Scheduling Based Wireless Sensor Networks Integrated with IoT Environment
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Wireless Sensor networks are widely adopt in military, target tracking, signal processing and monitoring applications like traffic and structural, the small and low cost unreliable sensor nodes in these applications uses batteries as the sole energy source. The energy efficiency becomes difficult task as the tiny and less weight battery act as source of each node. Scheduling the different category of data packets is a way to reduce the power consumption and increasing the lifetime of sensor nodes. The existing scheduling algorithms are not adapted to the environment changes. The basic FCFS (First Come First Serve) suffered by long delay while transmit the real time data packets. In DMP (Dynamic Multilevel Priority) real time data packets occupies highest priority, the remaining non real time data packets sent to lower priority level queues. Some real time task holds the resources for longer time, the other task have to wait, it makes the deadlock condition. The NJN (Nearest Job Next) will select the nearest requesting sensor node for service, real time packets have to wait long time. The proposed Adaptive weighted scheduling scheme changes the behavior of the network queue by adaptively changes the weights based on network traffic. Simulation results proof that, adaptive weighted scheduling algorithm works better than the FCFS and DMP data scheduling in terms of energy consumption and lifetime. Our future scheme to integrate Internet of Things (IoT) with the WSN to increase the performance of the wireless networks.
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