• 2018-07
  • 2018-10
  • 2018-11
  • br Domain specific survey of


    Domain specific survey of IoT cloud platforms This section presents 26 IoT cloud platforms according to their appropriateness into the specific application domains. It is obvious that there are many more platforms present in the market, but due to tech-specific and time limits 26 of these are chosen to provide a precise ideas about how they work, what are their strengths, what are their weaknesses, in which domain they are appropriate. While, studying these IoT platforms, each of these was tested in reality to disseminate their strengths and weaknesses. Further, based on applicability and suitability preferences in several domains the IoT cloud platforms have been revisited. 10 different domains are selected based on which most of IoT cloud platforms are currently evolving into the IT market. Management wise few technological sectors are envisioned where these platforms do best fit into such as: Device, System, Heterogeneity, Data, Deployment, and Monitoring. Similarly, Analytics, Research and Visualization fields are chosen where rest of the platforms may be accommodated. While describing the selected cloud platforms following parameters such as real time data capture capability, data visualization, cloud model type, data analytics, device configuration, API protocols, and usage cost are chosen as the key selective features. This section also provides Table 1 that compares IoT clouds according to their suitability and appropriateness in the prescribed division of application domains. Following platforms are proficient enough to be rigorously used for development and providing solutions to Weird cum actuator vetted problems.
    Problems of the existing IoT cloud platforms The existing cloud solutions have incorporated IoT based smarter applications for solving a number of challenges in various fields. I discuss the few important prospects of these applications to improve the existing solutions as below, whereas the following sections shall show the path to improve the current situation point-wise.
    Discussions and conclusions Fig. 2 (see left) shows the results obtained from Table 1 where suitable and applicability issues are depicted. Out of 26 IoT cloud platforms, Data management based platforms are currently trending in the market scoring 19.2%. Device and application management domains are performing same in IoT market valuing 11.5%. Heterogeneity, analytics, monitoring, visualization and research domain are equally valuating 7.6%. Deployment management is at the lowest priority at present time i.e., 3.8%. While looking at applicability graph (Fig. 2 see Right) values are changed. The reason behind is same IoT platform is chosen applicable for different domains. Monitoring management is leading at the moment with 42.3% of score. Application development, device management and visualization domains are standing at 2nd, 3rd, and 4th positions while scoring 38.4%, 30.7% and 19.2%, respectively. Applicability of research domain is 0%, because no other IoT cloud platforms are applicable in this domain.
    Introduction Indicators or key performance indicators (KPIs) in business environment are mostly quantitative information; helper T cells illustrates structures and processes of a company. Now KPIs are very important for planning and controlling through supporting information, creating transparency and supporting decision makers of the management [1]. Lord Kelvin defined KPIs as “When you can measure what are speaking about and measure it in numbers, you know something about it, when you cannot express it in numbers, your knowledge is of meager and unsatisfactory kind; it may be the beginning of knowledge but you have scarcely, in your thoughts advanced to the stage of science.”[2]. There are four types of performance measures (Fig. 1): [3] Wei Peng divides the KPI to three Types as follows: [4]
    Previews works
    Introduction Business Process Reengineering (BPR) project aims to rearrange managerial processes and to remove unnecessary processes in order to improve the overall quality management system of any organization [3,10]. In literatures BPR projects have detected many troubles and problems that are: inability of plan driven team software process methodologies to implement projects and to respond to frequent changes of customer requirements and to avoid risk factors. The strategic agility is preferred to accelerate BPR projects using the following concepts [12,14]: