable and competent mobility in urban environments. It introduced the city sensing framework which signified the feasibility of a more general crowd sensing platform that can be applied to different urban mobility areas. This platform helps to design various applications like traffic sensing and pollution sensing with the help of various add-on sensors which are internally and externally attached to mobile devices. This work suggested incorporating people involvement, reliability, security, and privacy in future work. [PERSON] et al. [5] introduces an algorithm called iLoCuS, that proposes an incentivizing method to realize a target sensing distribution in MCS using non-dedicated vehicle platforms. Because of different commercial aspects, the non-dedicated vehicles may not achieve the required spatio-temporal sensing coverage. iLoCuS gives an idea about incentivizing policies for vehicle agents to divide the sensing distribution into two different levels such as at the time location level and vehicle level so that more consistent results can be achieved with limited budgets. Furthermore, this work needed to consider the specific mobility and acceptance rate pattern of each vehicle agent. [PERSON] et al. [6] has studied and analyzed the data transfer opportunities among wireless devices carried by humans and it was found that the inter-contact time may be better approximated by a power law. Additionally, it recommended forwarding algorithms for the design of more efficient opportunistic. [PERSON] et al. [7] investigated opportunistic characteristics of human mobility in terms of sensing and transmission. This work explained some of the possibilities of data collection through opportunistic methods. It also specified some of the research issues related to human involvements in MCS. [PERSON] et al. [8] presented a survey on QOI in MCS. This work proposed a framework which includes the definition of the concept of QOI. Additionally, it defined and analyzed different research challenges in imposing and approximating QOI. It mentioned and discussed some of the previous related works and analyzed their strengths and weaknesses. [PERSON] et al. [9] introduced a QOS Sensitive Task Assignment problem for crowd sensing and proposed an idea of rewarding each user based on their level of QOS achievement against the threshold value in the data collection process. This work supported medium-range QOS models and proposed extensions for more complex models. [PERSON] et al. [10] devised a prototype to find inattentive driving behavior and authentication on urban roads. The data were sensed and Dynamic Time Wrapping (DWT) was used to detect the events collected from the smart phones with sensors.