###### Abstract
In the more recent past, progress has been made on the analysis of these real-time algorithms under the context of IoT, where the tasks need to be run on physical embedded devices. Research in the scheduling area for embedded systems can be leveraged in the context of IoT applications, but most of the algorithms that run well on general purpose systems do not scale well on these embedded devices because of their limited capabilities in terms of power and energy management. This issue becomes even more challenging for the periodic real-time tasks. Periodic real-time tasks are those tasks that repeat themselves after a certain time interval known as the period. The period plays a pivotal role in the overall system's performance and is regarded as an optimisation variable in many studies. Algorithms like Rate Monotonic (RM) [17], Earliest Deadline First (EDF) [18] and Deadlock Monotonic (DM) are among the most commonly used scheduling algorithms that also suffer from this period problem. These algorithms use a hyper-period to compute how much CPU clock cycles a given task set consumes and hence the power and energy consumption of these algorithms is dependent solely on the hyper-period of the real-time input tasks. In some scenarios, where periods of input tasks are not adequately modelled. For example, when all periods are prime numbers, they lead to enormously high hyper-periods despite the fact that the algorithm correctly schedules the tasks theoretically, but devices don't have sufficient resources to deal with such a high hyper-period. Some of the conventional approaches to limit the value of a hyper-period are bounded hyper-period and harmonization. In a bounded hyper-period approach [19], a static threshold is fixed, and the hyper-period is limited below this static threshold. In a harmonization approach [20; 21], though no such threshold is defined, the period values are in harmonic series and thus one value of a period will be the harmonic factor of another. The RM algorithm gives 100% utilization for a harmonic period. Both of these approaches minimise the value of hyper-period to a great extent but have their shortcomings. The static threshold does not scale well for dynamic scenarios like IoT where the resources are not fixed, and new IoT resources can register on the fly. The harmonization approach does not allow tasks to have a period value which could contribute to the high value of the hyper-period. Consequently, it dramatically decreases the tasks' acceptance ratio and, additionally, it is not a scalable approach that is considered a highly important attribute in the context of IoT applications. To cope with the above-mentioned shortcomings, we propose a systematic approach that models the periods of input tasks in such a manner that it always produces a hyper-period within an optimal threshold. It overcomes the problem of high power consumption that arises due to a high hyper-period. The optimal threshold is computed based on the profiles of IoT devices and the number of IoT resources connected to them. It is a safe bound by which a system can schedule tasks without any performance degradation. Moreover, this approach allows every possible value of the period within a specific bound unlike the traditional approach of harmonization. This method is general in the sense that it covers general priority-driven systems including both fixed-priority systems and dynamic-priority systems and all those algorithms that deal with a hyper-period to schedule input tasks on CPU. The proposed algorithm is compared with state-of-the-art technologies discussed earlier, and the results are found to be making significant improvements in terms of power, memory, scalability and tasks' acceptance rate. RM is considered as a fixed-priority algorithm, and EDF is taken as a dynamic-priority system to prove the claim that the proposed approach is generic and works well for both kinds of priority systems. The rest of the paper is organised as follows. Section 2 describes the related work in detail.