Toward Performance Optimization in IoT-based Next-Gen Wireless Sensor Networks

TL;DR

This paper introduces a comprehensive framework for optimizing performance in IoT-based wireless sensor networks, addressing resource depletion and data degradation through adaptive communication and robust data processing methods.

Contribution

It presents a novel layer-adaptive communication mechanism and a transform-domain algorithm to enhance resource efficiency and data integrity in next-generation sensor networks.

Findings

Improved resource utilization and battery life in simulations.

Effective data denoising with the proposed transform-domain algorithm.

Enhanced coverage and reduced data degradation in network models.

Abstract

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.