Priority-based Task Pre-processing in IoT Sensory Environments

Background: The motive of the Internet of Things (IoT) is to monitor and to control the devices that are connected to the internet. In IoT sensory environments, the application queries for the physical quantities in the spatiotemporal domain. The interaction between the sensors and the applications from the internet is the next big thing in the era of the internet of things. To minimise the resource utilisation, task scheduling mechanisms are implemented to the network. Methods: The PRITRAPS (Priority-based Task aware Pre-processing and Scheduling) is a mechanism that is employed in real time scenarios of industries. In which different applications units are accessing the gateway unit to measure and monitor the parameters of different service types. PRITRAPS employs priority among the tasks to reduce the network load. Results: The QoS parameters of the system are analysed and compared with the previous methodologies. The PRITRAPS mechanism consists of a task pre-processor unit, Scheduler and EMS module within the gateway unit. The scheduling algorithm employed in PRITRAPS is EDF (Earliest Deadline First) algorithm. The pre-processing task unit decreases the number of tasks by choosing the tasks having similar spatial and temporal requirements. The residual energy of the sensor nodes can help the scheduler for deciding the sensor nodes in respective of task requirements. The scheduler finds the best potential nodes and assigns them to the task for processing. Conclusion: To reduce the tasks arrivals at the wireless sensor unit, a priority based CCTs (Critical Covering Task sets) is proposed, and it effectively reduces the packet congestion and network overload. The results obtained are satisfactory and proven that PRITRAPS outperform TRAPS in energy consumption of a node by processing the tasks on the node. PRITRAPS require only 50 % of the time that has been taken by TRAPS for serving the tasks. The PRITRAPS mechanism is implemented in NS3 simulator and tested for different task sets.