## 1 Introduction  
In this era of innovation, we are experiencing a shift from physical space to a cyberspace [1], where every physical thing could be interconnected to form a mashup of things. This mashup is called Internet of Things (IoT) [2; 3; 4; 5]. Technologies like Quick Response (QR) codes and Radio Frequency Identification (RFID) are being exploited to identify physical objects. Similarly, the power of wireless sensor networks, wireless mesh networks, mobile networks and wireless Local Area Network (LAN) is employed to interconnect these smart objects. These connections, forming an IoT space, greatly enhance the business processes concerning efficiency and costs in enterprise systems [6; 7; 8; 9; 10]. In such smart space, every physical object connecting to IoT can be regarded as an edge node of it. These edge nodescan sense ambient scenarios and bridge the gap between the real world and networks [11]. The huge number of links is enabling the interaction between edge nodes which generate an enormous amount of data which can't be stored and processed on these edge nodes due to their limited capabilities. To address the challenge, cloud technologies [12] have been utilised which deals with huge sets of data. The use of cloud technologies has been dramatically increased due to the pervasive nature of IoT as it acts as a central server to process and hosts a massive volume of contextual data emitting from edge-nodes of IoT [12]. The IoT and cloud are so often integrated that it is recently known as Cloud of Things (CoT) [12; 13; 14]. In a recent study, cloud technologies have been overwhelmingly employed in smart homes and smart cities. In smart cities domain, some notable efforts have been made to highlight the state-of-the-art and overview the relevant technologies [15]. [PERSON] et al. [16] debate about the difference of smart cities and megacities and delineate the focus and scope of smart cities and megacities respectively. Moreover, as a fact of interdisciplinary research [17], the demand of cloud technologies are spotted for the big data processing. It is also envisioned that with the realisation of Industry 4.0, it would not be possible to process such an enormous chunk of manufacturing data without cloud technologies. In IoT applications, sometimes the interaction among IoT edge nodes has real-time requirements, and thus their behaviour needs to be determined, and the deadline must be formally proved beforehand. In the research by [PERSON] [18], it has been said that the tasks which edge nodes need to be scheduled and deployed on smart objects in the same fashion as in conventional real-time systems. In conventional real-time systems, task scheduling is regarded as a trivial issue, and thus many studies conducted for conventional real-time systems can be appropriated in real-time IoT applications. The development of real-time IoT based systems is an emerging application area which has attracted significant research attention in recent past. Currently, soft real-time systems such as streaming applications are supported by many existing IoT platforms [19; 20]. Recently, the term RT-IoT was introduced by [PERSON] et al. to address the associated challenges of both soft and hard real-time systems [18]. Specialised operating systems are developed to support real-time applications requirements in IoT based embedded systems. RT-IoT poses a significant challenge because of the additional Internet layer. Much like in traditional real-time applications, a task can have a valid deadline which must be ensured. One scenario in which tasks miss the deadline is the unrealistic system requirement and selection of inappropriate scheduling policies in accordance with them. In RT-IoT in addition to the careful selection of scheduling policies, communication delay also plays a vital role [21]. Therefore, extensive testing and simulation need to be carried out to understand the behaviour of both the scheduling policy and the communication medium before deploying them in a real situation.