Toward Performance Optimization in IoT-based Next-Gen Wireless Sensor Networks
This work addresses inefficiencies in IoT sensor networks for applications requiring reliable data and extended battery life, but it appears incremental as it builds on known bottlenecks with hybrid methods.
The paper tackles performance optimization in IoT-based wireless sensor networks by addressing resource drainage and data degradation, proposing a layer-adaptive method and a transform-domain algorithm that show desirable complexity for real-time applications in simulations.
In this paper, we propose a novel framework for performance optimization in Internet of Things (IoT)-based next-generation wireless sensor networks. In particular, a computationally-convenient system is presented to combat two major research problems in sensor networks. First is the conventionally-tackled resource optimization problem which triggers the drainage of battery at a faster rate within a network. Such drainage promotes inefficient resource usage thereby causing sudden death of the network. The second main bottleneck for such networks is that of data degradation. This is because the nodes in such networks communicate via a wireless channel, where the inevitable presence of noise corrupts the data making it unsuitable for practical applications. Therefore, we present a layer-adaptive method via 3-tier communication mechanism to ensure the efficient use of resources. This is supported with a mathematical coverage model that deals with the formation of coverage holes. We also present a transform-domain based robust algorithm to effectively remove the unwanted components from the data. Our proposed framework offers a handy algorithm that enjoys desirable complexity for real-time applications as shown by the extensive simulation results.