## 1 Introduction  
As we already know, Cloud Computing can offer several important features to users [1]. In particular, Cloud providers and customers are keen to build a more secure Cloud environment due to its unique function. In more detail, the following are the delivery data models of Cloud Computing [1, 2]: _Software as a Service (SaaS)_, _Platform as a Service (PaaS)_, and _Infrastructure as a Service (IaaS)_. Additionally, the major characteristics of Cloud Computing (_Cloud Computer Features_--_CCF_) are Storage over Internet (_CCF1_), Service over Internet (_CCF2_), Applications over Internet (_CCF3_), Energy Efficiency (_CCF4_), and Computationally Capable (_CCF5_). Nowadays, there are tremendous amounts of data generated daily in the sectors of manufacturing, business, science, and people's personal lives. These data, if processed properly, could reveal new knowledge about the market, society, and environment, additionally enabling people to cope with emerging opportunities and changes promptly [3, 4]. Furthermore, the accustomed data processing technologies, such as database sets and data warehouses, are becoming inadequate for the huge amounts of data that need to be dealt with. These challenges are known as Big Data (BD), and they constitute a novel field of study for researchers [3, 4]. Big Data is characterized as \"_a big thing in the field of modern technologies_\" [4]. The five Vs of BD are _Volume, Velocity, Variety, Veracity_, and _Value_ (Figure 1). All the data related to the term Big Data have a specific origin or \"source\", which, based on Variety, could give various data types. Here, we attempt to address several major Big Data sources (_BDSs_), as well as the respective challenges that arise from them regarding the overall use of Big Data. _BDS--Earth, Marine, and Space Sciences_: Large data sets are collected and generated every second and at different space-time scales for operations, as the presentation, monitoring, and understanding of complex earth, marine, and space systems are enabled by the preference of sensing and computing simulation technologies. So, as an example, earth, marine, and space observation software collects terabytes of images daily [5; 6; 7; 8], with gradual increases in space, time, and spectral analyses [5; 9]. _BDS2--Internet of Things_: The IoT is a broader aspect and is considered to be all the devices that would be able to connect to the internet and could interact with each other [1; 10; 11; 12]. The whole data that can be generated from the various IoT sensors includes spatiotemporal information, and thus, it can be described as BD. The combined use of IoT-BD in network environments, in addition to being integrated with technologies such as Cloud Computing, could offer new opportunities and lead to the accelerated development of Smart Cities [5; 13]. _BDS3--Social Sciences_: Big Data are being generated by various social networks, such as Instagram, Twitter, and Facebook, and thus they could transform social sciences [5]. _BDS4--Business_: The various decisions for strategy, managing optimization, and competition related to Big Data could be enhanced by business intelligence and analytics [5]. Data related to the previous scenarios contain harmful amounts of geospatial information, for example, where and when a transition occurred [14; 15]. _BDS5--Industry_: In Industry 4.0, which is considered the fourth industrial revolution, the products and production systems leverage technologies such as IoE-based BD, aiming to establish ad-hoc networks for self-managed applications [16; 17]. To operate and verify our proposed scenario and to evaluate our proposed algorithm, we used a Digital Twin scenario with the goal of simulating the operation of a Cloud server, through which it manages, transfers, and produces IoT-based Big Data. We selected the option of Digital Twin because it offers more reliable results for our proposal through its \"simulated\" scenario.