## 1 Introduction  
In recent years, Internet of Things (IOT) played an important role in connecting various physical environments from the real world for useful applications through sensing data and interconnecting various objects [1, 2]. In addition to this, ubiquitous computing provides more sensible data collection through sensors and transmission technologies. Now it is the time to exploit the capacity of ubiquitous computing through mobile devices such as smart phones which are owned today by most people for their daily routine communications [3]. In fact, the sensors are usually deployed to sense data over the limited areas. However, this technique is not so economically feasible for larger areas of data collection and transmission. Currently, a significant number of people carry smart mobile devices which are inherently pervasive in the real world and can be more effectively used instead of sensors. These mobile devices may be smart phones, wearable devices, or vehicle-mounted devices. These devices make it possible to sense and monitor a large geographical area in large-scale events or occurrences. Such a sensing prototype is known as mobile crowd sensing (MCS) [4, 5] or people/human centric sensing [6]. This prototype is more suitable for dense urban areas to measure/sense various parameters through specific/identified groups of mobile users. In recent mobile technology, all smart phones are equipped with different types of sensors, GPS, receivers, Bluetooth, and Wi-Fi capabilities. These functions made it possible to collect more useful data and send them to major monitoring and processing devices. These devices distribute/analyze the data based on the applications accordingly. There are various applications that have been emerged based on this prototype including traffic monitoring and control, air pollution control and monitoring, different security surveillance systems, and even a possible device to monitor and control the pandemic COVID-19 situation. MCS mainly requires greater participation of people involvement to attain better efficiency in sensing. Since the people are the better option in sensing coverage and data transmission. Different research in this field have been collectively devised two major models or prototypes of MCS, known as Participatory sensing and opportunistic sensing. The participatory sensing expects more conscious participant's involvement in sensing the data based on the demands of different application requirements. These requirements are judged based on the specific attributes such as when. Where, what, and how to sense data. On other hand opportunistic sensing does not requires dedicated/conscious participants to sense the data. here data can be collected just by running the required wireless communication model. This opportunistic sensing is more feasible for large area deployments with versatile applications. MCS consists of two types of paradigms that influence the different applications based on their demand of sensing and monitoring. These transmission paradigms are known as infrastructure-based transmission and opportunistic transmission. Infrastructure-based transmissions are mainly depending on the internet through which the user can send the sensed data to the sink or processing devices, whereas opportunistic transmission uses wireless communication modules such as Wi-Fi or Bluetooth supported by short range radio communications. These communication modules help the user to send the sensed data through the intermittent connections. The infrastructure-based transmission paradigm is one of the prioritized options in most of the current applications. However, this transmission paradigm showed poor performance due to lack of consistent network coverage areas, whereas opportunistic transmission performs better with higher efficiency and lower costs. More importantly, it does not require a centralized server or extra infrastructure for communication and management. This transmission minimizes the workload of the cellular network with negligible congestion rates.