  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.
